Abdelkarim, A, Gaber, A, Youssef, A & Pradhan, B 2019, 'Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling', Sensors, vol. 19, no. 5, pp. 1024-1024.
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This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.
Abdollahi, A, Pradhan, B & Shukla, N 2019, 'Extraction of road features from UAV images using a novel level set segmentation approach', International Journal of Urban Sciences, vol. 23, no. 3, pp. 391-405.
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© 2019, © 2019 The Institute of Urban Sciences. A novel hybrid technique for road extraction from UAV imagery is presented in this paper. The suggested analysis begins with image segmentation via Trainable Weka Segmentation. This step uses an immense range of image features, such as detectors for edge detection, filters for texture, filters for noise depletion and a membrane finder. Then, a level set method is performed on the segmented images to extract road features. Next, morphological operators are applied on the images for improving extraction precision. Eventually, the road extraction precision is calculated on the basis of manually digitized road layers. Obtained results indicated that the average proportions of completeness, correctness and quality were 93.52%, 85.79% and 81.01%, respectively. Therefore, experimental results validated the superior performance of the proposed hybrid approach in road extraction from UAV images.
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Development of lag time and time of concentration for a tropical complex catchment under the influence of long-term land use/land cover (LULC) changes', Arabian Journal of Geosciences, vol. 12, no. 3.
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© 2019, Saudi Society for Geosciences. Lag time (t L ) and time of concentration (T c ) are essential time parameters used in hydrological flood-flow design methods for estimating peak discharge and flood hydrograph shape. They form the basis of a number of hydrological models used among the scientists. Kelantan River basin, Malaysia, is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/land cover (LULC) changes making the area consistently flood prone thereby deteriorating water balance in the area. The most recent is that of December 2014 flood which lead to catastrophic loss of huge amount of properties worth millions of Malaysian ringgit. In view of this, the current research was conducted with the aim of developing (1) t L and T c based on 1984, 2002, and 2013 LULC conditions; (2) a relationship between t L and t L parameters; and (3) a relationship between t L and T c among different LULC conditions. Kelantan River basin was first delineated into four major catchments, viz., Galas, Pergau, Lebir, and Nenggiri, due to its large size (approximately 13,100 km 2 ). Soil map and LULC change maps corresponding to 1984, 2002, and 2013 were used for the calculation of CN values while NRCS lag equation was used for the estimation of t L and T c . The results showed that deforestation for logging activities and agricultural practices were the dominant LULC changes across the watershed. Low values of both t L and T c were obtained across the catchment which are typical for a tropical monsoon catchment characterized with high runoff and short peak discharge. Results of t L and T c in this study are not affected by LULC changes in the basin. Slope was observed to be highly correlated with t L . Correlation coefficient was used to determine the relationship between t L and t L factor, and hydraulic length and slope (L√S). The results showed high correlation in all the catchments from 1984 to 2013 except for Lebir catchment wher...
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Long-term runoff dynamics assessment measured through land use/cover (LULC) changes in a tropical complex catchment', Environment Systems and Decisions, vol. 39, no. 1, pp. 16-33.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The estimation of excess rainfall is critically important in water resource management as it provides the basis for calculating flood peak discharge that results in surface runoff. Kelantan River basin in Malaysia is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/cover (LULC) changes making the area consistently flood prone. The current study is therefore aimed to achieve the following goals: (1) to develop a curve number (CN) and runoff maps for 1984, 2002, and 2013 LULC conditions and (2) to determine runoff dynamics due to changes in LULC as well as to assess how the extent of LULC change will affect surface runoff generation. To achieve the aforementioned goals, land use maps corresponding to 1984, 2002, and 2014 LULC conditions were analyzed and prepared for the calculation of CN values using Soil Conservation Service (SCS-CN) method. CN and runoff maps corresponding to 1984, 2002, and 2013 LULC changes were successfully developed and the performance of the method was tested. The results indicated that forest was found to be the major land use type to have changed in all the LULC conditions across the watershed leading to intense runoff dynamics in the entire watershed. Higher runoff values were observed under 2013 LULC conditions across the watershed mainly due to intense deforestation relative to those of 1984 and 2002. The results of this study indicated that runoff generation is significantly affected by deforestation instead of changes in the rainfall pattern. The findings may be useful to water resource planners in controlling water loss for future planning.
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed', Geoscience Frontiers, vol. 10, no. 2, pp. 389-403.
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© 2017 China University of Geosciences (Beijing) and Peking University. The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing, wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc. which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover (LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km 2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation (USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor, topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential (reversible soil loss) or 0-1 t ha -1 yr -1 soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition. Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions (1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significan...
Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 2019, 'Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis', IEEE Transactions on Information Forensics and Security, 2020, vol. 15, pp. 3567-3579.
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In this paper, we work on intra-variable handwriting, where the writingsamples of an individual can vary significantly. Such within-writer variationthrows a challenge for automatic writer inspection, where the state-of-the-artmethods do not perform well. To deal with intra-variability, we analyze theidiosyncrasy in individual handwriting. We identify/verify the writer fromhighly idiosyncratic text-patches. Such patches are detected using a deeprecurrent reinforcement learning-based architecture. An idiosyncratic score isassigned to every patch, which is predicted by employing deep regressionanalysis. For writer identification, we propose a deep neural architecture,which makes the final decision by the idiosyncratic score-induced weightedaverage of patch-based decisions. For writer verification, we propose twoalgorithms for patch-fed deep feature aggregation, which assist inauthentication using a triplet network. The experiments were performed on twodatabases, where we obtained encouraging results.
Ahmed II, JB & Pradhan, B 2019, 'Spatial assessment of termites interaction with groundwater potential conditioning parameters in Keffi, Nigeria', Journal of Hydrology, vol. 578, pp. 124012-124012.
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© 2019 Elsevier B.V. Termite mounds are traditionally presumed to be good indicators of groundwater in places they inhabit but this hypothesis is yet to be scientifically substantiated. To confirm this assertion, it is expected that termite mounds would have strong correlations with groundwater conditioning parameters (GCPs). In this study, termite mounds distribution covering an area of about 156 km2 were mapped and their structural characteristics documented with the aim of examining their relationships with twelve (12) chosen GCPs. Other specific objectives were to identify specific mound types with affinity to groundwater and to produce a groundwater potential map of the study area. To achieve this, 12 GCPs including geology, drainage density, lineament density, lineament intersection density, land use/land cover, topographic wetness index (TWI), normalized difference vegetation index (NDVI), slope, elevation, plan curvature, static water level and groundwater level fluctuation were extracted from relevant sources. Frequency ratio (FR) and Spearman's rank correlation were used to find relationships and direction of such relationships. The result revealed a consistent agreement between FR and Spearman's rank correlation that tall (≥1.8 m) and Cathedral designed mounds are good indicators of groundwater. Further, the groundwater potential map produced from the Random Forest (RF) model via Correlation-based Feature Selection (CFS) using best-first algorithm depicted an erratic nature of groundwater distribution in the study area. This was then classified using natural break into very-high, high, moderate, low and very low potential classes and area under curve (AUC) of the receiver operating characteristics (ROC) showed an 86.5% validity of the model. About 75% of mapped termite mounds fell within the very-high to moderate potential classes thereby suggesting that although tall and cathedral mounds in particular showed good correlations with a number of GCPs, hi...
Ahmed II, JB, Pradhan, B, Mansor, S, Yusoff, ZM & Ekpo, SA 2019, 'Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters', Sensors, vol. 19, no. 9, pp. 2107-2107.
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In some parts of tropical Africa, termite mound locations are traditionally used to site groundwater structures mainly in the form of hand-dug wells with high success rates. However, the scientific rationale behind the use of mounds as prospective sites for locating groundwater structures has not been thoroughly investigated. In this paper, locations and structural features of termite mounds were mapped with the aim of determining the aquifer potential beneath termite mounds and comparing the same with adjacent areas, 10 m away. Soil and species sampling, field surveys and laboratory analyses to obtain data on physical, hydraulic and geo-electrical parameters from termite mounds and adjacent control areas followed. The physical and hydraulic measurements demonstrated relatively higher infiltration rates and lower soil water content on mound soils compared with the surrounding areas. To assess the aquifer potential, vertical electrical soundings were conducted on 28 termite mounds sites and adjacent control areas. Three (3) important parameters were assessed to compute potential weights for each Vertical Electrical Sounding (VES) point: Depth to bedrock, aquifer layer resistivity and fresh/fractured bedrock resistivity. These weights were then compared between those of termite mound sites and those from control areas. The result revealed that about 43% of mound sites have greater aquifer potential compared to the surrounding areas, whereas 28.5% of mounds have equal and lower potentials compared with the surrounding areas. The study concludes that termite mounds locations are suitable spots for groundwater prospecting owing to the deeper regolith layer beneath them which suggests that termites either have the ability to locate places with a deeper weathering horizon or are themselves agents of biological weathering. Further studies to check how representative our study area is of other areas with similar termite activities are recommended.
Ahmed, AA & Pradhan, B 2019, 'Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system', Environmental Monitoring and Assessment, vol. 191, no. 3.
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© 2019, Springer Nature Switzerland AG. This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
Ajibola, II, Mansor, S, Pradhan, B & Mohd. Shafri, HZ 2019, 'Fusion of UAV-based DEMs for vertical component accuracy improvement', Measurement, vol. 147, pp. 106795-106795.
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© 2019 Elsevier Ltd Most construction projects require data that comply with a certain standard of accuracy both in the horizontal and vertical components. This study aimed to develop a model for improving the quality of Digital Elevation Model (DEM) produced by UAV. UAV is the short form of Unmanned Aerial Vehicle, which is either fixed-wing or rotorcraft type. The study proposes a fusion approach that integrates a weighted averaging and additive median filtering algorithms to improve accuracy of the DEMs derived from fixed-wing UAVs. The low quality DEM was fused with high quality DEM produced by multi-rotor UAVs. Assessment of the DEM produced root mean square error of 1.14 cm and standard vertical accuracy of 2.24 cm at a 95% confidence level. This value represents a decrease in vertical standard error of 18.31 cm to 2.24 cm, which is an improvement of 88%. The result of the study indicates that the method is suitable for improving accuracy of DEM produced by UAVs.
Alaei, F, Alaei, A, Pal, U & Blumenstein, M 2019, 'A comparative study of different texture features for document image retrieval', Expert Systems with Applications, vol. 121, pp. 97-114.
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© 2018 Elsevier Ltd Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.
Alamdari, MM, Dang Khoa, NL, Wang, Y, Samali, B & Zhu, X 2019, 'A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge', Structural Health Monitoring, vol. 18, no. 1, pp. 35-48.
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A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.
Al-Amin Hoque, M, Billah, MM & Pradhan, B 2019, 'Spatio-temporal and demographic distribution of lightning related casualties in northeastern part of Bangladesh', International Journal of Disaster Risk Reduction, vol. 38, pp. 101197-101197.
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© 2019 Lightning is one of the frequent catastrophic hazards to people and properties across the world. Bangladesh is one of the major lightning prone countries in the world. Information regarding the spatial, temporal and demographic distribution of lightning casualties is required to develop mitigation policies to minimize the impacts of lightning. This study aims to analyse the spatial, temporal and demographic distribution of lightning-related casualties in the northeastern part of Bangladesh from 2016 to 2018. The database of lightning casualties was developed from a variety of sources including government and private agencies. Records dating from 2016 to 2018 indicate that about 78 and 60 people have been killed and injured, respectively by lightning strikes. The highest number of lightning fatalities were reported in the districts of Kishoreganj (31%), Habiganj (18%) and Sunamganj (15%). The overall fatality rate is 1.76 per million people per year, and fatality density rate is 0.00388 per million people km−2 year−1. The majority of fatalities and injuries occurred within the early morning 0800 and early evening 1700 at local time. The number of fatalities was higher in April–May during the pre-monsoon season. The maximum number of people died by lightning during farming activities, followed by fishing, boating or bathing in water bodies. The findings of the study are highly beneficial to the administrator and policymakers to develop lightning mitigation plans, improve public awareness and lightning safety campaign to reduce the impacts of lightning hazards.
Alazigha, DP, Vinod, JS, Indraratna, B & Heitor, A 2019, 'Potential use of lignosulfonate for expansive soil stabilisation', Environmental Geotechnics, vol. 6, no. 7, pp. 480-488.
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This study involved the laboratory evaluation of the effectiveness of lignosulfonate (LS) admixture in improving engineering properties (i.e. swell potential, unconfined compressive strength, durability, compaction characteristics, permeability, consolidation characteristics and shrinkage behaviour) of a remoulded expansive soil. Standard geotechnical laboratory tests performed on untreated and LS-treated soil specimens compacted at optimum moisture content and maximum dry unit weight showed significant and consistent improvements in the engineering properties of the soil. The swell potential of the soil decreased by 23% while maintaining its ductility and pH value. The improved soil resistance to repeated freeze–thaw/wet–dry cycles was also observed in the LS-treated specimens. Likewise, the compressive strength, consolidation characteristics and shrinkage limit improved appreciably. However, the compaction characteristics and permeability of the treated soil remained relatively unchanged. With over 50 Mt of global annual production of LS, the successful use of LS as an alternative admixture for expansive soil stabilisation provides viable solutions to the sustainable use of the lignin by-products from paper manufacturing industry.
Alilou, H, Rahmati, O, Singh, VP, Choubin, B, Pradhan, B, Keesstra, S, Ghiasi, SS & Sadeghi, SH 2019, 'Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria', Journal of Environmental Management, vol. 232, pp. 22-36.
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Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as 'a potentially critical zone'. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.
Al-Najjar, HAH, Kalantar, B, Pradhan, B, Saeidi, V, Halin, AA, Ueda, N & Mansor, S 2019, 'Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks', Remote Sensing, vol. 11, no. 12, pp. 1461-1461.
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In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants wer...
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Miniature tri‐wideband Sierpinski–Minkowski fractals metamaterial perfect absorber', IET Microwaves, Antennas & Propagation, vol. 13, no. 7, pp. 991-996.
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© The Institution of Engineering and Technology 2019. With rapidly growing adoption of wireless technologies, requirements for the design of a miniature wideband multiresonators are increasing. In this study, a compact fractal-based metamaterial structure with lumped resistors is described. The structure of the authors proposed absorber is a combination of Sierpinski curve and Minkowski fractal. The new combination provides larger capacitance and inductance in the system enabling perfect absorption at lower frequencies. The final structure with dimensions of 20 × 20 × 1.6 mm3 and an air gap of 12.5 mm provides three main resonances at frequencies of 2.1, 5.1, and 12.8 GHz with bandwidth (absorption ratio over 90%) of 840 MHz, 1.05 GHz, and 910 MHz, respectively.
Anis, Z, Wissem, G, Riheb, H, Biswajeet, P & Mohamed Essghaier, G 2019, 'Effects of clay properties in the landslides genesis in flysch massif: Case study of Aïn Draham, North Western Tunisia', Journal of African Earth Sciences, vol. 151, pp. 146-152.
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© 2018 Elsevier Ltd Heavy rainfall in Aïn Draham province in the North-Western of Tunisia lead to the formation of some landslides which could poses danger to lives and properties. The geological outcrops of the region mainly consist of Numidian flysch rocks. In this study, field based 15 undisturbed samples were taken, from 11 boreholes drilled in 4 landslide points, to understand the real behaviour of soils when landslides occur. For this purpose, the geotechnical characterization of all samples was carried out. The grain size distribution shows that the clay and silt fractions prevail. The clay fraction ranges between 4% and 64% with an average of 40.4%, the silt fraction ranging from 19% to 71% averaging 39.8% and the sand fraction was between 6% and 44% with an average of 19.8%. The Casagrande plasticity chart indicates that 33.3% of samples were in the high plasticity group (CH group) and 66.6% having a medium to low plasticity. The water content varies between 12% and 31%. The direct shear strength test shows that the cohesions values range from 41 KPa to 77 KPa and the internal friction angle values range widely from 12° to 27°. A statistical approach was taken to determine the most important factors responsible for the decrease of the cohesion and friction angle which are in charge of slope failure. For this, a correlation matrix of all soil properties was done. The coefficients of correlation show that the clay fraction is the most correlated parameter to the cohesion with an index of −0.872. Unfortunately, the internal friction angle is very low correlated to all geotechnical parameters. The clay fraction, as the most correlated to the cohesion, and the water content, which depends on rainfall (landslide triggering factor), were considered as two independent parameters for the establishment of a multiple linear and non-linear regression models of the cohesion. The multiple linear model showed that the cohesion decrease with the increase of water conten...
Arabameri, A, Chen, W, Blaschke, T, Tiefenbacher, JP, Pradhan, B & Tien Bui, D 2019, 'Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran', Water, vol. 12, no. 1, pp. 16-16.
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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach—bagging-based alternating decision-tree classifier (bagging-ADTree)—and use it to model a landscape’s susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model’s goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models’ analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = ...
Arabameri, A, Pradhan, B & Lombardo, L 2019, 'Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling', CATENA, vol. 183, pp. 104223-104223.
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© 2019 Elsevier B.V. The initiation and development of gullies as worldwide features in landscape have resulted in land degradation, soil erosion, desertification, flooding and groundwater level decrease, which in turn, cause severe destruction to infrastructure. Gully erosion susceptibility mapping is the first and most important step in managing these effects and achieving sustainable development. This paper attempts to generate a reliable map using four state-of-the-art models to investigate the Bayazeh Watershed in Iran. These models consists of boosted regression trees (BRT), binary logistic regression (BLR), numerical risk factor (NRF) and frequency ratio (FR), which are based on a geographic information system (GIS). The gully erosion inventory map accounts for 362 gully locations, which were randomly divided into two groups (70% for training and 30% for validation). Sixteen topographical, geological, hydrological and environmental gully-related conditioning factors were selected for modelling. The threshold-independent area under receiver operating characteristic (AUROC) and seed cell area index (SCAI) approaches were used for validation. According to the results of BLR and BRT, the conditioning parameters namely, NDVI and lithology, played a key role in gully occurrence. Validation results showed that the BRT model with AUROC = 0.834 (83.4%) had higher prediction accuracy than other models, followed by FR 0.823 (82.3%), NRF 0.746 (74.6%) and BLR 0.659 (65.9%). SCAI results indicated that the BRT, FR and BLR models had acceptable classification accuracy. The findings, in terms of model and predictor choice, can be used by decision-makers for hazard management and implementation of protective measures in gully erosion-prone areas.
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS', Journal of Environmental Management, vol. 232, pp. 928-942.
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© 2018 Elsevier Ltd Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWR–CF–RF approach has a higher predictive accuracy 0.967 (96....
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models', Geosciences Journal, vol. 23, no. 4, pp. 669-686.
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© 2019, The Association of Korean Geoscience Societies and Springer. Remote sensing is recognized as a powerful and efficient tool that provides a comprehensive view of large areas that are difficult to access, and also reduces costs and shortens the timing of projects. The purpose of this study is to introduce effective parameters using remote sensing data and subsequently predict gully erosion using statistical models of Density Area (DA) and Information Value (IV), and data mining based Random Forest (RF) model and their ensemble. The aforementioned models were employed at the Tororud-Najarabad watershed in the northeastern part of Semnan province, Iran. For this purpose, at first using various resources, the map of the distribution of the gullies was prepared with the help of field visits and Google Earth images. In order to analyse the earth’s surface and extraction of topographic parameters, a digital elevation model derived from PALSAR (Phased Array type L-band Synthetic Aperture Radar) radar data with a resolution of 12.5 meters was used. Using literature review, expert opinion and multi-collinearity test, 15 environmental parameters were selected with a resolution of 12.5 meters for the modelling. Results of RF model indicate that parameters of NDVI (normalized difference vegetation index), elevation and land use respectively had the highest effect on the gully erosion. Several techniques such as area under curve (AUC), seed cell area index (SCAI), and Kappa coefficient were used for validation. Results of validation indicated that the combination of bivariate (IV and DA models) with the RF data-mining model has increased their performance. The prediction accuracy of AUC and Kappa values in DA, IV and RF are (0.745, 0.782, and 0.792) and (0.804, 0.852, and 0.860) and these values in ensemble models of DA-RF and IV-RF are (0.845, and 0.911) and (0.872, and 0.951) respectively. Results of SCAI show that ensemble models had a good performance, so that, with...
Arabameri, A, Pradhan, B, Rezaei, K & Conoscenti, C 2019, 'Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques', CATENA, vol. 180, pp. 282-297.
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© 2019 Elsevier B.V. This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM)that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS)and the analytic hierarchy process (AHP)multi-criteria decision-making (MCDM)models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs)were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points)was obtained. Kriging and inverse distance-weighted (IDW)methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC)was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13)and distance to road (0.12)played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion.
Arabameri, A, Pradhan, B, Rezaei, K, Sohrabi, M & Kalantari, Z 2019, 'GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms', Journal of Mountain Science, vol. 16, no. 3, pp. 595-618.
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© 2019, Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature. In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory (70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party (ILWP), Forestry, Rangeland and Watershed Organisation (FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve (AUC), frequency ratio (FR) and seed cell area index (SCAI). Normalised difference vegetation index, land use/ land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models (AUC = 0.912 (91.2%) and 0.907 (90.7%), respectively) had high predictive accuracy than the LNRF model alone (AUC = 0.855 (85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslid...
Arabameri, A, Yamani, M, Pradhan, B, Melesse, A, Shirani, K & Tien Bui, D 2019, 'Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility', Science of The Total Environment, vol. 688, pp. 903-916.
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Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.
Arabameri, Cerda, Rodrigo-Comino, Pradhan, Sohrabi, Blaschke & Tien Bui 2019, 'Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)', Remote Sensing, vol. 11, no. 21, pp. 2577-2577.
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Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and man...
Arabameri, Pradhan, Rezaei & Lee 2019, 'Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-based FR–RF Integrated Model and Multiresolution DEMs', Remote Sensing, vol. 11, no. 9, pp. 999-999.
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Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1,072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most...
Arshad, B, Ogie, R, Barthelemy, J, Pradhan, B, Verstaevel, N & Perez, P 2019, 'Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review', Sensors, vol. 19, no. 22, pp. 5012-5012.
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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.
Ashournejad, Q, Hosseini, A, Pradhan, B & Hosseini, SJ 2019, 'Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model', Arabian Journal of Geosciences, vol. 12, no. 4.
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Aung, Y, Khabbaz, H & Fatahi, B 2019, 'Mixed hardening hyper-viscoplasticity model for soils incorporating non-linear creep rate – H-creep model', International Journal of Plasticity, vol. 120, pp. 88-114.
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© 2019 Elsevier Ltd. This paper focuses on the deformation of soils considering the time-dependent stress-strain evolution. In this paper, a new mixed hardening hyper-viscoplasticity model is proposed for the derivation of the time-dependent constitutive behaviour of soils, with the intention to capture the variation in the shapes of the yield loci by pursuing non-associated flow rules and accounting for kinematic hardening effects. The distinctive departure from the existing viscoplasticity models is the application of thermodynamics, based upon the use of internal variables, to postulate free-energy and dissipation potential functions, from which the corresponding yield locus, isotropic and kinematic hardening laws, flow rules and the elasticity law are deduced in a systematic procedure. The kinematic hardening behaviour of the yield locus is considered using the shift stress, resulting from the additional plastic component of the free-energy function. A non-linear creep formulation is postulated to address the limitation of over-estimating long-term settlement and incorporated into the model for more reliable predictions. The major parameters required for the model are identified, along with the summary of descriptions on how the model parameters can readily be determined. Non-associated behaviour is found to be a natural consequence of this approach, whenever the division between dissipated and stored plastic work is not equal. This study aims to provide a theoretical background and a numerical implementation for those who are interested in the advancement of constitutive modelling of soil behaviour under the framework of hyperplasticity. Validity and versatility of the proposed constitutive model are evaluated against triaxial and oedometer test results available in literature.
Azar, APK, Askari, G, Crispini, L, Pour, AB, Zoheir, B & Pradhan, B 2019, 'Field and spaceborne imagery data for evaluation of the paleo-stress regime during formation of the Jurassic dike swarms in the Kalateh Alaeddin Mountain area, Shahrood, north Iran', Arabian Journal of Geosciences, vol. 12, no. 17.
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© 2019, Saudi Society for Geosciences. Dike swarms are commonly linked with extensional structures in diverse geodynamic environments. Mafic dyke swarms are typically used to reconstruct the paleo-stress fields of a given region. These dikes are considered paleo-stress indicators and excellent time marker (if related geochronological data are available) of the local and regional stress fields. In the Middle-Late Jurassic, swarms of mafic dikes emplaced into the Neoproterozoic schists and amphibolites in the Kalateh Alaeddin Mountain area in south Shahrood, north Iran. These dikes with different thicknesses show a general east–west strike direction, with mostly a steep dip angle. In this paper, we present structural data of these dike swarms for the sake of assessing the paleo-stress state and the magma pressure ratio at the time of their emplacement. Field and structural data are integrated with ASTER Global Digital Elevation Model (GDEM) and Centre National d’Etudes Spatiales (CNES)/SPOT imagery data, to extract important parameters of the investigated dikes and controlling fault/joint sets. Orientation of the principal paleo-stress axes, quantification of the stress ratio, and the associated magma pressure ratio (driving stress ratio) were calculated using the stereographic projection and Mohr’s circle reconstruction techniques. The results reveal that the maximum paleo-stress component (σ1) was sub-vertical and the intermediate (σ2) and minimum (σ3) paleo-stresses components were sub-horizontal in N264° E and N173° E trends, respectively. Due to the low value of the driving stress ratio (R = 0.05), these dikes developed perpendicular to the minimum principal stress (in E–W direction). The stress ratio value (ø = 0.66) indicates a moderately oblate stress ellipsoid. The orientation of the principal paleo-stress axes and the oblate ellipsoid are indicative of the dike emplacement during a N–S-directed tectonic extension, in agreement with the Jurassic subsidence...
AzariJafari, H, Taheri Amiri, MJ, Ashrafian, A, Rasekh, H, Barforooshi, MJ & Berenjian, J 2019, 'Ternary blended cement: An eco-friendly alternative to improve resistivity of high-performance self-consolidating concrete against elevated temperature', Journal of Cleaner Production, vol. 223, pp. 575-586.
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© 2019 Elsevier Ltd The unique fresh and hardened properties of high-performance self-consolidating concrete (HPSCC) led to an extensive application of this mixture in high-rise buildings. In this paper, the elevated temperature resistivity of 19 HPSCC mixtures incorporating binary and ternary blends of fly ash, silica fume, natural zeolite, and metakaolin was investigated. Changes in mass, compressive strength and ultrasonic pulse velocity (UPV) of the mixtures were measured at different temperatures (20, 300, 500, and 700 °C). A life cycle assessment (LCA) was also employed to explore the environmental performance of the mixtures. The test results revealed that in ambient temperature, ternary mixtures incorporating natural zeolite and fly ash or natural zeolite and metakaolin have lower compressive strength than that of the control mixture. The residual compressive strengths of fly ash-silica fume-incorporated mixture was similar to those in binary mixtures. The UPV test results revealed a larger than 50% reduction in transition velocity when the temperature was above 500 °C, and there is a strong association between the UPV and compressive strength test results of the mixtures at different temperatures, but the correlation decreased inversely proportional to the exposure temperature. Among the ternary mixtures, those mixtures that incorporate natural zeolite indicate the most significant mass loss after exposing to elevated temperature. The environmental results indicate that the substitution of pozzolanic materials with Portland cement may not always be beneficial. The ecosystem quality results of binary fly ash mixtures were larger than the control mixture due to the extensive transportation distance of import. In addition, metakaolin binary mixture exposes larger damage to the resources. Silica fume-incorporated mixtures had significant damage to human health. The ternary blended mixtures can be a remedy to obtain the optimized fire-resistant results and to...
Azeez, O, Pradhan, B, Shafri, H, Shukla, N, Lee, C-W & Rizeei, H 2019, 'Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks', Applied Sciences, vol. 9, no. 2, pp. 313-313.
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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
Azeez, OS, Pradhan, B, Jena, R, Jung, HS & Ahmed, AA 2019, 'Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model', Korean Journal of Remote Sensing, vol. 35, no. 1, pp. 137-149.
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Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara–Selatan Expressway–NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.
Baral, P, Rujikiatkamjorn, C, Indraratna, B, Leroueil, S & Yin, J-H 2019, 'Radial Consolidation Analysis Using Delayed Consolidation Approach', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 10, pp. 04019063-04019063.
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© 2019 American Society of Civil Engineers. The paper offers an analytical solution for radial consolidation that captures isotaches with a strain-rate dependency of preconsolidation pressure. These relationships are obtained based on constant-rate-of-strain (CRS) and long-term consolidation (LTC) tests and then used in the radial consolidation model incorporating the field strain rate, which is generally much lower compared with the typical laboratory environment. In this study, the calculated settlement and associated excess pore-water pressure are obtained using the equivalent preconsolidation pressure from the reference isotache within the (σp′/σp0′)-(ϵv) domain. Moreover, the change in Cα/Cc ratio (i.e., secondary compression index/compression index) with decreasing strain rate is used to calculate the long-term settlement. This method is then validated using various case histories in Australia and Southeast Asia, where excess pore-water pressure is dissipated at a slower rate in relation to the observed settlement.
Beiranvand Pour, A, Park, Y, Crispini, L, Läufer, A, Kuk Hong, J, Park, T-YS, Zoheir, B, Pradhan, B, Muslim, AM, Hossain, MS & Rahmani, O 2019, 'Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data', Remote Sensing, vol. 11, no. 12, pp. 1408-1408.
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Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanic...
Biradar, J, Banerjee, S, Shankar, R, Ghosh, P, Mukherjee, S & Fatahi, B 2019, 'Response of square anchor plates embedded in reinforced soft clay subjected to cyclic loading', Geomechanics and Engineering, vol. 17, no. 2, pp. 165-173.
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Plate anchors are generally used for structures like transmission towers, mooring systems etc. where the uplift and lateral forces are expected to be predominant. The capacity of anchor plate can be increased by the use of geosynthetics without altering the size of plates. Numerical simulations have been carried out on three different sizes of square anchor plates. A single layer geosynthetic has been used as reinforcement in the analysis and placed at three different positions from the plate. The effects of various parameters like embedment ratio, position of reinforcement, width of reinforcement, frequency and loading amplitude on the pull out capacity have been presented in this study. The load-displacement behaviour of anchors for various embedment ratios with and without reinforcement has been also observed. The pull out load, corresponding to a displacement equal to each of the considered maximum amplitudes of a given frequency, has been expressed in terms of a dimensionless breakout factor. The pull out load for all anchors has been found to increase by more than 100% with embedment ratio varying from 1 to 6. Finally a semi empirical formulation for breakout factor for square anchors in reinforced soil has also been proposed by carrying out regression analysis on the data obtained from numerical simulations.
Bui, Moayedi, Kalantar, Osouli, Gör, Pradhan, Nguyen & Rashid 2019, 'Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility', Sensors, vol. 19, no. 16, pp. 3590-3590.
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In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.
Cheah, R, Billa, L, Chan, A, Teo, FY, Pradhan, B & Alamri, AM 2019, 'Geospatial Modelling of Watershed Peak Flood Discharge in Selangor, Malaysia', Water, vol. 11, no. 12, pp. 2490-2490.
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Conservative peak flood discharge estimation methods such as the rational method do not take into account the soil infiltration of the precipitation, thus leading to inaccurate estimations of peak discharges during storm events. The accuracy of estimated peak flood discharge is crucial in designing a drainage system that has the capacity to channel runoffs during a storm event, especially cloudbursts and in the analysis of flood prevention and mitigation. The aim of this study was to model the peak flood discharges of each sub-watershed in Selangor using a geographic information system (GIS). The geospatial modelling integrated the watershed terrain model, the developed Soil Conservation Service Curve Cumber (SCS-CN) and precipitation to develop an equation for estimation of peak flood discharge. Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) was used again to simulate the rainfall-runoff based on the Clark-unit hydrograph to validate the modelled estimation of peak flood discharge. The estimated peak flood discharge showed a coefficient of determination, r2 of 0.9445, when compared with the runoff simulation of the Clark-unit hydrograph. Both the results of the geospatial modelling and the developed equation suggest that the peak flood discharge of a sub-watershed during a storm event has a positive relationship with the watershed area, precipitation and Curve Number (CN), which takes into account the soil bulk density and land-use of the studied area, Selangor in Malaysia. The findings of the study present a comparable and holistic approach to the estimation of peak flood discharge in a watershed which can be in the absence of a hydrodynamic simulation model.
Chen, B, Huang, J & Ji, JC 2019, 'Control of flexible single-link manipulators having Duffing oscillator dynamics', Mechanical Systems and Signal Processing, vol. 121, pp. 44-57.
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© 2018 Elsevier Ltd Unwanted vibrations caused by the commanded motions lower the positioning accuracy and degrade the control performance of flexible link manipulators. Much work has been devoted to the dynamics and control of flexible link manipulators. However, there are few studies dedicated to the flexible link manipulators having Duffing oscillator dynamics. This paper develops a new model for a flexible single-link manipulator by assuming large mechanical impedance in the drives or large inertia of the motor hub and by considering the flexibility of the single-link manipulator. The derived model includes an infinite number of uncoupled Duffing oscillators by ignoring the modal coupling effects. Two new methods are presented for controlling the vibrations of the flexible manipulator governed by Duffing oscillators. One is designed for the single-mode Duffing oscillator, and the other is for the multi-mode Duffing oscillators. A comparison of these two methods is made using the results of numerical simulations and experimental measurements. Experimental investigations are also performed on a flexible single-link manipulator to validate the dynamic behavior of Duffing oscillators and the effectiveness of the new control methods.
Chen, W, Pradhan, B, Li, S, Shahabi, H, Rizeei, HM, Hou, E & Wang, S 2019, 'Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis', Natural Resources Research, vol. 28, no. 4, pp. 1239-1258.
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© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.
Chen, W, Yan, X, Zhao, Z, Hong, H, Bui, DT & Pradhan, B 2019, 'Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)', Bulletin of Engineering Geology and the Environment, vol. 78, no. 1, pp. 247-266.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation...
Chenari, RJ, Fatahi, B, Ghoreishi, M & Taleb, A 2019, 'Physical and numerical modelling of the inherent variability of shear strength in soil mechanics', Geomechanics and Engineering, vol. 17, no. 1, pp. 31-45.
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In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of FLAC 3D . The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.
Cheng, S, Dong, H, Yu, L, Zhang, D & Ji, J 2019, 'Consensus of Second-order Multi-agent Systems with Directed Networks Using Relative Position Measurements Only', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 85-93.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. This brief paper studies the consensus problem of second-order multi-agent systems when the agents’ velocity measurements are unavailable. Firstly, two simple consensus protocols which do not need velocity measurements of the agents are derived to guarantee that the multi-agent systems achieve consensus in directed networks. Secondly, a key constant which is determined by the complex eigenvalue of the nonsymmetric Laplacian matrix and an explicit expression of the consensus state are respectively developed based on matrix theory. The obtained results show that all the agents can reach consensus if the feedback parameter is bigger than the key constant. Thirdly, the theoretical analysis shows that the followers can track the position and velocity of the leader provided that the leader has a directed path to all other followers and the feedback parameter is bigger enough. Finally, numerical simulations are given to illustrate the effectiveness of the proposed protocols.
Dackermann, U, Smith, WA, Alamdari, MM, Li, J & Randall, RB 2019, 'Cepstrum-based damage identification in structures with progressive damage', Structural Health Monitoring, vol. 18, no. 1, pp. 87-102.
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This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis; while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies; as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available; second, a new damage feature is proposed and its superior performance is demonstrated;...
Dano, U, Balogun, A-L, Matori, A-N, Wan Yusouf, K, Abubakar, I, Said Mohamed, M, Aina, Y & Pradhan, B 2019, 'Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia', Water, vol. 11, no. 3, pp. 615-615.
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Understanding factors associated with flood incidence could facilitate flood disaster control and management. This paper assesses flood susceptibility of Perlis, Malaysia for reducing and managing their impacts on people and the environment. The study used an integrated approach that combines geographic information system (GIS), analytic network process (ANP), and remote sensing (RS) derived variables for flood susceptibility assessment and mapping. Based on experts’ opinion solicited via ANP survey questionnaire, the ANP mathematical model was used to calculate the relative weights of the various flood influencing factors. The ArcGIS spatial analyst tools were used in generating flood susceptible zones. The study found zones that are very highly susceptible to flood (VHSF) and those highly susceptible to flood (HSF) covering 38.4% (30,924.6 ha) and 19.0% (15,341.1 ha) of the study area, respectively. The results were subjected to one-at-a-time (OAT) sensitivity analysis to verify their stability, where 6 out of the 22 flood scenarios correlated with the simulated spatial assessment of flood susceptibility. The findings were further validated using real-life flood incidences in the study area obtained from satellite images, which confirmed that most of the flooded areas were distributed over the VHSF and HSF zones. This integrated approach enables network model structuring, and reflects the interdependences among real-life flood influencing factors. This accurate identification of flood prone areas could serve as an early warning mechanism. The approach can be replicated in cities facing flood incidences in identifying areas susceptible to flooding for more effective flood disaster control.
Daqamseh, S, Al-Fugara, A, Pradhan, B, Al-Oraiqat, A & Habib, M 2019, 'MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia', Sensors, vol. 19, no. 9, pp. 2069-2069.
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In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.
Darabi, H, Choubin, B, Rahmati, O, Torabi Haghighi, A, Pradhan, B & Kløve, B 2019, 'Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques', Journal of Hydrology, vol. 569, pp. 142-154.
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© 2018 Elsevier B.V. Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.
Dash, SK, Saikia, R & Nimbalkar, S 2019, 'Contact Pressure Distribution on Subgrade Soil Underlying Geocell Reinforced Foundation Beds', Frontiers in Built Environment, vol. 5.
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© Copyright © 2019 Dash, Saikia and Nimbalkar. High contact stresses generated in the foundation soil, owing to increased load, causes distress, instability, and large settlements. Present days, geocell reinforcement is being widely used for the performance improvement of foundation beds. Pressure distribution on subgrade soil in geocell reinforced foundation beds is studied through model tests and numerical analysis. The test data indicates that with provision of geocell reinforcement the contact pressure on the subgrade soil reduces significantly. Consequently, the subgrade soil tends to remain intact until large loadings on the foundation leading to significant performance improvement. Through numerical analysis it is observed that the geocells in the region under the footing were subjected to compression and beyond were in tension. This indicates that the geocell reinforcement right under the footing directly sustains the footing loading through mobilization of its compressive stiffness and bending rigidity. Whereas, the end portions of the geocell reinforcement, contribute to the performance improvement in a secondary manner through mobilization of anchorage derived from soil passive resistance and friction.
Deng, S, Ji, J, Yin, S & Wen, G 2019, 'Multistability in the Centrifugal Governor System Under a Time-Delay Control Strategy', Journal of Computational and Nonlinear Dynamics, vol. 14, no. 11.
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Abstract The centrifugal governor system plays an indispensable role in maintaining the near-constant speed of engines. Although different arrangements have been developed, the governor systems are still applied in many machines for its simple mechanical structure. Therefore, the large-amplitude vibrations of the governor system which can lead to the function failure of the system should be attenuated to guarantee reliable operation. This paper adopts a time-delay control strategy to suppress the undesirable large-amplitude motions in the centrifugal governor system, which can be regarded as the practical application of the delayed feedback controller in this system. The stability region of the trivial equilibrium of the controlled system is determined by investigating the characteristic equation and generic Hopf bifurcations. It is found that the dynamic behavior of multistability can be induced by the Bautin bifurcation, arising on the stability boundary of the trivial equilibrium with a constant delay. More specifically, a coexistence of two desirable stable motions, i.e., an equilibrium or a small-amplitude periodic motion, can be observed in the controlled centrifugal governor system without changing the physical parameters. This is a new feature of the motion control in the centrifugal governor systems, which has not yet been reported in the existing studies. Finally, the results of theoretical analyses are verified by numerical simulations.
Dikshit, A, Sarkar, R, Pradhan, B, Acharya, S & Dorji, K 2019, 'Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas', Water, vol. 11, no. 8, pp. 1616-1616.
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Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data a...
Dikshit, A, Satyam, N & Pradhan, B 2019, 'Estimation of Rainfall-Induced Landslides Using the TRIGRS Model', Earth Systems and Environment, vol. 3, no. 3, pp. 575-584.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this stu...
Ding, H, Ji, J & Chen, L-Q 2019, 'Nonlinear vibration isolation for fluid-conveying pipes using quasi-zero stiffness characteristics', Mechanical Systems and Signal Processing, vol. 121, no. Int. J. Non-linear Mech. 45 2010, pp. 675-688.
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© 2018 Fluid-conveying pipes are always subjected to various excitations to cause unwanted vibrations. A quasi-zero stiffness system consisting of three linear springs is adopted as the nonlinear isolator to attenuate the transverse vibrations of fluid-conveying pipes induced by foundation excitations. A dynamic model of nonlinear forced vibration of the fluid-conveying pipe coupled with two nonlinear isolators is established for the nonlinear continuous system and validated by using two methods, Galerkin method and the finite difference method. The influence of the quasi-zero stiffness isolators on the vibration characteristics and vibration transmission of the pipe is investigated by analyzing the natural frequency, vibration mode, and nonlinear vibration response. The effects of flow speed of the fluid and the system parameters of the isolator are studied to evaluate the isolation performance. It is found that the quasi-zero stiffness isolator and fluid flow can shift several natural frequencies of vibration of the pipeline to the low-frequency region. When the linear stiffness of the vibration isolation is zero in the vertical direction, the first two modes of the bending vibration of the fluid-conveying pipe tend to become rigid mode. While achieving high-efficiency vibration isolation in the high-frequency region, the vibration in the low-frequency region is complicated. The flow speed of the fluid can deteriorate the performance of vibration isolation.
Dong, W, Li, W, Long, G, Tao, Z, Li, J & Wang, K 2019, 'Electrical resistivity and mechanical properties of cementitious composite incorporating conductive rubber fibres', Smart Materials and Structures, vol. 28, no. 8, pp. 085013-085013.
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© 2019 IOP Publishing Ltd. Conductive cementitious composites with excellent conductivity and piezoresistivity can be potentially used for pavement deicing, concrete corrosion evaluations or structural health monitoring. Inspired by the practice of recycling rubber wastes for concrete manufacturing, the conductive rubbers are first added as enhanced fillers to improve the electrical conductivity of cementitious composite in this study. Based on the experimental investigations on electrical resistivity, mechanical properties and microstructure, the results show that cementitious composites containing conductive rubber fibres exhibit relatively low resistivity with nearly one order of magnitude to approximately 1 × 104 Ω cm. On the other hand, cementitious composites with aluminium/silver filled rubber (AR) exhibit better conductivity than the counterparts with carbon black filled rubber (CR). For CR reinforced composites (CRC) and AR reinforced composite (ARC) with more than 40 rubber fibres (0.64 vol%), the higher the rubber fibre content, the better is the conductivity but the slightly lower the compressive strength. The cementitious composites reinforced by 100 conductive rubber fibres (1.6 vol%) not only display excellent conductivity but also provides acceptable mechanical properties, with up to 30.6% increase in ultimate strain but only 17.3% reduction in compressive strength. Furthermore, cementitious composites with rubber fibres demonstrate better damping capacity by enlarging stress-strain hysteresis loops compared to the counterpart without rubber. Such promising conductivity and damping properties provide the cementitious composites with great potentials for being used as cementitious composite sensors and smart composites to self-monitor the structural health or traffic load of various transportation infrastructures, such as bridges, highways and pavements.
Dong, W, Li, W, Lu, N, Qu, F, Vessalas, K & Sheng, D 2019, 'Piezoresistive behaviours of cement-based sensor with carbon black subjected to various temperature and water content', Composites Part B: Engineering, vol. 178, pp. 107488-107488.
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© 2019 Elsevier Ltd Cement-based sensor possesses unique properties for structural health monitoring (SHM) applications, such as low cost, high durability, adaptability and excellent sensitivity. The piezoresistivity of cement-based sensor possesses is often affected by working environments, which may limit its real potentials. In this study, the piezoresistive sensitivity and repeatability of cement-based sensors with carbon black (CB) under various environmental conditions were investigated. Under various temperatures ranging from −20 °C to 100 °C, the piezoresistive sensitivity and repeatability were almost unchanged when eliminating the effects by thermal exchanges. The water content of cementitious composites caused significant fluctuations on the resistivity and piezoresistivity, and the optimal water content for cement-based sensor possesses was found to be approximately 8%. Subjected to freeze-thaw cycles, dry CB/cementitious composites slightly reduced the piezoresistive sensitivity. However, the saturated composites presented dramatic piezoresistivity reduction by 30.7%, due to the microstructural damages caused by the volume expansion and shrinkage of pore solution. The related outcomes provide scientific framework for the adoption of CB/cementitious composites sensors for the SHM of concrete infrastructures under various environmental conditions.
Dong, W, Li, W, Shen, L & Sheng, D 2019, 'Piezoresistive behaviours of carbon black cement-based sensors with layer-distributed conductive rubber fibres', Materials & Design, vol. 182, pp. 108012-108012.
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© 2019 Conductive rubber fibres filled carbon black (CB)/cementitious composites were developed to achieve the cement-based sensors with excellent piezoresistivity in this study. Ameliorations on the conductivity and piezoresistive sensitivity of CB filled composites were mainly explored with conductive rubber fibres embedded. Their compressive strengths were investigated to evaluate the practical application possibility. The results indicated that the composites with CB content <4.0 wt% possessed acceptable compressive strengths. In terms of conductivity and piezoresistivity, both conductivity and piezoresistivity of composites filled with 0.5 wt% CB increased with the rubber content, and their gauge factor raised to 91 when embedded with 80 rubber fibres (1.27 vol%). Moreover, phenomenon of “piezoresistive percolation” was observed by sharp fractional changes of resistivity for the composites filled with 1.0 wt% CB, where existed highest gauge factor reaching 482 when embedded with same rubber fibres. However, because of the excellent conductivity of 2.0 wt% CB filled composites, the gauge factor firstly increased but then slightly decreased around 100 with increase of rubber fibre content. Overall, conductive rubber fibres can significantly improve the piezoresistivity of CB/cementitious composites by the increased gauge factor.
Fanos, AM & Pradhan, B 2019, 'A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS', Earth Systems and Environment, vol. 3, no. 3, pp. 491-506.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Abstract: Rockfall is a common phenomenon in mountainous and hilly areas worldwide, including Malaysia. Rockfall source identification is a challenging task in rockfall hazard assessment. The difficulty rise when the area of interest has other landslide types with nearly similar controlling factors. Therefore, this research presented and assessed a hybrid model for rockfall source identification based on the stacking ensemble model of random forest (RF), artificial neural network, Naive Bayes (NB), and logistic regression in addition to Gaussian mixture model (GMM) using high-resolution airborne laser scanning data (LiDAR). GMM was adopted to automatically compute the thresholds of slope angle for various landslide types. Chi square was utilised to rank and select the conditioning factors for each landslide type. The best fit ensemble model (RF–NB) was then used to produce probability maps, which were used to conduct rockfall source identification in combination with the reclassified slope raster based on the thresholds obtained by the GMM. Next, landslide potential area was structured to reduce the sensitivity and the noise of the model to the variations in different conditioning factors for improving its computation performance. The accuracy assessment of the developed model indicates that the model can efficiently identify probable rockfall sources with receiver operating characteristic curve accuracies of 0.945 and 0.923 on validation and training datasets, respectively. In general, the proposed hybrid model is an effective model for rockfall source identification in the presence of other landslide types with a reasonable generalisation performance. Graphic Abstract: [Figure not available: see fulltext.].
Fanos, AM & Pradhan, B 2019, 'A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS', CATENA, vol. 172, pp. 435-450.
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© 2018 Elsevier B.V. Rockfall hazards occur widely in regions with steep terrain such as Kinta Valley, Malaysia. Rockfalls threaten urban areas and the transportation corridors that pass through such areas. This paper proposes a comprehensive rockfall hazard assessment strategy based on high-resolution laser scanning data (LiDAR), both airborne and terrestrial. It provides (1) rockfall source identification by developing a hybrid model based on a bagging neural network (BBNN), which is compared with various machine learning algorithms and ensemble models (bagging, boosting, voting) and a Gaussian mixture model; (2) 3D modelling of rockfall kinematic processes (trajectory distribution, frequency, velocity, kinetic energy, bounce height, impact location); and (3) hazard zonation based on spatial modelling in combination with an analytical hierarchy process (AHP) in a geographic information system (GIS). In addition, mitigation measures are suggested based on the modelling results. The proposed methodology was validated in three study areas to test the applicability and generalisability of the methods. The results show that the proposed hybrid model can accurately identify rockfall source areas at the regional scale. It achieved a 97% training accuracy and 5-fold cross-validation area under curve (AUC) value of 0.96. The mechanical parameters of the developed 3D model were calibrated with an accuracy of 97%, 93% and 95% for Gunung Lang, Gua Tambun and Gunung Rapat areas, respectively. In addition, the proposed spatial model effectively delineates areas at risk of rockfalls. This method provides a comprehensive understanding of rockfall hazards that can assist authorities to develop proper management and protection of urban areas and transportation corridors.
Fanos, AM & Pradhan, B 2019, 'A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS', IEEE Access, vol. 7, pp. 74570-74585.
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Fanos, AM, Pradhan, B, Mansor, S, Yusoff, ZM, Abdullah, AFB & Jung, HS 2019, 'Rockfall Source Identification Using a Hybrid Gaussian Mixture-Ensemble Machine Learning Model and LiDAR Data', Korean Journal of Remote Sensing, vol. 35, no. 1, pp. 93-115.
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The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms (ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.
Fatahi, B 2019, 'Editorial', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 1, pp. 1-2.
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Gao, C, Ji, J, Yan, F & Liu, H 2019, 'Oscillation induced by Hopf bifurcation in the p53–Mdm2 feedback module', IET Systems Biology, vol. 13, no. 5, pp. 251-259.
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This study develops an integrated model of the p53-Mdm2 interaction composed of five basic components and time delay in the DNA damage response based on the existing research work. Some critical factors, including time delay, system parameters, and their interactions in the p53-Mdm2 system are investigated to examine their effects on the oscillatory behaviour induced by Hopf bifurcation. It is shown that the positive feedback formed between p53 and the activity of Mdm2 in the cytoplasm can cause a slight decrease in the amplitude of the p53 oscillation. The length of the time delay plays an important role in determining the amplitude and period of the oscillation and can significantly extend the parameter range for the system to demonstrate oscillatory behaviour. The numerical simulation results are found to be in good agreement with the published experimental observation. It is expected that the results of this research would be helpful to better understand the biological functions of p53 pathway and provide some clues in the treatment of cancer.
Ghasemi, M, Rasekh, H, Berenjian, J & AzariJafari, H 2019, 'Dealing with workability loss challenge in SCC mixtures incorporating natural pozzolans: A study of natural zeolite and pumice', Construction and Building Materials, vol. 222, pp. 424-436.
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Ghodousi, Alesheikh, Saeidian, Pradhan & Lee 2019, 'Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data', Sustainability, vol. 11, no. 17, pp. 4595-4595.
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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.
Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Estimating chlorophyll content at leaf scale in viroid-inoculated oil palm seedlings (Elaeis guineensis Jacq.) using reflectance spectra (400 nm–1050 nm)', International Journal of Remote Sensing, vol. 40, no. 19, pp. 7647-7662.
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Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Selection of a Spectral Index for Detection of Orange Spotting Disease in Oil Palm (Elaeis guineensis Jacq.) Using Red Edge and Neural Network Techniques', Journal of the Indian Society of Remote Sensing, vol. 47, no. 4, pp. 639-646.
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© 2019, Indian Society of Remote Sensing. Spectral screening can play an important role in successful detection of viroid-infected oil palm seedlings from nursery stage prior to transplanting into the field. Coconut cadang–cadang viroid (CCCVd) is the main causal agent of orange spotting (OS) disease. OS disease is an emerging disease in Malaysian plantation. In this study, a glasshouse experiment was conducted with fifteen CCCVd-inoculated and five healthy oil palm seedlings in the growing season of 2015. Spectral screening was performed using a hyperspectral spectroradiometer, Analytic Spectral Device HandHeld 2 (325–1075 nm). The red edge, a steep gradient in reflectance between red and near-infrared bands (680–780 nm), was used for selection of red edge bands. A maximum point (i.e., 700 nm) and minimum point (i.e., 768 nm) of red edge were selected from healthy and inoculated spectra. Shifts of red edge inflection point from healthy to inoculated spectra were also studied. Four well-known spectral indices, namely simple ratio, red edge normalized difference vegetation index, two-band enhanced vegetation index 2 (EVI2), and chlorophyll index red edge, were evaluated using selected red edge bands. The multilayer perceptron neural network model was used to establish a nonlinear relationship between selected spectral bands and each spectral index. EVI2 was selected as a best spectral index which resulted in zero errors at the training, testing, and validation datasets. The highest coefficient of correlation (r = 1) was recorded between spectral bands (input values) and EVI2 (target values).
Gong, B, Wang, S, Sloan, SW, Sheng, D & Tang, C 2019, 'Modelling Rock Failure with a Novel Continuous to Discontinuous Method', Rock Mechanics and Rock Engineering, vol. 52, no. 9, pp. 3183-3195.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. The original discontinuous deformation and displacement (DDD) method is greatly refined using the statistical damage theory and contact mechanics. Next, a novel coupled method is proposed to model the continuous to discontinuous failure process of rocks. By hybridizing the finite element method (FEM) and discontinuous deformation analysis (DDA) method, the proposed method inherits the advantages of both and is able to provide a complete and unified description of rock deformation, crack initiation and propagation, and rock body translation, rotation and interaction. Moreover, to improve the deformation results and refine the stress distribution within the model blocks, finite elements are introduced into the blocks. The ability of an intact block to fracture is included as well, i.e., the deformable blocks that contain several finite elements may split into smaller blocks if the strength criteria are satisfied continuously. The boundaries of damaged elements represent newly formed joints, and sliding and opening may occur along these joints, i.e., mechanical interaction is allowed between adjacent blocks. The correctness and validity of the proposed method are verified through a series of benchmark tests. The simulated results are consistent with the analytical solutions, previous studies and experimental observations. Overall, the coupled method is an effective and reliable approach for modelling the entire rock failure process with satisfactory accuracy and shows considerable potential in geotechnical engineering.
Gowripalan, N, Nguyen, T, Yang, Y, Li, J & Sirivivatnanon, V 2019, 'Evaluation of elastic modulus reduction due to ASR', Concrete in Australia, vol. 45, no. 2, pp. 47-52.
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Evaluation of reduction in modulus of elasticity of concrete undergoing alkali silica reaction is carried out using an artificial neural network
Gu, X, Yu, Y, Li, Y, Li, J, Askari, M & Samali, B 2019, 'Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control', Mechanical Systems and Signal Processing, vol. 119, pp. 380-398.
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© 2018 Elsevier Ltd In this paper, a “smart” base isolation strategy is proposed in this study utilising a semi-active magnetorheological elastomer (MRE) isolator whose stiffness can be controlled in real-time and reversible fashion. By modulating the applied current, the horizontal stiffness of the MRE isolator can be controlled and thus the control action can be generated for the isolated structure. To overcome the inherent nonlinearity and hysteresis of the MRE isolator, radial basis function neural network based fuzzy logic control (RBF-NFLC) was developed due to its inherent robustness and capability in coping with uncertainties. The NFLC was optimised by a non-dominated sorting genetic algorithm type II (NSGA-II) for better suited fuzzy control rules as well as most appropriate parameters for the membership functions. To evaluate the effectiveness of the proposed smart base isolation system, four scenarios are tested under various historical earthquake excitations, i.e. bare building with no isolation, passive isolated structure, MRE isolated structure with Bang-Bang control, MRE isolated structure with proposed NFLC. A three-storey shear building model was adopted as the testing bed. Through the testing results, limited performance of passive isolation system was revealed. In contrast, the adaptability of the proposed isolation strategy was demonstrated and it is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
Habib, M, Alfugara, A & Pradhan, B 2019, 'A LOW-COST SPATIAL TOOL FOR TRANSFORMING FEATURE POSITIONS OF CAD-BASED TOPOGRAPHIC MAPPING', Geodesy and cartography, vol. 45, no. 4, pp. 161-168.
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In fact, Computer Aided Design (CAD) offers powerful design tools to produce digital large scale topographic mapping that is considered the backbone for construction projects, urban planning and landscape architecture. Nowadays local agencies in small communities and developing countries are facing some difficulties in map to map transformation and handling discrepancies between the physical reality and represented spatial data due to the need for implementing high cost systems such as GIS and the experienced staff required. Therefore, the require for providing a low-cost tool based on the most common CAD system is very important to guarantee a quality and positional accuracy of features. The main aim of this study is to describe a mathematical relationship to fulfil the coordinate conversion between two different grid references applying two-dimensional conformal polynomial models built on control points and a least squares fitting algorithm. In addition, the automation of this model was performed in the Microsoft Visual Studio environment to calculate polynomial coefficients and convert the positional property of entities in AutoCAD by developing spatial CAD tool. To evaluate the proposed approach the extracted coordinates of check points from the interpolation surface are compared with the known ones.
Hakdaoui, S, Emran, A, Pradhan, B, Lee, C-W & Nguemhe Fils, SC 2019, 'A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco', Remote Sensing, vol. 11, no. 9, pp. 1042-1042.
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This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.
Hassoun, M & Fatahi, B 2019, 'Novel integrated ground anchor technology for the seismic protection of isolated segmented cantilever bridges', Soil Dynamics and Earthquake Engineering, vol. 125, pp. 105709-105709.
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© 2019 Elsevier Ltd An external restraining system which is anchoring the bridge superstructure to the embankment backfill is proposed in this study for the seismic protection of isolated bridges. The restraining system is employed to reduce the seismic demands of the bridge deck by utilising the otherwise inactive ground behind the abutment back-walls. The system can be described as fastening the bridge end-diaphragms to the rocky strata that lie beneath the abutment backfill. The anchoring is achieved through a series of steel strands grouted to the rock to achieve a strong anchoring capacity. Indeed, the proposed anchor is flexible enough to allow the thermal, creep and shrinkage serviceability movements of the deck. A parametric study conducted in this paper shows that the ground anchor external restraining system is truly effective in reducing the seismic demands of the bridge deck.
He, L-X, Wu, C & Li, J 2019, 'Post-earthquake evaluation of damage and residual performance of UHPSFRC piers based on nonlinear model updating', Journal of Sound and Vibration, vol. 448, pp. 53-72.
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© 2019 Elsevier Ltd This paper presents an innovative approach for damage and residual performance evaluation of ultra-high performance steel fiber reinforced concrete (UHPSFRC) piers after earthquakes utilizing low-level vibration tests. A nonlinear fiber section element model is constructed in OpenSees to simulate the hysteretic behavior of a UHPSFRC bridge pier. Experimental data from a UHPSFRC column is utilized to verify the accuracy of the nonlinear numerical model. Based on the nonlinear fiber section element model, a new technique of nonlinear finite element model updating involving two updating stages is developed. This new method is designed to incorporate the maximum and minimum strains of section fibers as the updating parameters. By forming the objective function from the modal information, the damage parameters related to the nonlinear material model can be updated by solving the constrained optimization problem. To validate the efficiency of this updating approach, it has been applied to a numerically simulated UHPSFRC pier. With using the updated nonlinear finite element model, the residual axial loading capacity and post-seismic performance of the UHPSFRC pier are examined. The numerical results indicate that the updated nonlinear finite element model can be used not only to assess the current damage state of the UHPSFRC pier but also to predict its future performance after an earthquake. Finally, the noise effect on the proposed method is also investigated. The results reveal that the post-earthquake evaluation approach for UHPSFRC piers based on this study's updating algorithm is robust to noise.
He, X, Wu, W & Wang, S 2019, 'A constitutive model for granular materials with evolving contact structure and contact forces—Part I: framework', Granular Matter, vol. 21, no. 2.
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He, X, Wu, W & Wang, S 2019, 'A constitutive model for granular materials with evolving contact structure and contact forces—part II: constitutive equations', Granular Matter, vol. 21, no. 2.
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© 2019, The Author(s). This and the companion paper present a constitutive model for granular materials with evolving contact structure and contact forces, where the contact structure and contact forces are characterised by some statistics of grain-scale entities such as contact normals and contact forces. And these statistics are actually the “fabric” or “force” terms in the “stress–force–fabric” (SFF) equation. The stress–strain response is obtained by inserting the predicted “fabric” or “force” terms from evolution equations into the SFF equation. In the model, the critical state is characterised by two fitting equations and three critical state parameters. A semi-mechanistic analysis is conducted about the change of the contact number and the obtained results are combined with observed phenomena in DEM virtual experiments to give the constitutive equations for the “fabric” terms. The change of fabric anisotropy is related to the strain rate, current fabric anisotropy and also contact forces. The change of coordination number is induced by two terms related to volumetric and shear deformations, and also an additional term related to the change of fabric anisotropy. The constitutive equations regarding the “force” terms are also proposed. All the “fabric” or “force” terms are modelled to tend toward their critial state value, which agrees with Li and Dafalias’s (J Eng Mech 138(3):263–275, 2012. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000324) basic philosophy in their evolution equation for the fabric tensor. These equations along with the SFF equation form a constitutive model.
Heitor, A & Ngo, T 2019, 'Editorial', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 4, pp. 211-212.
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Hoque, M, Tasfia, S, Ahmed, N & Pradhan, B 2019, 'Assessing Spatial Flood Vulnerability at Kalapara Upazila in Bangladesh Using an Analytic Hierarchy Process', Sensors, vol. 19, no. 6, pp. 1302-1302.
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Floods are common natural disasters worldwide, frequently causing loss of lives and huge economic and environmental damages. A spatial vulnerability mapping approach incorporating multi-criteria at the local scale is essential for deriving detailed vulnerability information for supporting flood mitigation strategies. This study developed a spatial multi-criteria-integrated approach of flood vulnerability mapping by using geospatial techniques at the local scale. The developed approach was applied on Kalapara Upazila in Bangladesh. This study incorporated 16 relevant criteria under three vulnerability components: physical vulnerability, social vulnerability and coping capacity. Criteria were converted into spatial layers, weighted and standardised to support the analytic hierarchy process. Individual vulnerability component maps were created using a weighted overlay technique, and then final vulnerability maps were produced from them. The spatial extents and levels of vulnerability were successfully identified from the produced maps. Results showed that the areas located within the eastern and south-western portions of the study area are highly vulnerable to floods due to low elevation, closeness to the active channel and more social components than other parts. However, with the integrated coping capacity, western and south-western parts are highly vulnerable because the eastern part demonstrated particularly high coping capacity compared with other parts. The approach provided was validated by qualitative judgement acquired from the field. The findings suggested the capability of this approach to assess the spatial vulnerability of flood effects in flood-affected areas for developing effective mitigation plans and strategies.
Hoque, MA-A, Ahmed, N, Pradhan, B & Roy, S 2019, 'Assessment of coastal vulnerability to multi-hazardous events using geospatial techniques along the eastern coast of Bangladesh', Ocean & Coastal Management, vol. 181, pp. 104898-104898.
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© 2019 Elsevier Ltd The eastern coastal region of Bangladesh, which has a 377 km-long coastline, is highly vulnerable to multi-hazardous events, such as tropical cyclones, coastal floods, coastal erosion and salinity intrusion. The vulnerability of this coastal region is likely to increase under the future climate change context. This research aims to develop a coastal vulnerability index (CVI) of multi-hazardous events for the eastern coastal region of Bangladesh. Eight parameters, mostly focused on physical vulnerability, were considered in this study. Various thematic layers were prepared for each parameter using spatial techniques, and all parameters were assigned a vulnerability ranking. Finally, a CVI was developed and the related values were categorised into five distinct classes (i.e., very high, high, moderate, low, and very low). Results indicate that approximately 121 km (32%) of the coastline of the study area is in high-to very high-vulnerability zones. Low elevations, gentle slopes, high storm surge impacts, sandy coastlines, high shoreline erosion rates and high sea-level changes are the most important factors of high to very-high vulnerability zones. The moderately vulnerable area covers approximately 119 km (32%) of the coastline. Meanwhile, 78 (21%) and 59 (16%) km of the coastlines are in low-to very low-vulnerability zones, respectively. These coastlines are characterised by steep slopes with high elevations, low tide range and storm surge heights as well as less erosion. The CVI results were validated by qualitative observations acquired from the field. The findings of this study can be applied by policymakers and administrators to develop effective mitigation plans and minimise the likely impacts of coastal multi-hazards.
Hoque, MA-A, Pradhan, B, Ahmed, N & Roy, S 2019, 'Tropical cyclone risk assessment using geospatial techniques for the eastern coastal region of Bangladesh', Science of The Total Environment, vol. 692, pp. 10-22.
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© 2019 Elsevier B.V. Tropical cyclones frequently affect millions of people, damaging properties, livelihoods and environments in the coastal region of Bangladesh. The intensity and extent of tropical cyclones and their impacts are likely to increase in the future due to climate change. The eastern coastal region of Bangladesh is one of the most cyclone-affected coastal regions. A comprehensive spatial assessment is therefore essential to produce a risk map by identifying the areas under high cyclone risks to support mitigation strategies. This study aims to develop a comprehensive tropical cyclone risk map using geospatial techniques and to quantify the degree of risk in the eastern coastal region of Bangladesh. In total, 14 spatial criteria under three risk components, namely, vulnerability and exposure, hazard, and mitigation capacity, were assessed. A spatial layer was created for each criterion, and weighting was conducted following the Analytical Hierarchy Process. The individual risk component maps were generated from their indices, and subsequently, the overall risk map was produced by integrating the indices through a weighted overlay approach. Results demonstrate that the very-high risk zone covered 9% of the study area, whereas the high-risk zone covered 27%. Specifically, the south-western (Sandwip and Sonagazi), western (Patiya, Kutubdia, Maheshkhali, Chakaria, Cox's Bazar and Chittagong Sadar) and south-western (Teknaf) regions of the study site are likely to be under a high risk of tropical cyclone impacts. Low and very-low hazard zones constitute 11% and 28% of the study area, respectively, and most of these areas are located inland. The results of this study can be used by the concerned authorities to develop and apply effective cyclone impact mitigation plans and strategies.
Indraratna, B, Babar Sajjad, M, Ngo, T, Gomes Correia, A & Kelly, R 2019, 'Improved performance of ballasted tracks at transition zones: A review of experimental and modelling approaches', Transportation Geotechnics, vol. 21, pp. 100260-100260.
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© 2019 Elsevier Ltd Track transitions such as bridge approaches, road crossings and shifts from slab track to ballasted track are common locations where track degradation accelerates due to dynamic and high impact forces; as a consequence there is higher differential settlement. These types of discontinuities cause an abrupt change in the structural response of the track due mainly to variations in stiffness and track damping. Track transition zones are prone to an accelerated deterioration of track material and geometry that leads to increased maintenance costs. Track deterioration also leads to vehicle degradation due to enhanced acceleration, low frequency oscillation, and high frequency vibrations. While ballast deterioration is a major factor affecting the stability and longevity of rail tracks, the cost of tackling transition related problems that detract from passenger comfort is also high. A good transition zone lessens the impact of dynamic load of moving trains by minimising the abrupt variations in track stiffness and ensuring a smooth and gradual change from a less stiff (ballasted track) to a stiff (slab track) structure. This paper presents a critical review of various problems associated with transition zones and the measures adopted to mitigate them; it also includes critical review of research work carried out using large-scale laboratory testing, mathematical and computational modelling and field measurements on track transition zones.
Indraratna, B, Qi, Y, Heitor, A & Vinod, JS 2019, 'The influence of rubber crumbs on the critical state behavior of waste mixtures', E3S Web of Conferences, vol. 92, pp. 06004-06004.
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The practical application of waste materials such as steel furnace slag (SFS) and coal wash (CW) is becoming more prevalent in many geotechnical projects. It was found that the inclusion of rubber crumbs (RCs) from recycled tyres into mixtures of SFS and CW not only solves the problem of large stockpiles of waste tyres, it also can provide an energy-absorbing medium that will reduce track degradation. In order to investigate the influence of RC on the geotechnical properties of the granular waste matrix (SFS+CW+RC), a series of monotonic consolidated drained triaxial tests were conducted on waste mixtures. The test results reveal that the inclusion of RC significantly affects the geotechnical properties of the waste mixtures, especially their critical state behaviour. Specifically, the waste matrix can achieve a critical state with a low RC content (<20%), whereas those mixtures with higher RC contents (20-40%) cannot attain a critical state within the ultimate strain capacity that can be applied to specimens using the traditional triaxial equipment. Therefore, for the waste matrix with higher RC contents extrapolation of the measured volumetric strains had to be adopted to obtain the appropriate critical state parameters. Moreover, the influence of energy absorbing property by adding RC on the critical state behaviour has also been captured through an empirical equation.
Indraratna, B, Qi, Y, Ngo, TN, Rujikiatkamjorn, C, Neville, T, Ferreira, FB & Shahkolahi, A 2019, 'Use of Geogrids and Recycled Rubber in Railroad Infrastructure for Enhanced Performance', Geosciences, vol. 9, no. 1, pp. 30-30.
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Railway tracks are conventionally built on compacted ballast and structural fill layers placed above the natural (subgrade) foundation. However, during train operations, track deteriorations occur progressively due to ballast degradation. The associated track deformation is usually accompanied by a reduction in both load bearing capacity and drainage, apart from imposing frequent track maintenance. Suitable ground improvement techniques involving plastic inclusions (e.g., geogrids) and energy absorbing materials (e.g., rubber products) to enhance the stability and longevity of tracks have become increasingly popular. This paper presents the outcomes from innovative research and development measures into the use of plastic and rubber elements in rail tracks undertaken at the University of Wollongong, Australia, over the past twenty years. The results obtained from laboratory tests, mathematical modelling and numerical modelling reveal that track performance can be improved significantly by using geogrid and energy absorbing rubber products (e.g., rubber crumbs, waste tire-cell and rubber mats). Test results show that the addition of rubber materials can efficiently improve the energy absorption of the structural layer and also reduce ballast breakage. Furthermore, by incorporating the work input parameters, the energy absorbing property of the newly developed synthetic capping layer is captured by correct modelling of dilatancy. In addition, the laboratory behavior of tire cells and geogrids has been validated by numerical modelling (i.e., Finite Element Modelling-FEM, Discrete Element—DEM), and a coupled DEM-FEM modelling approach is also introduced to simulate ballast deformation.
Indraratna, B, Rujikiatkamjorn, C, Baral, P & Ameratunga, J 2019, 'Performance of marine clay stabilised with vacuum pressure: Based on Queensland experience', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 3, pp. 598-611.
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© 2018 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Stabilising soft marine clay and estuarine soils via vacuum preloading has become very popular in Australasia over the past decades because it is a cost-effective and time-efficient approach. In recent times, new land on areas outside but adjacent to existing port amenities, the Fisherman Islands at the Port of Brisbane (POB), was reclaimed to cater for an increase in trade activities. A vacuum preloading method combined with surcharge to stabilise the deep layers of soil was used to enhance the application of prefabricated vertical drains (PVDs). This paper describes the performance of this combined surcharge fill and vacuum system under the embankment and also compares it with a surcharge loading system to demonstrate the benefits of vacuum pressure over conventional fill. The performance of this embankment is also presented in terms of field monitoring data, and the relative performance of the vacuum together with non-vacuum systems is evaluated. An analytical solution to radial consolidation with time-dependent surcharge loading and vacuum pressure is also presented in order to predict the settlement and associated excess pore water pressure (EPWP) of deposits of thick soft clay.
Indraratna, B, Rujikiatkamjorn, C, Tawk, M & Heitor, A 2019, 'Compaction, degradation and deformation characteristics of an energy absorbing matrix', Transportation Geotechnics, vol. 19, pp. 74-83.
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© 2019 Elsevier Ltd The reuse of waste materials as an alternative to natural aggregates is becoming more popular in engineering projects. It offers a sustainable and economical solution to address the environmental concerns arising from the scarcity of natural quarries as well as the increase in waste generation. Coal wash (CW) and rubber crumbs (RC) are industrial by-products that could potentially be used in railway substructures. In this study, different RC levels are introduced into CW (i.e. CWRC mixture) to reduce potential breakage of CW and increase the ductility and energy absorbing capacity of the matrix. The compaction and degradation characteristics of CWRC mixtures to be used as a construction fill are investigated under five energy levels ranging from standard to modified Proctor compaction. An optimum compaction energy is determined so as to minimize breakage but still yield an acceptable void ratio (compact packing) to avoid excessive settlements. The compressibility of rubber particles and the induced change in the volume of solids is addressed with regard to the overall void ratio of the matrix. Furthermore, the results of triaxial tests on four CWRC mixtures compacted to the same void ratio under three different confining pressures (25, 50 and 75 kPa) are presented, and the effect of RC content on the stress-strain relationship is elucidated.
Isola, A, Mansor, S, Shafri, HM, Pradhan, B & Mansor, Y 2019, 'Impact of externai forces on the quality of digital elevation model derived from drone technology', International Journal of Geoinformatics, vol. 15, no. 1, pp. 81-91.
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Platform instability is one of the sources of error of Digital Elevation Model (DEM) derived from a low altitude aircraft. This paper examines the influence of atmospheric pressure (AP) on the DEM produced by drone system. To achieve the research objective, an experimental-based ftxed-wing drone platform was set up at the Universiti Putra Malaysia Campus. First, Ground Control Points (GCPs) and CheckPoints (CPs) were established within the study area by a real-time kinematic differential global positioning system. The drone flew seven times at different altitudes over the study area. In the process, an on-board canon digital camera took a series of overlapping photos. The photos were processed with an image-matching algorithm. Then orthorectified the photos using the GCPs. Photo orthorectification entails orientation of aerial photos with respect to the ground control points. It helps to remove distortions that might occur while acquiring or Processing the aerial photographs. In the end, seven DEMs were exported in tiff file format. Analysis of impact of AP on the resulting DEMs was conducted using a proposed model and obtained 0.072m, 0.05m, 0.014m, 0.0lm, 0.004m, 0.003m, and 0.002m for lOOm, I50m, 200m, 250m, 350m, 400m, and 500m altitudes, respectively. To confirm the efficiency of the proposed model, the results were tested using the CPs and their corresponding points on the DEMs and obtained root mean square error of 0.03m, O.OSm, 0.07m, O.lm, 13m, 0.14m, and 0.16m. On a final note, a close look at the validation and impact of AP results unveils a small gap. Hence, suggests that platform instability should be ignored amidst of other externai forces that can influence the performance of drone system.
Israr, J & Indraratna, B 2019, 'Study of Critical Hydraulic Gradients for Seepage-Induced Failures in Granular Soils', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 7, pp. 04019025-04019025.
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© 2019 American Society of Civil Engineers. This paper reports on a series of laboratory hydraulic tests on a select range of granular soils compacted at relative densities between 0% and 100%. The critical hydraulic gradient at the onset of seepage failure (i.e., heave and suffusion) is considerably smaller than unity for internally unstable (i.e., nonuniform) sand-gravel mixtures due to stress reduction in their finer fraction. For example, stable uniform fine sands have been shown to exhibit heave at hydraulic gradients ≥1.0, whereas sand-gravel mixtures suffer from suffusion at hydraulic gradients ≥1.0. The boundary friction from the cell walls of test equipment would influence the development of heave, while suffusion is controlled by interparticle friction. In this study, the critical hydraulic gradient is modeled theoretically by considering the effects of interparticle and boundary frictions, and stress reduction in the soil. The experimental results from both this and past studies are used to verify the proposed model, which showed good agreement with experimental observations with less than 5% standard error.
Jamilu Bala Ahmed, II, Pradhan, B, Mansor, S, Tongjura, JDC & Yusuf, B 2019, 'Multi-criteria evaluation of suitable sites for termite mounds construction in a tropical lowland', CATENA, vol. 178, pp. 359-371.
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© 2019 Elsevier B.V. Termite mounds influence ecosystem heterogeneity and contribute to the stabilization of the system under global change. A number of environmental factors influence the distribution, height, diameter and designs of termite mounds but these factors are not only poorly understood, they cannot be extrapolated for everywhere. In this study, we employed a ground based survey and Geographical Information System (GIS) technique to map 156 km 2 study area in Keffi, Nigeria. The aims were to (1) estimate the density and area covered by termite mounds, (2) sample and identify species types and how they are distributed, and (3) use five environmental factors (elevation, geology, surface water drainage, land use/land cover and static water level) to model suitable sites for mounds construction. A total of 361 mounds were mapped representing a density of about 0.8 mounds ha −1 and covering only about 0.31% of the studied area. Next, the effect of the five chosen environmental factors on the geographic distribution, life status, height and diameter of mounds and species diversity were analysed and their relationships plotted in pairwise comparison matrices using the Saaty's Analytical Hierarchy Process. Normalized rates for classes in each factor and corresponding weights were computed and aggregated using the Weighted Linear Combination method. The result depicted that moderate to low elevation (270–330 m amsl), rock cover types that are more susceptible to weathering (schist), cultivated areas and shallow water table zones are most favourable for termites to build mounds. The result obtained in this study shows a promising correlation between the environmental factors and termite mounds distribution. The proposed model can easily be replicated in a different but similar multi-land use and rock cover types.
Jamshidi Chenari, R, Alaie, R & Fatahi, B 2019, 'Constrained Compression Models for Tire-Derived Aggregate-Sand Mixtures Using Enhanced Large Scale Oedometer Testing Apparatus', Geotechnical and Geological Engineering, vol. 37, no. 4, pp. 2591-2610.
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© 2018, Springer Nature Switzerland AG. Tire derived aggregates have recently been in wide use both in industry and engineering applications depending on the size and the application sought. Five different contents of tire derived aggregates (TDA) were mixed with sand thoroughly to ensure homogeneity. A series of large scale oedometer experiments were conducted to investigate the compressibility properties of the mixtures. Tire shreds content, TDA aspect ratio, skeletal relative density and overburden pressure are studied parameters. Constrained deformation modulus and coefficient of earth pressure at rest are measured parameters. All tests were conducted at seven overburden pressure levels. It was concluded that deformability of TDA-sand mixture increases with soft inclusion. Overburden pressure and skeletal relative density are also important parameters which render more rigidity and less lateral earth pressure coefficient accordingly. TDA size or aspect ratio was shown to have minor effect at least for the constrained strain conditions encountered in current study. An EPR-based parametric study and also sensitivity analyses based on cosine amplitude method revealed quantitative evaluation of the relative importance of each input parameter in varying deformation and lateral earth pressure coefficient as the outputs.
Javdanian, H & Pradhan, B 2019, 'Assessment of earthquake-induced slope deformation of earth dams using soft computing techniques', Landslides, vol. 16, no. 1, pp. 91-103.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Evaluating behavior of earth dams under dynamic loads is one of the most important problems associated with the initial design of such massive structures. This study focuses on prediction of deformation of earth dams due to earthquake shaking. A total number of 103 real cases of deformation in earth dams due to earthquakes that has occurred over the past years were gathered and analyzed. Using soft computing methods, including feed-forward back-propagation and radial basis function based neural networks, two models were developed to predict slope deformations in earth dams under variant earthquake shaking. Earthquake magnitude (Mw), yield acceleration ratio (ay/amax), and fundamental period ratio (Td/Tp) were considered as the most important factors contributing to the level of deformation in earth dams. Subsequently, a sensitivity analysis was conducted to assess the performance of the proposed model under various conditions. Finally, the accuracy of the developed soft computing model was compared with the conventional relationships and models to estimate seismic deformations of earth dams. The results demonstrate that the developed neural model can provide accurate predictions in comparison to the available practical charts and recommendations.
Jayasuriya, C, Indraratna, B & Ngoc Ngo, T 2019, 'Experimental study to examine the role of under sleeper pads for improved performance of ballast under cyclic loading', Transportation Geotechnics, vol. 19, pp. 61-73.
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© 2019 Elsevier Ltd The degradation and deformation of ballast critically affect the track geometry, safety, and passenger comfort. The increase in axle loads and train speed increases the stress applied on the ballast and exacerbates the rate of ballast degradation. This situation is more critical when tracks are built on stiff subgrades (e.g. bridges, tunnels and crossings), hence the use of energy absorbing (damping) layers in track substructure is a countermeasure to minimize track damage. In this study, a series of large-scale laboratory tests using the track process simulation testing apparatus (TPSA) is carried out to assess the performance of under sleeper pads (USP) to reduce ballast degradation and to decrease permanent deformation. When placed beneath the sleeper, the energy absorbing nature of USP reduces the energy transferred to the ballast and other substructure components. Subsequently, the ballast layer experiences less deformation and degradation. Innovative tactile surface sensors (matrix-based) are used to measure the pressure and contact area between sleeper and ballast. The measured data show that an increase in contact area between sleeper and ballast decreases the stress applied on ballast, and thus a reduction in ballast breakage and corresponding reduced ballast deformation can be achieved. Furthermore, the influence of the USP stiffness is examined and the measured data offer an insightful understanding of the role of USP for given track and loading conditions in terms of energy dissipation and reduced ballast deformation.
Jia, Y, Tang, L, Xu, B & Zhang, S 2019, 'Crack Detection in Concrete Parts Using Vibrothermography', Journal of Nondestructive Evaluation, vol. 38, no. 1.
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Jiang, Y & Nimbalkar, S 2019, 'Finite Element Modeling of Ballasted Rail Track Capturing Effects of Geosynthetic Inclusions', Frontiers in Built Environment, vol. 5.
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© 2019 Jiang and Nimbalkar. This paper presents a two dimensional finite element (FE) approach to investigating beneficial aspects of geogrids in the railway track. The influences of different factors including the subgrade strength, the geogrid stiffness, the placement depth of geogrid, the effective width of geogrid, the strength of ballast-geogrid interface and the combination of double geogrid layers were investigated under the monotonic loading. The results indicated the role of geogrid reinforcement is more pronounced over the weak compressible subgrade. A stiffer geogrid reduces ballast settlement and produces a more uniform stress distribution along a track. The placement location of a geogrid is suggested at the ballast-sub-ballast interface to achieve better reinforcement results. Although the width of a geogrid layer should be sufficient to cover an entire loaded area, excessive width does not guarantee additional benefits. Higher interface strength between a ballast and a geogrid is beneficial for effective reinforcement. Increasing the number of geogrid layers is an effective way to reinforce the ballast over weak subgrades. The results of the limited cyclic FE simulations revealed the consistency of the reinforcement effect of the geogrids under monotonic and cyclic loads.
Kalantar, Al-Najjar, Pradhan, Saeidi, Halin, Ueda & Naghibi 2019, 'Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping', Water, vol. 11, no. 9, pp. 1909-1909.
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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial m...
Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2019, 'A scene image classification technique for a ubiquitous visual surveillance system', Multimedia Tools and Applications, vol. 78, no. 5, pp. 5791-5818.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods.
Kaljahi, MA, Shivakumara, P, Hu, T, Jalab, HA, Ibrahim, RW, Blumenstein, M, Lu, T & Ayub, MNB 2019, 'A geometric and fractional entropy-based method for family photo classification', Expert Systems with Applications: X, vol. 3, pp. 100008-100008.
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© 2019 Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.
Kaljahi, MA, Shivakumara, P, Idris, MYI, Anisi, MH & Blumenstein, M 2019, 'A new image size reduction model for an efficient visual sensor network', Journal of Visual Communication and Image Representation, vol. 63, pp. 102573-102573.
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© 2019 Elsevier Inc. Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.
Kaljahi, MA, Shivakumara, P, Idris, MYI, Anisi, MH, Lu, T, Blumenstein, M & Noor, NM 2019, 'An automatic zone detection system for safe landing of UAVs', Expert Systems with Applications, vol. 122, pp. 319-333.
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© 2019 Elsevier Ltd As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems.
Khabbaz, H, Gibson, R & Fatahi, B 2019, 'Effect of constructing twin tunnels under a building supported by pile foundations in the Sydney central business district', Underground Space, vol. 4, no. 4, pp. 261-276.
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© 2019 Tongji University and Tongji University Press In congested cities such as Sydney, competition for underground space escalates within the built environment because various assets require finite geotechnical strength and support. Specific problems such as damage to buildings may develop when high-rise buildings on piled foundations are subject to ground movements as tunnels are constructed. This paper focuses on the risks of tunneling beneath Sydney's Martin Place and how buildings are subject to additional loads caused by tunneling. The key objective of this study is to improve the understanding of tunnel–rock–pile interactions and to encourage sustainable development. A finite element model is developed to predict the interaction between tunnel construction and piled foundations. The tunnel, rock, and pile components are studied separately and are then combined into a single model. The ground model is based on the characteristics of Hawkesbury Sandstone and is developed through a desktop study. The piles are designed using Australian Standards and observations of high-rise buildings. The tunnel construction is modeled based on the construction sequence of a tunnel boring machine. After combining the components, a parametric study on the relationship between tunnel location, basements, and piles is conducted. Our findings, thus far, show that tunneling can increase the axial and flexural loads of piles, where the additional loading exceeds the structural capacity of some piles, especially those that are close to basement walls. The parametric study reveals a strong relationship between tunnel depth and lining stresses, while the relationship between tunnel depth and induced pile loads is less convincing. Furthermore, the horizontal tunnel position relative to piles shows a stronger relationship with pile loads. Further research into tunnel–rock–pile interactions is recommended, especially beneath basements, to substantiate the results of this study.
Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2019, 'A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs', IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6964-6974.
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© 2019 IEEE. To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.
Khare, V, Shivakumara, P, Chan, CS, Lu, T, Meng, LK, Woon, HH & Blumenstein, M 2019, 'A novel character segmentation-reconstruction approach for license plate recognition', Expert Systems with Applications, vol. 131, pp. 219-239.
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© 2019 Elsevier Ltd Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characters of license plates to enhance the performance of license plate recognition systems. Partial character reconstruction is proposed based on the characteristics of stroke width in the Laplacian and gradient domain in a novel way. This results in character components with incomplete shapes. The angular information of character components determined by PCA and the major axis are then studied by considering regular spacing between characters and aspect ratios of character components in a new way for segmenting characters. Next, the same stroke width properties are used for reconstructing the complete shape of each character in the gray domain rather than in the gradient domain, which helps in improving the recognition rate. Experimental results on benchmark license plate databases, namely, MIMOS, Medialab, UCSD data, Uninsbria data Challenged data, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, namely, ICDAR 2013, ICDAR 2015, SVT, MSRA, show that the proposed technique is effective and useful.
Khoa, NLD, Wang, Y & Chawla, S 2019, 'Incremental commute time and its online applications', Pattern Recognition, vol. 88, pp. 101-112.
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Khosravi, K, Shahabi, H, Pham, BT, Adamowski, J, Shirzadi, A, Pradhan, B, Dou, J, Ly, H-B, Gróf, G, Ho, HL, Hong, H, Chapi, K & Prakash, I 2019, 'A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods', Journal of Hydrology, vol. 573, pp. 311-323.
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© 2019 Elsevier B.V. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.
Kordestani, MD, Naghibi, SA, Hashemi, H, Ahmadi, K, Kalantar, B & Pradhan, B 2019, 'Groundwater potential mapping using a novel data-mining ensemble model', Hydrogeology Journal, vol. 27, no. 1, pp. 211-224.
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© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.
Lake, C & Sheng, D 2019, 'Note of appreciation / Note de reconnaissance', Canadian Geotechnical Journal, vol. 56, no. 12, pp. v-vii.
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Lamqadem, AA, Saber, H & Pradhan, B 2019, 'Long-Term Monitoring of Transformation from Pastoral to Agricultural Land Use Using Time-Series Landsat Data in the Feija Basin (Southeast Morocco)', Earth Systems and Environment, vol. 3, no. 3, pp. 525-538.
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The expansion of agricultural land at the cost of pastoral land is the common cause of land degradation in the arid areas of developing countries, especially in Morocco. This study aims to assess and monitor the transformation of pastoral land to agricultural land in the arid environment of the Feija Basin (Southeast of Morocco) and to find the key drivers and the issues resulting from this transformation. Spectral mixture analysis was applied to multi-temporal (1975–2017) and multi-sensor (i.e. Multi-spectral Scanner, Thematic Mapper, and Operational Land Imager) Landsat satellite images, from which land use classifications were derived. The remote sensing data in combination with ground reference data (household level), groundwater and climate statistics were used to validate and explain the derived land use change maps. The results of the spatiotemporal changes in agricultural lands show two patterns of changes, a middle expansion from 1975 to 2007, and a rapid expansion from 2008 to 2017. In addition, the overall accuracy demonstrated a high accuracy of 94.4%. In 1975 and 1984, the agricultural lands in Feija covered 0.17 km and 1.32 km , respectively, compared with 20.10 km in 2017. Since the adoption of the Green Morocco Plan in 2008, the number of watermelon farms and wells has increased rapidly in the study area, which induced a piezometric level drawdown. The results show that spectral mixture analysis yields high accuracies for agricultural lands extraction in arid dry lands and accounts for mixed pixels issues. Results of this study can be used by local administrators to prepare an effective environmental management plan of these fragile drylands. The proposed method can be replicated in other regions to analyse land transformation in similar arid conditions. 2 2 2
Lay, US, Pradhan, B, Yusoff, ZBM, Abdallah, AFB, Aryal, J & Park, H-J 2019, 'Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data', Sensors, vol. 19, no. 16, pp. 3451-3451.
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Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Lee, J, Jung, H-S, Zlatanoya, S & Pradhan, B 2019, 'Editorial', International Journal of Urban Sciences, vol. 23, no. 3, pp. 301-302.
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Li, K, Ni, W, Abolhasan, M & Tovar, E 2019, 'Reinforcement Learning for Scheduling Wireless Powered Sensor Communications', IEEE Transactions on Green Communications and Networking, vol. 3, no. 2, pp. 264-274.
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© 2017 IEEE. In a wireless powered sensor network, a base station transfers power to sensors by using wireless power transfer (WPT). Inadequately scheduling WPT and data transmission causes fast battery drainage and data queue overflow of some sensors who could have potentially gained high data reception. In this paper, scheduling WPT and data transmission is formulated as a Markov decision process (MDP) by jointly considering sensors' energy consumption and data queue. In practical scenarios, the prior knowledge about battery level and data queue length in MDP is not available at the base station. We study reinforcement learning at the sensors to find a transmission scheduling strategy, minimizing data packet loss. An optimal scheduling strategy with full-state information is also investigated, assuming that the complete battery level and data queue information are well known by the base station. This presents the lower bound of the data packet loss in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcement learning scheduling algorithm significantly reduces network packet loss rate by 60%, and increases network goodput by 67%, compared to existing non-MDP greedy approaches. Moreover, comparing the optimal solutions, the performance loss due to the lack of sensors' full-state information is less than 4.6%.
Li, L-Q, Ju, N-P, Zhang, S, Deng, X-X & Sheng, D 2019, 'Correction to: Seismic wave propagation characteristic and its effects on the failure of steep jointed anti-dip rock slope', Landslides, vol. 16, no. 1, pp. 125-126.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The published version of this article, unfortunately, contained error. A compass went unconverted in the upper-right corner of Fig. 1. Given in this article is the correct image. The original article has been corrected.
Li, L-Q, Ju, N-P, Zhang, S, Deng, X-X & Sheng, D 2019, 'Seismic wave propagation characteristic and its effects on the failure of steep jointed anti-dip rock slope', Landslides, vol. 16, no. 1, pp. 105-123.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Discontinuities, such as joints and beddings, usually play a significant role in the seismic response and corresponding failure process of slopes, especially for anti-dip rock slide according to field observations. Shaking table tests associated with numerical analyses are carried out in this paper to explore the effect of seismic wave on response of jointed anti-dip rock slopes. Shaking table tests involve anti-dip rock slope models with different rock types and different excitation intensities. Ten accelerometers are installed inside each slope model to monitor the dynamic response and spectrum shifting characteristics. It is found that the area of failure zone in the soft rock anti-dip slope is approximate 1.5 times the size of that in the hard rock anti-dip slope. Meanwhile, the width and ridge number of the fast Fourier-transformation spectrum along the slope surface can reveal the internal damage features within the anti-dip rock slopes, and the multiple failure planes can also be recognized according to the variation of distance between the innermost and outermost ridges in the fast Fourier-transformation spectrum. Moreover, the distinct element method incorporating a damage model is used to interpret the test results and to identify the main influencing factors for seismic instability. It is found that the failure pattern of a jointed anti-dip rock slope is more sensitive to bedding inclination than to joint inclination.
Li, W, Huang, L & Ji, J 2019, 'Periodic solution and its stability of a delayed Beddington‐DeAngelis type predator‐prey system with discontinuous control strategy', Mathematical Methods in the Applied Sciences, vol. 42, no. 13, pp. 4498-4515.
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This paper investigates the periodic solution of a delayed Beddington‐DeAngelis (BD) type predator‐prey model with discontinuous control strategy. Firstly, the regularity and visibility analysis of the delayed predator‐prey model is carried out by using the principle of differential inclusion. Secondly, the positiveness and boundeness of the solution is discussed by employing the comparison theorem. Based on the boundary conditions of the model and the Mawhin‐like coincidence theorem, it is shown that the solution of the delayed BD system is asymptotically stable in finite time. Furthermore, it is found that there exists at least one periodic solution of the nonautonomous delayed predator‐prey model by using the principle of topological degree and set value mapping. Specially, when the nonautonomous delayed BD system degenerates into an autonomous system, some criteria are obtained to guarantee the convergence behavior of the harvesting solutions for the corresponding autonomous delayed BD system. Finally, numerical examples are given to demonstrate the applicability and effectiveness of main results. It is worthy to point out that the discontinuous control strategy is superior to the continuous harvesting policies adopted in existing literature.
Li, Y & Li, J 2019, 'Overview of the development of smart base isolation system featuring magnetorheological elastomer', Smart Structures and Systems, vol. 24, no. 1, pp. 37-52.
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Despite its success and wide application, base isolation system has been challenged for its passive nature, i.e., incapable of working with versatile external loadings. This is particularly exaggerated during near-source earthquakes and earthquakes with dominate low-frequency components. To address this issue, many efforts have been explored, including active base isolation system and hybrid base isolation system (with added controllable damping). Active base isolation system requires extra energy input which is not economical and the power supply may not be available during earthquakes. Although with tunable energy dissipation ability, hybrid base isolation systems are not able to alter its fundamental natural frequency to cope with varying external loadings. This paper reports an overview of new adventure with aim to develop adaptive base isolation system with controllable stiffness (thus adaptive natural frequency). With assistance of the feedback control system and the use of smart material technology, the proposed smart base isolation system is able to realize real-time decoupling of external loading and hence provides effective seismic protection against different types of earthquakes.
Liu, L, Yu, J, Ji, J, Miao, Z & Zhou, J 2019, 'Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots', International Journal of Systems Science, vol. 50, no. 8, pp. 1556-1567.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.
Lu, S, Oberst, S, Zhang, G & Luo, Z 2019, 'Bifurcation analysis of dynamic pricing processes with nonlinear external reference effects', Communications in Nonlinear Science and Numerical Simulation, vol. 79, pp. 104929-104929.
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© 2019 Elsevier B.V. Dynamic pricing has been widely implemented to hedge against volatile demand. One challenging problem is the study of optimal price choices under the influence of this volatility. Stochastic demand is a prevalent assumption when it comes to model the volatility on pricing decisions. However, the demand volatility might also be produced by deterministic chaos, which has rarely been studied in this field of research to-date. We propose deterministic dynamic pricing processes that aim to maximise the revenue and to mimic a real pricing decision. Our model includes nonlinear consumer expectations that explain the effects of external information on consumers and discrete optimisations due to a non-smooth demand function that considers asymmetries in the perceptions of gains or losses of consumers and finite price choices of companies. Volatile markets can show up because of non-periodic consumer expectations, period adding bifurcations, codimension-2 points and coexisting solutions. Results highlight that optimal pricing strategies should agree with the dynamics of consumer expectations. Disregarding deterministic dynamics may not only cause revenue losses in practice but might also mislead regulators about the underlying mechanisms that consumers and companies respond to. We introduce for the first time an irregular pricing strategy: a company can make the first return iteration of each sales price non-periodic to follow non-periodic consumer expectations when having finite price choices. These results may justify implementing irregular pricing strategies in the case of practical pricing decisions. Here, the existence of coexisting solutions can assist to identify potential market manipulations within a monopoly market. This not only contributes to a fresh look on volatile markets but also emphasises the importance of initial conditions to pricing decisions and price regulations.
Mahdavi, H, Fatahi, B & Khabbaz, H 2019, 'A comparison of frictional and socketed concrete injected columns in a transition zone', Geosynthetics International, vol. 26, no. 5, pp. 497-514.
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This paper sets out to investigate the options available for the transition from Concrete Injected Columns (CICs) to other ground improvement methods, used away from the bridge abutment. Two possible alternatives, widely spaced CICs socketed into stiff material and shorter, closely spaced, frictional CICs, were numerically simulated using FLAC3D software considering the dissipation of porewater pressure and variation of soil permeability with time. The total length of the CICs and the total volume of concrete used for their construction were the same for both alternatives. A geosynthetic layer was introduced into the load transfer platform, and interface elements were incorporated to simulate CIC-soil interaction. The numerical results were also compared with an established analytical solution and a good agreement was achieved. A comparison was then made between the two scenarios; indeed, the embankment on frictional CICs experienced less settlement on the surface, less loads in the geosynthetic, and the bending moments and shear forces generated in the columns were less than the corresponding values for socketed CICs. This study offers an enhanced understanding of the available options to practising engineers when designing road embankments on soft soil.
Makhdoom, I, Abolhasan, M, Abbas, H & Ni, W 2019, 'Blockchain's adoption in IoT: The challenges, and a way forward', Journal of Network and Computer Applications, vol. 125, pp. 251-279.
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© 2018 Elsevier Ltd The underlying technology of Bitcoin is blockchain, which was initially designed for financial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and cryptographic security benefits such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain's viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identified the gaps by mapping the security and performance benefits inferred by the blockchain technologies and some of the blockchain-based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the significant challenges to the blockchain's adoption in IoT.
Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to the Internet of Things', IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1636-1675.
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© 1998-2012 IEEE. The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.
Mandal, R, Roy, PP, Pal, U & Blumenstein, M 2019, 'Bag-of-visual-words for signature-based multi-script document retrieval', Neural Computing and Applications, vol. 31, no. 10, pp. 6223-6247.
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© 2018, The Natural Computing Applications Forum. An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/) document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.
Meena, NK & Nimbalkar, S 2019, 'Effect of Water Drawdown and Dynamic Loads on Piled Raft: Two-Dimensional Finite Element Approach', Infrastructures, vol. 4, no. 4, pp. 75-75.
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The piled raft foundations are widely used in infrastructure built on soft soil to reduce the settlement and enhance the bearing capacity. However, these foundations pose a potential risk of failure, if dynamic traffic loading and ground conditions are not adequately accounted in the construction phase. The ground conditions are complex because of frequent groundwater fluctuations. The drawdown of the water table profoundly influences the settlement and load sharing capacity of piled raft foundation. Further, the dynamic loading can also pose a potential risk to these foundations. In this paper, the two-dimensional finite element method (FEM) is employed to analyze the impact of water drawdown and dynamic loading on the stability of piled raft. The seismic response of piled raft is also discussed. The stresses and deformations occurring in and around the raft structure are evaluated. The results demonstrate that water drawdown has a significant effect on the stability and seismic response of piled raft. Various foundation improvement methods are assessed, such as the use of geotextile and increasing thickness of the pile cap, which aids of limiting the settlement.
Meilianda, E, Pradhan, B, Syamsidik, Comfort, LK, Alfian, D, Juanda, R, Syahreza, S & Munadi, K 2019, 'Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia', International Journal of Disaster Risk Reduction, vol. 41, pp. 101292-101292.
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Melnikov, A, Chiang, YK, Quan, L, Oberst, S, Alù, A, Marburg, S & Powell, D 2019, 'Acoustic meta-atom with experimentally verified maximum Willis coupling', Nature Communications, vol. 10, no. 1, pp. 3148-3148.
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AbstractAcoustic metamaterials are structures with exotic acoustic properties, with promising applications in acoustic beam steering, focusing, impedance matching, absorption and isolation. Recent work has shown that the efficiency of many acoustic metamaterials can be enhanced by controlling an additional parameter known as Willis coupling, which is analogous to bianisotropy in electromagnetic metamaterials. The magnitude of Willis coupling in a passive acoustic meta-atom has been shown theoretically to have an upper limit, however the feasibility of reaching this limit has not been experimentally investigated. Here we introduce a meta-atom with Willis coupling which closely approaches this theoretical limit, that is much simpler and less prone to thermo-viscous losses than previously reported structures. We perform two-dimensional experiments to measure the strong Willis coupling, supported by numerical calculations. Our meta-atom geometry is readily modeled analytically, enabling the strength of Willis coupling and its peak frequency to be easily controlled.
Miao, Z, Yu, J, Ji, J & Zhou, J 2019, 'Multi-objective region reaching control for a swarm of robots', Automatica, vol. 103, no. IEEE Transaction Robotics and Automation 14 6 1998, pp. 81-87.
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© 2019 Elsevier Ltd This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.
Moayedi, H, Mehrabi, M, Mosallanezhad, M, Rashid, ASA & Pradhan, B 2019, 'Modification of landslide susceptibility mapping using optimized PSO-ANN technique', Engineering with Computers, vol. 35, no. 3, pp. 967-984.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CE...
Moayedi, H, Tien Bui, D, Gör, M, Pradhan, B & Jaafari, A 2019, 'The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes', ISPRS International Journal of Geo-Information, vol. 8, no. 9, pp. 391-391.
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In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neu...
Mohammadinia, A, Saeidian, B, Pradhan, B & Ghaemi, Z 2019, 'Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches', BMC Infectious Diseases, vol. 19, no. 1.
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Abstract Background Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence. Methods This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models. Results Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R2 (0.85, 0.80, 0.78 and 0.75) are used. Conclusion Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM...
Mojaddadi Rizeei, H, Pradhan, B & Saharkhiz, MA 2019, 'Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery', International Journal of Remote Sensing, vol. 40, no. 3, pp. 1092-1119.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.
Motevalli, A, Naghibi, SA, Hashemi, H, Berndtsson, R, Pradhan, B & Gholami, V 2019, 'Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater', Journal of Cleaner Production, vol. 228, pp. 1248-1263.
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© 2019 Elsevier Ltd Nitrate pollution of groundwater has increased dramatically worldwide due to increase of population and agricultural productivity. The resulting nitrate concentration in groundwater is usually a combination of various types of point and non-point pollutant sources. It is often difficult to distinguish between these sources since groundwater is formed in large and complex catchments with various natural processes and anthropogenic influence that contribute to a certain downstream nitrate concentration. For such conditions, this paper uses a methodology that can be used to inversely determine type and location of main nitrate pollutant source. The methodology builds on two state-of-the-art data mining techniques, boosted regression tree (BRT)and k-nearest neighbor (KNN). These techniques are used to produce a nitrate pollution vulnerability map. The methodology can mitigate effects of subjective judgement on determining importance of different sources and mechanisms for nitrate transport. The investigated mechanisms are hydrogeological, hydrological, anthropogenic, topography, and soil conditioning factors. Thus, the proposed methodology is used to separate between natural processes and anthropogenic effects on nitrate pollution. To calculate the groundwater vulnerability maps, a groundwater nitrate concentration of 40 mg/L (suggested by WHO with a 20% risk margin)was selected as a general threshold for identifying polluted areas that resulted in 96 polluted wells. Non-polluted locations were selected from well data with nitrate concentration less than 15 mg/L (96 non-polluted). The models were trained on 70% polluted and 70% non-polluted site data. The remaining data, 30% polluted and 30% non-polluted sites, were used to validate the simulation results. Results showed that the BRT produced outputs with higher performance than the KNN algorithm. The final ranking results based on the BRT model showed the higher importance of hydraulic ...
Naderpour, M, Rizeei, HM, Khakzad, N & Pradhan, B 2019, 'Forest fire induced Natech risk assessment: A survey of geospatial technologies', Reliability Engineering & System Safety, vol. 191, pp. 106558-106558.
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© 2019 Elsevier Ltd Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.
Naghibi, SA, Dolatkordestani, M, Rezaei, A, Amouzegari, P, Heravi, MT, Kalantar, B & Pradhan, B 2019, 'Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential', Environmental Monitoring and Assessment, vol. 191, no. 4.
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© 2019, Springer Nature Switzerland AG. Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ensemble methodology based on evidential belief function and tree-based models (EBFTM) for preparing groundwater potential maps (GPM). The performance of these new models is then compared with three previously implemented models, i.e., boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). For this purpose, spring locations in the Meshgin Shahr in Iran were detected. The spring locations were randomly categorized into training (70% of the locations) and validation (30% of the locations) datasets. Furthermore, several groundwater conditioning factors (GCFs) such as hydrogeological, topographical, and land use factors were mapped and regarded as input variables. The tree-based algorithms (i.e., BRT, CART, RF, and RTF) were applied by implementing the input variables and training dataset. The groundwater potential values (i.e., spring occurrence probability) obtained by the BRT, CART, RF, and RTF models for all the pixels of the study area were classified into four potential classes and then used as inputs of the EBF model to construct the new ensemble model (i.e., EBFTM). At last, this paper implemented a receiver operating characteristics (ROC) curve for determining the efficiency of the EBFTM, RTF, BRT, CART, and RF methods. The findings illustrated that the EBFTM had the highest efficacy with an area under the ROC curve (AUC) of 90.4%, followed by the RF, BRT, CART, and RTF models with AUC-ROC values of 90.1, 89.8, 86.9, and 86.2%, respectively. Thus, it could be inferred that the ensemble approach is capable of improving the efficacy of the single tree-based models in GPM production.
Ngo, TN, Indraratna, B & Rujikiatkamjorn, C 2019, 'Improved performance of ballasted tracks under impact loading by recycled rubber mats', Transportation Geotechnics, vol. 20, pp. 100239-100239.
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© 2019 Elsevier Ltd Ballasted tracks at transition locations such as approaches to bridges and road crossings experience increasing degradation and deformation due to dynamic and high impact forces, a key factor that decreases the stability and longevity of railroads. One solution to minimise ballast degradation at the transition zones is using rubber energy absorbing drainage sheets (READS)manufactured from recycled tyres. When placed beneath the ballast layer, READS distributes the load over wider area and attenuate of the load over a longer duration thus decreasing maximum stress, apart from reducing the energy transferred to the ballast and other substructure components. Subsequently, the track substructure experiences less plastic deformation and degradation. These mats also provide an environmentally friendly and cost-effective alternative. In this study, a series of large-scale drop hammer impact tests was carried out to investigate how effectively the READS could attenuate impact loads and help mitigate ballast deformation and degradation. Soft and stiff subgrade were used to investigate the load-deformation response of ballast (with and without READS), subjected to impact loads from a hammer dropped from various heights (hd = 100–250 mm). Laboratory test results show that the inclusion of READS helps to reduce the dynamic impact load transferred to the ballast layer resulting in significantly less permanent deformation and degradation of ballast, apart from significant attenuation of load magnitude and vibration to the underlying subgrade layers.
Ngoc, TP, Fatahi, B & Khabbaz, H 2019, 'Impacts of Drying-Wetting and Loading-Unloading Cycles on Small Strain Shear Modulus of Unsaturated Soils', International Journal of Geomechanics, vol. 19, no. 8, pp. 04019090-04019090.
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© 2019 American Society of Civil Engineers. The small strain shear modulus (Gmax) is an important parameter in geodynamic problems. To predict the Gmax of unsaturated soils that are normally subjected to complex drying and wetting processes, the effect of hydraulic hysteresis needs to be evaluated. Although several equations have been proposed in recent years, limitations still exist, requiring more research studies in this field. In this study, Gmax was investigated in a multistage test during several drying-wetting cycles and a loading-unloading cycle of net stress. The results revealed four key factors that directly influence the magnitude of Gmax: the void ratio, net stress, matric suction, and degree of saturation. Although variations of the void ratio, net stress, and matric suction cause persistent responses of Gmax (i.e., if all other factors remain unchanged, Gmax would then be reversely proportional to the void ratio and directly proportional to the net stress and matric suction), variations in the degree of saturation result in different responses. A decrease in the degree of saturation may induce a reduction or growth of Gmax because, on the one hand, it reduces the effect of matric suction, whereas on the other hand, it increases the total effect of van der Waals attractions and electric double-layer repulsions. At the same stress state, a reverse trend, induced by an increase in the degree of saturation, will occur with a growth in the effect of matric suction and a reduction in the combined effect of van der Waals attractions and electric double-layer repulsions. An analysis of the results showed that hydraulic hysteresis occurred in all the stress loops, and it directly influenced the response of Gmax. The effect of hydraulic hysteresis can only be captured if the van der Waals attractions and electric double-layer repulsions are considered. A model to estimate Gmax while incorporating the van der Waals attractions and electric double-la...
Nguyen, HH, Khabbaz, H & Fatahi, B 2019, 'A numerical comparison of installation sequences of plain concrete rigid inclusions', Computers and Geotechnics, vol. 105, pp. 1-26.
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© 2018 Elsevier Ltd Soil displacement induced when installing controlled modulus columns (CMC) as ground reinforcement could affect the columns installed close by. Realising numerical analyses may provide useful insights, this paper describes a numerical approach to investigate how groups of CMC installed in different sequences could affect columns installed previously. Coupled consolidation analyses in large strain mode and incorporating soil-CMC interaction were carried out using the three-dimensional finite difference software package FLAC3D. The CMCs were modelled using advanced non-linear Hoek-Brown material with a tensile yield criterion while soils with a typical profile were characterised using the modified Cam Clay and the elastic-perfectly plastic material with a Mohr-Coulomb yield criterion. Where possible, the predicted responses of ground surrounding the CMCs were compared to a number of existing analytical methods. Predictions revealed that lateral soil movement and soil heave near existing CMCs induced by installing new CMCs towards the existing CMCs were approximately 15% and 25% greater than corresponding predictions when a reverse installation sequence was adopted. The maximum excess pore water pressures, induced near existing columns due to installing new columns towards the existing ones, were almost twice more than those caused by the reverse sequence of installation. Moreover, the predicted bending moments generated in the existing columns induced by installing new columns towards the existing CMCs were almost 22% greater than the corresponding values when the reverse installation sequence was adopted. This shows the importance of selecting an appropriate installation sequence in the CMC construction process as well as considering the initial stress field and bending moments in the surrounding soil and CMCs, respectively when designing embankments on improved soft soils.
Nguyen, TN, Yu, Y, Li, J, Gowripalan, N & Sirivivatnanon, V 2019, 'Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network', Computers and Concrete, vol. 24, no. 6, pp. 541-553.
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Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.
Nguyen, TT & Indraratna, B 2019, 'Micro-CT Scanning to Examine Soil Clogging Behavior of Natural Fiber Drains', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 9, pp. 04019037-04019037.
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© 2019 American Society of Civil Engineers. The use of jute and coir fibers as natural fiber drains to facilitate drainage and soft soil stabilization has been proposed for decades. However, their uncertain hydraulic behavior has often hampered their wider application in major infrastructure projects. Because these drains have a complex porous structure that can trap soil particles and reduce their discharge capacity, a comprehensive laboratory investigation in which soft soil was used to interact with different fiber drains under varying confining pressure was conducted via a discharge capacity test scheme. Nondestructive micro-computed tomography (CT) scanning followed by a series of image processing techniques was applied to the drains to capture their three-dimensional porous characteristics, which were then used to clarify their hydraulic behavior. The study revealed that there are two major types of components - intra- and interbundle voids - making porosity in a fiber drain, and they can be used to evaluate the drain discharge capacity. The larger the interbundle porosity, the higher the drain discharge capacity. Jute filters not only enlarge the interbundle porosity but also - if they are thick enough - help drains resist undue lateral pressure and clogging. Fiber drains are more sensitive to confinement than polymeric drains, because their discharge capacity decreases considerably at higher confining pressures. This study enables the hydraulic properties of natural fiber drains subjected to soil clogging to be properly understood so that drain designs can be optimized to make them more competitive with conventional polymeric drains.
Nguyen, TT, Indraratna, B, Kelly, R, Phan, NM & Haryono, F 2019, 'Mud pumping under railtracks: Mechanisms, assessments and solutions', Australian Geomechanics Journal, vol. 54, no. 4, pp. 59-80.
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Mud pumping under railway tracks has received increasing attention from academic and practical perspectives in recent decades, however, the actual mechanisms and possible solutions are still not understood or well established. Frequent investigations in countries such as Japan, Canada, the USA, China, Australia, the UK, and other European regions where railway systems are the largest and most advanced, indicate that mud pumping still leads to high annual maintenance costs. On this basis, a thorough review is therefore essential, so this paper presents a systematic and comprehensive review of mud pumping in railways. In particular three primary aspects of mud pumping are addressed: (i) the phenomena and mechanisms; (ii) assessments; and (iii) solutions. The review shows the three essential factors that trigger mud pumping, i.e., excess fines, excess water, and cyclic loads. While excess fines can be induced by subgrade fluidisation, ballast breakdown and external sources, the excess water is mainly due to insufficient drainage in the foundations. Given these 3 factors, different contexts where mud pumping can be instigated are summarised such as subgrade fluidisation and infiltration, peat boils from soft roadbeds and upward migration of non-subgrade fines. Unfavourable weather condition, poor sleeper-ballast contact and stress/strain concentration at particular sections such as rail joints, switches, crossings and transition zones can accelerate the inception of mud pumping. In all cases, the generation of excess pore pressure is the driving mechanism. The study also summarises the laboratory and in-situ techniques currently used to assess mud pumping. 4 major groups of mud pumping solutions are highlighted with their advantages and disadvantages: (1) clean, modify and renew problematic layers; (2) enhance drainage condition; (3) geosynthetics; and (4) chemical stabilisations.
Nohani, Moharrami, Sharafi, Khosravi, Pradhan, Pham, Lee & Melesse 2019, 'Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models', Water, vol. 11, no. 7, pp. 1402-1402.
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Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the ...
Noori, AM, Pradhan, B & Ajaj, QM 2019, 'Dam site suitability assessment at the Greater Zab River in northern Iraq using remote sensing data and GIS', Journal of Hydrology, vol. 574, pp. 964-979.
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Noori, L, Pour, A, Askari, G, Taghipour, N, Pradhan, B, Lee, C-W & Honarmand, M 2019, 'Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran', Remote Sensing, vol. 11, no. 5, pp. 495-495.
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Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The appr...
Oberst, S, Lenz, M, Lai, JCS & Evans, TA 2019, 'Termites manipulate moisture content of wood to maximize foraging resources', Biology Letters, vol. 15, no. 7, pp. 20190365-20190365.
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Animals use cues to find their food, in microhabitats within their physiological tolerances. Termites build and modify their microhabitat, to transform hostile environments into benign ones, which raises questions about the relative importance of cues. Termites are desiccation intolerant and foraging termites are attracted to water, so most research has considered moisture to be a cue. However, termites can also transport water to food, and so moisture may play other roles than previously considered. To examine the role of moisture, we compared Coptotermes acinaciformis termite foraging decisions in laboratory experiments when they were offered dry and moist wood, with and without load. Without load, termites preferred moist wood and ate it without any building, whereas they moistened dry wood after wrapping it in a layer of clay. For the ‘With load’ units, termites substituted some of the wood for load-bearing clay walls, and kept the wood drier than on the unloaded units. As drier wood has higher compressive strength and higher rigidity, it allows more of the wood to be consumed. These results suggest that moisture plays a more important role in termite ecology than previously thought. Termites manipulate the moisture content according to the situational context and use it for multiple purposes: increased moisture levels soften the fibre, which facilitates foraging, yet keeping the wood dry provides higher structural stability against buckling which is especially important when foraging on wood under load.
Ogie, RI & Pradhan, B 2019, 'Natural Hazards and Social Vulnerability of Place: The Strength-Based Approach Applied to Wollongong, Australia', International Journal of Disaster Risk Science, vol. 10, no. 3, pp. 404-420.
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© 2019, The Author(s). Natural hazards pose significant threats to different communities and various places around the world. Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques, however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index (SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.
Pallewattha, M, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2019, 'Shear strength of a vegetated soil incorporating both root reinforcement and suction', Transportation Geotechnics, vol. 18, pp. 72-82.
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© 2018 Shear strength of the root permeated soil increases due to the mechanical effects of root reinforcement and most of the past studies have been conducted to capture this effect under saturated soil conditions. However, the soil adjacent to the tree roots is usually in an unsaturated condition and this leads to alterations in root-soil interaction mechanisms and associated shear strength of the root permeated soil system. In this paper, the increment in shear strength is studied considering both the effect of suction and root reinforcement patterns. A number of direct shear tests were conducted for different suction levels in root-permeated and unreinforced soil specimens. The results indicate that the shear strength behaviour of the soil-root system is governed by the level of suction and root failure patterns and a new mathematical model incorporating the effect of both parameters is proposed.
Pathirage, U, Indraratna, B, Pallewattha, M & Heitor, A 2019, 'A theoretical model for total suction effects by tree roots', Environmental Geotechnics, vol. 6, no. 6, pp. 353-360.
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Strengthening soft and weak soil by way of root reinforcement is a well-known strategy that is adopted worldwide. In Australia, native gum trees remain evergreen throughout the year and have been utilised to stabilise transportation corridors by way of reinforcement provided by the roots and the suction generated within the root domain as a function of evapotranspiration through the canopy. A mature gum tree can induce a missive total suction pressure exceeding 30 MPa through its root water and solute uptake in terms of matric plus osmotic suction. This cumulative effect of matric and osmotic suctions contributes to the overall shear strength of the soil, but the significant osmotic suction is often ignored in classical geotechnical engineering that does not consider the presence of trees. This study is an attempt to demonstrate the important role of osmotic suction, because it is directly proportional to the solute concentration in the soil and the solute uptake mechanisms of the surrounding vegetated ground.
Pour, Park, Park, Hong, Muslim, Läufer, Crispini, Pradhan, Zoheir, Rahmani, Hashim & Hossain 2019, 'Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland', Remote Sensing, vol. 11, no. 20, pp. 2430-2430.
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Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/bio...
Qi, Y, Indraratna, B & Coop, MR 2019, 'Predicted Behavior of Saturated Granular Waste Blended with Rubber Crumbs', International Journal of Geomechanics, vol. 19, no. 8, pp. 04019079-04019079.
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Qi, Y, Indraratna, B, Heitor, A & Vinod, JS 2019, 'Closure to “Effect of Rubber Crumbs on the Cyclic Behavior of Steel Furnace Slag and Coal Wash Mixtures” by Yujie Qi, Buddhima Indraratna, Ana Heitor, and Jayan S. Vinod', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 1, pp. 07018035-07018035.
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Rahmati, O, Samadi, M, Shahabi, H, Azareh, A, Rafiei-Sardooi, E, Alilou, H, Melesse, AM, Pradhan, B, Chapi, K & Shirzadi, A 2019, 'SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors', Geoscience Frontiers, vol. 10, no. 6, pp. 2167-2175.
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© 2019 China University of Geosciences (Beijing) and Peking University The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources. Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner. Hence, this study aimed at developing a user-friendly geographic information system (GIS) tool, Sub-Watershed Prioritization Tool (SWPT), using the Python programming language to decrease any possible uncertainty. It used geospatial–statistical techniques for analyzing morphometric and topo-hydrological factors and automatically identifying critical and priority sub-watersheds. In order to assess the capability and reliability of the SWPT tool, it was successfully applied in a watershed in the Golestan Province, Northern Iran. Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds. It provided a cost-effective approach for prioritization of sub-watersheds. Therefore, the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.
Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-Dimensional Slope Stability Analysis Incorporating Coupled Effects of Pile Reinforcement and Reservoir Drawdown', International Journal of Geomechanics, vol. 19, no. 4, pp. 06019002-06019002.
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© 2019 American Society of Civil Engineers. In pile-reinforced dams and bank slopes, the antislide effect of piles and drawdown of reservoirs are two aspects that could significantly affect the slope stability. However, existing studies have incorporated these two factors separately, albeit not in tandem. Moreover, stability assessment of these earth structures is usually performed ignoring the three-dimensional (3D) effect. To address these issues, the kinematic approach of limit analysis is adopted in this technical note for evaluating slope stability based on the 3D rotational failure mechanism. In addition, the coupled effects of pile reinforcement and water drawdown are considered. The analysis is performed for four types of drawdown cases. The results demonstrate that the optimal pile location undergoes significant change during the external drawdown process, while the effect of the declining water level on slope stability follows the similar pattern for varying pile locations.
Rasouli, H & Fatahi, B 2019, 'A novel cushioned piled raft foundation to protect buildings subjected to normal fault rupture', Computers and Geotechnics, vol. 106, pp. 228-248.
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© 2018 Elsevier Ltd Recent earthquake events have shown that besides the earthquake forces, interaction between the fault rupture and structure could cause a lot of damage to the surface and underground structures. Field observations have revealed a need to design structures for fault induced loading in regions with active faults. In this present study, three-dimensional numerical modelling using ABAQUS finite element software is used to study the interactive mechanism of normal fault rupture with a 20-story moment-resisting building frame sitting on a raft, connected piled raft, and cushioned piled raft foundations. The performance of a foundation-structure system is examined by considering geotechnical and structural performance objectives such as structural inter-story drift, raft displacement, and the bending moment and shear forces within the raft and piles. In order to improve the geotechnical and structural performance of foundations and buildings, a new foundation system with cushioned piles below the raft is proposed because of its superior performance with regards to raft rocking and permanent structural inter-story drifts under normal fault rupture. This proposed foundation system also curtailed the bending moments induced in the piles.
Razzak, I, Blumenstein, M & Xu, G 2019, 'Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1117-1127.
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© 2001-2011 IEEE. Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.
Rehman, J, Sohaib, O, Asif, M & Pradhan, B 2019, 'Applying systems thinking to flood disaster management for a sustainable development', International Journal of Disaster Risk Reduction, vol. 36, pp. 101101-101101.
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© 2019 Elsevier Ltd The rapid urbanization and environmental imbalance have significantly challenged Pakistan's organizational capacity to respond and initiate relief efforts and hence increasing its vulnerability to flood disaster situations. This study considers systems thinking approaches such as, Causal Loop Diagram (CLD) and Driver-Pressures-States-Impacts-Responses (DPSIR) framework to identify key stakeholders to disaster risk reduction and analyze various social, technical, institutional, cultural, infrastructural and environmental factors that contribute to flooding in Pakistan. Based on the information collected through expert interviews with key government officials and analyzing the existing literature and research reports on floods and disaster management, policy recommendations for long-term flood disaster response strategies have been made. The comprehensive set of recommendations towards effective flood management and mitigation would help build resilience from floods by raising community awareness and enhancing institutional capacities at federal, provincial and district government levels in the countries like Pakistan and other developing nations facing catastrophic flood situations.
Rizeei, H & Pradhan, B 2019, 'Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries', Remote Sensing, vol. 11, no. 6, pp. 692-692.
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Orthorectification is an important step in generating accurate land use/land cover (LULC) from satellite imagery, particularly in urban areas with high-rise buildings. Such buildings generally appear as oblique shapes on very-high-resolution (VHR) satellite images, which reflect a bigger area of coverage than the real built-up area on LULC mapping. This drawback can cause not only uncertainties in urban mapping and LULC classification, but can also result in inaccurate urban change detection. Overestimating volume or area of high-rise buildings has a negative impact on computing the exact amount of environmental heat and emission. Hence, in this study, we propose a method of orthorectfiying VHR WorldView-3 images by integrating light detection and ranging (LiDAR) data to overcome the aforementioned problems. A 3D rational polynomial coefficient (RPC) model was proposed with respect to high-accuracy ground control points collected from the LiDAR data derived from the digital surface model. Multiple probabilities for generating an orthrorectified image from WV-3 were assessed using 3D RCP model to achieve the optimal combination technique, with low vertical and horizontal errors. Ground control point (GCPs) collection is sensitive to variation in number and data collection pattern. These steps are important in orthorectification because they can cause the morbidity of a standard equation, thereby interrupting the stability of 3D RCP model by reducing the accuracy of the orthorectified image. Hence, we assessed the maximum possible scenarios of resampling and ground control point collection techniques to bridge the gap. Results show that the 3D RCP model accurately orthorectifies the VHR satellite image if 20 to 100 GCPs were collected by convenience pattern. In addition, cubic conventional resampling algorithm improved the precision and smoothness of the orthorectified image. According to the root mean square error, the proposed combination techni...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models', International Journal of Disaster Risk Reduction, vol. 38, pp. 101205-101205.
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© 2019 The increases in the frequency and intensity of rainfall events due to global climate change and the development of additional pavement, roads and water storage sites due to population growth have enhanced the probability of pluvial flooding (PF) in urban areas. The estimation of urban pluvial flood vulnerability and prompt emergency responses are crucial steps towards urban planning and risk mitigation. However, uncertainties exist in the optimal allocation of emergency response centres (ERCs). This study assessed the current situation of ERCs in terms of PF-prone demand points. In this study, fire and police stations, hospitals and military camps were defined as ERCs, and residential buildings, where people spend most of their time, were considered demand points. Our study area was Damansara City in Peninsular Malaysia, which is frequently affected by PF. We combined an optimised PF probability model with ideal location allocation methods on a geographic information system platform to construct the proposed model for achieving accurate ERC spatial planning. Firstly, PF-prone urban areas were identified using a recent machine learning multiple layer perceptron (MLP) model. Then, a Taguchi method was used to calibrate the MLP variables, namely, seed, momentum, learning rate, hidden layer attribute and class. Fourteen important PF contributing parameters were weighted on the basis of historical flood events. The predicted PF-prone areas were validated by comparing the predictions with the data from meteorological stations and observed inventory events. In addition, the current locations of ERCs were utilised in the location allocation model to assess the ideal time for providing essential services to elements at risk. Minimum impedance and maximum coverage location problem models were implemented to assess the current allocated location of ERCs and multiple scenarios. The coverage of existing ERCs was calculated, and their suitable and optimal locations wer...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS', Complex & Intelligent Systems, vol. 5, no. 3, pp. 283-302.
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Rizeei, HM, Pradhan, B, Saharkhiz, MA & Lee, S 2019, 'Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique', Journal of Hydrology, vol. 579, pp. 124172-124172.
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© 2019 Machine learning and data-driven models have achieved a favorable reputation in the field of advanced geospatial modeling, particularly for models of groundwater aquifer potential over large areas. Such models built using standalone machine learning techniques retain some uncertainty, including errors associated with the modeling process, sampling approach, and input hyper-parameters. Some of these techniques cannot be applied in data-scarce regions because high bias and variance can lead to oversimplification. Therefore, in the current study, we developed and validated a novel ensemble multi-adaptive boosting logistic regression (MABLR) model for groundwater aquifer potential mapping. This model was validated in a large area of the Gyeongsangbuk-do basin in South Korea and the results were compared to those of different types of machine learning models including multiple-layer perception (MPL), logistic regression (LR), and support vector machine (SVM) models. A forward stepwise LR technique was implemented to assess the importance of contributing morphological factors; we found 15 factors that contributed significantly: topographic wetness index (TWI), topographic roughness index (TRI), stream power index (SPI), topographic position index (TPI), multi-resolution valley bottom flatness (MVBF), slope, aspect, slope length (LS), distance from the river, distance from the fault, profile curvature, plane curvature, altitude, land use/land cover (LULC), and geology. We optimized the MABLR model using a fuzzy logic supervised (FLS) approach with 184 iterations and then validated the results using accuracy assessment metrics including the κ coefficient, root-mean-square error (RMSE), receiver operating characteristics (ROC), and the precision-recall curve (PRC). Our model had superior predictive performance among the models tested, with higher overall goodness-of-fit and validation values according to the κ coefficient (0.819 and 0.781, respectively), ROC (0.917...
Roodposhti, M, Aryal, J & Pradhan, B 2019, 'A Novel Rule-Based Approach in Mapping Landslide Susceptibility', Sensors, vol. 19, no. 10, pp. 2274-2274.
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Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.
Saeidian, B, Mesgari, MS, Pradhan, B & Alamri, AM 2019, 'Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms', Water, vol. 11, no. 12, pp. 2611-2611.
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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method.
Sahoo, S, Dey, S, Dhar, A, Debsarkar, A & Pradhan, B 2019, 'On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC', Ecological Indicators, vol. 106, pp. 105440-105440.
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© 2019 Elsevier Ltd Assessing the impact of climate variability is important for water resources planning and management. In the present study, climate model data were utilized in conjunction with the hydrological model to analyze the effect of climate change on projected streamflow and groundwater recharge values for the Dwarakeswar-Gandherswari basin, India. Regional Climate Model (RCM) data [Representative Concentration Pathway (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5)] were considered for future climate change scenarios. Five bias correction methods [linear scaling (LS), local intensity scaling (LOCI), power transformation (PWTR), distribution mapping (DM) and variance scaling (VARI)] were applied for RCM based precipitation and temperature data. Projected Land Use and Land Cover (LULC) values were obtained from Dyna-CLUE model. Discharge data (1990–2016) was utilized for model calibration and validation purpose. Total twelve scenarios (4 RCPs per year for the years 2030, 2050 and 2080) were considered. The results showed increasing trend in simulated discharge for the months June to September and reverse trend for the months October to December. The results also showed that groundwater recharge increased for the maximum number of sub-watersheds for the interval 2016–2030 compared to 2016–2050 and 2016–2080 under all RCPs. Uncertainties in streamflow were quantified in terms of exceedance probability and recurrence interval. ALPHA_BF was the most sensitive parameter for the river basin. However, gross increase in groundwater recharge was observed for all the scenarios. These results can be effectively utilized for irrigation planning purpose.
Salmasi, F, Pradhan, B & Nourani, B 2019, 'Prediction of the sliding type and critical factor of safety in homogeneous finite slopes', Applied Water Science, vol. 9, no. 7.
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AbstractIn this paper, the effect of soil material parameters including soil specific weight (γ), cohesion (C), angle of internal friction ($$\emptyset$$∅), and geometric parameter of slope including angle with the horizontal (β) for a constant slope height (H) on factor of safety (Fs) was investigated.Fswas considered in two scenarios: (1) slope with dry condition, and (2) with steady-state saturated condition that comprises water level drawdown circumstances. In addition, the type of slip circle was also investigated. For this purpose, theSLOPE/Wsoftware as a subgroup ofGeo-Studiosoftware was implemented. Results showed that decreasing of water table level and omitting the hydrostatic pressure on the slope consequently would result in safety factor decrement. Comparison of the plane and circular failure surfaces showed that plane failure method produced good results for near-vertical slopes only. Determination of slip type showed that for state (30° < β < 45°), the three types of failure circles (toe, slope or midpoint circle) may occur. For state (45° < β < 60°), two modes of failure may occur: midpoint circle and toe circle. For state (β > 60°), the mode of failure circle is only toe circle. Linear and nonlinear regression equations were obtained for estimation of slope safety factor.
Samadi-Boroujeni, H, Altaee, A, Khabbaz, H & Zhou, J 2019, 'Application of buoyancy-power generator for compressed air energy storage using a fluid–air displacement system', Journal of Energy Storage, vol. 26, pp. 100926-100926.
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© 2019 Elsevier Ltd This study proposes a gravity power generator based on the fluid–air displacement system using Compressed Air Energy Storage from renewable energy sources to increase the solar and wind power system penetration in the power network. A computer model was applied to estimate the performance of the fluid–air displacement system, taking into account the effects of key design and operating parameters. Analysis of the system was performed to calculate the net energy generation as the difference between the energy input and the energy output. Simulation results indicated that the round-trip efficiency of the fluid–air displacement system was between 47% and 60%, assuming 80% compressor efficiency. Results also showed that a system generating the maximum energy density should have a speed of cylinders movement of 0.65 m/s, a cylinder-wall distance of 0.25 × diameter of the cylinder and a gap distance between centers of two tandem cylinders is equal to 1.25. Furthermore, a sensitivity analysis conducted on the main parameters of the system identified that the gap ratio and the buckets moving speed were the highly sensitive parameters to the design and operation of the proposed system. This study also demonstrates the feasibility of using the fluid-displacement system in energy storage from renewable energy technologies.
Sameen, MI & Pradhan, B 2019, 'Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information', IEEE Access, vol. 7, pp. 114363-114373.
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Sameen, MI, Pradhan, B & Lee, S 2019, 'Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas', Natural Resources Research, vol. 28, no. 3, pp. 757-775.
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© 2018, International Association for Mathematical Geosciences. Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.
Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2019, 'Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks', IEEE Access, vol. 7, pp. 35317-35329.
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© 2013 IEEE. Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd. Most of the existing approaches use regression on density maps for the crowd count from a single image. However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment. On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations. To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections. Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos. We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians. After pedestrian detection, the proposed motion guided filter is employed. We evaluate the performance of our approach on three publicly available datasets. The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.
SarojiniAmma, BK, Indraratna, B & Vinod, JS 2019, 'A semi-empirical dilatancy model for ballast fouled with plastic fines', Geomechanics and Geoengineering, vol. 14, no. 1, pp. 12-17.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In the era of high speed trains, it is very important to ensure the stability of rail tracks under adverse conditions including the fouling of ballast. Fouling of ballast from unstable and saturated soft subgrade soil is one of the major reasons for track deterioration. The reported results of a number of large-scale laboratory experiments on the shear behaviour of ballast and fouled ballast are analysed, herein. It was observed that fines have a significant influence on the shear behaviour of ballast. Shear strength increases and dilatancy decreases with the addition of fines. In this paper, a semi-empirical mathematical model has been proposed to capture the dilatancy of ballast fouled with fines during shearing. The empirical constants a, b and c proposed in the model are a function of the fines content Void Contamination Index (VCI). The results of the model have been compared with the laboratory experiments and are found to be in good agreement.
Sheikhrahimi, A, Pour, AB, Pradhan, B & Zoheir, B 2019, 'Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran', Advances in Space Research, vol. 63, no. 10, pp. 3315-3332.
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© 2019 COSPAR The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.
Shit, RC, Sharma, S, Puthal, D, James, P, Pradhan, B, Moorsel, AV, Zomaya, AY & Ranjan, R 2019, 'Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World', IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3532-3564.
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© 1998-2012 IEEE. The 'Smart World' envisioned by technology will be achieved by the penetration of intelligence into ubiquitous things, including physical objects, cyber-entities, social-elements or individuals, and human thinking. The development of Smart World is enabled by diverse applications of wireless sensor networks (WSNs) into those components identified as things. Such a smart-world will have features controlled significantly by the location information. Control and Policy information of Smart World services, often addressed as location-based services (LBSs), are governed by location data. Localization thus becomes the key enabling technology for Smart World facilities. It is generally classified as active and passive techniques in nature. Active localization is a widely adopted localization scheme where the target is detected and tracked carries a tag or attached device. The other category, Passive methods, defines targets to be localized as free of carrying a tag or device, hence also referred to as device-free localization (DFL) or sensor-less localization. The passive approach is a well suited for the development of diverse smart world applications with ubiquitous localization. DFL schemes fall into a wide range of application scenarios within the Smart World ecosystem. A few notable examples are occupancy detection, identity definition, positioning, gesture detection, activity monitoring, pedestrian and vehicle-traffic flow surveillance, security safeguarding, ambient intelligence-based systems, emergency rescue operations, smart work-spaces and patient or elderly monitoring. In this paper, the revolution of DFL technologies have been reviewed and classified comprehensively. Further, the emergence of the Smart World paradigm is analyzed in the context of DFL principles. Moreover, the inherent challenges within the application domains have been extensively discussed and improvement strategies for multi-target localization and counting approach are ...
Skilodimou, HD, Bathrellos, GD, Chousianitis, K, Youssef, AM & Pradhan, B 2019, 'Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study', Environmental Earth Sciences, vol. 78, no. 2.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capaci...
Stender, M, Oberst, S & Hoffmann, N 2019, 'Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction', Vibration, vol. 2, no. 1, pp. 25-46.
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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.
Stender, M, Oberst, S, Tiedemann, M & Hoffmann, N 2019, 'Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data', Nonlinear Dynamics, vol. 97, no. 4, pp. 2483-2497.
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Complex machine dynamics, as caused by friction-induced vibrations and related to brake squeal, have gained significant attention in research and industry for decades. Today, remedies heavily rely on experimental testing due to the low prediction quality of numerical models. However, there is considerable lack of in-depth studies in characterizing self-excited oscillations encoded in scalar measurements. We complement previous works on phase-space reconstruction and recurrence plots analysis to a larger data base by applying a novel systematic approach using a large
data base. This framework considers appropriate delay embedding, time series partitioning into squealing and non-squealing parts and comparison to operational parameters of the brake system. By means of recurrence plot analysis, we illustrate that friction-excited vibrations are multi-scale in nature. Results confirm the existence of low-dimensional attractors in squealing regimes with increasing values of determinism and periodicity with rising vibration levels. It is shown that the squeal propensity can be directly linked to recurrence quantification measures. Using determinism and the clustering coefficient as metrics, we show for the first time that is possible to predict instabilities in regions of non-squealing conditions.
Sun, Q, Indraratna, B & Heitor, A 2019, 'Behaviour of a capping layer reinforced with recycled tyres', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 3, pp. 127-137.
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In this paper, a sustainable approach for reducing lateral displacement in a track by increasing the confining pressure in the track substructure is demonstrated by placing a cellular rubber (tyre) membrane infilled with crushed ballast, as an alternative to a traditional capping layer of compacted granulates. Plate-load tests on a single tyre filled with gravel and subjected to a vertical load were carried out to investigate the interaction between tyre and gravel. A track model with tyre reinforcement was created to evaluate the performance of a tyre-reinforced capping layer under cyclic loading, and a numerical model was developed to determine the benefit that tyres would provide to railway substructure, especially when spent ballast is recycled as capping layer materials.
Sun, Q, Indraratna, B & Ngo, NT 2019, 'Effect of increase in load and frequency on the resilience of railway ballast', Géotechnique, vol. 69, no. 9, pp. 833-840.
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This paper presents the results of a series of large-scale cyclic triaxial tests conducted on ballast subjected to increased load and frequency of loading. For a given loading, the laboratory test data demonstrate that the resilient modulus of ballast is influenced by the frequency of loading. Both strain hardening and strain softening can be observed in response to increasing magnitude of load and frequency. A correlation between the resilient modulus and bulk stress is introduced to describe both the strain-hardening and strain-softening behaviour of ballast under different frequencies. A good corroboration between the cyclic stress ratio and the accumulated permanent strain and the resilient strain is demonstrated.
Sun, Y & Nimbalkar, S 2019, 'Stress-fractional soil model with reduced elastic region', Soils and Foundations, vol. 59, no. 6, pp. 2007-2023.
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Sun, Y, Nimbalkar, S & Chen, C 2019, 'Particle breakage of granular materials during sample preparation', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 2, pp. 417-422.
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© 2019 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Particle breakage is commonly observed in granular materials when subjected to external loads. It was found that particle breakage would occur during both sample preparation and loading stages. However, main attention was usually paid to the particle breakage behaviour of samples during loading stage. This study attempts to explore the breakage behaviour of granular materials during sample preparation. Triaxial samples of rockfill aggregates are prepared by layered compaction method to achieve different relative densities. Extents of particle breakage based on the gradings before and after test are presented and analysed. It is found that particle breakage during sample preparation cannot be ignored. Gradings after test are observed to shift away from the initial grading. Aggregates with larger size that appear to break are more than the smaller-sized ones. Irrespective of the initial gradings, an increase in the extent of particle breakage with the increasing relative density is observed during sample preparation.
Sutton, GJ, Zeng, J, Liu, RP, Ni, W, Nguyen, DN, Jayawickrama, BA, Huang, X, Abolhasan, M, Zhang, Z, Dutkiewicz, E & Lv, T 2019, 'Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives', IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2488-2524.
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© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements.
Teng, J, Kou, J, Zhang, S & Sheng, D 2019, 'Evaluating the Influence of Specimen Preparation on Saturated Hydraulic Conductivity Using Nuclear Magnetic Resonance Technology', Vadose Zone Journal, vol. 18, no. 1, pp. 1-7.
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Core IdeasSpecimen preparation methods have a significant influence on hydraulic conductivity.The difference caused by different methods can be large as one order of magnitude.Soil pore structure should be considered in predicting hydraulic conductivity.A pore‐information‐based model is presented to predict hydraulic conductivity.The new model is more accurate than traditional particle information based models.A series of laboratory tests were performed to investigate the influences of specimen preparation on pore size distribution of soil and saturated hydraulic conductivity (Ks). Nuclear magnetic resonance technology was used to measure the pore size distribution of the saturated samples of silty soil, which were prepared by three different kinds of methods: Proctor compaction, static compaction, and the consolidation method. The Ks of the samples was measured by the falling head permeability test. The results show that the difference in Ks caused by different specimen preparations can be large as one order of magnitude, as the measured Ks varied from 3.09 × 10−3 to 3.36 × 10−4 cm s−1. The consolidated specimen tended to have the greatest Ks value, followed by those prepared by Proctor compaction and static compaction. The observed difference highlights the importance of pore structure in determining
Teng, J, Shan, F, He, Z, Zhang, S, Zhao, G & Sheng, D 2019, 'Experimental study of ice accumulation in unsaturated clean sand', Géotechnique, vol. 69, no. 3, pp. 251-259.
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A series of laboratory experiments is carried out to replicate moisture accumulation in an unsaturated coarse-grained soil underneath an impervious cover. The results show that significant moisture accumulation occurs in relatively dry specimens when the temperature at the cover drops below the freezing point. The tested soil is a coarse-grained sand and is not expected to generate much moisture accumulation according to existing frost susceptibility criteria in the literature. The primary mechanism of moisture migration in the soil is observed to be vapour diffusion, and the primary mechanism of moisture accumulation is ice formation by way of vapour–ice desublimation. It is also observed that two peak values exist along the total water content profile of the 13·5 cm long specimen. For a non-freezing condition, water content gradually decreases from the warmer end to the colder end without any peak value, and the amount of moisture accumulation is less than occurs under freezing conditions. The test results are considered to be useful for understanding vapour diffusion in unsaturated freezing soils, and for validating theoretical and numerical models.
Teng, J, Zhang, X, Zhang, S, Zhao, C & Sheng, D 2019, 'An analytical model for evaporation from unsaturated soil', Computers and Geotechnics, vol. 108, pp. 107-116.
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© 2018 Evaporation from unsaturated soil is characterized by vapor transfer in the upper part and liquid water transfer in the lower part of the soil. This study develops an analytical model for identifying the vaporization plane where the evaporation occurs, and the model consists of three partial differential equations that respectively govern the vapor flow, liquid water flow and heat transfer. These equations are solved simultaneously for the transient water content profile, evaporation rate, transient temperature profile and location of the vaporization plane. A series of experiments are used to validate the proposed analytical model, which indicates that this model can reasonably well predict the temporal water content profile and evaporation rate during the evaporation process. The result shows that the evaporation rate during falling rate stage is proportional to the inverse of the square root of elapsed time, and the proportionality is affected by the vapor diffusion coefficient, heat diffusion coefficient, and critical water content. The depth of vaporization plane is found to be independent of soil hydraulic properties, but only dependent on the heat diffusion coefficient of the soil. It is also revealed that heat diffusion coefficient has a pronounced influence on the evaporation process, which has not been observed in previous studies. A larger thermal diffusion coefficient leads to a faster advancing and a deeper vaporization plane, as well as a faster decreasing evaporation rate. The analytical model provides a useful tool for investigating the mechanism of the evaporation process.
Tien Bui, D, Khosravi, K, Shahabi, H, Daggupati, P, Adamowski, JF, M.Melesse, A, Thai Pham, B, Pourghasemi, HR, Mahmoudi, M, Bahrami, S, Pradhan, B, Shirzadi, A, Chapi, K & Lee, S 2019, 'Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model', Remote Sensing, vol. 11, no. 13, pp. 1589-1589.
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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and ...
Tien Bui, D, Shahabi, H, Omidvar, E, Shirzadi, A, Geertsema, M, Clague, J, Khosravi, K, Pradhan, B, Pham, B, Chapi, K, Barati, Z, Bin Ahmad, B, Rahmani, H, Gróf, G & Lee, S 2019, 'Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm', Remote Sensing, vol. 11, no. 8, pp. 931-931.
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We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.
Tien Bui, D, Shirzadi, A, Chapi, K, Shahabi, H, Pradhan, B, Pham, B, Singh, V, Chen, W, Khosravi, K, Bin Ahmad, B & Lee, S 2019, 'A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping', Water, vol. 11, no. 10, pp. 2013-2013.
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This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.
Tien Bui, D, Shirzadi, A, Shahabi, H, Chapi, K, Omidavr, E, Pham, BT, Talebpour Asl, D, Khaledian, H, Pradhan, B, Panahi, M, Bin Ahmad, B, Rahmani, H, Gróf, G & Lee, S 2019, 'A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)', Sensors, vol. 19, no. 11, pp. 2444-2444.
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In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Low-Frequency Metamaterial Absorber Using Space-Filling Curve', Journal of Electronic Materials, vol. 48, no. 10, pp. 6451-6459.
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© 2019, The Minerals, Metals & Materials Society. The extensive use of metamaterials and metamaterial absorbers increases the demand for compact structures in various frequencies. Designing electrically small absorbers for lower frequencies, especially sub-gigahertz applications, is one of the open issues in this field. In this paper, a space filling curve is used to design an absorber operating on low frequencies. The unit cell design is based on a Sierpinski curve with the size of 25×25×1.6mm3 and air-gap of 10 mm. The structure shows 99.9% absorption at 900 MHz on the third step. The system also shows multiple resonances due to its structure. The proposed structure is fabricated and tested and shows a good agreement with simulation results.
Tong, C-X, Zhang, K-F, Zhang, S & Sheng, D 2019, 'A stochastic particle breakage model for granular soils subjected to one-dimensional compression with emphasis on the evolution of coordination number', Computers and Geotechnics, vol. 112, pp. 72-80.
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© 2019 Elsevier Ltd Prediction of the evolution of particle size distribution (PSD) is of great importance for studying particle breakage. This paper presents a stochastic approach, namely a Markov chain model, for predicting the evolution of PSD of granular materials during one-dimensional compression tests. The model requires the survival probability of each size group particles in an assembly, named as the survival probability matrix. The Weibull distribution is used to capture the particle size and particle strength effects of single particles. The evolution of the coordination number is investigated via 3D discrete element simulations. The proposed analytical form of survival probability matrix with consideration of the coupling effect of particle-scale factors (i.e., particle size, particle strength) and evolution of the coordination number during one-dimensional compression shows that the largest particles in an assembly do not always have the maximum breakage probability (or the minimum survival probability). This also confirms the dominant role of the coordination number on the balance of evolution of PSD within granular soils. The proposed model is validated against experimental data from one-dimensional compression tests on different granular materials. The limitations as well as possible future developments of the model are discussed.
Wang, B, Gao, Y, Sun, C, Blumenstein, M & La Salle, J 2019, 'Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves', IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 5963-5976.
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Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behavior that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.
Wang, D, Tawk, M, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2019, 'A mixture of coal wash and fly ash as a pavement substructure material', Transportation Geotechnics, vol. 21, pp. 100265-100265.
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Wang, G, Ji, J & Zhou, J 2019, 'Practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises and its applications to electrical systems', IET Control Theory & Applications, vol. 13, no. 1, pp. 96-105.
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© The Institution of Engineering and Technology 2018. This study focuses on the practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises, where the dissipative and restorative couplings are no longer required in the connected network topologies. By employing the variational approach in combination with Lyapunov-like analysis, some simple yet generic practical synchronisation criteria are established in the sense of probability distribution and of mean square for coupled harmonic oscillator with directed network topology. Three main issues on stochastic synchronisation, including practical distribution synchronisation, stochastic distribution synchronisation, and practical mean square synchronisation, as well as their differences and relationships are fully addressed. The developed practical synchronisation criteria are then applied to a representative model of electrical systems which are composed of LC oscillators with linear time-invariant (LTI) resistors and inductors. Finally, numerical simulations are provided to show the effectiveness of the developed methods.
Wijayaratna, KP, Cunningham, ML, Regan, MA, Jian, S, Chand, S & Dixit, VV 2019, 'Mobile phone conversation distraction: Understanding differences in impact between simulator and naturalistic driving studies', Accident Analysis & Prevention, vol. 129, pp. 108-118.
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A current issue within the driver distraction community centres around different findings regarding the impact of mobile phone conversation on driving found in driving simulators versus instrumented vehicles employed in real-world naturalistic driving studies (NDSs). This paper compares and contrasts the two types of studies and aims to provide reasons for the differences in findings that have been documented. A comprehensive review of literature and consultations with human factors experts highlighted that simulator studies tend to show degradation in driving performance, suggestive of increased crash risk as a result of mobile phone conversation. Whilst NDSs, at times, present data suggesting that mobile phone conversation distraction actually reduces crash risk. This study identifies that these differences may be attributed to behavioural hypotheses associated with driver self-regulation, arousal from cognitive loading, task displacement and gaze concentration - all of which need to be explicitly tested in future driving studies. Metric estimation and application was also revealed to be polarising results and the subsequent assessment of the crash risk. A common metric applied in this domain is the 'Odds Ratio', particularly prevalent in NDSs. This study presents a detailed investigation into the assumptions and application of the Odds Ratio which revealed the potential for over- and under-estimation of the metric depending on the core data and sampling assumptions. Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. The findings from this investigation implores the need for greater consistency in the application of analysis methods and metrics across both simulator and NDSs. Improvements can yield a more robust platform to systematically compare and interpret data across both approaches, ultimatel...
Xu, B, He, N, He, B, Li, D & Wu, S 2019, 'Experiment study on pipeline bending deformation monitoring based on distributed optical fiber sensor', Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 40, no. 8, pp. 20-30.
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Scientific and reasonable pipeline safety monitoring technology is of great significance to pipeline engineering operation. This paper carries out the experiment study on pipeline bending deformation monitoring based on distributed optical fiber sensing technology. Aiming at the deficiency of existing distributed fiber deformation calculation method, a calculation method of pipeline bending deformation monitoring using distributed optical fiber sensor is proposed, and the calculation program of pipeline bending deformation using distributed optical fiber sensor based on MATLAB is written. The study results show that the proposed pipeline bending deformation monitoring method based on distributed optical fiber sensing technology has high overall measurement accuracy. Within the bending deformation range of 180 mm, the absolute error is less than 4 mm and the average relative error is within 2%. When the bending deformation is getting larger, the absolute error increases, however the average relative error is below 3.2%. The pipeline force analysis based on distributed optical fiber sensing technology was carried out preliminarily. The results show that the simulated pipeline shear force pattern is in good agreement with the theoretical pattern and the actual situation. The proposed pipeline bending deformation monitoring method based on distributed optical fiber sensing technology possesses high measurement accuracy and small error, which can meet the requirements of pipeline bending deformation monitoring and has good application prospect. The method is an ideal deformation monitoring technology and can also be extended to the application of other safety analysis such as pipeline force analysis and etc.
Xu, H & Ji, J 2019, 'Analytical-numerical studies on the stability and bifurcations of periodic motion in the vibro-impact systems with clearances', International Journal of Non-Linear Mechanics, vol. 109, pp. 155-165.
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© 2018 This paper investigates the stability and bifurcations of periodic solutions in three-degree-of-freedom vibro-impact systems based on the explicit critical criteria and the discontinuous mapping method. Firstly, a six-dimensional Poincaré map is established by taking the impact surface as the Poincaré section. The explicit criteria including eigenvalue assignment and transversality condition are applied to determine the bifurcation point of co-dimension one pitchfork bifurcation. The stability and direction of the bifurcation solution are then studied by using center manifold reduction theory and normal form approach. Secondly, the bifurcation points of co-dimension-two Hopf–Hopf interaction bifurcation and pitchfork–Neimark–Sacker bifurcation are determined by applying the explicit critical criteria, and the local dynamic behaviors are examined in the neighborhood of these co-dimension-two bifurcation points. Finally, a six-dimensional Poincaré map formed by choosing the constant phase angle as the Poincaré section is used to investigate the existence and stability of grazing bifurcation based on the piecewise compound normal form map. The causes of the discontinuous jump and the coexistence of attractors near the grazing periodic motion are explained for the three-degree-of-freedom vibro-impact system with a moving constraint.
Xu, R & Fatahi, B 2019, 'Novel application of geosynthetics to reduce residual drifts of mid-rise buildings after earthquakes', Soil Dynamics and Earthquake Engineering, vol. 116, pp. 331-344.
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© 2018 Elsevier Ltd Geosynthetics have been used in variety of geotechnical engineering projects, such as ground improvement, erosion control, slope stabilisation and foundation strength improvement and they have been proved to be cost and time effective in many cases. In this study, a geosynthetic reinforced composite soil (GRCS) foundation system is proposed for seismic protection of mid-rise buildings supported by a shallow foundation potentially suffering from residual structural drift or permanent foundation settlement. To evaluate the proposed GRCS, a conventional reinforced concrete moment resisting building sitting on this composite ground under the earthquake excitations of 1978 Tabas, 1994 Northridge and 1995 Kobe was numerically simulated using FLAC3D software. The effect of soil-structure interaction (SSI) was captured using direct method of analysis adopting a three-dimensional numerical model. By adopting direct calculation method, the soil deposit, the geosynthetic reinforcement, the foundation and the structure were simulated simultaneously. Inelastic behaviour of the structure was considered, while hysteretic damping algorithm was adopted representing the variation of the shear modulus and corresponding damping ratio of the soil with cyclic shear strain capturing the energy dissipation characteristics of the soil. Both material and geometry nonlinearities were taken into account at the interface between the foundation and ground surface. The results are then presented in terms of mobilised tensile force in geosynthetic layers, the response spectra at bedrock and ground surface level, the shear force developed in the superstructure, the maximum foundation rocking angle, the maximum lateral deflection, the maximum inter-storey drift, and most importantly the residual inter-storey drift and permanent foundation settlement. The results showed that the proposed GRCS could offer design engineers a rational and cost-effective alternative solution to con...
Xue, C, Li, W, Li, J & Wang, K 2019, 'Numerical investigation on interface crack initiation and propagation behaviour of self-healing cementitious materials', Cement and Concrete Research, vol. 122, pp. 1-16.
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© 2019 Elsevier Ltd Based on the extended finite element method (XFEM) and cohesive surface (CS) technique, the interface cracks between healing agent and cementitious materials in the self-healing mortar beam under three-point bending are numerically investigated in this study. After obtaining original crack feature using XFEM, a parametric study was conducted to comprehensively discuss effects of the elastic ratio between self-healing agent and cementitious materials, bonding strength and fracture toughness of the self-healing agent-cementitious material interface on crack initiation and propagation. The results reveal that crack initiation seriously degrades stiffness of cementitious materials. Flexible healing agent increases the probability of new crack initiation and healed crack propagation, while stiffer healing agent induces obvious stress concentration around the interface, increasing fracture chance of interfacial zone. The numerical model and methodology developed in this study are useful to investigate the self-healing behaviours and develop high efficient self-healing cementitious materials.
Xue, C, Li, W, Li, J, Tam, VWY & Ye, G 2019, 'A review study on encapsulation‐based self‐healing for cementitious materials', Structural Concrete, vol. 20, no. 1, pp. 198-212.
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Encapsulation‐based self‐healing technology is an effective method for healing the crack‐deteriorated cementitious material. Encapsulation‐based self‐healing initiates by crack occurrence and progresses by chemical reaction of released self‐healing agents in the cracks, which are contained in capsules. In this paper, a review has been conducted on various healing agents, encapsulation techniques, as well as experimental approaches, basing on existing substantial studies. Recently, there is no consistent agreement on the effective criteria for evaluating encapsulation‐based self‐healing and mature solution for increasing the survival ratio of capsules during mixing. However, the polyurethane‐based healing agents filled in glass or ceramic tubes are popularly applied for self‐healing cementitious materials. Besides, the polymer capsules present promising attractions for engineering application. Mechanical strength and durability are the most widely used self‐healing efficiency assessment indexes. On the other hand, nondestructive technique and numerical modeling have also extensively adopted to visualize and evaluate the self‐healing behavior of cementitious materials. However, there are still some challenges, which require further investigations, such as behavior of crack propagation, kinetics of healing agent in discrete crack surfaces, effect of inserted capsules on the mechanical properties of self‐healed cementitious materials.
Yang, G, Jiang, Y, Nimbalkar, S, Sun, Y & Li, N 2019, 'Influence of Particle Size Distribution on the Critical State of Rockfill', Advances in Civil Engineering, vol. 2019, pp. 1-7.
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In order to study the effect of particle size distribution on the critical state of rockfill, a series of large-scale triaxial tests on rockfill with different maximum particle sizes were performed. It was observed that the intercept and gradient of the critical state line in thee−p′plane decreased as the grading broadened with the increase in particle size while the gradient of the critical state line in thep′−qplane increased as the particle size increased. A power law function is found to appropriately describe the relationship between the critical state parameters and maximum particle size of rockfill.
Yang, G, Yan, X, Nimbalkar, S & Xu, J 2019, 'Effect of Particle Shape and Confining Pressure on Breakage and Deformation of Artificial Rockfill', International Journal of Geosynthetics and Ground Engineering, vol. 5, no. 2.
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© 2019, Springer Nature Switzerland AG. The rockfill exhibits a substantial amount of particle breakage when subjected to higher range of stresses. The deformations of rockfill under such excessive stresses often lead to failure and cannot be ignored. The degree of particle breakage is related to the type of the material as well as the particle shape. Based on this, artificially simulated rockfill materials with three different aggregate shapes (prism, cube, and cylinder) were prepared by cement paste-casting method. Through a series of medium-sized triaxial shear tests, the effects of confining pressure and particle shape on the fracture characteristics of the artificial rockfill and its secant modulus were investigated. The useful relationships between particle sphericity and roundness with deformation modulus and particle breakage rate were proposed.
Yang, R, Huang, J, Griffiths, DV, Li, J & Sheng, D 2019, 'Importance of soil property sampling location in slope stability assessment', Canadian Geotechnical Journal, vol. 56, no. 3, pp. 335-346.
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Site investigations provide characterization of soil properties, but inevitable uncertainty remains at locations that have not been examined. Only a limited scope of site investigation can be conducted due to budget and time constraints, hence there are always risks associated with design based on limited investigation information. An efficient geotechnical site investigation should involve choosing the optimal number and location of borehole sites to gain adequate information for a given cost. Using a slope as an example, this paper proposes a framework to find the best sampling location that gives the most information while minimizing the probability of making the wrong decisions. The results suggest that the slope crest appears to be the optimal location to conduct geotechnical site exploration for slope stability assessment.
Ye, K & Ji, J 2019, 'Current, wave, wind and interaction induced dynamic response of a 5 MW spar-type offshore direct-drive wind turbine', Engineering Structures, vol. 178, pp. 395-409.
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© 2018 Elsevier Ltd This paper studies the dynamic response of a spar-type direct-drive wind turbine subjected to external and internal excitations. A free-free end model is developed for the wind turbine structure with a spar-type floating platform under deep sea condition. Firstly, the spar supported platform with tower structure is modelled as a rigid body while the nacelle is considered as a point mass attached on the top of the tower. Then the dynamic interaction between the drive-train system and the tower is considered by incorporating the modelling of a direct-drive drive-train system. The hydrodynamic and aerodynamic excitations applied include current, wave, and wind excitations as well as buoyant forces. The misalignments of the wind, wave and current are also considered to examine the induced response. With the help of the time history and FFT spectrum, the effects of both hydrodynamic and aerodynamic excitations along with the dynamic interaction between the drive-train system and tower structure on the dynamic behaviour of the spar-type floating platform are investigated under different sea conditions.
Ye, X, Wang, Q, Wang, S, Sloan, S & Sheng, D 2019, 'Performance of a compaction-grouted soil nail in laboratory tests', Acta Geotechnica, vol. 14, no. 4, pp. 1049-1063.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This study proposed a new soil nail known as the compaction-grouted soil nail, and a physical model was established to investigate its pull-out behaviour with different grouting pressures. The study on scale effect of the physical model was performed subsequently via numerical modelling. Additionally, interface shear tests were performed using the same boundary conditions as the physical model test. The strength parameters obtained were used to estimate the pull-out resistance of a conventional soil nail. The merits of these two soil nail types were compared based on their pull-out resistances. The physical model test results showed that the pull-out resistance of the compaction-grouted soil nail increases with increasing grouting pressure. In addition, the pull-out resistance exhibits hardening behaviour without a yield point, indicating that the compaction-grouted soil nail enables soils to remain stable against a relatively large deformation before ultimate failure. Furthermore, a higher grouting pressure results in a higher rate of increase for pull-out resistance versus pull-out displacement, which improves the performance of the compaction-grouted soil nail in the stabilization of large deformation problems. A comparison of the two types of soil nails suggests that the new compaction-grouted soil nail is more sensitive to grouting pressure than the conventional soil nail in terms of pull-out resistance improvement. In other words, the performance (pull-out resistance) of the compaction-grouted soil nail can be markedly improved by increasing the grouting pressure without inducing any accidental or undesired cracking or soil displacement.
Ye, X, Wang, S, Wang, Q, Sloan, SW & Sheng, D 2019, 'The influence of the degree of saturation on compaction-grouted soil nails in sand', Acta Geotechnica, vol. 14, no. 4, pp. 1101-1111.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. A series of large-scale model tests was conducted on compaction-grouted soil nails to study the influence of the degree of saturation on the soil response to compaction grouting and pull-out. The experimental results show that the initial degree of saturation of the soil strongly influences the grout injectability, thus the formed diameter of grout bulb. Subsequently, the diameter of the grout bulb alters the pull-out force, with larger grout bulbs generating higher pull-out forces and exhibiting greater hardening behaviour. Interestingly, the initial pull-out forces are the same for the same grouting pressure, regardless of the initial degree of saturation and the subsequently grout bulb. In addition, some of the main factors influencing the pressure grouting and pull-out of the soil nail, as the initial degree of saturation varies, are as follows. First, the variations in the soil pressure and density with the initial degree of saturation are similar to that of the volume of grout injected, and the compression of the soil induced by pressure grouting exhibits a similar evolution with the initial degree of saturation at different locations. Second, the initial degree of saturation of the soil sample plays a dominant role in the change in suction during pressure grouting and pull-out of soil nail. Third, the horizontal soil pressure derived from the pull-out of soil nail propagates closely in the soil sample of lower initial degree of saturation. The vertical soil pressure induced by the vertical soil dilation and squeezing effect varies in accidence with the initial degree of saturation and the grout bulb.
Ye, X, Wang, S, Xiao, X, Sloan, S & Sheng, D 2019, 'Numerical Study for Compaction-Grouted Soil Nails with Multiple Grout Bulbs', International Journal of Geomechanics, vol. 19, no. 2, pp. 04018193-04018193.
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© 2018 American Society of Civil Engineers. A finite-element model was adopted to numerically simulate compaction-grouted soil nailswithmultiple grout bulbs. The numerical model was first verified by the corresponding experimental results. Then a series of numerical simulations were carried out to investigate the pull-out behavior of compaction-grouted soil nails with multiple grout bulbs. Numerical results show that the pull-out force increases with the increasing diameter of the grout bulb and the spacing between the grout bulbs. Furthermore, the pull-out displacement at failure of the soil nail decreases for the bigger grout bulb. Soil nails with larger back-end and smaller front-end grout bulbs experience the higher peak pull-out force and larger pull-out displacement at failure. Two types of failure surfaces were found for the soil nails with a double-grouted bulb, and those with a curved failure surface gave the largest pull-out displacement at failure. It indicates that the grouting point placed at the end of the nail rod is more preferable in field application. An equal spacing and grout bulb diameter can help to maximize the performance of a compaction-grouted soil nail with multiple grout bulbs.
Yeganeh, N & Fatahi, B 2019, 'Effects of choice of soil constitutive model on seismic performance of moment-resisting frames experiencing foundation rocking subjected to near-field earthquakes', Soil Dynamics and Earthquake Engineering, vol. 121, pp. 442-459.
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© 2019 Elsevier Ltd The current study investigated the extent to which the choice of the soil constitutive models can impact the predicted seismic performance of a 20-story reinforced concrete moment-resisting building with a mat foundation considering the Seismic Soil-Structure Interaction (SSSI). Since the soil, in general, is the weakest material, involved in the commonplace geotechnical engineering projects, a soil constitutive model would be able to rule the dynamic response of the system. In this research, the hardening plasticity-based soil constitutive model, named “hyperbolic hardening with hysteretic damping” in conjunction with the two simple, conventional soil models, namely, the isotropic elastic with hysteretic damping model, and elastic-perfectly plastic Mohr-Coulomb with hysteretic damping model, were invoked in the three-dimensional coupled soil-structure numerical simulations using FLAC3D software. The direct method of analysis was used for analyzing the soil-foundation-structure system in one single step without a need to separately analyze each part of the domain. The cherry-picked earthquake excitations, viz, the 1999 Chi-Chi (Taiwan), and 2011 Kohriyama (Japan), were scaled by means of the widely-used response spectrum matching method as per the design response spectrum of a strong rock. The plastic moment concept was employed so as to assign the elastic-perfectly plastic model to the superstructure and its foundation. Additionally, the strain-compatible shear modulus and damping dependency on the cyclic shear strain were considered via the programmed hysteretic damping algorithm. The numerical predictions included the response spectra at the seismic bedrock and ground surface, base shear forces, shear force distributions along the building height, maximum and permanent foundation displacements, and foundation rocking, plus the flooring lateral deflections and inter-story drifts. The life safety limits for the transient and residual total in...
Yin, S, Ji, J & Wen, G 2019, 'Complex near-grazing dynamics in impact oscillators', International Journal of Mechanical Sciences, vol. 156, pp. 106-122.
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© 2019 Elsevier Ltd Impact oscillators frequently appear in various physical and engineering systems with non-smooth characteristics and can exhibit different dynamic behavior from the smooth nonlinear systems, including grazing bifurcation in which an impact with zero velocity occurs. This paper investigates the near-grazing dynamics of the multi-degree-of-freedom impact oscillators in the small neighborhood of degenerate grazing points, with a focus on the stability and potential bifurcations of near-grazing period-one impact motions. The high order zero time discontinuity mapping method is applied to perform the prospective analyses of stability and bifurcations. Particularly, this paper shows that the peculiar Neimark-Sacker bifurcations regaining the stability of near-grazing period-one impact motion can be induced by two different ways, either through the interaction between the singular and regular real eigenvalues or via a grazing bifurcation directly. A two degree-of-freedom impact oscillator is taken as an example to present detailed numerical results for the verification of proposed analysis.
Yin, S, Ji, J, Deng, S & Wen, G 2019, 'Degenerate grazing bifurcations in a three-degree-of-freedom impact oscillator', Nonlinear Dynamics, vol. 97, no. 1, pp. 525-539.
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© 2019, Springer Nature B.V. This paper presents the analysis of the degenerate grazing bifurcation in a three-degree-of-freedom impact oscillator by studying the bifurcations of near-grazing period-one impact motion near the degenerate grazing point. Actually, this paper extends the higher-order zero time discontinuity mapping to perform the perturbation analysis of characteristic equation of period-one impact motion and obtains feasible eigenvalue approximation to study the potential bifurcations. The shooting method is applied to verify the validity of the derived approximation and corresponding computation results. In addition to the known bifurcation scenarios of saddle-node and period-doubling, novel Neimark–Sacker bifurcation and related co-dimension two bifurcation points of near-grazing period-one impact motion are also found to arise near the degenerate grazing point in a three-degree-of-freedom impact oscillator. For the in-depth understanding of near-grazing dynamics, the obtained results are compared with the reported results in the single- and two-degree-of-freedom impact oscillators.
Yin, S, Ji, J, Wen, G & Wu, X 2019, 'Use of degeneration to stabilize near grazing periodic motion in impact oscillators', Communications in Nonlinear Science and Numerical Simulation, vol. 66, pp. 20-30.
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© 2018 In controlling the discontinuous grazing bifurcations in impact oscillators, a discrete-in-time linear feedback control strategy in the existing literature was used to change the conditions at the grazing point based on the grazing stability criterion. Though this strategy is effective for its linear inequality constraint of the control parameter domain, a smooth and predictable bifurcating response cannot be obtained for the controlled system, but the grazing induced chaos or period-adding phenomena. To improve this control strategy and stabilize the elementary near-grazing impact periodic motion in impact oscillators, one feasible control criterion is established in this paper by performing the perturbation analysis of the eigenvalues of the Jacobian matrix. It is found that the degeneration of both eigenvalues and grazing bifurcation can stabilize the elementary near-grazing impact periodic motion and eliminate the discontinuous jump phenomenon at grazing.
Youssef, AM, Abu Abdullah, MM, Pradhan, B & Gaber, AFD 2019, 'Agriculture Sprawl Assessment Using Multi-Temporal Remote Sensing Images and Its Environmental Impact; Al-Jouf, KSA', Sustainability, vol. 11, no. 15, pp. 4177-4177.
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In this paper, multispectral and multi-temporal satellite data were used to assess the spatial and temporal evolution of the agriculture activities in the Al-Jouf region, Kingdom of Saudi Arabia (KSA). In the current study, an attempt was made to map the agriculture sprawl from 1987 to 2017 using temporal Landsat images in a geographic information system (GIS) environment for better decision-making and sustainable agriculture expansion. Our findings indicated that the agriculture activities developed through two crucial stages: high and low rise stages. Low rise stages occurred during three sub-stages from April 1987 to April 1988, from September 1993 to August 1998, and from April 2008 to May 2015, with overall change rates of 37.9, 44.4, and 30.5 km2/year, respectively. High rise stages occurred during three sub-stages from April 1988 to February 1993, from September 2000 to March 2006, and from April 2016 to August 2017, with overall change rates of 132.4, 159.1, and 119.5 km2/year, respectively. Different environmental problems due to uncontrolled agriculture activities were observed in the area, including substantial depletion of the groundwater table. Another environmental impact observed was the appearance of sinkholes that occurred suddenly with no warning signs. These environmental impacts will increase in the future if no regulated restrictions are implemented by decision-makers.
Yu, C, Wang, H, Wu, Z-X, Sun, W-J & Fatahi, B 2019, 'Analytical Solution for Pollutant Diffusion in Soils with Time-Dependent Dispersion Coefficient', International Journal of Geomechanics, vol. 19, no. 10, pp. 04019109-04019109.
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Yu, J, Ji, J, Miao, Z & Zhou, J 2019, 'Neural network-based region reaching formation control for multi-robot systems in obstacle environment', Neurocomputing, vol. 333, no. Automatica 53 53 2015, pp. 11-21.
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© 2018 Elsevier B.V. This study is concerned with the region reaching formation control problem with collision and obstacle avoidance for multi-robot systems in the presence of model uncertainties and external disturbances. A novel neural network based robust control scheme combining with the adaptive compensator and the adaptive control gain is proposed to achieve the region reaching formation control with collision and obstacle avoidance. It is shown that under the proposed control method, all the robots can always reach into the objective region, maintain their formation, and guarantee collision and obstacle avoidance. Illustrative examples are presented to show the effectiveness of the proposed control scheme.
Yu, Li, Li, Li, Li & Wang 2019, 'Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices', International Journal of Molecular Sciences, vol. 20, no. 13, pp. 3216-3216.
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Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.
Yu, Y, Dackermann, U, Li, J & Niederleithinger, E 2019, 'Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory', Structural Health Monitoring, vol. 18, no. 1, pp. 123-142.
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This article presents a novel assessment framework to identify the health condition of wood utility poles. The innovative approach is based on the integration of data mining and machine learning methods and combines advanced signal processing, multi-sensor data fusion and decision ensembles to classify different damage condition types of wood poles. In the proposed framework, wavelet packet analysis is employed to transform captured multi-channel stress wave signals into energy information, which is consequently compressed by principal component analysis to extract a feature vector. Furthermore, support vector machine multi-classifier, optimized by genetic algorithm, is designed to identify the pole condition type. Finally, evidence theory is applied to fuse different assessment results from different sensors for a final decision. For validation of the proposed approach, the wood pole specimens with three common damage condition types are tested using a novel multi-sensor narrow-band frequency-excitation non-destructive testing system in the laboratory. The final experimental analysis results confirm that the proposed approach is capable of making full use of multi-sensor information and providing an effective and accurate identification on types of conditions in wood poles.
Yu, Y, Li, Y, Li, J & Gu, X 2019, 'Characterizing nonlinear oscillation behavior of an MRF variable rotational stiffness device', Smart Structures and Systems, vol. 24, no. 3, pp. 303-317.
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Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.
Yu, Y, Subhani, M, Dackermann, U & Li, J 2019, 'Novel Hybrid Method Based on Advanced Signal Processing and Soft Computing Techniques for Condition Assessment of Timber Utility Poles', Journal of Aerospace Engineering, vol. 32, no. 4, pp. 04019032-04019032.
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© 2019 American Society of Civil Engineers. Recently, a variety of nondestructive evaluation (NDE) approaches have been developed for health assessment and residual capacity estimation of timber structures. Among these methods, guided wave (GW)-based techniques are highly regarded as effective tools for potential use in real situations. Nevertheless, because it is hard to comprehensively grasp the behavior of wave propagation in a wood structure, existing NDE-based techniques mainly depend on an oversimplified hypothesis, which can result in inaccurate or even misleading results in practice. Understanding the complex behavior of GW propagation in wood structures and extracting appropriate information from captured GW signals is a key for successful assessments of in situ conditions of timber structures. This paper analyzes the existing feature extraction and damage detection algorithms, and proposes a novel approach based on an integration of wavelet packet transform (WPT) and ensemble empirical mode decomposition (EEMD) for extracting damage-sensitive patterns, and then a soft computing method like support vector machine (SVM) for pole condition identification. In the proposed method, GW signals measured from a multisensing system with pole health condition as the baseline are divided into a series of subfrequency bands based on WPT. Then EEMD is adopted to extract the intrinsic mode functions (IMFs) that possess the features extracted at corresponding subfrequency bands. Hence, the IMF component was segregated from the original signals of tested poles, and the IMF Shannon entropy was employed to build up the feature vector to effectively demonstrate the health condition. To decrease the size of the feature vector and avoid multiple collinearity among obtained patterns, principal component analysis was employed and entropy information in the feature vector was replaced with main principal components, which will be employed as input variables of the dev...
Yu, Y, Wang, C, Gu, X & Li, J 2019, 'A novel deep learning-based method for damage identification of smart building structures', Structural Health Monitoring, vol. 18, no. 1, pp. 143-163.
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In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.
Zhang, X, Fatahi, B, Khabbaz, H & Poon, B 2019, 'Assessment of the Internal Shaft Friction of Tubular Piles in Jointed Weak Rock Using the Discrete-Element Method', Journal of Performance of Constructed Facilities, vol. 33, no. 6, pp. 04019067-04019067.
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© 2019 American Society of Civil Engineers. This study focuses on the internal shaft friction of open-ended tubular piles induced by jointed weak rock plugs. To investigate the bearing mechanism of the plug, push-up load tests were carried out on the jointed mudstone inside a tubular pile. The discrete-element method (DEM) was used in order to consider heterogeneity and to reproduce the discrete nature of the rock mass. A flat-joint model was used to reproduce the mechanical behavior of mudstone, and a smooth-joint contact model was used to replicate natural joints. The push-up load tests were carried out using the calibrated properties of a weak mudstone. The effects of joint density and joint dip were examined in detail and, as expected, the push-up force of the rock plug was influenced by the joint properties because joint density and joint dip had to some extent affected the plug resistance. The existing joints reduced the push-up force when the joints were steep, whereas the horizontal joints had a minimal effect on altering the inner shaft friction compared with the intact rock mass. The reduced friction along the pile was amplified with joint density, while the exponential increase of vertical stress from the top of the rock plug to the bottom revealed that the inner shaft resistance was mainly mobilized at the bottom portion of the rock plug. The findings of this study increase our understanding of joint dip and joint density affecting the internal shaft resistance of open-ended tubular piles; this knowledge can be used further to develop a design methodology for open-ended tubular piles in weak rock while assessing plugging effects.
Zhang, X, Ji, J & Xu, J 2019, 'Parameter identification of time-delayed nonlinear systems: An integrated method with adaptive noise correction', Journal of the Franklin Institute, vol. 356, no. 11, pp. 5858-5880.
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© 2019 The Franklin Institute This paper proposes a novel method called the adaptive-noise-correction integrated parameter identification (ANCPI) for time-delayed nonlinear systems. Compared with the existing de-noising techniques, the significance of the proposed method is the use of the system itself to correct the noise-polluted components so that the accuracy of parameter identification is enhanced. To achieve the goal of adaptive noise correction, this study starts from the case of periodic response and then parameterizes the noise correction as the coefficient correction of harmonic basis. In this way, the parameter identification integrated with noise correction can be performed as the parameter optimization of the error function. For the convenience of application, a user-friendly program package is further provided and a detailed tutorial is presented in the supplementary material.
Zhang, X, Ji, J, Fu, J & Xu, J 2019, 'Denoising identification for nonlinear systems with distorted streaming', International Journal of Non-Linear Mechanics, vol. 117, pp. 103224-103224.
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© 2019 Elsevier Ltd Streaming usually happens in systems with asymmetric nonlinearity. It is a dynamic phenomenon that the midpoint of the motion shifts away from the equilibrium point of the system. It is also an ultra-low-frequency process so that its observation often distorts because of signal acquisition limitations. Extensive studies have shown that the precision of parameter identification will drop greatly if this distortion is not accurately corrected. Motivated by the intention of enhancing the parameter identification's precision via signal correction, the present paper proposes a novel approach called the orthonormal Legendre polynomial based denoising identification method (OLP-DIM). In this method, the distorted response is decomposed by the orthonormal Legendre polynomials. The polynomial coefficients corresponding to the distortion are treated as uncertain parameters and then jointly identified with the system parameters. Numerical and experimental examples with different responses show that the OLP-DIM returns parameters in excellent precision and, distinct from traditional parameter identification methods, accurately recovers the system's streaming.
Zhang, Z, Oberst, S & Lai, JCS 2019, 'A non-linear friction work formulation for the analysis of self-excited vibrations', Journal of Sound and Vibration, vol. 443, pp. 328-340.
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Even though much research has been devoted to understand friction-induced vibrations, its root cause is not yet fully understood. Reliable prediction of friction-induced unstable vibrations such as in brake squeal or hip squeak remains a challenge because of nonlinearities involved and because the complex eigenvalue analysis (CEA) widely used in industry is linear. The energy fed back into the system by friction has been shown to be useful for analysis of measurements and numerical simulations. In numerical simulations, the linearised method of feed-in energy, calculated purely based on friction work has provided some insights into the physical mechanism for instabilities. However, the dynamics due to friction-induced instabilities is highly nonlinear and damping may offset some or all of the excess friction energy provided to the system. By using a nonlinear 2-DOF dry friction oscillator, a nonlinear friction work formulation is proposed to demonstrate that in combination with viscous damping the energy budget provides an improved analysis capability over linearised friction work. The results highlight the potential of nonlinear friction work as a reliable tool to study friction-induced instabilities to gain deeper physical insights into squeal triggering mechanisms and to better understand the over- and under-predictive character inherent to linear methods.
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2019, 'High-Quality Image Captioning With Fine-Grained and Semantic-Guided Visual Attention', IEEE Transactions on Multimedia, vol. 21, no. 7, pp. 1681-1693.
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© 1999-2012 IEEE. The soft-attention mechanism is regarded as one of the representative methods for image captioning. Based on the end-to-end convolutional neural network (CNN)-long short term memory (LSTM) framework, the soft-attention mechanism attempts to link the semantic representation in text (i.e., captioning) with relevant visual information in the image for the first time. Motivated by this approach, several state-of-the-art attention methods are proposed. However, due to the constraints of CNN architecture, the given image is only segmented to the fixed-resolution grid at a coarse level. The visual feature extracted from each grid indiscriminately fuses all inside objects and/or their portions. There is no semantic link between grid cells. In addition, the large area 'stuff' (e.g., the sky or a beach) cannot be represented using the current methods. To address these problems, this paper proposes a new model based on the fully convolutional network (FCN)-LSTM framework, which can generate an attention map at a fine-grained grid-wise resolution. Moreover, the visual feature of each grid cell is contributed only by the principal object. By adopting the grid-wise labels (i.e., semantic segmentation), the visual representations of different grid cells are correlated to each other. With the ability to attend to large area 'stuff,' our method can further summarize an additional semantic context from semantic labels. This method can provide comprehensive context information to the language LSTM decoder. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can accurately link the relevant visual information with each semantic meaning inside the text. Demonstrated by three experiments including both qualitative and quantitative analyses, our model can generate captions of high quality, specifically high levels of accuracy, completeness, and diversity. Moreover, our model significantly outperforms all other meth...
Zhao, L-S, Zhou, W-H, Geng, X, Yuen, K-V & Fatahi, B 2019, 'A closed-form solution for column-supported embankments with geosynthetic reinforcement', Geotextiles and Geomembranes, vol. 47, no. 3, pp. 389-401.
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© 2019 Elsevier Ltd Soil arching effect results from the non-uniform stiffness in a geosynthetic-reinforced and column-supported embankment system. However, most theoretical models ignore the impact of modulus difference on the calculation of load transfer. In this study, a generalized mathematical model is presented to investigate the soil arching effect, with consideration given to the modulus ratio between columns and the surrounding soil. For simplification, a cylindrical unit cell is drawn to study the deformation compatibility among embankment fills, geosynthetics, columns, and subsoils. A deformed shape function is introduced to describe the relationship between the column and the adjacent soil. The measured data gained from a full-scale test are applied to demonstrate the application of this model. In the parametric study, certain influencing factors, such as column spacing, column length, embankment height, modulus ratio, and tensile strength of geosynthetic reinforcement, are analyzed to investigate the performance of the embankment system. This demonstrates that the inclusion of a geosynthetic reinforcement or enlargement of the modulus ratio can increase the load transfer efficiency. When enhancing the embankment height or applying an additional loading, the height of the load transfer platform tends to be reduced. However, a relatively long column has little impact on the load transfer platform.
Zhao, Y, Chen, J, Wu, D, Teng, J, Sharma, N, Sajjanhar, A & Blumenstein, M 2019, 'Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern', Information, vol. 10, no. 8, pp. 262-262.
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Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
Zhu, S, Li, JC, Casciati, S & Li, J 2019, 'Special Issue on Smart Devices for Structural Control:Preface', Smart Structures and Systems, vol. 24, no. 1, p. I.
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Abdollahi, A, Nezhad, MP & Pradhan, B 1970, 'Determining the desertification risks of the Mashhad regions using integrated indices based on the AHP method', 2019 13th International Conference on Sensing Technology (ICST), 2019 13th International Conference on Sensing Technology (ICST), IEEE, Macquarie Univ, Sydney, AUSTRALIA.
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Abdollahi, A, Nezhad, MP & Pradhan, B 1970, 'Investigation of the Vegetation Cover and the Vulnerability of the Mashhad Regions to Desertification by Using MODIS Image and EVI', 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE, Banda Aceh, Indonesia, pp. 46-49.
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© 2019 IEEE. Desertification is a natural phenomenon that threatens the biomass of the world in various forms, and the adverse effects of this phenomenon can be observed in different parts of the planet. Some events and complications of the earth's surface, such as vegetation coverage, have changed over time due to natural or human factors, thereby affecting the ecosystem's condition and performance. Vegetation coverage is a critical factor in the assessment of desertification, and continuous production of accurate vegetation maps is an important tool for monitoring natural resources and the environment. Therefore, this paper used MODIS images to investigate the vulnerability of the Mashhad regions in Iran to desertification according to the enhanced vegetation index (EVI) for various periods. Experimental results showed that the Mashhad regions had the highest vulnerability to desertification during 2001-2005, given the highest variation in the EVI in this period.
Abdollahi, M, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 1970, 'A Routing Protocol for SDN-based Multi-hop D2D Communications', 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, USA, pp. 895-898.
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© 2019 IEEE. This paper presents a new Multi-hop Device-to-Device (MD2D) routing protocol, referred to as SMDRP (SDN-based Multi-hop D2D Routing Protocol), for SDN-based wireless networks. Our proposed protocol can be considered as a semi-distributed routing protocol, where an SDN controller manages and controls part of the overall MD2D routing functionality to increase scalability while enabling network operators to control and maintain the out-of-band packet forwarding network. This paper also extends prior work on the Hybrid SDN Architecture for Wireless Distributed Networks (HSAW) [1] and is adapted to the framework presented in this paper. In HSAW, since all link state information is flooded by the controller to the nodes, the network will experience scalability problem. In our approach, this problem is overcome by only passing the next hop for each active route to the mobile nodes. To investigate this, we performed a theoretical and simulation studies comparing HSAW with SMDRP. From our result, it can be seen that for larger density populated networks, SMDRP shows better scalability than HSAW. In addition, mobile nodes need less memory and energy for their communications.
Abeywickrama, A, Indraratna, B & Rujikiatkamjorn, C 1970, 'Excess Pore-Water Pressure Generation and Mud Pumping in Railways Under Cyclic Loading', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 371-383.
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© 2019, Springer Nature Singapore Pte Ltd. High-speed heavy haul trains have become one of the most popular and economical modes of transportation in the modern world to cater for increased demand in freight for agricultural and mining activities. However, when these trains travel through vulnerable areas occupying soft subgrade formations, frequent maintenance is required to prevent differential settlement and localized failures of track. The poor performance of track caused by ballast fouling is also often observed where fines are fluidized and pumped into the ballast voids (mud pumping), which in turn create ballast pockets, mud holes and track instability. When saturated subgrade is subjected to short-term undrained cyclic loading, the pore-water pressure can accumulate inducing fine particles to migrate upwards into the ballast layer. Mud pumping causes millions of dollars of damage to heavy haul rail networks every year in Australia. This paper presents a critical review primarily focused on the role of excess pore-water pressure generation on mud pumping under cyclic loading. Mitigation of these issues can result in considerable savings to rail authorities on recurrent track maintenance activities.
Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 1970, 'Detecting Named Entities in Unstructured Bengali Manuscript Images', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 196-201.
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© 2019 IEEE. In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.
Aghayarzadeh, M, Khabbaz, H & Fatahi, B 1970, 'Evaluation of Reaction Piles Effect on Test Piles in Static Load Testing Using Three-Dimensional Numerical Analysis', ASTM Special Technical Publication, International Conference on Stress Wave Theory and Testing Methods for Deep Foundations, ASTM International, San Diego, California, USA, pp. 68-80.
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Copyright © 2019 by ASTM International. Static load testing includes the direct measurement of pile head displacements when a physical test load is applied. It is known as the most fundamental form of pile load testing and generally considered as a benchmark for pile performance assessment. During static load testing, the load is commonly applied using a hydraulic jack acting against a reaction beam, which is restrained by an anchorage system. The anchorage system may be in the form of cable anchors or reaction piles installed into the ground to provide tension resistance. In this paper, PLAXIS 3D software incorporating elastic-perfectly-plastic Mohr-Coulomb and hardening-soil constitutive models is initially used to simulate a real static load test conducted in stiff overconsolidated clay. Then, in order to assess the effect of the reaction system on the test results, a similar model using the hardening-soil model is simulated. In the three-dimensional model, different numbers of reaction piles, identical to the test pile, are located in different distances from the test pile. Subsequently, the influences of spacing, length, diameter of reaction piles, and type of reaction piles on the load-displacement behavior of test piles are assessed. This paper can provide insight to practicing civil engineers on how to design the loading systems for static pile load tests.
Ahmed, AA, Kalantar, B, Pradhan, B, Mansor, S & Sameen, MI 1970, 'Land Use and Land Cover Mapping Using Rule-Based Classification in Karbala City, Iraq', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1019-1027.
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© Springer Nature Singapore Pte Ltd. 2019. Land use and land cover are important and useful geographic information system (GIS) layers that have been used for a wide range of geospatial applications. These layers are usually generated by applying digital image processing steps for a satellite image or images captured from an aircraft. Several methods are available in literature to produce such GIS layers. Image classification is the main method that has been used by many researchers to produce thematic maps. In the current study, a decision tree was used to develop rulesets at object level. These rules were applied and a thematic map of Karbala city was produced from SPOT image. The overall accuracy of the classification image was 96% and the kappa index was 0.95. The results indicated that the proposed classification method is effective and can produce promising results.
Alaei, F, Alaei, A, Pal, U & Blumenstein, M 1970, 'Document Image Retrieval Based on Visual Saliency Maps', 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, pp. 7-12.
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There has been a massive growth in the production of various unstructured, complex and multi-lingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.
Al-Najjar, HAH, Kalantar, B, Pradhan, B & Saeidi, V 1970, 'Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms', Earth Resources and Environmental Remote Sensing/GIS Applications X, Earth Resources and Environmental Remote Sensing/GIS Applications X, SPIE, Strasbourg, FRANCE.
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Ashtari, S, Tofigh, F, Abolhasan, M, Lipman, J & Ni, W 1970, 'Efficient Cellular Base Stations Sleep Mode Control Using Image Matching', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, MALAYSIA.
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© 2019 IEEE. Green cellular network helps to decrease environmental pollution. In contrast, massive connectivity and demand for higher data rate promise the presence of new generation of cellular system (5G) and small cell networks. Hence, expectation on increasing the number of base stations (BSs), which leads to increase in energy usage. One way to improve energy consumption is by shutting down the redundant BSs while sustaining the Quality-of-Service (QoS) for each user. In this paper, we propose a dynamic structural algorithm based on transportation problem, to switch on/off the BSs in cellular networks without compromising its coverage, and maintain the networks load by neighboring cells. We use weighted graphs to translate our problem as a transportation problem and then use linear programming to solve it. The cost of transport, turning a BS into sleep mode, is illustrated as a function of energy usage,coverage area and load on the BSs. Running the propose method consecutively provides the maximum number of BSs whom are at sleep mode. The methodology explained in this paper reduces energy consumption to almost 40%, whereas maintaining all the existing loads in the network.
Athayde, J, De Silva Wijayaratna, K & Robson, E 1970, 'Employment Decentralisation in Sydney', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
Baral, P, Indraratna, B & Rujikiatkamjorn, C 1970, 'An Elastic Visco-Plastic Model for Soft Soil with Reference to Radial Consolidation', Geotechnics for Transportation Infrastructure, International Symposium on Transportation Geotechnics, Springer Singapore, Delhi (India), India, pp. 369-380.
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© Springer Nature Singapore Pte Ltd 2019. The time-dependent stress–strain behaviour of soft soil due to its viscous nature affects its long-term settlement and pore water dissipation. A novel mathematical model developed using the Peaceman–Rachford ADI scheme (P–R FD Scheme) can describe the visco-plastic behaviour of soft clay with a non-Darcian flow function; this model is a combination of the basic radial consolidation equation developed by Barron and Bjerrum’s time-equivalent (Bjerrum in Geotechnique 17:81–118, 1967) concept that incorporates Yin and Graham’s (Can Geotech J 26:199–209, 1989b) visco-plastic parameters. The settlement and excess pore water pressure obtained from this model are then compared with preexisting models such as a Class C prediction for the Ballina trial embankment at National Field Testing Facility (NFTF). This elastic visco-plastic model provides better results in terms of settlement and pore water pressure with the field data, although the excess pore water pressure that did not dissipate after one year is mainly due to the piezometers becoming biologically and chemically clogged in terrain with acid sulphate soil (ASS).
Basavaraja, V, Shivakumara, P, Guru, DS, Pal, U, Lu, T & Blumenstein, M 1970, 'Age Estimation using Disconnectedness Features in Handwriting', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 1131-1136.
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© 2019 IEEE. Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods.
Blumenstein, M & Pal, U 1970, 'Welcome Message from the General Chairs', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. xxxvi-xxxvii.
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Chang, Y, Li, Z, Luo, L, Luo, S, Sowmya, A, Wang, Y & Chen, F 1970, 'Exploring Latent Structure Similarity for Bayesian Nonparameteric Model with Mixture of NHPP Sequence', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer International Publishing, Sydney, NSW, Australia, pp. 432-444.
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© 2019, Springer Nature Switzerland AG. Temporal point process data has been widely observed in many applications including finance, health, and infrastructures, so that it has become an important topic in data analytics domain. Generally, a point process only records occurrence of a type of event as 1 or 0. To interpret the temporal point process, it is important to estimate the intensity of the occurrence of events, which is challenging especially when the intensity is dynamic over time, for example non-homogeneous Poisson process (NHPP) which is exactly what we will analyse in this paper. We performed a joint task to determine which two NHPP sequences are in the same group and to estimate the intensity resides in that group. Distance dependent Chinese Restaurant Process (ddCRP) provides a prior to cluster data points within a Bayesian nonparametric framework, alleviating the required knowledge to set the number of clusters which is sensitive in clustering problems. However, the distance in previous studies of ddCRP is designed for data points, in this paper such distance is measured by dynamic time warping (DTW) due to its wide application in ordinary time series (e.g. observed values are in $$\mathcal {R}$$). The empirical study using synthetic and real-world datasets shows promising outcome compared with the alternative techniques.
Chang, Y, Li, Z, Zhang, B, Luo, L, Sowmya, A, Wang, Y & Chen, F 1970, 'Recovering DTW Distance Between Noise Superposed NHPP', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 229-241.
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© Springer Nature Switzerland AG 2019. Unmarked event data is increasingly popular in temporal modeling, containing only the timestamp of each event occurrence without specifying the class or description of the events. A sequence of event is usually modeled as the realization from a latent intensity series. When the intensity varies, the events follow the Non-Homogeneous Poisson Process (NHPP). To analyze a sequence of such kind of events, an important task is to measure the similarity between two sequences based on their intensities. To avoid the difficulties of estimating the latent intensities, we measure the similarity using timestamps by Dynamic Time Warping (DTW), which can also resolve the issue that observations between two sequences are not aligned in time. Furthermore, real event data always has superposed noise, e.g. when comparing the purchase behaviour of two customers, we can be mislead if one customer visits market more often because of some occasional shopping events. We shall recover the DTW distance between two noise-superposed NHPP sequences to evaluate the similarity between them. We proposed two strategies, which are removing noise events on all possibilities before calculating the DTW distance, and integrating the noise removal into the DTW calculation in dynamic programming. We compare empirical performance of all the methods and quantitatively show that the proposed methods can recover the DTW distance effectively and efficiently.
Choudhary, K, Rujikiatkamjorn, C, Indraratna, B & Choudhury, PK 1970, 'Analytical Modeling of Indian-Made Biodegradable Jute Drains for Soft Soil Stabilization: Progress and Challenges', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 99-109.
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© Springer Nature Singapore Pte Ltd. 2019. Installation of vertical drains in soft soil is probably the most popular preloading method of ground improvement today. These drains reduce the consolidation time of the soil by providing alternative pathways to relieve the pore water pressure in the soil quickly thus reducing construction time. Jute drains have been introduced as an environmentally friendly alternative to synthetic drains in recent times. However, owing to higher absorption capacity of jute and their tendency to degrade in soil their consolidation behavior can be vastly different from that of synthetic drains. In this review, the paper provides in detail the properties of jute drains along with significant developments that have been achieved over the years in understanding their consolidation behavior. The clogging and degradation behavior in these drains is investigated in relation to the limitations in analytical modeling. This article aimed to discuss not only the challenges associated with modeling this phenomenon but also suggests approaches by which this problem can be solved.
Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Ghenescu, M, Piatrik, T, De Cubber, G, Nalamati, M, Kapoor, A, Saqib, M, Sharma, N, Blumenstein, M, Magoulianitis, V, Ataloglou, D, Dimou, A, Zarpalas, D, Daras, P, Craye, C, Ardjoune, S, De la Iglesia, D, Mendez, M, Dosil, R & Gonzalez, I 1970, 'Drone-vs-Bird Detection Challenge at IEEE AVSS2019', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. This paper presents the second edition of the 'drone-vs-bird' detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.
Dang, LC, Dang, CC & Khabbaz, H 1970, 'Numerical Modelling of Embankment Supported by Fibre Reinforced Load Transfer Platform and Cement Mixed Columns Reinforced Soft Soil', 17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019 - Proceedings.
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This paper presents the numerical modelling of a new ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and columns supported (CS) embankment constructed on top of multilayers of soft soil. To investigate the influences of thickness and tensile strength of FRLTP on the embankment behaviour, a series of finite element analyses (FEA) was conducted on the full geometry of a CS embankment reinforced without or with an FRLTP. The FRLTP thickness varied in a range of 0∼3 m, and the FRLTP tensile strength ranged from 10 kPa to 240 kPa, which were considered in this numerical modelling. The numerical results reveal that an increase in the FRLTP thickness significantly improved the stress concentration ratio between columns and surrounding soil, meanwhile resulted in a considerable reduction of the lateral deformation and hence, effectively improved the stability of the embankment system. The findings of the parametric study also indicate that when the FRLTP tensile strength increased in the investigated range, the embankment lateral displacement was found to reduce to a certainly low value, and then it remained almost unchanged. It is also found that the time-dependent embankment behaviour was considerably affected by the changes in the tensile strength and the thickness of FRLTP.
Das, A, Pal, U, Blumenstein, M, Wang, C, He, Y, Zhu, Y & Sun, Z 1970, 'Sclera Segmentation Benchmarking Competition in Cross-resolution Environment', 2019 International Conference on Biometrics (ICB), 2019 International Conference on Biometrics (ICB), IEEE, Crete, Greece, pp. 1-7.
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© 2019 IEEE. This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using.a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.
De Silva Wijayaratna, S, Huang, Y & De Silva Wijayaratna, K 1970, 'Applications of Public Transport Accessibility Levels (PTAL) in Sydney', Australian Institute of Traffic Planning and Management National Conference, Adelaide, Australia.
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Encouraging public transport adoption and utilisation is imperative to achieving sustainable and mobile cities. The concept of public transport accessibility and its measurement is a complex topic that has received considerable attention in research and practice. One metric, the Public Transport Accessibility Level (PTAL), is used extensively around the world and has been increasingly used in Australia to inform parking rates, trip generation and transport impact assessments. This study reviews the relationship between PTAL and public transport use at a number of selected sites in Sydney to consider the suitability of PTAL in practice. The paper then proposes potential factors which could be further developed to provide a more robust assessment of public transport accessibility.
El-Hawat, O, Fatahi, B & Edmonds, C 1970, 'Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions', 7th International Conference on Earthquake Geotechnical Engineering, CRC Press, Roma, Italy, pp. 2241-2248.
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El-Hawat, O, Fatahi, B & Edmonds, C 1970, 'The Effectiveness of Restrainers to Enhance the Seismic Performance of Bridges with Rocking Foundations', Australian Earthquake Engineering Society 2019 Conference, Newcastle, NSW, Australia.
Gamal, M, Abolhasan, M, jafarizadeh, S, Lipman, J & Ni, W 1970, 'Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Ho Chi Minh City, Vietnam, pp. 328-333.
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© 2019 IEEE. Within the context of Software-Defined Networking (SDN), the problem of resource allocation for a set of incoming Virtual Network Functions (VNF) service requests has been the focus of many studies. In this paper, a new optimization model has been developed to find the near to optimal mapping and scheduling for the incoming VNF service requests. This model while considering delay, aims to achieve three objectives functions, namely, minimizing the transmission delays occurring in every link, minimizing the processing capacity for every Virtual Machine (VM) and minimizing the processing delay at every VM. The resultant problem is formulated as a multi-objective optimization problem and the developed solution is based on a multi-objective evolutionary algorithm utilizing the decomposition algorithm. Simulation results illustrate that the resulting algorithm is scalable while considering delay and it outperforms the genetic bandwidth link allocation (GA-BA) and genetic non-bandwidth link allocation (GA-NBA) algorithms.
Gamal, M, Jafarizadeh, S, Abolhasan, M, Lipman, J & Ni, W 1970, 'Mapping and Scheduling for Non-Uniform Arrival of Virtual Network Function (VNF) Requests', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA.
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© 2019 IEEE. As a new research concept for both academia and industry, there are several challenges faced by the Network Function Virtualization (NFV). One such challenge is to find the optimal mapping and scheduling for the incoming service requests which is the focus of this study. This optimization has been done by maximizing the number of accepted service requests, minimizing the number of bottleneck links and the overall processing time. The resultant problem is formulated as a multi- objective optimization problem, and two novel algorithms based on genetic algorithm have been developed. Through simulations, it has been shown that the developed algorithms can converge to the near to optimal solutions and they are scalable to large networks.
Ge, M, Pineda, JA, Sheng, D, Burton, GJ & Li, N 1970, 'Collapse behaviour of compacted loess: role of the stress level on soil microstructure', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 209-214.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. The paper presents preliminary results of an experimental study aimed at evaluating the influence of soil microstructure on the collapse behaviour of compacted loess from Xi'an, Shannxi province, China. Collapse behaviour was evaluated from one-dimensional compression tests in which compacted specimens were loaded to different vertical stresses, under constant water content conditions, prior soaking. Mercury intrusion porosimetry (MIP) tests and Scanning Electron Microscopy (SEM) analysis reveals a strong influence of the stress level on the soil microstructure formed by soaking under zero lateral deformation conditions.
Gong, S, Oberst, S & Wang, X 1970, 'Dynamic analysis of vibrating flip-flow screens equipped with support and shear rubber springs', Journal of Physics: Conference Series, Recent Advances in Structural Dynamics, IOP Publishing, Lyon, France, pp. 012061-012061.
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Abstract Vibrating flip-flow screens provide an effective means of screening highly viscous or fine materials, and the dynamic characteristics of the main and the floating screen frames are largely responsible for a flip-flow screen’s screen performance and its processing capacity. An accurate dynamic model of the rubber shear springs used within the frame of the screen is critical for its dynamic analysis – to understand deficiencies and improve its performance. In this paper, the Sjöberg model is used to predict the frequency-and amplitude-dependent behaviour of the rubber shear springs. A friction model represents the amplitude dependency of the rubber shear springs. The fractional derivative model is used to describe its frequency dependency with its elasticity being represented by a linear spring. This model is further validated by cyclic tests of the rubber shear springs. Furthermore, dynamic response of the VFFS have been analysed using the Sjöberg model and the Kelvin-Voigt model, respectively. Experimental results indicate that dynamic response of VFFS can be better predict using the Sjöberg model than Kelvin-Voigt model in time region as well as in the frequency domain.
Guo, T, Zhu, X, Wang, Y & Chen, F 1970, 'Discriminative Sample Generation for Deep Imbalanced Learning', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 2406-2412.
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In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.
Hadgraft, R, Francis, B, Brown, T, Fitch, R & Halkon, B 1970, 'Renewing Mechanical and Mechatronics Programs', AAEE2019, AAEE2019, Brisbane, Australia.
Halkon, B 1970, 'On the possibility of UAV-mounted LDVS for response-only dynamic characterisation of remote infrastructure', 8th IOMAC - International Operational Modal Analysis Conference, Proceedings, International Operational Modal Analysis Conference, Curran, Copenhagen, pp. 547-551.
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Laser Doppler vibrometers are technically well suited to general application but they offer special benefits in a variety of challenging measurement scenarios which are now well documented and accepted. An interesting and potentially powerful example of such a challenging measurement scenario is one where the laser vibrometer is mounted on/in an unmanned aerial vehicle in order that autonomous measurement campaigns can be undertaken in remote and/or harsh environments. One important challenge to overcome in such a scenario is the measurement sensitivity to vibration of the instrument itself or indeed of any steering optics used to point the probe laser beam toward the target of interest. In this paper, recently reported means by which this measurement sensitivity can be rectified by simultaneously obtained correction measurements will be described. Specifically, this development opens up the possibility of laser Doppler vibrometry from unmanned aerial vehicles for response-only dynamic assessment of remote infrastructure, a measurement challenge of significant potential value.
Halkon, B, Rauter, A, Oberst, S & Marburg, S 1970, 'Research and development of an air-puff excitation system for lightweight structures', 8th IOMAC - International Operational Modal Analysis Conference, Proceedings, International Operational Modal Analysis Conference, Curran Associates, Copenhagen, Denmark, pp. 627-634.
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Lightweight, thin-walled structures appear in numerous engineering and natural structures. Due to their sensitivity, vibration excitation by, now traditional, contacting techniques, such as modally-tuned impact hammers or electrodynamic shakers, to investigate their dynamics is challenging since it typically adds substantial mass and/or stiffness at the excitation location. The research presented in this article, therefore, is intended to yield a system for the non-contact excitation of thin-walled structures through small, controlled blasts of air. An air-puff system, consisting of two fast-acting solenoid-controlled valves, a small air outlet nozzle and bespoke control software with a programmable valve control sequence, is researched and developed. The excitation impulse characteristics are investigated experimentally and described in detail for varying input control parameters. Ultimately, suitability of the system for the excitation of thin-walled structures is explored, for both a 3D-printed micro-satellite panel and a natural bee honeycomb, with promising results when compared to that of an impact hammer.
Hassoun, M, Fatahi, B & Mirlatifi, S 1970, 'Seismic effectiveness of different restraining systems for skewed bridge decks supported on elastomeric bearings', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering, CRC Press, Rome, Italy, pp. 2812-2819.
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Past earthquakes have shown that isolated bridges are susceptible to excessive movements at the expansion joints. Reconnaissance reports have exposed various isolated bridges that have collapsed due to this excessive movement in a phenomenon known as unseating. This problem is particularly evident in skewed bridges as the bridge experience coupled lateral and rotational movements. The study seeks to understand the effectiveness of various restrainers, namely cable restrainers and viscous dampers in limiting both lateral and rotational movements of the bridge deck. In order to approximate the complex behavior of isolated restrained bridges, the models used in this study include the effect of soil-structure interaction behind the abutment backwall and the pile foundations. Moreover, the inelastic behavior of highly damaged bridge elements such as abutment backwall, pier and shear-keys are studied, and results are presented.
Ho, P, Wijayaratna, K & Dixit, V 1970, 'Understanding driver behavior in response to variable message signs for smart motorway management systems', Proceedings of the 24th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2019: Transport and Smart Cities, The 24th International Conference of Hong Kong Society for Transportation Studies, Hong Kong, pp. 73-80.
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Variable message signs (VMS) are a key component of information systems used to manage congestion. To improve the accuracy of real time predictions for decision support systems, it is important to understand and model driver behavior in response to messages displayed on VMS during incidents. In this empirical study, an aggregate analysis was undertaken to study the impact of VMS on off-ramp diversion behavior on the M4 Motorway in Sydney, Australia. Hypothesis testing was first used to confirm the statistically significant impact of VMS on diversion proportions revealing a shift of up to 3.5%. A linear regression model was then estimated using over 4000 observed incident messages to identify influencing factors such as downstream congestion, accident events, and the content and duration of messages. The results provide objective insight into the aggregate diversion behaviour. This has implications in providing more accurate predictions and evaluation of incident management strategies.
Huang, B, Fatahi, B & Terzaghi, S 1970, 'Investigating effects of wave incident angle and joint depth on ground surface motion considering multiple reflections via coupled discrete element and finite difference method', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering (VII ICEGE), CRC Press, Roma, Italy, pp. 2883-2890.
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In this study, the effects of incident angle and joint depth on the ground surface motion induced by the shear wave propagation across the rock mass were numerically assessed. A three-dimensional coupled discrete element-finite difference model consisting of a 40-m deep rock mass with a single joint, was developed using 3DEC software. The continuously yielding joint model was adopted to replicate the nonlinear behaviour of the joint under the influence of the seismic wave. Moreover, the role of the multiple reflections occurring between the ground surface and the joint in the ground surface motion was determined via the comparison between the models with and without the presence of the free surface. The results of this parametric study showed that a larger incident angle could lead to the amplification of both the horizontal and vertical components of the peak particle velocity captured on the ground surface. In addition, it was found that the multiple reflections can significantly amplify the ground surface motion, particularly when the joint with the shallow depth was present. Hence, it is critical for practicing engineers to take into account the joint spatial properties such as the joint orientation and depth, in conjunction with the multiple reflections, when making the prediction of the ground surface motion.
Huang, B, Fatahi, B & Zargarbashi, S 1970, 'Coupled discrete element and finite difference modelling of wave propagation across rock mass considering multiple reflections between ground surface and joint', 13th Australia New Zealand Conference on Geomechanics, Australia New Zealand Conference on Geomechanics, Australian Geomechanics Society, Perth, Australia, pp. 889-896.
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Ground surface motion induced by the upward propagating seismic waves could cause severe damage to structures on ground surface. The propagation of seismic wave could be significantly affected by the presence of joints embedded in a rock mass. The joints could attenuate and slow down the travelling wave. Moreover, the multiple wave reflections occurring between the ground surface and the joint could significantly alter the behaviour of the wave travelling through the near-ground rock mass. In this study, the S-wave propagating through the jointed rock mass and its induced ground surface motion were numerically investigated. A three-dimensional coupled discrete element-finite difference model was developed using 3DEC software to study seismic wave propagation across a 30-m deep rock mass with a single horizontal joint in the middle. Continuously yielding joint model was adopted to capture the nonlinear progressive damage of joints under shear. The influences of the shear stress ratio and the frequency of the incident S-wave on the wave propagation across the rock mass and the associated ground surface motion were studied, considering multiple wave reflections between the free ground surface and rock joint. Moreover, a comparison was made between the models with and without the presence of the free surface, to better understand the impact of the multiple reflections on the wave propagation and the ground surface motion. It was showed that the multiple reflections could conspicuously intensify the wave propagation across the rock mass, and therefore amplifying the ground surface motion, particularly when the frequency was low. Moreover, the ground surface motion became insensitive to the variation of the shear stress ratio when the ratio was either too small or too large, in conjunction with the multiple wave reflections. Hence, the effect of multiple reflections should be carefully considered, when predicting the ground surface motion caused by the wave p...
Idrees, MO & Pradhan, B 1970, 'Frontier in Three-Dimensional Cave Reconstruction—3D Meshing Versus Textured Rendering', GCEC 2017: Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1029-1038.
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© Springer Nature Singapore Pte Ltd. 2019. Underground caves and their specific structures are important for geomorphological studies. This paper investigates the capabilities of a new modelling approach advanced for true-to-life three-dimensional (3D) reconstruction of cave with full resolution scan relative to 3D meshing. The cave was surveyed using terrestrial laser scanner (TLS) to acquire high resolution scans. The data was processed to generate a 3D-mesh model and textured 3D model using sub-sampled points and full resolution scan respectively. Based on both point and solid surface representation, comparative analysis of the strengths and weaknesses of the two approaches were examined in terms of data processing efficiency, visualization, interactivity and geomorphological feature representation and identification. The result shows that full scan point representation offers advantage for dynamic visualization over the decimated xyz point data because of high density of points and availability of other surface information like point normal, intensity and height which can be visualized in colour scale. For the reconstructed surface, mesh model is better with respect to interactivity and morphometric but 3D rendering shows superiority in visual reality and identification of micro detail of features with high precision. Complementary use of the two will provide better understanding of the cave, its development and processes.
Indraratna, B, Baral, P, Qi, Y, Ngo, T, Rujikiatkamjorn, C & Ferreia, F 1970, 'Advances in Ground Improvement and Principles of Track Geomechanics for Future Railways', Proc. 17th African Regional Conference on Soil Mechanics and Geotechnical Engineering, 17th African Regional Conference on Soil Mechanics and Geotechnical Engineering, Cape Town, pp. 21-36.
Indraratna, B, Ngo, NT, Sun, Q, Rujikiatkamjorn, C & Ferreira, FB 1970, 'Concepts and Methodologies for Track Improvement and Associated Physical Modelling and Field Monitoring', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, New Delhi, India, pp. 219-246.
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© 2019, Springer Nature Singapore Pte Ltd. As the heavy haul freight trains become longer and heavier, ballast grain experience pronounced breakage and deformation, resulting in the deterioration of the ballasted track substructure. Suitable soil stabilisation approaches using geosynthetics and/or energy-absorbing rubber mats are commonly employed to enhance the stability and longevity of ballasted tracks. This paper reviews the research studies that have been conducted at the University of Wollongong on track technology using advanced laboratory and computational modelling, as well as real-life health monitoring of selected track sections. Full-scale instrumented field monitoring supported by Australian rail organisations has been carried out to obtain measurements of actual stresses and displacements and thereby evaluate track performance supplemented by computational models. In the past decade, the authors have tested varied types of geosynthetics and rubber mats both in the laboratory and in the field where these geoinclusions were put underneath the ballast layer in tracks built on various subgrade types (i.e. soft and hard subgrades). Stresses induced by traffic, ballast degradation, vertical and lateral displacements of the ballast aggregates were routinely recorded using extensive instrumentation systems. These results provide suitable approaches that can be considered into current track design for future heavy and long freight train travelling at higher speeds.
Indraratna, B, Qi, Y, Jayasuriya, C, Heitor, A & Sinniah, KN 1970, 'Use of Rubber Tyre Elements in Track Stabilization', 15th International Conference on Geotechnical Engineering, Larhore, Pakistan.
Jayathilaka, P, Indraratna, B & Heitor, A 1970, 'Influence that Osmotic Suction and Tree Roots has on the Stability of Coastal Soils', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 669-680.
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© Springer Nature Singapore Pte Ltd 2019. The contribution made by osmotic suction to unsaturated shear strength analysis has not been considered for the past few decades. Osmotic suction is generated by the salt in pore water, especially in coastal environments, and it can be more significant than matric suction. Tree roots can also induce osmotic and matric suction by continuous transpiration, and when these saline and rooted environments are combined under unsaturated conditions, they can challenge conventional shear strength models. Electrical resistivity can be used as a proper tool to evaluate the properties of soil in a large scale. This review summarizes the historical development of studies related to osmotic suction as well as the present situation of osmotic suction for soil shear strength.
Jena, R & Pradhan, B 1970, 'A Model To Detect Forest Change Relating To Mining Using Google Earth Engine Application In Belitung Island, Indonesia', 2019 6th International Conference on Space Science and Communication (IconSpace), 2019 6th International Conference on Space Science and Communication (IconSpace), IEEE, Johor Bahru, Malaysia, pp. 47-52.
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© 2019 IEEE. Belitung Island is one of the biodiversity hotspots in Indonesia that is best known for its multi-use landscape, tourism, large agricultural land, tin mining and all other activities. The main earning possibility of local people of the island is most efficiently lies in coastal activities and tin mining. Main challenges are persistent cloud cover over the steep and vegetated terrain that creates a problem in forest change mapping. This research was conducted to identify and visually analyse the forest loss or gain due to tin mining activity and settlement along with the consequences of illegal logging using the Google earth engine application. Furthermore, this study will also help to understand the areas of water bodies filled after mining making it inactive. Therefore, NDVI and MNDWI analysis have been conducted to calculate the index values using the (GEE) Google earth engine and graphically presented. Landsat +ETM, MODIS global land cover, Hansen global forest change and other remote sensing data applied to conduct this research. The results obtained from this study shows that the width of forestry land cover is decreased gradually from 2012 to 2017 and the active tin mining, agricultural land, and settlement are widely increased. The inactive tin-mined areas are filled with water that can be well understood from the elevation modelling. Furthermore, the forest gain is also increasing mildly as per the results of change detection in forest gain analysis from 2012 to 2017. This clearly indicates the change of forest resulting due to the active tin mining and inactive tin-mined water filled land as well as the human settlement.
Jena, R & Pradhan, B 1970, 'Earthquake Vulnerability Assessment using Expert-based Approach in GIS', 2019 6th International Conference on Space Science and Communication (IconSpace), 2019 6th International Conference on Space Science and Communication (IconSpace), IEEE, Johor Bahru, Malaysia, pp. 53-56.
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© 2019 IEEE. Several techniques of earthquake vulnerability assessment exist for the evaluation of social, economic and buildings vulnerability that has been carried out to investigate their suitability in the application of earthquake risk assessment. The primary challenge is the prediction of earthquakes that is almost seemingly impossible in the current time. The main concern is mitigation and preparedness, which is dominant for the human, animals, and environment. However, exposed assets and the determination of their fragilities/vulnerabilities are essential and will be challenging in the future for the viability and reliability during the assessment of rapid loss. Therefore, this study proposes an expert's decision-based approach for the assessment of earthquake vulnerability in Banda Aceh city, Indonesia that could help in future risk assessment on a city scale. It was analysed that the proposed method adequately satisfies all the necessary criteria that can be involved in earthquake vulnerability assessment in Banda Aceh city to reduce the earthquake impacts. The results shows that the proposed method is good for city-scale earthquake vulnerability assessment with significant consistency ratio of 0.04. This research observes the current practices involved in regional and urban earthquake vulnerability assessment.
Kalhori, H, Halkon, B & Alamdari, MM 1970, 'Wavelet transform-based strategy for identifying impact force on a composite panel', Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019, International Congress on Sound and Vibration, Montreal, Canada.
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An algorithm based on wavelet analysis for automatically estimating the location and magnitude of impact forces exerted on a rectangular carbon fibre-epoxy honeycomb composite panel is developed. The technique employs a single piezoelectric sensor mounted distant from the impact zone and presumes that an impact is applied at one of several pre-established locations. Furthermore, it is presumed that the recorded vibration response is the superposition of the simultaneous 'assumed' impacts at these locations, with the aim of simultaneously identifying the actual impact location and force magnitude through an under-determined regularisation scheme. The algorithm aims to detect the most probable impact location amongst the spurious locations. Since a normal impact introduces a narrow-band time-localised event with high energy, the wavelet transform is an effective tool to locate this event, with the wavelet coefficient representing how closely correlated the wavelet is with the reconstructed forces. The larger the coefficient is in absolute value, the greater the similarity. As a case study, an under-determined problem with four potential impact locations is considered. The results demonstrate successful localisation and reconstruction of the impact force using both orthogonal and non-orthogonal wavelets
Khoa, NLD, Tian, H, Wang, Y & Chen, F 1970, 'Online Data Fusion Using Incremental Tensor Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 357-369.
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© Springer Nature Switzerland AG 2019. Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources.
Kim, I, De Silva Wijayaratna, K & Jian, S 1970, 'Travel Behaviour variation across Sydney', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
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Increasing numbers of people are opting to move to the outskirts of metropolitan areas and into “semi-regional” areas of Greater Sydney as a result of rapid population growth. In addition, public transport service provisions differ across the network where frequencies and capacities change between stations and lines. Hence, communities adjusttheir travel behaviour to align with the availability of services within their locality, particularly affecting departure times. Thus, this behaviour raises the key research question, “Do Semi-Regional Sydney commuters have more consistent work travel patterns than those living in Metropolitan Sydney?” This was investigated by using the average total travel time of frequent rail commuters and a novel accessibility metric, “Tapon Time Deviation”, based on the tap on times from smart card data. This metric was used to quantify travel behaviour consistency and gain a better understanding of geographic impacts on individuals travel characteristics.
Lay, US & Pradhan, B 1970, 'Identification of Debris Flow Initiation Zones Using Topographic Model and Airborne Laser Scanning Data', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 915-940.
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© Springer Nature Singapore Pte Ltd. 2019. Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm)...
Lay, US, Jibrin, G, Tijani, I & Pradhan, B 1970, 'Geomorphometric Analysis of Landform Pattern Using Topographic Position and ASTER GDEM', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1139-1160.
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© Springer Nature Singapore Pte Ltd. 2019. A number of research have been carried out on geomorphology using a conventional approach to classify the landform; this has a tendency of producing misleading result, due to ruggedness and inaccessibility of the terrain. Geographic Information System (GIS) and remote sensing techniques are capable of generating automated landform classes using Topographic Position Index techniques (TPI). This research is set to achieve the following objectives: to categorize landform elements and to illustrate the complexity of the terrain in Negeri Sembilan state based on ASTER GDEM with 30 m resolution. TPI-based algorithm for landscape classification was applied to slope position and landform classification automation. We used 300 and 3000 neighbourhood size on the TPI grids to determine the landform categories. To quantify the spatial pattern of topographic position, Deviation from mean elevation (DEV) is adopted. Maximum Elevation Deviation was selected to measure the spatial landscape pattern at the maximum (3000) scale of the absolute DEV value within the scale (DEVmax), and finally, high-pass filter algorithm was used to identify the extreme topography (ridges/valleys). The combination of the TPI and slope position of DEV that formed the landform classification results show four prominent landform classes these include canyons, U-shape valley, local ridges/ hill valleys, and mountaintops/high ridges. The slope position classes revealed only two (valley/cliff base and ridges/canyons edge) classes based on slope position index. The canyons had the maximum of 63% and minimum was U-shaped valley with 1.04% for the landform of the area of interest. To achieve better results, there is a need to utilize a high spatial resolution remotely sensed DEM derived data and sensitivity analysis need to be incorporated. For that, laser scanning data is capable of improving the results.
Le, TM, Dang, LC & Khabbaz, H 1970, 'Combined Effects of Bottom Ash and Lime on Behaviour of Expansive Soil', Recent Advancements on Expansive Soils, International Congress and Exhibition on Sustainable Civil Infrastructures, Springer International Publishing, Cairo, Egypt, pp. 28-44.
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This study illustrates the effectiveness of combining bottom ash and hydrated lime to enhance the engineering properties of expansive soil. The bottom ash was collected from Eraring Power Station in New South Wales, Australia, as a by-product of coal-fired power stations, and soil specimens were used as artificial soil including kaolinite, bentonite and fine sand in a reasonable ratio to stimulate soil samples with characteristics of expansive soil. The stabilised soil samples were prepared by altering the bottom ash content from 0% to 30% on a dry weight basis of expansive soil as well as with constant percentage of 5% in hydrated lime. Through conducting a series of experimental tests including linear shrinkage and unconfined compressive strength (UCS) in various curing time, the shrinkage and strength behaviour of treated soils were investigated and compared with untreated soil samples. The results revealed that the combination of bottom ash and hydrated lime significantly reduced the linear shrinkage, while it increased the strength of expansive soil. The use of bottom ash alone is not recommended due to a slight increase of linear shrinkage and a minor negative impact on the soil strength. The optimum content of combined bottom ash and hydrated lime to stabilise expansive soils is also presented.
Liang, B, Vitanage, D, Doolan, C, Li, Z, Taib, R, Mathews, G, Wang, Y, Lu, S, Chen, F, Hua, T & Peters, A 1970, 'Predicting Water Quality for the Woronora Delivery Network with Sparse Samples', 2019 IEEE International Conference on Data Mining (ICDM), 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China, pp. 1210-1215.
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© 2019 IEEE. Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.
Liu, C, Ngo, NT & Indraratna, B 1970, 'Improved Performance of Railroad Ballast Using Geogrids', International Symposium on Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 151-163.
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© Springer Nature Singapore Pte Ltd. 2019. Geogrids are commonly used to stabilise and reinforce ballast, and over the various laboratory tests have been carried out to determine how geogrids affect the interface between geogrid and ballast aggregates. This paper presents a critical review and interpretation of the results of large-scale direct shear tests and cyclic tests on key parameters such as the interlocking effects of aperture size and the location of geogrids. Field investigations from sites at Bulli and Singleton as well as findings from Discrete Element Modelling, including the influence zone of geogrid and the linear relationship between geometric anisotropy and stress ratio, are examined and discussed. It also includes a presentation and discussion of analytical modelling for quantifying the geogrid reinforcing effect (pullout tests).
Lu, S, Oberst, S, Zhang, G & Luo, Z 1970, 'Period adding bifurcations in dynamic pricing processes', 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), IEEE, Shenzhen, China, pp. 71-76.
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Price information enables consumers to anticipate a price and to make purchasing decisions based on their price expectations, which are critical for agents with pricing decisions or price regulations. A company with pricing decisions can aim to optimise the short-term or the long-term revenue, each of which leads to different pricing strategies thereby different price expectations. Two key ingredients play important roles in the choosing of the short-term or the long-term optimisation objectives: the maximal revenue and the robustness of the chosen pricing strategy against market volatility. However the robustness is rarely identified in a volatile market. Here, we investigate the robustness of optimal pricing strategies with the short-term or long-term optimisation objectives through the analysis of nonlinear dynamics of price expectations. Bifurcation diagrams and period diagrams are introduced to compare the change in dynamics of the optomal pricing strategies. Our results highlight that period adding bifurcations occur during the dynamic pricing processes studied. These bifurcations would challenge the robustness of an optimal pricing strategy. The consideration of the long-term revenue allows a company to charge a higher price, which in turn increases the revenue. However, the consideration of the short-term revenue can reduce the occurrence of period adding bifurcations, contributing to a robust pricing strategy. For a company, this strategy is a robust guarantee of optimal revenue in a volatile market; for consumers, this strategy avoids rapid changes in price and reduce their dissatisfaction of price variations.
Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 1970, 'Multitask Learning for Sparse Failure Prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 3-14.
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© Springer Nature Switzerland AG 2019. Sparsity is a problem which occurs inherently in many real-world datasets. Sparsity induces an imbalance in data, which has an adverse effect on machine learning and hence reducing the predictability. Previously, strong assumptions were made by domain experts on the model parameters by using their experience to overcome sparsity, albeit assumptions are subjective. Differently, we propose a multi-task learning solution which is able to automatically learn model parameters from a common latent structure of the data from related domains. Despite related, datasets commonly have overlapped but dissimilar feature spaces and therefore cannot simply be combined into a single dataset. Our proposed model, namely hierarchical Dirichlet process mixture of hierarchical beta process (HDP-HBP), learns tasks with a common model parameter for the failure prediction model using hierarchical Dirichlet process. Our model uses recorded failure history to make failure predictions on a water supply network. Multi-task learning is used to gain additional information from the failure records of water supply networks managed by other utility companies to improve prediction in one network. We achieve superior accuracy for sparse predictions compared to previous state-of-the-art models and have demonstrated the capability to be used in risk management to proactively repair critical infrastructure.
Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 1970, 'PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities', Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, 16th International Conference on Security and Cryptography, SCITEPRESS - Science and Technology Publications, Prague, Czech Republic, pp. 363-371.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce, smart cities, supply chain management, smart cars, cyber-physical systems and a lot more. However, the data collected and processed by IoT systems especially the ones with centralized control are vulnerable to availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative framework for integrity and privacy-preserving IoT data sharing in a smart city environment. The proposed scheme is distinct from existing technologies on many aspects. The data privacy is preserved by dividing the blockchain network into various channels, where every channel processes a specific type of data such as health, smart car, smart energy or financial data. Moreover, access to user data within a channel is controlled by embedding access control rules in the smart contracts. In addition, users' data within a channel is further isolated and secured by using private data collection. Likewise, the REST API that enables clients to interact with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed solution also conforms to some of the significant requirements outlined in the European Union General Data Protection Regulation. Lastly, we present a system of reward in the form of a digital token “PrivyCoin” for the users for sharing their data with the stakeholders/third parties.
Medawela, S, Indraratna, B, Pathirage, U & Heitor, A 1970, 'Controlling Soil and Water Acidity in Acid Sulfate Soil Terrains Using Permeable Reactive Barriers', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 413-426.
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© 2019, Springer Nature Singapore Pte Ltd. There are about 12–14 million ha of acid sulfate soils (ASSs) found throughout the length and breadth of the earth. Bridge and building foundations, pipelines, culverts, and other buried infrastructure in such acidic environments are deteriorated when they are exposed to higher acidity, which is generated due to leaching of sulfuric acid from ASS. Thus, acidic groundwater should be properly treated to avoid detrimental effects on natural environment and strenuous efforts on repairing damaged manmade structures. Since the early 90s, permeable reactive barriers (PRBs) were implemented in several places worldwide and it was proven that PRBs are capable of competently treating poor-quality groundwater with various contaminants. While the acidic groundwater flows through a PRB, contaminants (toxic cations) are removed by mineral precipitation and due to the chemical reactions occur, a near-neutral pH is maintained in the effluent. Nevertheless, longevity of the PRB is alleviated due to coupled clogging in porous media. Physical, chemical, and biological clogging mechanisms and the existing PRB design criteria have been critically reviewed in this paper, including precursory numerical models. It is imperative to extend existing equations and models combining all possible clogging mechanisms, to assure the maximum acid removal capacity of a PRB. Hence, water and soil quality would be enhanced to make the land safe for transport and other infrastructure developments.
Melnikov, A, Chiang, YK, Oberst, S, Quan, L, Alu, A, Marburg, S & Powell, D 1970, 'Experimental validation of maximal Willis coupling in an acoustic meta-atom', 13th International Congress on Artificial Materials for Novel Wave Phenomena – Metamaterials 2019, International Congress on Artificial Materials for Novel Wave Phenomena, Rome, pp. 1-2.
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Willis coupling is the acoustic analog of bianisotropy, representing coupling between the monopolar and dipolar degrees of freedom. It has recently been theoretically demonstrated that there is an upper bound on the strength of this coupling, imposed by the conservation of energy. Here we present a scalable meta-atom design, and experimentally demonstrate that it approaches the theoretical limit for Willis coupling.
Mezaal, MR, Pradhan, B, Shafri, HZM, Mojaddadi, H & Yusoff, ZM 1970, 'Optimized Hierarchical Rule-Based Classification for Differentiating Shallow and Deep-Seated Landslide Using High-Resolution LiDAR Data', GCEC 2017: Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 825-848.
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© Springer Nature Singapore Pte Ltd. 2019. Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influenc...
Mirzababaei, M, Decourcy, T & Fatahi, B 1970, 'Sustainable Use of Reclaimed Ballast Rejects for Construction of Rail Corridor Access Road-an Australian Experience', Springer International Publishing, pp. 257-269.
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Moayedi, H, Foong, LK, Nazir, R & Pradhan, B 1970, 'Investigation of Aqueous and Light Non-aqueous Phase Liquid in Fractured Double-Porosity Soil', Springer International Publishing, pp. 207-210.
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Moayedi, H, Nazir, R, Foong, LK, Mosallanezhad, M & Pradhan, B 1970, 'Experimental Investigation of Several Different Types of Soil Erosion Protection Systems', Springer International Publishing, pp. 481-483.
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Mokhtar, ES, Pradhan, B, Ghazali, AH & Shafri, HZM 1970, 'Assessing Vertical Accuracy and the Impact of Water Surface Elevation from Different DEM Datasets', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference 2017, Springer Singapore, Malaysia, pp. 849-862.
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© Springer Nature Singapore Pte Ltd. 2019. Digital elevation models (DEMs) are essential to provide continuous terrain elevation for water surface elevation (WSE) with a variety of horizontal and vertical accuracies in flood inundation modelling. The WSE forecasting depends on the appropriateness of the DEM data used. The comparative methodology is applied to various DEM sources: LiDAR and IFSAR DEM based on different types of land use at each of the cross-sectional lines. The accuracy of the IFSAR DEMs was assessed with LiDAR data, which is a high-precision DEM and was applied in hydraulic modelling to simulate the WSE in Padang Terap, Kedah, Malaysia. Furthermore, Bjerklie’s model is used as predicted discharge to support the analysis. The relationship of the DEMs is established by natural logarithm (ln). Then, the equation is interpolated on the original and resampled IFSAR DEMs to improve the medium-resolution data for WSE delineation. Next, the WSE was validated with observed WSE obtained along the upstream (Kuala Nerang) to the downstream parts (Kampung Kubu) Kedah using R 2 , mean absolute error (MAE), and root-mean-square error (RMSE). By applying this method, the WSE can be improved by considering uncertainties and lead to produce a better flood hazard map using medium-high-resolution images.
Moylan, E, De Silva Wijayaratna, K, Jian, S & Waller, ST 1970, 'The Unreliability of journey-time reliability measurements', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
Nalamati, M, Kapoor, A, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Drone Detection in Long-Range Surveillance Videos', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.
Ngo, NT & Indraratna, B 1970, 'Interface Behavior of Geogrid-Reinforced Sub-ballast: Laboratory and Discrete Element Modeling', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 195-209.
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© Springer Nature Singapore Pte Ltd. 2019. This paper shows a study on the interface behavior of biaxial geogrids and sub-ballast using a direct shear box and computational modeling. A series of large-scale direct shear tests are performed on sub-ballast (capping layer) with and without geogrid inclusions. The laboratory test data indicate that the interface shear strength is mainly decided by applied normal stresses and types of geosynthetics tested. Discrete element modeling approach is used to investigate the interface shear behavior of the sub-ballast subjected to direct shear loads. Irregular-shaped sub-ballast particles are modeled by clumping of many spheres together in pre-determined sizes and positions. Biaxial geogrids are simulated in the DEM by bonding small balls together to build desired geogrid shapes and opening apertures. The numerical results reasonably match with the measured test data, showing that the introduced DEM model can simulate the interface behavior of sub-ballast stabilized by the geogrids. In addition, the triaxial geogrid presents the highest interface shear strength compared to the biaxial geogrids; and this can be associated with the symmetric geometry of grids’ apertures that can distribute load in all directions. Evolutions of contact forces of unreinforced/reinforced sub-ballast specimens and contour strain distributions during shear tests are also investigated.
Nguyen, T, Indraratna, B & Carter, J 1970, 'Influence of soil clogging on the performance of jute fibre drains installed in Ballina clay', In Proceedings of the 13th ANZ Geomechanics-Australia New Zealand Conference on Geomechanics, the 13th ANZ Geomechanics-Australia New Zealand Conference on Geomechanics, Perth.
Nguyen, TN, Yu, Y, Li, J, Gowripalan, N & Sirivivatnanon, V 1970, 'Mechanical properties of ASR affected concrete: a critical review', Concrete 2019, Concrete 2019, Sydney.
Norman, M, Shafri, HZM, Pradhan, B & Yusuf, B 1970, 'Improved Building Roof Type Classification Using Correlation-Based Feature Selection and Gain Ratio Algorithms', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 863-873.
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© Springer Nature Singapore Pte Ltd. 2019. Of late, application of data mining for pattern recognition and feature classification is fast becoming an essential technique in remote sensing research. Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. Geographic object-based image analysis (GEOBIA) has made it possible to derive varieties of object attribute for this purpose; however, the analysis is more computationally intensive. The aim of this study is to develop feature selection technique that will provide the most suitable attributes to identify different roofing materials and their conditions. First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. Then, the quality of the selected features was assessed using correlation-based feature selection (CFS). The classification results using SVM classifier produced an overall accuracy of 83.16%. The study has shown that the ability to exploit rich image feature attribute through optimization process improves accurate extraction of roof material with greater reliability.
Ou, K, Pineda, JA, Liu, X & Sheng, D 1970, 'Osmotic effects on the microstructure of Ashfield shale', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 669-674.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. Preliminary results of a comprehensive microstructural investigation aimed at studying the influence of osmotic effects in Ashfield shale, a low permeability sedimentary rock from the Sydney Basin (Australia), are presented in the paper. Natural rock specimens were exposed to different brine solutions to assess their influence on rock microstructure. Qualitative as well as quantitative experimental techniques were used to evaluate changes in mineralogical composition (XRD analysis), Cation Exchange Capacity (CEC), cation/ion concentration (chromatographic analysis), specific surface, structural arrangement (Scanning Electron Microscopy) as well as pore size distribution (Mercury Intrusion Porosimetry). Test results show an important influence of the applied osmotic potential on specific surface, CEC and pore size distribution and to a lesser extend the structural arrangement assessed via SEM. These changes occur without important variations in the mineralogical composition of the rock.
Peng, X, Long, G, Shen, T, Wang, S, Jiang, J & Blumenstein, M 1970, 'Temporal Self-Attention Network for Medical Concept Embedding', Proceedings - IEEE International Conference on Data Mining, ICDM, International Conference on Data Mining, Beijing, China, pp. 498-507.
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In longitudinal electronic health records (EHRs), the event records of apatient are distributed over a long period of time and the temporal relationsbetween the events reflect sufficient domain knowledge to benefit predictiontasks such as the rate of inpatient mortality. Medical concept embedding as afeature extraction method that transforms a set of medical concepts with aspecific time stamp into a vector, which will be fed into a supervised learningalgorithm. The quality of the embedding significantly determines the learningperformance over the medical data. In this paper, we propose a medical conceptembedding method based on applying a self-attention mechanism to represent eachmedical concept. We propose a novel attention mechanism which captures thecontextual information and temporal relationships between medical concepts. Alight-weight neural net, 'Temporal Self-Attention Network (TeSAN)', is thenproposed to learn medical concept embedding based solely on the proposedattention mechanism. To test the effectiveness of our proposed methods, we haveconducted clustering and prediction tasks on two public EHRs datasets comparingTeSAN against five state-of-the-art embedding methods. The experimental resultsdemonstrate that the proposed TeSAN model is superior to all the comparedmethods. To the best of our knowledge, this work is the first to exploittemporal self-attentive relations between medical events.
Pham Ngoc, T, Fatahi, B & Khabbaz, H 1970, 'Impact of Liquid Whey Waste on Strength and Stiffness of Cement Treated Clay', New Developments in Soil Characterization and Soil Stability, Civil Infrastructures Confronting Severe Weathers and Climate Changes Conference, Springer International Publishing, Hangzhou, China, pp. 1-10.
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The reuse of whey waste, a by-product of the dairy industry, is an emerging issue due to the environmental impacts. Some previous experimental studies have indicated that whey waste can be used as an admixture for cement-based materials, including mortar and concrete, to reduce the setting time and increase the workability, thus reduce the amount of required cement. However, influence of whey waste on cemented soil has not received sufficient attention. This study investigates variations of unconfined compressive strength (UCS) and Young's modulus (E) of cemented Kaolin clay when water in cement slurry was replaced by different whey waste proportions. Unconfined compression tests were conducted on treated specimens after two different curing times, namely 14 days and 56 days. Stress-strain relationship in each test was used to compute UCS and E at different dosages of cement and whey waste. Results of the experiments show improvements of UCS and E only for specimens when less than 10% water in cement slurry was replaced by liquid whey waste at 56 day-curing age, regardless of cement dosage. For the other cases, the presence of whey waste resulted in reductions of both UCS and E, indicating that although whey waste can be used to improve mechanical properties of cement treated clay, the optimum dosage should be selected very carefully to minimize the adverse effects. Different responses of UCS and E with curing age, dosages of cement and liquid whey waste are explained while discussing about the effects of lactose (milk sugar) available in whey waste acting as a retarding agent.
Phan, NM, Indraratna, B & Nguyen, T 1970, 'The response of granular soil to increasing hydraulic gradient through LBM-DEM coupling', In Proceedings of the 9th Asian Young Geotechnical Engineers (9YGEC), Lahore.
Pineda, JA & Sheng, D 1970, 'Environmental degradation of clayey rocks', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 8-20.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. This paper explores the mechanisms that lead to the degradation of clayey rocks when exposed to environmental effects as those caused by unloading and cyclic variations in relative humidity. The following aspects are evaluated: (i) the number of applied RH cycles, N, (ii) the amplitude of relative humidity cycles, RH, (iii) the stress level (p-ua) and (iv) the effect of the fluid used to induce rock saturation (liquid water or vapour). The implementation of nonconventional experimental techniques for inducing and tracking rock degradation, at 'macro' and 'micro' scales, is described. An experimentally-based framework of behaviour is presented which may be used in practice for the evaluation of the degradation potential of clayey rocks.
Pour, AB, Park, T-YS, Park, Y, Hong, JK & Pradhan, B 1970, 'Application of Constrained Energy Minimization (CEM) algorithm to ASTER data for alteration mineral mapping', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Japan.
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Pour, AB, Park, T-YS, Park, Y, Hong, JK & Pradhan, B 1970, 'Fusion of DPCA and ICA algorithms for mineral detection using Landsat-8 spectral bands', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 6067-6070.
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© 2019 IEEE. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) algorithms was applied to some selected Landsat-8 mineral indices for mapping gossan and clay-rich zones for Zn-Pb exploration in the Franklinian Basin of North Greenland. This region contains a unique potential for exploration of world-class zinc deposits. Numerous potential zones for Zn-Pb deposits were identified using the fusion technique of DPCA/ICA. Their identification was based on detecting alteration mineral patterns consist of ferric iron, ferrous iron and clay minerals within a background of sedimentary rocks using Landsat-8 spectral bands. Several zones of gossan and clay mineral assemblages were identified in the trough sequences of the Citronen Fjord, Peary Land that may represent potential undiscovered CD Zn-Pb deposits and warrant further investigation. This investigation indicates that satellite remote sensing mapping techniques can aid in identifying unknown/undiscovered Zn-Pb sulfide deposits in the High Arctic Franklinian Basin by targeting the location of alteration zones that are feasible location for mineral occurrences.
Pradhan, B 1970, 'Geospatial Computation for Urban Applications', 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE.
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Qi, Y, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 1970, 'Recycled Rubber Derivatives for Resilient Transport Corridors', 15th International Conference on Geotechnical Engineering, Lahore, Pakistan.
Qi, Y, Indraratna, B, Heitor, A & Vinod, JS 1970, 'The Influence of Rubber Crumbs on the Energy Absorbing Property of Waste Mixtures', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 271-281.
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© Springer Nature Singapore Pte Ltd. 2019. The practical application of waste materials such as steel furnace slag (SFS) and coal wash (CW) is becoming more prevalent in civil engineering. While the addition of rubber crumbs (RC) derived from waste tyres can influence the geotechnical properties of the mixtures of SFS and CW significantly, especially the energy absorbing property. In this paper, the energy absorbing property of the SFS + CW + RC mixtures under static loading has been evaluated by the strain energy density. As expected, the energy absorbing capacity of the waste mixture increases with the addition of RC. To further illustrate the influence of rubber crumbs on the energy absorbing property of the waste mixtures, particle degradation has also be examined after finishing the triaxial tests. It has been found that the addition of RC can significantly reduce the particle breakage of the waste mixtures. Therefore, with high energy absorbing property, the SFS + CW + RC mixtures can be further extended to dynamic loading projects, such as railway capping layer.
Rahim, MS, Anh Nguyen, K, Stewart, RA, Giurco, D & Blumenstein, M 1970, 'Predicting Household Water Consumption Events: Towards a Personalised Recommender System to Encourage Water-conscious Behaviour', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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© 2019 IEEE. Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.
Rizeei, HM, Pradhan, B & Saharkhiz, MA 1970, 'Surface Runoff Estimation and Prediction Regarding LULC and Climate Dynamics Using Coupled LTM, Optimized ARIMA and Distributed-GIS-Based SCS-CN Models at Tropical Region', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1103-1126.
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© Springer Nature Singapore Pte Ltd. 2019. The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed.
Saki, M, Abolhasan, M & Lipman, J 1970, 'A Big Sensor Data Offloading Scheme in Rail Networks', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, Malaysia, Malaysia.
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© 2019 IEEE. In this paper, we propose an offloading scheme to transfer massive stored sensor data from rolling stock to railway data centers. We apply a delayed offloading strategy for non-critical stored data assuming that the critical data has been already separated through an appropriate edge processing task and has been sent via a real-time communication such as cellular networks. We propose train stations as potential and feasible spots for data offloading via available wireless local area networks (WLAN) such as existing WiFi network at stations. Thus, stations will not only be the places of passenger exchange but also data exchange. We develop an analytical model customized for the proposed offloading strategy in rail applications. Then we validate the performance of our model through simulation in various scenarios in Omnet. The simulation results shows an accuracy of %98.67 for the proposed analytical model with reference to the simulation results in Omnetpp. Additionally, by using our proposed scheme, we can theoretically offload up to 5.43 GB per each stopping station.
Sameen, MI, Pradhan, B, Shafri, HZM & Hamid, HB 1970, 'Applications of Deep Learning in Severity Prediction of Traffic Accidents', Global Civil Engineering Conference 2017, Springer Singapore, Malaysia, pp. 793-808.
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© Springer Nature Singapore Pte Ltd. 2019. This study investigates the power of deep learning in predicting the severity of injuries when accidents occur due to traffic on Malaysian highways. Three network architectures based on a simple feedforward Neural Networks (NN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) were proposed and optimized through a grid search optimization to fine tune the hyperparameters of the models that can best predict the outputs with less computational costs. The results showed that among the tested algorithms, the RNN model with an average accuracy of 73.76% outperformed the NN model (68.79%) and the CNN (70.30%) model based on a 10-fold cross-validation approach. On the other hand, the sensitivity analysis indicated that the best optimization algorithm is “Nadam” in all the three network architectures. In addition, the best batch size for the NN and RNN was determined to be 4 and 8 for CNN. The dropout with keep probability of 0.2 and 0.5 was found critical for the CNN and RNN models, respectively. This research has shown that deep learning models such as CNN and RNN provide additional information inherent in the raw data such as temporal and spatial correlations that outperform the traditional NN model in terms of both accuracy and stability.
Singh, M, Indraratna, B & Rujikiatkamjorn, C 1970, 'Use of Geosynthetics in Mitigating the Effects of Mud Pumping: A Railway Perspective', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 609-618.
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© Springer Nature Singapore Pte Ltd 2019. In Australia, where the major network of railways traverses along the coastal regions, millions of dollars are spent on track maintenance annually to mitigate track differential settlement. One of the recurring problems faced with ballasted tracks on estuarine soils is mud pumping. Mud pumping is a complex phenomenon involving the migration of fine soft subgrade particles into the coarser ballast/sub-ballast layer. The problem has been widely reported and is of interest among the railway engineers over the last couple of decades. The migration of fines causes excessive settlements and track degradation leading to track instability, thereby incurring excessive maintenance costs. The primary objective of this paper was to assess the existing remediation measures for mud pumping reported. The current mitigation techniques range from the in situ mixing of additives to the use of geosynthetics to separate the layers in a track structure. On the other hand, the use of geosynthetics has proven to act as a separator between the track layers; their effectiveness is highly dependent on the type of subgrade soil. The comprehensive study reveals the probable causes of mud pumping and a better understanding of the phenomenon.
Tawk, M, Indraratna, B, Rujikiatkamjorn, C & Heitor, A 1970, 'Review on Compaction and Shearing-Induced Breakage of Granular Material', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 259-270.
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© Springer Nature Singapore Pte Ltd. 2019. With ongoing expansion of the transport infrastructure to accommodate the need of growing population, the stress on natural construction resources, such as quarried aggregates, has been increasing. Hence, the use of alternative non-traditional waste material is becoming more popular. Coal wash, a by-product of coal mining, has been recently suggested as a substitute to traditional quarried materials. However, recent research showed that these waste aggregates have a weaker structure than conventional materials, which translates into significant potential for breakage upon compaction and loading. Therefore, it is important to quantify breakage and evaluate its influence on the final structure of the soil body and the associated geotechnical properties. This paper presents a critical literature review on compaction and shearing-induced breakage of granular material. The review addresses the available breakage indices developed in the literature to quantify breakage and their limitations. The factors affecting the degree of breakage and the influence of the latter on the different geotechnical properties of compacted granular materials is also discussed. The findings of this review could be extrapolated to waste materials and corresponding treatment methods could be developed to reduce their breakage potential, so they can be more confidently accepted as substitutes to traditional materials in transport infrastructure.
Tello, AMD & Abolhasan, M 1970, 'SDN Controllers Scalability and Performance Study', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Malaysia.
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Software Defined Networks (SDN) is a networking approach that decouples the intelligent control plane from networking devices and establishes a separate entity called ”controller” that rule the behaviour of the data plane on physical networking devices. Due to the rapid evolution and growth of SDN controllers in the market, this paper aims to present an extensive study on performance and scalability of different open source SDN controllers available in the existing literature. This work covers previous studies and expands them with updated information and official benchmarking methodologies. The study provides a framework based on the standards recommended by the IETF (Internet Engineering Task Force) and it will serve as a guideline to the SDN community to benchmark different SDN controllers.
Teng, J, Zhong, Y, Zhang, S & Sheng, D 1970, 'An interpretation of soil freezing characteristic curve of unsaturated freezing soils', 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019.
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The Soil Freezing Characteristic Curve (SFCC) plays a crucial role in describing the behavior of frozen soils, which is as important as the Soil Water Characteristic Curve (SWCC) in unsaturated soil mechanics. SFCC describes the relationship between freezing temperature and liquid water content in soil, which is of great significance to reveal the mechanism of frost heave. In the 1960s, some researchers have studied the similarity between SFCC and SWCC, and found some explanations in theory. But a reasonable theoretical description model for SFCC is still not available. It is noted that Dash et al. (2006) put forward a theory of pre-melting film, which gives the function between the thickness of water film on ice surface and temperature and curvature. Cahn et al. (1992) considered the pre-melting film and the Gibbs-Thomson effect (G-T effect) of the bending interface. They measured and compared the freezing characteristic curves of polystyrene microspheres, and found a good agreement between each other. However, the polystyrene particle size is about 10 μm, which is far different from soil particles. This study extends Cahn's study and applies it to soil mechanics. The pre-melting phenomenon and the G-T effect are considered in this study. A theoretical framework for SFCC is then derived. Under the assumption of uniform particle radius and particle contact, two kinds of arrangement modes that are saturated by liquid water and ice are considered firstly. As for the first case, the most loose simple cube (SC) arrangement. The expression of the volumetric water content can be determined by considering the pre-melting phenomenon and G-T effect: (equation presented) where fSC(α)=(1+α)3-α2(4.5+3α), α=d/r, β=rbend/r, r is the soil particle radius, d is the thickness of pre-melting film, d=3.5×10-3/(Tm-T)×[1-0.0158/(r×(Tm-T))], and Tm=273.15 K, rbend is the bending interface radius of ice surface, rbend=0.0516/(Tm-T). The second case is the closest compact tetr...
Tian, H, Khoa, NLD, Anaissi, A, Wang, Y & Chen, F 1970, 'Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 2813-2821.
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© 2019 Association for Computing Machinery. Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.
Wang, H, Li, Y, Zhang, G, Wang, J & Li, J 1970, 'Behaviours of lithium-based magnetorheological grease under triangular quasi-static test', Proceedings of 30th International Conference on Adaptive Structures and Technologies, ICAST 2019, pp. 131-132.
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This paper investigates the behaviour of lithium-based magnetorheological (MR) grease under the triangular quasi-static test. Three types of MR grease are prepared with weight fractions of carbon iron particles (CIP) as 30%, 50% and 70%, respectively. Quasi-static test of periodical triangular inputs, with various shear strain and strain rates, are employed to evaluate the performance of the MR greases, figure 1 and 2. Further evaluations are conducted by cross-checking the behaviour of the MR grease under various strain rate at a given max strain and the cases under various shear strains at a fixed strain rate.
Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'DeepText: Detecting Text from the Wild with Multi-ASPP-Assembled DeepLab', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 208-213.
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© 2019 IEEE. In this paper, we address the issue of scene text detection in the way of direct regression and successfully adapt an effective semantic segmentation model, DeepLab v3+ [1], for this application. In order to handle texts with arbitrary orientations and sizes and improve the recall of small texts, we propose to extract features of multiple scales by inserting multiple Atrous Spatial Pyramid Pooling (ASPP) layers to the DeepLab after the feature maps with different resolutions. Then, we set multiple auxiliary IoU losses at the decoding stage and make auxiliary connections from the intermediate encoding layers to the decoder to assist network training and enhance the discrimination ability of lower encoding layers. Experiments conducted on the benchmark scene text dataset ICDAR2015 demonstrate the superior performance of our proposed network, named as DeepText, over the state-of-the-art approaches.
Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'ReELFA: A Scene Text Recognizer with Encoded Location and Focused Attention', 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, Australia.
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Wijayaratna, K, Moylan, E, Jian, S, Jones, M & Waller, ST 1970, 'The unified reliability model', Australasian Transport Research Forum, ATRF 2019 - Proceedings, Australasian Transport Research Forum, Australia.
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Reliability of products and systems are fundamental to consumer choice. In the context of transport systems, journey time reliability is seen as a key determinant of traveller choices. Existing research has found that on-time arrival can be valued more than travel time savings. Thus, the quantification of reliability is paramount to monitoring and assessing the performance of transport systems, especially considering road transport systems. This paper presents the development of the Unified Reliability Model (URM), a supplementary tool for the simple and robust measurement of reliability on a road network. The URM unifies aspects of the UK Reliability Model and the New Zealand (NZ) model, both of which are currently applied as best practice. Applications of the URM using data from the Sydney road network present robust measurements of reliability that are comparable or exceed the accuracy of the existing approaches.
Wilson, KJ, Alabd, R, Abolhasan, M, Franklin, DR & Safavi-Naeini, M 1970, 'Localisation of the Lines of Response in a Continuous Cylindrical Shell PET Scanner.', EMBC, IEEE Engineering in Medicine and Biology Conference, IEEE, Berlin, Germany, pp. 4844-4850.
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This work presents a technique for localising the endpoints of the lines of response in a PET scanner based on a continuous cylindrical shell scintillator. The technique is demonstrated by applying it to a simulation of a sensitivity-optimised continuous cylindrical shell PET system using two novel scintillator materials - a transparent ceramic garnet, GLuGAG:Ce, and a LuF$_3$:Ce-polystyrene nanocomposite. Error distributions for the endpoints of the lines of response in the axial, tangential and radial dimension as well as overall endpoint spatial error are calculated for three source positions; the resultant distribution of error in the placement of the lines of response is also estimated.
Wu, D, Chen, J, Sharma, N, Pan, S, Long, G & Blumenstein, M 1970, 'Adversarial Action Data Augmentation for Similar Gesture Action Recognition', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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Human gestures are unique for recognizing and describing human actions, and video-based human action recognition techniques are effective solutions to varies real-world applications, such as surveillance, video indexing, and human-computer interaction. Most existing video human action recognition approaches either using handcraft features from the frames or deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN); however, they have mostly overlooked the similar gestures between different actions when processing the frames into the models. The classifiers suffer from similar features extracted from similar gestures, which are unable to classify the actions in the video streams. In this paper, we propose a novel framework with generative adversarial networks (GAN) to generate the data augmentation for similar gesture action recognition. The contribution of our work is tri-fold: 1) we proposed a novel action data augmentation framework (ADAF) to enlarge the differences between the actions with very similar gestures; 2) the framework can boost the classification performance either on similar gesture action pairs or the whole dataset; 3) experiments conducted on both KTH and UCF101 datasets show that our data augmentation framework boost the performance on both similar gestures actions as well as the whole dataset compared with baseline methods such as 2DCNN and 3DCNN.
Wu, D, Hu, R, Zheng, Y, Jiang, J, Sharma, N & Blumenstein, M 1970, 'Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics. Most existing graph embedding approaches either embed the topological information and node features separately or learn one regularized embedding with both sources of information, however, they mostly overlook the interdependency between structural characteristics and node features when processing the graph data into the models. Moreover, existing methods only reconstruct the structural characteristics, which are unable to fully leverage the interaction between the topology and the features associated with its nodes during the encoding-decoding procedure. To address the problem, we propose a framework using autoencoder for graph embedding (GED) and its variational version (VEGD). The contribution of our work is two-fold: 1) the proposed frameworks exploit a feature-dependent graph matrix (FGM) to naturally merge the structural characteristics and node features according to their interdependency; and 2) the Graph Convolutional Network (GCN) decoder of the proposed framework reconstructs both structural characteristics and node features, which naturally possesses the interaction between these two sources of information while learning the embedding. We conducted the experiments on three real-world graph datasets such as Cora, Citeseer and PubMed to evaluate our framework and algorithms, and the results outperform baseline methods on both link prediction and graph clustering tasks.
Xu, B, He, N & Li, D 1970, 'Study on the treatments and countermeasures for liquefiable foundation', MATEC Web of Conferences, EDP Sciences, pp. 01012-01012.
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This paper summarizes the current treatments and countermeasures for liquefiable foundations, and divides the existing anti-liquefaction countermeasures into two categories. One of the ideas is proceeding from the properties of liquefiable foundation soils, by the means of improvement for the soil’s qualities to enhance the capacity of soil’s anti-liquefaction in the early stage. The other idea is considering from the stress conditions of liquefiable foundation soils, and to reduce the liquefaction-induced disasters by changing the stress conditions of the soil. The advantages and disadvantages of various anti-liquefaction measures were analysed by verifying the effectiveness of field applications of anti-liquefaction measures against ground liquefaction hazards, and the applicable conditions of various anti-liquefaction measures were classified. This paper provides experience for resisting soil liquefaction disasters.
Xu, R & Fatahi, B 1970, 'Assessment of Soil Plasticity Effects on Seismic Response of Mid-Rise Buildings Resting on End-Bearing Pile Foundations', Springer International Publishing, pp. 146-159.
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Yan, H, Teng, J, Zhang, S & Sheng, D 1970, 'A mathematical model for tortuosity of soil with considering particles arrangement', 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019.
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Tortuosity is an important variable to understand air or water permeability of soil. The existing studies have revealed that tortuosity is monotonously related to porosity. It has been recognized that tortuosity is only related to porosity, as shown in table 1. But the effect of particle redistribution on tortuosity that caused by penetration destroy or soil deformation has been less understood in literature. The previous formula of tortuosity was similar at high porosity, but different at low porosity. This study will conduct a theoretical study on tortuosity with considering the effect of particle arrangement on tortuosity By assuming that fluid passes through the squared particles in forms of laminar flow, a new mathematical model is developed in this study to compute the tortuosity of soil. The flow paths are shown in Fig.1, where the particle is assumed to be a square. There are some parameters should be defined, A is the side length of the square, B is the distance between the two particle center lines, C is the horizontal projection distance between the two particle center lines, and is the angle between the two particle center lines and the horizontal direction. According to the model, the relationship between A, B, C and θ can be determined as: (equation presented) We can then get the maximum flow path and the minimum flow path: (equation presented) Taking the average value of flow paths and considering the influence of overlap, we can obtain expression of tortuosity: (equation presented) This expression indicates that the tortuosity will change with the change of particle arrangement, but the range of this change has an interval, which corresponds to triangle arrangement (TA) and square arrangement (SA) (Fig. 2). By comparing this formula with the tortuosity expression proposed in literature, we can find that most data are within the range determined by this formula, indicating that the formula proposed in this paper has universality and accur...
Yeganeh, N, Fatahi, B & Mirlatifi, S 1970, 'Effects of hyperbolic hardening parameters on seismic response of high rise buildings considering soil-structure interaction', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering, Associazione Geotecnica Italiana, Roma, Italy, pp. 5754-5761.
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Seismic soil-structure interaction is referred to the process wherein the soil dynamic response is influenced by the structure motion whilst the latter is also affected by the soil motion. To assess the seismic response of the soil-structure systems, selecting the appropriate soil model parameters is of great importance and the predictions can be significantly impacted if the simply assumptive parameters, presented in the literature, are employed. In this study, the strain hardening soil constitutive model, named “hyperbolic hardening with hysteretic damping”, was employed in the 3D coupled soil-structure interaction numerical simulations using FLAC3D. Utilizing the numerical simulations, the impact of the choice of the soil model parameters, in the range, recommended in the literature, on the seismic response of a moment-resisting building was assessed. It was concluded that the relation between the hyperbolaand Mohr-Coulomb failurecriterion hasa major contribution to the prediction of the seismic response of a building considering the soil-structure interaction.
Yu, Y, Li, Y, Li, J, Nguyen, TN, Li, S & Erkmen, E 1970, 'Vibration control of MRE isolator-embedded smart building using genetic algorithm', Proceedings of 30th International Conference on Adaptive Structures and Technologies, ICAST 2019, International Conference on Adaptive Structures and Technologies, Montreal, Canada, pp. 9-10.
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This study developed the adaptive genetic algorithm (GA) for vibration control of building structures subjected to ambient hazard excitations. An innovative smart building system was designed based on magnetorheological elastomer (MRE) isolators under each storey of the structure instead of being only installed beneath the entire structure. Such innovative system allows high authority semi-active control of storey responses by instantly changing the stiffness of the isolator, the control process of which can be considered as solving a global multi-objective optimization problem. Finally, a numerical investigation was conducted using a 5-storey international benchmark model under four benchmark earthquakes.
Yu, Y, Nguyen, TN, Li, J & Sirivivatnanon, V 1970, 'Soft computing techniques for evaluation of elastic modulus of ASR affected concrete', Concrete 2019, Concrete 2019, Sydney.
Yu, Y, Nguyen, TN, Li, J & Sirivivatnanon, V 1970, 'Soft computing techniques for evaluation of elastic modulus of ASR affected concrete', Concrete 2019: Concrete in Practice – Progress Through Knowledge, Concrete 2019: Concrete in Practice – Progress Through Knowledge, Sydney.
Zhang, M, Gao, Y, Sun, C & Blumenstein, M 1970, 'Kernel Mean P Power Error Loss for Robust Two-Dimensional Singular Value Decomposition', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 3432-3436.
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© 2019 IEEE. Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers. To overcome this problem, in this paper we propose a new robust 2DSVD method based on the kernel mean p power error loss (KMPE-2DSVD). Different from the MSE and the correntropy based ones which are second order statistics based measurements, the KMPE-2DSVD is based on the non-second order statistics in the kernel space, and thus is more flexible in controlling the representation error. Experimental results show that the proposed method significantly improves the accuracy of facial image clustering.
Zhang, M, Gao, Y, Sun, C & Blumenstein, M 1970, 'Robust Sparse Learning Based on Kernel Non-Second Order Minimization', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 2045-2049.
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© 2019 IEEE. Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods.
Zhang, X, Fatahi, B & Khabbaz, H 1970, 'Investigating Effects of Individual Fracture Length on Behaviour of Weak Rock Using Discrete Element Method', Proceedings of the 5th GeoChina International Conference 2018 – Civil Infrastructures Confronting Severe Weathers and Climate Changes: From Failure to Sustainability, GeoChina International Conference, Springer International Publishing, Wuhan, pp. 46-56.
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In this paper weak rock specimens with different individual fracture lengths are numerically simulated using the discrete element method (DEM). Effects of micro or macro-mechanical responses of intact and fractured specimens subjected to triaxial test have been studied. Various individual fracture lengths with a given fracture density within the weak rock specimens were reproduced using the particle flow code in three-dimension software (PFC3D). Different lengths of fractures were simulated by altering the size of each fracture to give insight over the influence of continual fractures and non-persistent fractures within bonded assemblies. As expected, for a given fracture density the individual fracture length affected the strength and deformability of rock mass. For an individual fracture length to specimen width ratio (the normalized fracture length) less than a limiting value, the effects of the individual fracture length on the stress-strain behaviour of rock specimens were more evident. Indeed, the strength decreased with decreasing the normalized fracture length. However, with a ratio above the limiting value, the effects of the individual fracture length were minimal. It can be concluded that for a given fracture density, present of shorter mini-fractures could be potentially more detrimental to stiffness and strength of the rock mass in comparison to longer major fractures.
Zhang, X, Zhang, X, Verma, S, Liu, Y, Blumenstein, M & Li, J 1970, 'Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data', PRICAI 2019: Trends in Artificial Intelligence, The 16th Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Fiji, pp. 307-321.
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Zhang, Y, Li, R, Guo, T, Li, Z, Wang, Y & Chen, F 1970, 'A conditional Bayesian delay propagation model for large-scale railway traffic networks', Australasian Transport Research Forum, ATRF 2019 - Proceedings, Canberra, Australia, pp. 1-12.
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Reliability is one of the critical success factors for both passenger and freight rail service delivery. One major factor that significantly impacts reliability performance is delays spanning over spatial and temporal dimensions. One way to increase reliability is to avoid systematic delay propagation through better timetable design to reduce the interdependencies between trains caused by route conflicts and train connections. In this paper, we aim to predict the propagation of delays on a railway network by developing a conditional Bayesian delay propagation model. In the model, the propagation satisfies the Markov property that determination of delay propagation for the future of the process is based solely on its present state, and that the history does not have an influence on the future. For the cases of delay caused by cross line conflicts and train connection, throughput estimation is considered in the model. The proposed model benefits from scalable computing time and complexity advantages over the Markov property. Implementation of actual operational data shows the feasibility and accuracy of the proposed model when compared to traditional probability models. The proposed model can be used for timetable evaluation and operations management decision support.
Zhao, Y, Liang, B, Wang, Y, Dang, S, Taib, R, Chen, F, Hua, T, Vitanage, D & Doolan, C 1970, 'Optimising Pump Scheduling for Water Distribution Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 433-444.
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© 2019, Springer Nature Switzerland AG. Energy costs can be a major component of operational costs for water utilities. Operational efficiencies including optimising energy costs while maintaining continuity of supply is one area to reduce overall operational costs. To address the challenge, we have proposed an effective optimisation model to minimise the energy cost for water distribution networks. A simulation of the model over a water distribution network in Sydney demonstrated that 15% saving in energy cost could be achieved using this approach, as compared with the existing rule-based method.
Zhou, F, Zhang, Y, Li, Z, Fan, X, Wang, Y, Sowmya, A & Chen, F 1970, 'Hawkes Process with Stochastic Triggering Kernel', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 319-330.
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© Springer Nature Switzerland AG 2019. The impact from past to future is a vital feature in modelling time series data, which has been described by many point processes, e.g. the Hawkes process. In classical Hawkes process, the triggering kernel is assumed to be a deterministic function. However, the triggering kernel can vary with time due to the system uncertainty in real applications. To model this kind of variance, we propose a Hawkes process variant with stochastic triggering kernel, which incorporates the variation of triggering kernel over time. In this model, the triggering kernel is considered to be an independent multivariate Gaussian distribution. We derive and implement a tractable inference algorithm based on variational auto-encoder. Results from synthetic and real data experiments show that the underlying mean triggering kernel and variance band can be recovered, and using the stochastic triggering kernel is more accurate than the vanilla Hawkes process in capacity planning.
Zhou, I, Makhdoom, I, Abolhasan, M, Lipman, J & Shariati, N 1970, 'A Blockchain-based File-sharing System for Academic Paper Review', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Australia.
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Zhou, J, Li, Z, Hu, H, Yu, K, Chen, F, Li, Z & Wang, Y 1970, 'Effects of Influence on User Trust in Predictive Decision Making', Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI '19: CHI Conference on Human Factors in Computing Systems, ACM, Glasgow, SCOTLAND.
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© 2019 Copyright held by the owner/author(s). This paper introduces fact-checking into Machine Learning (ML) explanation by referring training data points as facts to users to boost user trust. We aim to investigate what influence of training data points, and how they affect user trust in order to enhance ML explanation and boost user trust. We tackle this question by allowing users check the training data points that have the higher influence and the lower influence on the prediction. A user study found that the presentation of influences significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts.
