Abdollahi, A, Liu, Y, Pradhan, B, Huete, A, Dikshit, A & Nguyen Tran, N 2022, 'Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 3, pp. 673-685.
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In the present work, a deep learning-based network called LeNet is applied for accurate grassland map production from Sentinel-2 data for the Greater Sydney region, Australia. First, we apply the technique to the base date Sentinel-2 data (non-seasonal) to make the vegetation maps. Then, we combine short time-series (seasonal) data and enhanced vegetation index (EVI) information to the base date imagery to improve the classification results and generate high-resolution grassland maps. The proposed model obtained an overall accuracy (OA) of 88.36% for the mono-temporal data, and 92.74% for the multi-temporal data. The experimental products proved that, by combining the short time-series images and EVI information to the base date, the classification maps' accuracy is increased by 4.38%. Moreover, the Sentinel-2 produced grassland maps are compared with the pre-existing maps such as Australian Land Use and Management (ALUM) 50 m resolution and Dynamic Land Cover Dataset (DLCD) with 250 m resolution as well as some traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest (RF). The results show the effect of the LeNet network's performance and efficiency for grassland map production from short time-series data. As a result, decision-makers and urban planners can benefit from this work in terms of grassland change identification, monitoring, and planning assessment.
Abdollahi, A, Pradhan, B & Alamri, A 2022, 'SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 99, pp. 1-15.
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Existing automated road extraction approaches concentrate on regional accuracy rather than road shape and connectivity quality. Most of these techniques produce discontinuous outputs caused by obstacles, such as shadows, buildings, and vehicles. This study proposes a shape and connectivity-preserving road identification deep learning-based architecture called SC-RoadDeepNet to overcome the discontinuous results and the quality of road shape and connectivity. The proposed model comprises a state-of-the-art deep learning-based network, namely, the recurrent residual convolutional neural network, boundary learning (BL), and a new measure based on the intersection of segmentation masks and their (morphological) skeleton called connectivity-preserving centerline Dice (CPclDice). The recurrent residual convolutional layers accumulate low-level features for segmentation tasks, thus allowing for better feature representation. Such representation enables us to construct a UNet network with the same number of network parameters but improved segmentation effectiveness. BL also aids the model in improving the road’s boundaries by penalizing boundary misclassification and fine-tuning the road form. Furthermore, the CPclDice method aids the model in maintaining road connectivity and obtaining accurate segmentations. We demonstrate that CPclDice ensures connection preservation for binary segmentation, thereby allowing for efficient road network extraction at the end. The proposed model improves F1 score accuracy to 5.49%, 4.03%, 3.42%, and 2.27% compared with other comparative models, such as LinkNet, ResUNet, UNet, and VNet, respectively. Furthermore, qualitative and quantitative assessments demonstrate that the proposed SC-RoadDeepNet can improve road extraction by tackling shadow and occlusion-related interruptions. These assessments can also produce high-resolution results, particularly in the area of road network completeness.
Abdollahi, A, Pradhan, B & Alamri, AM 2022, 'An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images', Geocarto International, vol. 37, no. 12, pp. 3355-3370.
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Building objects is one of the principal features that are essential for updating the geospatial database. Extracting building features from high-resolution imagery automatically and accurately is challenging because of the existence of some obstacles in these images, such as shadows, trees, and cars. Although deep learning approaches have shown significant improvements in the results of image segmentation in recent years, most deep neural networks still cannot achieve highly accurate results with correct segmentation map when processing high-resolution remote sensing images. Therefore, we implemented a new deep neural network named Seg–Unet method, which is a composition of Segnet and Unet techniques, to exploit building objects from high-resolution aerial imagery. Results obtained 92.73% accuracy carried on the Massachusetts building dataset. The proposed technique improved the performance to 0.44%, 1.17%, and 0.14% compared with fully convolutional neural network (FCN), Segnet, and Unet methods, respectively. Results also confirmed the superiority of the proposed method in building extraction.
Abdollahi, M, Ashtari, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8796-8809.
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Abharian, S, Sarfarazi, V, Marji, MF & Rasekh, H 2022, 'Experimental and numerical evaluation of the effects of interaction between multiple small holes and a single notch on the mechanical behavior of artificial gypsum specimens', Theoretical and Applied Fracture Mechanics, vol. 121, pp. 103462-103462.
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The mechanical behavior of cubic gypsum specimens containing five small circular holes in a linear configuration and a single notch under uniaxial compression test were studied to evaluate interactions between these flaws during crack development under loading. Multiple angles between the line of holes and the horizontal axis were evaluated (15°, 45°, and 75°), as were different notch apertures (2, 4, 6 and 8 mm). Acoustic emission (AE) data were used to evaluate the fracture development process in each case. Following the experiments, numerical simulations of the tests were conducted using the particle flow code (PFC2D). The compressive strengths of the specimens were found to be associated with the failure mechanism and fracturing geometry, which were in turn controlled by the geometric attributes of the flaws considered. The compressive strength of specimens were affected by the number of tensile cracks. The induced tensile cracked number were increased by decreasing the joint length. Only few AE events were detected in the initial phase of loading, but then AE hits grew rapidly prior to reaching the peak stress. The AE hits increased by increasing the filling thickness. Failure pattern and compressive strength of specimens were nearly similar in both numerical and experimental approaches.
Abharian, S, Sarfarazi, V, Rasekh, H & Behzadinasab, M 2022, 'Effects of concrete/gypsum bedding layers and their inclination angles on the tensile failure mechanism: Experimental and numerical studies', Case Studies in Construction Materials, vol. 17, pp. e01272-e01272.
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This paper investigates the influence of concrete/gypsum bedding layers and their orientation angles on the tensile failure mechanism in the three-point bending test based on experiments and numerical simulations. Rectangular samples containing different combinations of concrete and gypsum layers were prepared, i.e. one layer of gypsum and one layer of concrete, one layer of gypsum and two layers of concrete, and two layers of gypsum and two layers of concrete. In each configuration, bedding layer angles varied between 0° and 90° with increment of 30°. A total of 36 specimens including 12 configurations were prepared and tested. In addition, numerical simulations were conducted on the concrete/gypsum bedding layers at different angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90°. Results show that the bedding layer orientation and bedding layer thickness affect the observed tensile failure process including the failure pattern and tensile strength. A pure tensile failure occurred when the bedding layer angle was 0°, while a sliding failure evolved by increasing the joint angle. When the bedding layer angle was 90°, the failure in boundary of layer was observed. Specimens with one layer of concrete and one layer of gypsum at 0° inclination angle had the highest tensile strength. However, increasing the number of layers and inclination angles decreased the tensile strength of specimens as the number of weak layers in the direction of loading increased.
Abraham, MT, Satyam, N & Pradhan, B 2022, 'Effect of data splitting and selection of machine learning algorithms for landslide susceptibility mapping'.
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<p>Landslide susceptibility maps (LSMs) are inevitable parts of regional scale landslide forecasting models. The susceptibility maps can provide the spatial probability of occurrence of landslides and have crucial role in the development and planning activities of any region. With the wide availability of satellite-based data and advanced computational facilities, data driven LSMs are being developed for different regions across the world. Since a decade, machine learning (ML) algorithms have gained wide acceptance for developing LSMs and the performance of such maps depends highly on the quality of input data and the choice of ML algorithm. This study employs a k fold cross validation technique for evaluating the performance of five different ML models, viz., Na&#239;ve Bayes (NB), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbors (KNN) and Support Vector Machines (SVM), to develop LSMs, by varying the train to test ratio. The ratio is varied by changing the number folds used for k fold cross validation from 2 to 10, and its effect on each algorithm is assessed using Receiver Operating Characteristic (ROC) curves and accuracy values. The method is tested for Wayanad district, Kerala, India, which is highly affected by landslides during monsoon. The results show that RF algorithm performs better among all the five algorithms considered, and the maximum accuracy values were obtained with the value of k as 8, for all cases. The variation between the minimum and maximum accuracy values were found to be 0.6 %, 0.74 %, 1.71 %, 1.92 % and 1.83 % for NB, LR, KNN, RF and SVM respectively.</p>
Abraham, MT, Satyam, N, Pradhan, B & Segoni, S 2022, 'Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach', All Earth, vol. 34, no. 1, pp. 243-258.
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Abraham, MT, Satyam, N, Pradhan, B & Tian, H 2022, 'Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 6, pp. 1747-1760.
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Abraham, MT, Satyam, N, Pradhan, B, Segoni, S & Alamri, A 2022, 'Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring', Geosciences Journal, vol. 26, no. 2, pp. 289-301.
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Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, which uses the statistical distribution of rainfall for forecasting the occurrence of landslides. The threshold curves are functions of standard deviation of the cumulated rainfall data, taking into account both long-term and short term-rainfall. To overcome the limitations of statistical rainfall threshold, the real-time monitoring data from MicroElectroMechanical Systems (MEMS) tilt sensors have been integrated with SIGMA model using a decisional algorithm for a test site (Kalimpong) in Darjeeling Himalayas, in the northeastern part of India. Three different models, the SIGMA model, tilt meter readings and the combination of both are compared quantitatively using the precipitation and landslide data of Kalimpong town between July 2017 and September 2020. The results indicate that the integration of tilt meter readings has lowered the number of false alarms issued by SIGMA model from 70 to 38 in the studied period, with an increase in the likelihood ratio from 18.10 to 20.23. The Receiver Operating Characteristic (ROC) curves indicate that the combined approach has the best performance among the models considered in this study, with an area under the curve 0.976. The proposed method was found to have better performance than the other rainfall thresholds derived for Kalimpong region so far, and the prototypal model can be further fine-tuned to develop an operational Landslide Early Warning System (LEWS) for the region.
Adak, A, Pradhan, B & Shukla, N 2022, 'Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review', Foods, vol. 11, no. 10, pp. 1500-1500.
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During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.
Adak, A, Pradhan, B, Shukla, N & Alamri, A 2022, 'Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique', Foods, vol. 11, no. 14, pp. 2019-2019.
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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN...
Ahmed, N, Hoque, MA-A, Howlader, N & Pradhan, B 2022, 'Flood risk assessment: role of mitigation capacity in spatial flood risk mapping', Geocarto International, vol. 37, no. 25, pp. 8394-8416.
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Alibeikloo, M, Khabbaz, H & Fatahi, B 2022, 'Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters', Reliability Engineering & System Safety, vol. 219, pp. 108254-108254.
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Low embankment strategy is one of the effective methods to control time-dependent settlement of soft soils in infrastructure construction projects. Spatial variability of soil characteristics is a crucial factor, affecting the reliability of predictions of the long-term settlement in soft soils. In this paper, the time-dependent behaviour of soft soils is analysed incorporating spatial variability of elastic visco-plastic model parameters. Standard Gaussian random fields for correlated elastic-plastic model parameter (λ/V) and the initial creep coefficient (ψ0/V) are generated adopting Karhunen-Loeve expansion method based on the spectral decomposition of correlation function into eigenvalues and eigenfunctions. Then the generated random fields are incorporated in the proposed non-linear elastic visco-plastic (EVP) creep model. The impacts of spatially variable elastic visco-plastic model parameters (i.e. ψ0/V and λ/V) on long-term settlement predictions are evaluated through random field analysis (RF) with different spatial correlation lengths, and results are then compared to a single random variable (SRV) analysis. The probability of failure (PF) is calculated adopting RF and SRV analysis to determine the critical spatial correlation length, resulted in a maximum probability of failure. This study can be employed by design engineers to determine the critical spatial correlation length for safe design in the absence of adequate data to determine the exact spatial correlation length. The results also confirm that SRV analysis is not always the most conservative analysis in predicting time-dependent settlement of soft soils; and it is essential to perform RF analysis considering the spatial correlation length to reduce the risk and increase the reliability of the design to be applied in construction.
Alotaibi, AA, Maerz, NH, Boyko, KJ, Youssef, AM & Pradhan, B 2022, 'Temporal LiDAR scanning in quantifying cumulative rockfall volume and hazard assessment: A case study at southwestern Saudi Arabia', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 2, pp. 435-443.
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Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall changes, calculation of rockfall volume, and evaluate rockfall hazards along the Habs Road, Jazan Region, Saudi Arabia. A series of temporal LiDAR scans were acquired at three selected sites. Our results show that these three sites have different degrees of hazard due to their geological differences. The mean volume loss of sites A1, A2, and A3 is 327.1, 424.4, and 3.7 L, respectively. Statistical analysis confirms the significance of the influence of site type on rockfall volume, with a probability value of < 0.0105. The rockfall volume and change detection values are then correlated with precipitation, which is a triggering factor. The study also reveals that the use of terrestrial LiDAR could reduce time and effort, increase accessibility, and produce effective solutions. LiDAR could be an indispensable tool for disaster risk assessment, response and recovery process.
Alsenwi, M, Abolhasan, M & Lipman, J 2022, 'Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21582-21592.
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Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.
Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6785-6794.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.
Andaryani, S, Nourani, V, Pradhan, B, Jalali Ansarudi, T, Ershadfath, F & Torabi Haghighi, A 2022, 'Spatiotemporal evaluation of future groundwater recharge in arid and semi-arid regions under climate change scenarios', Hydrological Sciences Journal, vol. 67, no. 6, pp. 979-995.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Deformation and degradation behaviour of Rubber Intermixed Ballast System under cyclic loading', Engineering Geology, vol. 307, pp. 106786-106786.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Geotechnical characteristics of a Rubber Intermixed Ballast System', Acta Geotechnica, vol. 17, no. 5, pp. 1847-1858.
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This study aims to promote the concept of using rubber granules from waste tyres as elastic aggregates blended with traditional ballast particles for better performance of rail tracks, i.e. a Rubber Intermixed Ballast System (RIBS). This paper describes the mechanical and compressibility characteristics of RIBS under monotonic loads and a criterion designed to determine the optimum rubber content in the proposed RIBS. The most interesting findings of this study embrace how the rubber granules in the blended rockfill assembly significantly reduce the dilation and modulus degradation, and the breakage of ballast aggregates. RIBS with more than 10% of rubber demonstrates a seemingly consistent reduction in dilation under changing confining pressures. Increased deviator stress and larger effective confining pressure compress the rubber particles within the RIBS which may cause relatively large initial settlements in the ballast layer, if the rubber content becomes excessive. It is also evident from the results that rubber particles ranging from 9.5 to 19 mm with similar angularity to ballast aggregates is advantageous, because, they reduce the breakage of load-bearing larger aggregates, thus effectively controlling ballast fouling within the granular matrix.
Arivalagan, J, Indraratna, B, Rujikiatkamjorn, C & Warwick, A 2022, 'Effectiveness of a Geocomposite-PVD system in preventing subgrade instability and fluidisation under cyclic loading', Geotextiles and Geomembranes, vol. 50, no. 4, pp. 607-617.
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Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Open RAN is an emerging vision and an advancement of the Radio Access Network (RAN). Its purpose is to implement a vendor and network-generation agnostic RAN, provide networking solutions across all service requests, and implement artificial intelligence solutions in different stages of an end-to-end communication path. The 5th Generation (5G) and beyond the 5th Generation (B5G) of networking introduce and support new use cases, such as tactile internet and autonomous driving. The complexity and innovative nature of these use cases require continuous innovation at a high pace in the RAN. The traditional approach of building end-to-end RAN solutions by only one vendor hampers the speed of innovation - furthermore, the lack of a standard approach to implementing artificial intelligence complicates the compatibility of products with the RAN ecosystem. O-RAN Alliance, a community of industry and academic experts in RAN, works on writing Open RAN specifications on top of the 3rd Generation Partnership Project (3GPP) standards. Founded on these specifications, the aim of this paper is to introduce open research topics in Open RAN that overlap the interests of both AI and telecommunication researchers. The paper provides an overview of the architecture and components of Open RAN, then explores AI use cases in Open RAN. Also, this survey includes some plausible AI deployment scenarios that the specifications have not covered. Open RAN in future cities creates opportunities for various use cases across different sectors, including engineering, operations, and research that this paper addresses.
Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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Wireless cellular networks have rapidly evolved to be software-defined in nature. This has created opportunities to improve their performance. One such opportunity is through enabling programming and integration of multi-hop device-to-device (MD2D) at the edge. However, efficient integration of MD2D at the edge requires a highly adaptable and scalable routing protocol, where its development is underpinned through understanding of which type of current routing characteristics and architectures are suitable over dynamic networking conditions. To develop such understanding, we conducted a detailed analysis and performance study on three routing protocols, namely virtual ad-hoc routing protocol-source based (VARP-S) Abolhasan et al. (2018), SDN-based multi-hop device-to-device routing protocol (SMDRP) Abdollahi et al. (2019) and hybrid SDN architecture for wireless distributed networks (HSAW) Abolhasan et al. (2015). Our investigations illustrate that VARP-S and SMDRP perform best in terms of energy consumption and cellular routing overhead. However, HSAW shows better performance in terms of end-to-end (E2E) delay and packet loss over lower network and traffic densities.
ashtari, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Joint Mobile Node Participation and Multihop Routing for Emerging Open Radio-Based Intelligent Transportation System', IEEE Access, vol. 10, pp. 85228-85242.
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This paper proposes joint mobile node participation and routing protocol for multi-hop device-to-device (MD2D) networking in intelligent transportation systems, called fuzzy-based participation and routing protocol for MD2D (FPRM). Our proposed protocol is designed to operate over future open-radio access networks (O-RANs). We introduce a sub-layer at the network layer that can determine nodes with the highest participation probability in routing using a fuzzy logic system, thus building a framework to create more stable routes. To ensure the participating nodes are capable of handling the data traffic, two constraints are proposed, mobility and coverage constraints. The former enables the creation of sustainable communication links, and the latter enforces the communication service to the entire MD2D network. Simulation results show that our approach can increase the network lifetime, decrease the end-to-end (E2E) delay, and increase the packet delivery ratio (PDR) compared to the existing proactive routing protocol. Our protocol outperforms the benchmarked MD2D protocols and other investigated ad hoc protocols.
Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.
Atif, Y, Soulaimani, A, Ait lamqadem, A, Pour, AB, Pradhan, B, Nouamane, EA, Abdelali, K, Muslim, AM & Hossain, MS 2022, 'Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine', International Journal of Image and Data Fusion, vol. 13, no. 4, pp. 337-361.
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Baharvand, S & Pradhan, B 2022, 'Erosion and flood susceptibility evaluation in a catchment of Kopet-Dagh mountains using EPM and RFM in GIS', Environmental Earth Sciences, vol. 81, no. 20, p. 490.
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Erosion and flood events can damage soils, water, quality, and sediment transportation, causing many cumulative hazards. In developing countries, such as Iran, the empirical models, which are low-cost procedures to mitigate environmental hazards, are necessary to plan the watersheds. Hence, the main aim of this study is to evaluate erosion and flood susceptibility using empirical models of erosion potential method (EPM) and rational flood model (RFM) to prioritize the GIS-based prone zones in a catchment of the Kopet-Dagh Mountains. The results revealed that the heavy classes of erosion and flood susceptibility include 40.4–58.2% of the total study area, dominantly in the upstream catchments. The correlation test revealed a strong, significant, and direct association (R equal to 0.705) between W and Qp at the 99% confidence level. Consequently, the results of our research indicated the prioritization of the three sub-catchments based on their slight sensitivity and susceptibility to occurrences of soil erosion and flood events through future spatial developments. Ultimately, the model validity explained the AUC (area under the curve) values averagely equal to 0.898 and 0.917 for erosion and flood susceptibility evaluations (i.e., EPM and RFM), explaining the very good performance of the models and excellent sensitivities.
Balogun, A-L, Sheng, TY, Sallehuddin, MH, Aina, YA, Dano, UL, Pradhan, B, Yekeen, S & Tella, A 2022, 'Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study', Geocarto International, vol. 37, no. 26, pp. 12989-13015.
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This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities.
Bardhan, A, GuhaRay, A, Gupta, S, Pradhan, B & Gokceoglu, C 2022, 'A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor', Transportation Geotechnics, vol. 32, pp. 100678-100678.
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This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model's superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects.
Basack, S, Goswami, G, Khabbaz, H & Karakouzian, M 2022, 'Flow Characteristics through Granular Soil Influenced by Saline Water Intrusion: A Laboratory Investigation', Civil Engineering Journal, vol. 8, no. 5, pp. 863-878.
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The coastal geoenvironment initiates saline water intrusion into the freshwater aquifers, producing a geohydraulic problem. Such intrusion not only contaminates the fresh groundwater resources, making them unsuitable for human use, but also alters the hydraulic conductivity of the aquifer materials, which affects the coastal groundwater flow, influencing the water resources planning and management. Past investigations reveal that the groundwater flow can be linear or nonlinear depending upon the hydraulic gradient. Thus, the coefficients of nonlinear hydraulic conductivities are affected by saltwater intrusion. The present study focuses on an in-depth laboratory investigation into the influence of saltwater submergence on the nonlinear flow characteristics through granular soil. The fine sand samples have been submerged under saline water of specified concentrations for a specific duration, and the alteration in their nonlinear geohydraulic properties has been studied. It is observed that the flow characteristics through fine sand are significantly affected by the period of submergence and saline concentration. Appropriate analyses of the test results are performed to interpret the experimental data, and relevant conclusions are drawn therefrom. The novelty of this study is an in-depth analysis of nonlinear flow characterization affected by saline water intrusion. Doi: 10.28991/CEJ-2022-08-05-02 Full Text: PDF
Basack, S, Loganathan, MK, Goswami, G & Khabbaz, H 2022, 'Saltwater Intrusion into Coastal Aquifers and Associated Risk Management: Critical Review and Research Directives', Journal of Coastal Research, vol. 38, no. 3, pp. 654-672.
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Coastal regions mainly rely on sources of local fresh groundwater for domestic, irrigational, and industrial usages, which are vulnerable to high-risk of getting intruded by saltwater. Excessive pumping of fresh groundwater initiates advances of saltwater-freshwater interface inward due to hydraulic equilibrium and continuity. This introduces saline water intrusion into coastal aquifers. This is also caused by natural hazards like sea-level rise and storm-surge. The saltwater intrusion in coastal aquifers contaminates the freshwater storage, thereby emerging as a major environmental issue. To incorporate adequate coastal groundwater control and management techniques that are effective and conveniently implementable, understanding the phenomenon of saline water intrusion and the risk assessment is of utmost importance. Several scientific contributions including theoretical (analytical and numerical) solutions, experimental (laboratory and field) results, design recommendations, and risk analysis are available, indicating remarkable advances in the research area. The authors have attempted to summarize the significant contributions over the last few decades in each of these study aspects through extensive literature survey and critical analysis of the existing knowledge. It is observed that risk prevention and control methodologies such as qanat-well structure, shallow and deep wells might not be effective in many coastal areas as the complex intrusion process is yet to be understood clearly. Moreover, the high intensity coastal hazards that often occur due to climate change continue to make aquifers more vulnerable, adversely affecting the coastal groundwater management. The paper presents a critical overview of existing studies on saline water intrusion into coastal aquifers and associated risks and management techniques. Furthermore, adequate research directives with recommendations for future development are also provided.
Basack, S, Nimbalkar, S, Karakouzian, M, Bharadwaj, S, Xie, Z & Krause, N 2022, 'Field Installation Effects of Stone Columns on Load Settlement Characteristics of Reinforced Soft Ground', International Journal of Geomechanics, vol. 22, no. 4.
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Bordbar, M, Neshat, A, Javadi, S, Pradhan, B, Dixon, B & Paryani, S 2022, 'Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches', Natural Hazards, vol. 110, no. 3, pp. 1799-1820.
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The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area. Graphic abstract: [Figure not available: see fulltext.]
Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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The majority of existing DDoS defense mechanisms in SDN impose a significant computational burden on the controller and employ limited flow statistics and packet features. Tackling these issues, this paper presents a multi-layer defense mechanism that detects and mitigates three distinct types of flooding DDoS attacks. In the proposed framework, the detection process consists of flow-based and packet-based attack detection mechanisms employing Extreme Learning Machine-based Single-hidden Layer Feedforward Networks (ELM-SLFNs) and Case-based Information Entropy (C-IE), respectively. Moreover, the affected switches are avoided in the optimal path determined by the Floyd-Warshall algorithm, where the switches are classified based on the Hidden Markov Model (HMM) using the extracted packet features. Our simulation demonstrates the improved performance of our framework compared to similar schemes proposed in the literature in terms of different metrics, including attack detection rate, detection accuracy, false-positive rate, switch failure ratio, packet loss rate, response time, and CPU utilization.
Bui, P, Ngo, T & Huynh, T 2022, 'Effect of ground rice husk ash on engineering properties and hydration products of SRC eco‐cement', Environmental Progress & Sustainable Energy, vol. 41, no. 2.
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AbstractThe effect of ground rice husk ash (GRHA) (R) on engineering properties and hydration products of eco‐cements containing ground granulated blast furnace slag (GGBFS) (S) and circulating fluidized bed combustion ash (CFA) (C) was studied. Four mixture proportions of SRC eco‐cements with GRHA replacement at levels of 0%, 15%, 30%, and 45% by mass of binder were investigated. A reference mixture proportion of paste with 100% ordinary Portland cement (OPC) was prepared for comparison purposes. A series of laboratory tests including setting time, compressive strength, water absorption, porosity, thermal conductivity, scanning electron microscope coupled with energy dispersive spectroscopy, X‐ray diffraction, and Fourier‐transform infrared spectroscopy analysis was carried out. Measured results showed that the GRHA increased setting time and porosity in the SRC eco‐cements having a water‐to‐powder (w/p) of 0.4, leading to the decrease in compressive strength and thermal conductivity while the increase in water absorption. The GRHA increased the cristobalite amount and decreased the portlandite amount in the SRC eco‐cements at the age of 28 days, resulting in the more significant long‐term compressive strength development when compared with the reference paste with 100% OPC. Consequently, the GRHA could be used at a level of 15% by mass of binder to produce the SRC eco‐cement with the compressive strength at 28 days of higher than 30 MPa and the thermal conductivity of 0.713 W/mK, resulting from the formations of AFt, C–S–H, and C–A–S–H gels.
Cai, G, Wang, C, Li, J, Xu, Z, He, X & Zhao, C 2022, 'Study on Tensile Properties of Unsaturated Soil Based on Three Dimensional Discrete Element Method', Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, vol. 30, no. 5, pp. 1228-1244.
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Based on the discrete element method for unsaturated materials proposed by the author, the PFC3D (Particle Flow Code in Three Dimensions) particle flow discrete element analysis program is improved, and a discrete element model suitable for both clay and sand under uniaxial tension is established. The relationship between uniaxial tensile stress and displacement and uniaxial tensile strength are studied. The influence of different microstructure parameters on the tensile failure of soil is explored, and the relationship between saturation and cohesive strength between particles is established by taking uniaxial tensile strength as a bridge. The uniaxial tensile test of clay and sand with different initial void ratio and saturation is studied, and the tensile properties of unsaturated soil and the applicability of discrete element model and program to simulate unsaturated soil are deeply studied. The results show that:among the five microstructure parameters of normal bond strength, shear bond strength, Young's modulus, stiffness ratio and friction coefficient, the influence of normal bond strength on uniaxial tensile simulation is the largest, followed by shear bond strength, Young's modulus and stiffness ratio, and the friction coefficient has the least influence; the uniaxial tensile strength of clay increases at first and then decreases with the increase of saturation. The results show that the increase rate of uniaxial tensile strength on the left side (dry side) is greater than that on the right side (wet side); the uniaxial tensile strength of sand shows a 'increase-decrease-increase' rule with the increase of saturation; the simulation results are in good agreement with the experimental results, which verifies the applicability of the discrete element model and the numerical analysis program in the simulation of uniaxial tensile properties of unsaturated materials.
Chandra Shit, R, Sharma, S, Watters, P, Yelamarthi, K, Pradhan, B, Davison, R, Morgan, G & Puthal, D 2022, 'Privacy‐preserving cooperative localization in vehicular edge computing infrastructure', Concurrency and Computation: Practice and Experience, vol. 34, no. 14.
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SummaryAdvancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.
Chen, C, Ding, L, Liu, B, Du, Z, Liu, Y, Di, X, Shan, X, Lin, C, Zhang, M, Xu, X, Zhong, X, Wang, J, Chang, L, Halkon, B, Chen, X, Cheng, F & Wang, F 2022, 'Exploiting Dynamic Nonlinearity in Upconversion Nanoparticles for Super-Resolution Imaging', Nano Letters, vol. 22, no. 17, pp. 7136-7143.
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Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of nonlinear fluorescence response in the obtained images. The higher orders of fluorescence response can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images to generate an image with higher resolution than superlinear microscopy images. We apply this approach to resolve single nanoparticles in a large area, improving the resolution to 132 nm. This work suggests a new scope for the development of dynamic nonlinear fluorescent probes in super-resolution nanoscopy.
Chen, J, Vinod, JS, Indraratna, B, Ngo, NT, Gao, R & Liu, Y 2022, 'A discrete element study on the deformation and degradation of coal-fouled ballast', Acta Geotechnica, vol. 17, no. 9, pp. 3977-3993.
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AbstractThis paper presents the results of Discrete Element Modelling (DEM) which quantitively examine the effect of coal fouling on the deformation and degradation of ballast upon cyclic loading. The degradation model described herein considers the Weibull distribution effects in tandem with a granular medium hardening law that incorporates the maximum contact criterion to capture surface abrasion and corner breakage of angular ballast. The DEM model had been calibrated initially with laboratory data obtained from large-scale direct shear testing. Subsequently, a series of cubical shear test simulations have been carried out using DEM to understand the behaviour of fouled ballast whereby the numerical particle degradation modelling could simulate the experimental response of the ballast assembly at various fouling levels. The results show that the increased level of fouling exacerbates the sleeper settlement, while decreasing the resilient modulus and the particle breakage. Ballast beneath the sleeper experiences significant breakage compared to the crib ballast, and not surprisingly, the extent of damage decreases with depth. Rigorous microscopic analysis is also presented in relation to inter-particle contacts, particle velocity and anisotropy of the ballast assembly. This micromechanical examination highlights that the decrease in ballast breakage for fouled assemblies is predominantly attributed to the inevitable decrease in inter-particle contact pressures as effected by the coating of ballast aggregates by the coal fines.
Chen, Q, Guo, D, Ke, W, Xu, C & Nimbalkar, S 2022, 'Novel Open Trench Techniques in Mitigating Ground-Borne Vibrations due to Traffic under a Wide Range of Ground Conditions', International Journal of Geomechanics, vol. 22, no. 6.
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Darwish, A, Halkon, B & Oberst, S 2022, 'Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks', Sensors, vol. 22, no. 23, pp. 9360-9360.
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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.
Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2022, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction', Journal of Sound and Vibration, vol. 520, pp. 116607-116607.
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Deng, S, Ji, J, Wen, G & Xu, H 2022, 'Two-parameter dynamics of an autonomous mechanical governor system with time delay', Nonlinear Dynamics, vol. 107, no. 1, pp. 641-663.
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A deep understanding of the dynamical behavior in the parameter-state space plays a vital role in both the optimal design and motion control of mechanical systems. By combining the GPU parallel computing technique with two determinate indicators, namely the Lyapunov exponents and Poincaré section, this paper presents a detailed study on the two-parameter dynamics of a mechanical governor system with different time delays. By identifying different responses in the two-parameter plane, the effect of time delay on the complexity of the evolutionary process is fully revealed. The path-following calculation scheme and time domain collocation method are used to explore the detailed bifurcation mechanisms. An interesting phenomenon that the number of intersection points of some periodic responses on the specified Poincaré section differs from the actual period characteristics is found in classifying the dynamic behavior. For example, the commonly exhibited period-one orbit may have two or more intersection points on the Poincaré section rather than one point. The variations of the basins of attraction are also discussed in the plane of initial history conditions to demonstrate the multistability phenomena and chaotic transitions.
Dikshit, A, Pradhan, B & Santosh, M 2022, 'Artificial neural networks in drought prediction in the 21st century–A scientometric analysis', Applied Soft Computing, vol. 114, pp. 108080-108080.
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Droughts are the most spatially complex geohazard, which often lasts for years, thereby severely impacting socio-economic sectors. One of the critical aspects of drought studies is developing a reliable and robust forecasting model, which could immensely help drought management planners in adopting adequate measures. Further, the prediction of drought events are extremely challenging due to the involvement of several hydro-meteorological factors, which are further aggravated by the effect of climate change. Among the several techniques such as statistical, physical and data-driven that are used to forecast droughts, artificial neural networks provide one of the most robust approach. As droughts are inherently non-linear and multivariate in nature, the capability of neural networks to capture the dynamic relationship easily and efficiently has seen a rise in its use. Here we evaluate the most used architectures in the last two decades, using scientometric analysis. A general framework used in drought prediction studies is explained and examples from various continents are provided, thus exploring the topic in a global context. The findings show that using sophisticated input representation, the artificial intelligence-based solutions applied to drought prediction of hydro-meteorological variables have promising success, particularly in complex geographical scenarios. The future works need to focus on interpretable models, use of deep learning architectures for long lead time forecasting and use of neural networks to predict different drought characteristics like drought propagation and flash droughts. We also summarize the most widely used neural network approaches in spatial drought prediction, which would serve as a foundation for future research in drought prediction studies.
Dikshit, A, Pradhan, B, Assiri, ME, Almazroui, M & Park, H-J 2022, 'Solving transparency in drought forecasting using attention models', Science of The Total Environment, vol. 837, pp. 155856-155856.
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Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the different drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors. These variables are non-linear in nature, and neural networks have been found to effectively forecast drought. However, classical neural nets often succumb to over-fitting due to various lag components among the variables and therefore, the emergence of new deep learning and explainable models can effectively solve this problem. The present study uses an Attention-based model to forecast meteorological droughts (Standard Precipitation Index) at short-term forecast range (1-3 months) for five sites situated in Eastern Australia. The main aim of the work is to interpret the model outcomes and examine how a deep neural network achieves the forecasting results. The plots show the importance of the variables along with its short-term and long-term dependencies at different lead times. The results indicate the importance of large-scale climatic indices at different sequence dependencies specific to the study site, thus providing an example of the necessity to build a spatio-temporal explainable AI model for drought forecasting. The use of such interpretable models would help the decision-makers and planners to use data-driven models as an effective measure to forecast droughts as they provide transparency and trust while using these models.
Dikshit, A, Pradhan, B, Huete, A & Park, H-J 2022, 'Spatial based drought assessment: Where are we heading? A review on the current status and future', Science of The Total Environment, vol. 844, pp. 157239-157239.
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Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessments have significantly evolved in recent times. In this paper, we review recent methods used to examine and model droughts from a spatial viewpoint. Our analysis was conducted at three spatial scales (point-wise, regional and global) and we evaluated how recent spatial methods have advanced our understanding of drought through case study examples. Further, we also examine and provide a broad overview of relevant case studies related to future drought occurrences under climate change. This study is a comprehensive synthesis of the various quantitative techniques used to assess the spatial characteristics of droughts at different spatial scales, and not an exhaustive review of all drought aspects. However, this serves as a basis for understanding the key milestones and advances accomplished through new spatial concepts relative to the traditional approaches to study drought. This work also aims to address the gaps in knowledge that are in need of further attention and provides recommendations to improve our understanding of droughts.
Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy', Advanced Science, vol. 9, no. 32, pp. e2203354-2203354.
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AbstractLight scattering from nanoparticles is significant in nanoscale imaging, photon confinement. and biosensing. However, engineering the scattering spectrum, traditionally by modifying the geometric feature of particles, requires synthesis and fabrication with nanometre accuracy. Here it is reported that doping lanthanide ions can engineer the scattering properties of low‐refractive‐index nanoparticles. When the excitation wavelength matches the ion resonance frequency of lanthanide ions, the polarizability and the resulted scattering cross‐section of nanoparticles are dramatically enhanced. It is demonstrated that these purposely engineered nanoparticles can be used for interferometric scattering (iSCAT) microscopy. Conceptually, a dual‐modality iSCAT microscopy is further developed to identify different nanoparticle types in living HeLa cells. The work provides insight into engineering the scattering features by doping elements in nanomaterials, further inspiring exploration of the geometry‐independent scattering modulation strategy.
Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy (Adv. Sci. 32/2022)', Advanced Science, vol. 9, no. 32.
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Dong, W, Li, W, Guo, Y, Qu, F, Wang, K & Sheng, D 2022, 'Piezoresistive performance of hydrophobic cement-based sensors under moisture and chloride-rich environments', Cement and Concrete Composites, vol. 126, pp. 104379-104379.
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Silicone hydrophobic powder (SHP) and crystalline waterproofing admixture (CWA) were used to improve the impermeability of carbon black (CB)/cement-based sensors. The mechanical, electrical and piezoresistive properties, waterproofing and chloride resistance of CB/cementitious composites were investigated in this study. The piezoresistivity before or after different durations of immersion in freshwater and 3% sodium chloride solution and the stability in freshwater and marine environment were studied and compared. The results show that compressive strength increased with the additions of CWA and SHP, while the tensile strength slightly decreased with CWA, due to the formation of crystalline. Moreover, cementitious composites with SHP exhibited the best water impermeability, while the counterpart containing CWA presented the optimal chloride resistance. Although cementitious composites with SHP exhibited the highest electrical resistivity, the most stable piezoresistivity occurred after 90 days of immersion in freshwater. On the other hand, cementitious composites incorporating CWA presented the lowest electrical resistivity, but the piezoresistivity continually decreased with the immersion duration. Because of the free ions, piezoresistivity increased as a result of the immersion in sodium chloride solution. The related results will provide an insight into the piezoresistivity of hydrophobic cement-based sensors under moisture and chloride environments for future structural health monitoring.
Dong, W, Li, W, Guo, Y, Wang, K & Sheng, D 2022, 'Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load', Construction and Building Materials, vol. 327, pp. 126978-126978.
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The electrical, mechanical properties, and piezoresistive performances of intrinsic graphene nanoplate (GNP)/cementitious composites were investigated after subjected to impact load in this paper. The stabilized electrical resistivity before/after exposure to impact load and real-time electrical response under dynamic load were simultaneously studied. The cement hydration and microstructures of (GNP)/cementitious composites were characterized by thermal gravity analysis (TGA) and scanning electron microscope. The nearly identical hydration degree of 1.0% GNP filled cement mortar (1GNPCM) and mortar with 2% GNP (2GNPCM) indicates the physical interactions between the GNP and cement matrix. The excellent intrinsic physical properties of GNP played an important role in the enhancements of GNP/cementitious composites. After exposed to impact, the stabilized electrical resistivity, mechanical performance, and piezoresistivity of 1GNPCM were greatly changed, whereas the counterpart of 2GNPCM was well-maintained and nearly unaffected. Therefore, the severe microstructural deteriorations in 1GNPCM could be responsible for the variations, which damaged the conductive passages. The almost unchanged mechanical, electrical and piezoresistive properties enable 2GNPCM as a promising cement-based senor to provide stable piezoresistivity even after exposure to impact load. The related outcomes provide an insight into the development of impact-resistant cement-based sensors and promote the applications of cement-based sensors under extreme loading conditions.
Dong, W, Li, W, Sun, Z, Ibrahim, I & Sheng, D 2022, 'Intrinsic graphene/cement-based sensors with piezoresistivity and superhydrophobicity capacities for smart concrete infrastructure', Automation in Construction, vol. 133, pp. 103983-103983.
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Dong, W, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Multifunctional cementitious composites with integrated self-sensing and self-healing capacities using carbon black and slaked lime', Ceramics International, vol. 48, no. 14, pp. 19851-19863.
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This study aims to develop multifunctionality of cementitious composites with the integrated self-sensing and self-healing capacities by incorporating conductive carbon black (CB) with CB-encapsulated slaked lime (SL). The microsized SL particles were premixed with a half of designed content of nanosized CB particles. When CB agglomerations coat around the SL surfaces, SL does not hydrate until the CB coating is removed. Another half of designed weight of CB is uniformly dispersed using ultrasonication with superplasticizer and added to obtain piezoresistivity. The results show that the stress sensing capacity of CB-SL-cementitious composite performs well with the compressive stress. Autogenous healing performances presented significantly can improve the self-healing capacity with the increase of SL. Furthermore, the healing efficiency is affected by the crack width and dispersion of SL, and the smaller cracks with SL are more easily healed. The size of CB agglomerations decreases with the added SL, and the main product of self-healing is calcium carbonate.
Eager, D, Zhou, S, Hossain, I, Ishac, K & Halkon, B 2022, 'Research on Impact Attenuation Characteristics of Greyhound Racing Track Padding for Injury Prevention', Vibration, vol. 5, no. 3, pp. 497-512.
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To reduce injuries to greyhounds caused by collisions with fixed racing track objects such as the outside fence or the catching pen structures, padding systems are widely adopted. However, there are currently neither recognised standards nor minimum performance thresholds for greyhound industry padding systems. This research is the first of its kind to investigate the impact attenuation characteristics of different padding systems for use within the greyhound racing industry for the enhanced safety and welfare of racing greyhounds. A standard head injury criterion (HIC) meter was used to examine padding impact attenuation performance based on the maximum g-force, HIC level and the HIC duration. Initially, greyhound racing speed was recorded and analysed with the IsoLynx system to understand the potential impact hazard to greyhounds during racing which indicates the necessity for injury prevention with padding. A laboratory test was subsequently conducted to compare the impact attenuation performance of different kinds of padding. Since padding impact attenuation characteristics are also affected by the installation and substrate, onsite testing was conducted to obtain the padding system impact attenuation performance in actual greyhound racing track applications. The test results confirm that the padding currently used within the greyhound industry is adequate for the fence but inadequate when used for rigid structural members such as the catching pen gate supports. Thus, increasing the padding thickness is strongly recommended if it is used at such locations. More importantly, it is also recommended that, after the installation of padding on the track, its impact attenuation characteristics be tested according to the methodology developed herein to verify the suitability for protecting greyhounds from injury.
El‐Hawat, O, Fatahi, B & Taciroglu, E 2022, 'Novel post‐tensioned rocking piles for enhancing the seismic resilience of bridges', Earthquake Engineering & Structural Dynamics, vol. 51, no. 2, pp. 393-417.
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AbstractThe rocking pile foundation system is a relatively new design concept that can be implemented in bridges to improve their seismic performance. This type of foundation prevents plastic damage at the bridge piers and the foundation system, which are difficult to repair and can lead to collapse. However, lack of adequate energy dissipation in this type of foundation can result in large deck displacements and subsequent catastrophic failures of the bridge. The present study proposes a novel foundation system that integrates post‐tensioned piles with the rocking foundation to simultaneously prevent plastic hinging at the piers and reduce the deck displacements during severe earthquakes. The effectiveness of the proposed foundation system is investigated and compared against the rocking pile and conventional fixed‐base foundation systems using identical bridge configurations. Three‐dimensional finite element models of these bridges were developed to capture possible nonlinear behavior of the bridge as well as soil‐structure interaction effects. Six strong earthquakes with both horizontal components were selected and scaled to the appropriate seismic hazard level with a return period of 2475 years. Static pushover and nonlinear time‐history analyses were then performed to compare the dynamic response of the bridges, including deck displacements, pier and pile inertial forces, and other nonlinear behavior experienced by the structure. The results reveal that by integrating the post‐tensioned piles with the rocking foundation, the deck displacements were reduced to an acceptable limit without subjecting the bridge to any damage. In contrast, the bridge with the fixed base foundation experienced extensive damage at the piers, and the bridge with the rocking foundation experienced substantial deck displacements that ultimately led to unseating, resulting in the collapse of both bridges. It was therefore concluded that the p...
Fallahpoor, M, Chakraborty, S, Heshejin, MT, Chegeni, H, Horry, MJ & Pradhan, B 2022, 'Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection', Computers in Biology and Medicine, vol. 145, pp. 105464-105464.
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Farooq, MA & Nimbalkar, S 2022, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366.
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Fathipour, H, Payan, M, Jamshidi Chenari, R & Fatahi, B 2022, 'General failure envelope of eccentrically and obliquely loaded strip footings resting on an inherently anisotropic granular medium', Computers and Geotechnics, vol. 146, pp. 104734-104734.
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Feng, K, Ji, JC, Li, Y, Ni, Q, Wu, H & Zheng, J 2022, 'A novel cyclic-correntropy based indicator for gear wear monitoring', Tribology International, vol. 171, pp. 107528-107528.
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Gearbox is a vulnerable component of a turbine's drivetrain and plays a vital role in the power transmission in wind turbines. Wind turbines usually operate under harsh working environments, such as in deserts, oceans, and on hills. The adverse operating conditions (such as inevitable fluctuating wind loads and speeds) make the gearbox transmission prone to reliability degradation and premature failure. Gear wear is a common and unavoidable surface degradation phenomenon during the lifespan of the gear transmission system. The gear wear propagation can result in severe failures, such as gear surface spalling, gear root crack, and gear tooth breakage, all of which could lead to the failure of the drivetrain system of wind turbines and bring unexpected economic loss, even serious accidents. Thus, it is crucial to monitor the gear wear propagation progression in order to enable reliable and safe operation. To this end, this paper develops a novel vibration-based health indicator to monitor the gear surface degradation induced by gear wear progression. With the help of the novel indicator developed, the health status of the gearbox can be well evaluated and thus predictive maintenance-based decisions can be made to reduce maintenance costs and minimize gearbox failures in wind turbines. A series of endurance tests under different lubrication conditions and operational conditions are carried out to verify the effectiveness of the gear wear monitoring indicator.
Feng, K, Ji, JC, Wang, K, Wei, D, Zhou, C & Ni, Q 2022, 'A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines', Ocean Engineering, vol. 266, pp. 112920-112920.
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Vold-Kalman order tracking filter is an effective technique for dealing with non-stationary vibrations which offshore wind turbines often encounter. It has a unique capability to extract and track the time waveforms of harmonics in short transients without phase bias, and this capability is beneficial to the condition monitoring of offshore wind turbines. In general, the accuracy of the tracking results of the Vold-Kalman filer for condition monitoring is heavily dependent on the selection of filter bandwidth. A fixed filter bandwidth becomes problematic when processing different types of signals under varying operating conditions. Significant errors may arise in the tracking, rendering the condition monitoring of offshore wind turbines unreliable. To address this issue, this paper proposes a novel scheme for Vold-Kalman filter bandwidth selection to guarantee the consistency and accuracy of the offshore wind turbine condition monitoring process, ensuring reliable fault diagnosis. A numerical model is used to evaluate the effectiveness of the proposed bandwidth selection scheme first. Then the proposed scheme is further validated through the offshore wind turbine planetary gearbox datasets, together with the demonstration of the fault diagnosis capability of the filtered results.
Ganbat, N, Altaee, A, Zhou, JL, Lockwood, T, Al-Juboori, RA, Hamdi, FM, Karbassiyazdi, E, Samal, AK, Hawari, A & Khabbaz, H 2022, 'Investigation of the effect of surfactant on the electrokinetic treatment of PFOA contaminated soil', Environmental Technology & Innovation, vol. 28, pp. 102938-102938.
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Gao, F, Zhang, S, He, X & Sheng, D 2022, 'Experimental Study on Migration Behavior of Sandy Silt under Cyclic Load', Journal of Geotechnical and Geoenvironmental Engineering, vol. 148, no. 5.
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This paper presents experimental investigation into the effects of particle size distribution of subgrade soil on mud pumping. The results show that subgrade soils with higher fine contents do not necessarily lead to more serious mud pumping. A soil with a higher silt content tends to cause the formation of a less permeable interlayer at the bottom of the ballast, which effectively reduces the particle migration magnitude. Increasing the median particle size (d50) or reducing the coefficient of uniformity (d60/d10) of the studied sandy silt promotes the migration distance of particles. While mud pumping is essentially an internal erosion problem caused by cyclic loads, existing filter theories do not directly apply to mud pumping. The findings from this study can help selecting proper rail embankment fills to reduce mud pumping.
Grzybowska, H, Wijayaratna, K, Shafiei, S, Amini, N & Travis Waller, S 2022, 'Ramp Metering Strategy Implementation: A Case Study Review', Journal of Transportation Engineering, Part A: Systems, vol. 148, no. 5.
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Guo, Y, Li, W, Dong, W, Wang, K, He, X, Vessalas, K & Sheng, D 2022, 'Self-sensing cement-based sensors with superhydrophobic and self-cleaning capacities after silane-based surficial treatments', Case Studies in Construction Materials, vol. 17, pp. e01311-e01311.
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A novel cement-based sensors was developed with integrated self-sensing superhydrophobicity, and self-cleaning functions in this paper. The synthesis was carried out by penetrating precast graphene nanoplate/cement-based sensors with silane/isopropanol solutions. The silane-treated cement-based sensors showed satisfactory stress/strain sensing performance with an average gauge factor of 141.8, and exhibited excellent hydrophobic behaviour with the highest water contact angle of 163° on the intact surface. The contact angle decreased to 148° and 142°, for the surface with scratches and for the inner part of sensors, respectively. The reduction was due to the spalling and less amount of silane particles within the scratches and the harder entry of silane to the inner part of sensor. The self-cleaning properties of silane-treated cement-based sensor were evaluated by the visual observation of removing efficiency of hydrophilic carbon black dust and lipophilic sauces after water rinsing. It was found that the silane-treated cement-based sensor showed excellent self-cleaning performance using hydrophilic carbon dust. Despite the removing efficiency decreased for the lipophilic sauces, the silane-treated cement-based sensors maintained much less stain than that of untreated ones on the surface. The related results will promote the synthesis and practical applications of multifunctional cement-based sensors for the application of intrisic structural health monitoring.
Hao, J, Zhu, X, Yu, Y, Zhang, C & Li, J 2022, 'Damage localization and quantification of a truss bridge using PCA and convolutional neural network', Smart Structures and Systems, vol. 30, no. 6, pp. 673-686.
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Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.
Hasan, H 2022, 'NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN LOOSE AND SILTY SAND AFTER TREATING WITH MICROBIALLY INDUCED CALCITE PRECIPITATION', International Journal of GEOMATE, vol. 22, no. 90, pp. 32-39.
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It is essential to provide a stable foundation system for construction projects to reduce the geotechnical risk of failure due to static or dynamic loads. Pile foundations are recommended to increase bearing capacity and decrease the dynamic oscillations of soils. Recently, soil stabilization using microbially induced calcite precipitation (MICP) was widely used to increase shear strength parameters and reduce the hydraulic conductivity of sand. In this study, the technique of using MICP was reviewed based on previous studies and analyzed using Plaxis 3D to evaluate the enhancement of a single pervious concrete pile under static, free vibration and earthquake stages of loose and silty sand. In the static stage, under the applying load to reach prescribed displacement of 76 mm, the results of loose sand demonstrate that the static load capacity was increased from 470 kN of untreated loose sand to 582, 598 and 612 kN after treating by MICP along the shaft and tip of a concrete pile with 0.5,0.75 and 1 m, respectively. In the earthquake stage, the result of treated loose sand such as vertical and lateral displacement was insignificant compared with untreated loose sand. The Plaxis 3D models have clarified the benefit of using MICP with the pile foundation model.
He, HS, Teng, JD, Zhang, S & Sheng, DC 2022, 'Rationality of frost susceptibility of soils', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 44, no. 2, pp. 224-234.
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The frost heave and thaw weakening are the critical issues for the infrastructures in cold regions. How to reasonably assess the frost susceptibility of soils has been a hotspot in cold-region geotechnics. The frost susceptibility has been studied for about one hundred years since Casagrande (1931) proposed fine content as a main index to evaluate the frost susceptibility of soils. In most cases, the frost characteristics defined by fines content are clear and very simple, and work well in guiding the engineering construction in cold regions. However, the recent studies show that: (1) The frost heave occurs frequently in the subgrade which is designed and constructed absolutely according to the existing frost susceptibility criteria. (2) The current frost susceptibility criteria vary greatly in different countries and regions with different accuracies. (3) The vapour flow can lead to considerable frost heave in coarse-grained soils, which is not considered in the existing frost susceptibility. The above issues challenge the existing frost susceptibility. It is worth to analyze whether the concept of frost susceptibility is reasonable or not as well as its evaluation system. This study tries to analyze the advantages and disadvantages of the existing frost susceptibility criteria. The main findings are: (1) The reliability of the existing frost susceptibility is generally low, within the range of 50% to 80%. (2) The existing frost susceptibility criteria are not suitable to the case that the frost heave in coarse-grained soils is caused by vapour transfer. The freezing environmental factors should be considered in defining the frost susceptibility. (3) The existing frost susceptibility may be suitable to indicate the thaw weakening property of soils. The outcome of this study is helpful to replenishing the classification of frost susceptibility criteria. It would be of great significance to frost disaster prevention in cold regions.
He, X, Wang, F, Li, W & Sheng, D 2022, 'Deep learning for efficient stochastic analysis with spatial variability', Acta Geotechnica, vol. 17, no. 4, pp. 1031-1051.
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Using machine-learning models as surrogate models is a popular technique to increase the computational efficiency of stochastic analysis. In this technique, a smaller number of numerical simulations are conducted for a case, and obtained results are used to train machine-learning surrogate models specific for this case. This study presents a new framework using deep learning, where models are trained with a big dataset covering any soil properties, spatial variabilities, or load conditions encountered in practice. These models are very accurate for new data without re-training. So, the small number of numerical simulations and training process are not needed anymore, which further increases efficiency. The prediction of bearing capacity of shallow strip footings is taken as an example. We start with a simple scenario, and progressively consider more complex scenarios until the full problem is considered. More than 12,000 data are used in training. It is shown that one-hidden-layer fully connected networks can give reasonable results for simple problems, but they are ineffective for complex problems, where deep neural networks show a competitive edge, and a deep-learning model achieves a very high accuracy (the root-mean-square relative error is 3.1% for unseen data). In testing examples, this model is proven very accurate if the parameters of specific cases are well in the defined limits. Otherwise, the capability of deep-learning models can be extended by simply generating more data outside the current limits and re-training the models.
Inan, DI, Beydoun, G & Pradhan, B 2022, 'Disaster Management Knowledge Analysis Framework Validated.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2077-2097.
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In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements.
Indraratna, B, Haq, S, Rujikiatkamjorn, C & Israr, J 2022, 'Microscale boundaries of internally stable and unstable soils', Acta Geotechnica, vol. 17, no. 5, pp. 2037-2046.
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This study presents a microscale approach for evaluating the internal instability of natural granular soils using the discrete element method. The coordination number and the stress reduction factor are combined to assess the internal instability of soil. Distinct boundaries are identified between various soils that are internally stable and unstable. The microscale investigations are then compared with constriction and particle size-based criteria. The findings reveal that the constriction-based criterion predicts internal instability with significantly better accuracy. The relationship between microscale parameters and the constriction-based retention ratio is also examined for practical purposes.
Indraratna, B, Medawela, SK, Athuraliya, S, Heitor, A & Baral, P 2022, 'Chemical clogging of granular media under acidic groundwater conditions', Environmental Geotechnics, vol. 9, no. 7, pp. 450-462.
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Generation of acidic groundwater attributed to pyrite oxidation in low-lying acid sulfate soil has caused substantial damage to the soil-water environment and civil infrastructure in coastal Australia. The installation of permeable reactive barriers (PRBs) is a frontier technology in the field of acid neutralisation and removal of toxic heavy metal cations – for example, soluble iron (Fe) and aluminium (Al). This study aims to assess the potential of limestone (calcite) aggregates as the PRB’s main reactive material in low-lying pyritic land. During long-term laboratory column experiments, a significant capacity of limestone for removing contaminant chemical species was observed. Nevertheless, the formation of secondary mineral precipitates upon geochemical reactivity within the granular media in the PRB caused armouring and chemical clogging, which diminished the rate of reactivity – that is, the treatment capacity of calcite aggregates – mainly at the entrance zone of the porous media. Flow properties were altered due to blockage of pores; for instance, hydraulic conductivity was reduced by 25% at the inlet zone. Non-homogeneous clogging towards the outlet was analysed, and the time-dependent effect on the longevity of a limestone column was studied and quantified.
Indraratna, B, Mehmood, F, Mishra, S, Ngo, T & Rujikiatkamjorn, C 2022, 'The role of recycled rubber inclusions on increased confinement in track substructure', Transportation Geotechnics, vol. 36, pp. 100829-100829.
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Large cyclic and impact loads exerted by heavy haul trains can cause significant deformation and degradation of ballast, leading to poor track geometry and track instability. The application of recycled rubber elements in track substructure to increase confinement of both sub-ballast and shoulder ballast is an innovative solution. In Australia, there is a lack of adequate recycling that leads to large stockpiles of waste tyres. In addition, the reusability of giant off-the-road tyres discarded from mining industry is seriously limited due to their size and weight (over 3.0 m in diameter weighing about 3 tonnes). This study presents a real-size prototype test using the Australia's first and only National Facility for Cyclic Testing of High-speed Rail to investigate the performance of a hybrid track where tyre-infilled granular waste materials were placed below the ballast layer to replace the traditional capping layer, and arc segments cut from the giant off-the-road tyres were used to confine shoulder ballast. The performance of this hybrid track is compared with an unreinforced track conducted earlier at the same loading conditions. Test results demonstrate that the use of this hybrid system with recycled rubber elements significantly decreases vertical and lateral displacements of ballast and effectively controls the distribution of vertical stress with depth, while reducing vibration and ballast breakage. The outcomes of this study provide a unique solution in a circular economy perspective to strengthen railways to cater for heavier and faster freight trains.
Indraratna, B, Qi, Y, Malisetty, RS, Navaratnarajah, SK, Mehmood, F & Tawk, M 2022, 'Recycled materials in railroad substructure: an energy perspective', Railway Engineering Science, vol. 30, no. 3, pp. 304-322.
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AbstractGiven that the current ballasted tracks in Australia may not be able to support faster and significantly heavier freight trains as planned for the future, the imminent need for innovative and sustainable ballasted tracks for transport infrastructure is crucial. Over the past two decades, a number of studies have been conducted by the researchers of Transport Research Centre (TRC) at the University of Technology Sydney (UTS) to investigate the ability of recycled rubber mats, as well as waste tyre cells and granulated rubber to improve the stability of track substructure including ballast and subballast layers. This paper reviews four applications of these novel methods, including using recycled rubber products such as CWRC mixtures (i.e., mixtures of coal wash (CW) and rubber crumbs (RC)) and SEAL mixtures (i.e., mixtures of steel furnace slag, CW and RC) to replace subballast/capping materials, tyre cells reinforcements for subballast/capping layer and under ballast mats; and investigates the energy dissipation capacity for each application based on small-scale cyclic triaxial tests and large-scale track model tests. It has been found that the inclusion of these rubber products increases the energy dissipation effect of the track, hence reducing the ballast degradation efficiently and increasing the track stability. Moreover, a rheological model is also proposed to investigate the effect of different rubber inclusions on their efficiency to reduce the transient motion of rail track under dynamic loading. The outcomes elucidated in this paper will lead to a better understanding of the performance of ballast tracks upgraded with resilient rubber products, while promoting environmentally sustainable and more affordable ballasted tracks for greater passenger comfort and increased safety.
Indraratna, B, Qi, Y, Tawk, M, Heitor, A, Rujikiatkamjorn, C & Navaratnarajah, SK 2022, 'Advances in ground improvement using waste materials for transportation infrastructure', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 175, no. 1, pp. 3-22.
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Recycling waste materials for transport infrastructure such as coal wash (CW), steel furnace slag (SFS), fly ash (FA) and recycled tyre products is an efficient way of minimising the stockpiles of waste materials while offering significant economic and environmental benefits, as well as improving the stability and longevity of infrastructure foundations. This paper presents some of the most recent state-of-the-art studies undertaken at the University of Wollongong, Australia on the use of waste materials such as (a) CW-based granular mixtures (i.e. SFS + CW, CW + FA) for port reclamation and road base/subbase and (b) using recycled tyre products (i.e. rubber crumbs, tyre cell, under-sleeper pads and under-ballast mats) to increase track stability and reduce ballast degradation. Typical methods of applying these waste materials for different infrastructure conditions are described and the results of comprehensive laboratory and field tests are presented and discussed.
Indraratna, B, Singh, M, Nguyen, TT, Rujikiatkamjorn, C, Malisetty, RS, Arivalagan, J & Nair, L 2022, 'Internal Instability and Fluidisation of Subgrade Soil under Cyclic Loading', Indian Geotechnical Journal, vol. 52, no. 5, pp. 1226-1243.
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AbstractRapid globalisation and the rise in population have substantially increased the demand for rail infrastructure which have been critical in transporting passengers and freight across landmasses for over a century. The surge in demand often leads to the construction of railway lines along with unfavourable soil conditions which result in different forms of substructure challenges such as uneven track deformations, ballast degradation, and subgrade mud pumping. A widespread site investigation along the eastern coast of New South Wales, Australia, indicated the prevalence of mud holes or bog holes along the tracks. The field studies suggest that low-to-medium plasticity soils are highly susceptible to mud pump when subjected to heavy axle loads under impeding drainage conditions. Subsequent laboratory investigations conducted on the remoulded soil samples collected from the sites indicated the sharp rise in cyclic axial strains and excess pore pressures along with the internal redistribution of moisture content as the governing mechanism for mud pumping. Numerical simulations performed using discrete element method coupled with computational fluid dynamics show that at a high hydraulic gradient, there is a substantial loss of soil contact network which leads to the upward migration of soil particles. The role of plastic fines and the inclusion of geosynthetic layer between the ballast and subgrade are also discussed in this paper. It was observed that the addition of 10% of cohesive fines increased the resistance of subgrade soils to mud pumping. On the other hand, geosynthetic inclusions not only assist in dissipating high cyclic excess pore pressures but also inhibit the upward migration of fine particles.
Jaafari, A, Panahi, M, Mafi-Gholami, D, Rahmati, O, Shahabi, H, Shirzadi, A, Lee, S, Bui, DT & Pradhan, B 2022, 'Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides', Applied Soft Computing, vol. 116, pp. 108254-108254.
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The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness.
Jain, K, Pradhan, B & Mishra, V 2022, 'Preface', Springer Proceedings in Mathematics and Statistics, vol. 404, pp. v-vi.
Japelaghi, M, Hajian, F, Gholamalifard, M, Pradhan, B, Maulud, KNA & Park, H-J 2022, 'Modelling the Impact of Land Cover Changes on Carbon Storage and Sequestration in the Central Zagros Region, Iran Using Ecosystem Services Approach', Land, vol. 11, no. 3, pp. 423-423.
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Central Zagros region in Iran is a major hotspot of carbon storage and sequestration which has experienced severe land cover change in recent decades that has led to carbon emission. In this research, using temporal Landsat images, land cover maps were produced and used in Land Change Modeler to predict land cover changes in 2020, 2030, 2040 and 2050 using Multilayer Perceptron Neural Network and Markov Chain techniques. Next, resultant maps were used as inputs to Ecosystem Services Modeler. The Intergovernmental Panel on Climate Change (IPCC) report data was used to extract carbon data. Results show that between 1989–2013 about half of forests have been destroyed. Prediction results show that by 2050 about 75% of existing forests will be lost and between 2013–2020 about 157,000 Mg carbon and by 2050 about 565,000 Mg carbon will be lost with more than US$1.9 million to 2020 and AU$3.2 million by 2050 economic compensation.
Jena, R, Pradhan, B, Beydoun, G, Alamri, A & Shanableh, A 2022, 'Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India', Geocarto International, vol. 37, no. 25, pp. 8080-8099.
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Jennifer, JJ, Saravanan, S & Pradhan, B 2022, 'Persistent Scatterer Interferometry in the post-event monitoring of the Idukki Landslides', Geocarto International, vol. 37, no. 5, pp. 1514-1528.
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Jifroudi, HM, Mansor, SB, Pradhan, B, Halin, AA, Ahmad, N & Abdullah, AFB 2022, 'A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree', Measurement, vol. 192, pp. 110781-110781.
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Kolli, MK, Opp, C, Karthe, D & Pradhan, B 2022, 'Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India', Geocarto International, vol. 37, no. 26, pp. 11173-11189.
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Kouretzis, G, Sheng, D & Thomas, HR 2022, 'In memory of Scott William Sloan (1954–2019)', Computers and Geotechnics, vol. 143, pp. 104593-104593.
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Le, A, Nimbalkar, S, Zobeiry, N & Malek, S 2022, 'An efficient multi-scale approach for viscoelastic analysis of woven composites under bending', Composite Structures, vol. 292, pp. 115698-115698.
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Li, H, Li, J & Bi, K 2022, 'A quasi-active negative stiffness damper for structural vibration control under earthquakes', Mechanical Systems and Signal Processing, vol. 173, pp. 109071-109071.
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This paper proposes a novel quasi-active negative stiffness damper (QANSD) for effective and robust seismic protection. By integrating the negative stiffness element and controllable damping element together, the proposed device enables to closely achieve active control performance with much less energy to operate compared to an active control system. Such a control system has been named as “Quasi-Active” control (QAC) in this study. To introduce the concept of QAC, this paper reiterates the fundamentals of active and semi-active vibration control systems from the perspective of control force, and numerically examines a few examples via comprehensive evaluation indices. The inherent shortfall of semi-active methods on control effectiveness is illustrated by an example of semi-active dampers. It is clearly revealed that the incapacity of semi-active control to capture the entire required active control force (RACF) is due to the fact that the amount of control force that can be generated by a semi-active control system is based on the responses of the structure, which prevents the semi-active control to achieve equivalent active control performance. To address this issue, this paper introduces the QAC concept and a specific realization, i.e. QANSD, including its principles, control strategy and realization. Furthermore, a generalized design approach and related formulae for designing the QANSD are developed with a special interest in obtaining its negative stiffness thresholds. Moreover, to demonstrate its control effectiveness and superiorities, comparative numerical studies are conducted based on a three-storey frame model. The comparisons are made among the same structure without control, with active control, semi-active control, passive control, as well as QAC. In the study, four scaled earthquakes are used as ground motion excitations and five evaluation criteria are adopted to assess the control performances. The results show that, with much less r...
Li, N, Asteris, PG, Tran, TT, Pradhan, B & Nguyen, H 2022, 'Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization', Steel and Composite Structures, vol. 42, no. 6, pp. 733-745.
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This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.
Li, N, Nguyen, H, Rostami, J, Zhang, W, Bui, X-N & Pradhan, B 2022, 'Predicting rock displacement in underground mines using improved machine learning-based models', Measurement, vol. 188, pp. 110552-110552.
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Displacement of rock mass in tunnels and underground mines is considered one of the most hazardous phenomena that can cause the collapse of the structures. In this study, the rock properties, such as the depth of the tunnels (H), the angle of rock layers (α), anti-bending moment (Wc), the width of the tunnels (b), the tensile strength of rock layers (Rn), and monitoring distance (Lb), and observation time (t), were investigated to predict rock displacement in tunnels and underground mines. Two novel soft computing models, namely Harris Hawks optimization algorithm (HHOA)-based support vector machine (SVM) model (i.e., HHOA-SVM) and Grasshopper optimization algorithm (GOA)-based SVM model (i.e., GOA-SVM), were developed for this aim based on the field measurements. A total of 12 measurement stations and 63 observations of vertical rock mass displacement, rock properties, and observation time in some underground coal mines in the Donbas region (Ukraine) were compiled as the dataset for developing soft computing models. In addition, a constraint was also added to the proposed HHOA-SVM and GOA-SVM models to prevent the model from offering negative results in predicting rock displacement. The conventional models, such as SVM (without optimization) and artificial neural network (ANN), were also investigated to compare favorably with the two proposed HHOA-SVM and GOA-SVM models. Furthermore, linear and nonlinear equations were also established to predict rock displacement and compared to the soft computing models. The results showed that the novel HHOA-SVM and GOA-SVM models provided better performances than conventional SVM and ANN models. Besides, the sensitivity of the input variables was also analyzed to discover the certain characteristics of the rock displacement phenomenon through the properties of rock and observation time. The findings show that H, Lb, t, and α are the most influential parameters for predicting rock displacement in tunnels and undergr...
Li, W, Ji, J & Huang, L 2022, 'Global dynamics analysis of a water hyacinth fish ecological system under impulsive control', Journal of the Franklin Institute, vol. 359, no. 18, pp. 10628-10652.
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Control of a water hyacinth-fish ecological system is required for a healthy and sustainable environment. This paper aims to investigate the global dynamics of a water hyacinth fish ecological system under ratio-dependent state impulsive control. First, we study the positivity and boundedness of the solution of the controlled system. By studying the local stability of the equilibrium, we find that the system has two situations. One is that there are two equilibria, namely a saddle point and a boundary equilibrium. In the second case, there are four equilibria, namely, two saddle points, a boundary equilibrium, and a focus point. For the first case, when we select an appropriate ratio-dependent control threshold, the trajectory will globally converge to the boundary equilibrium. For the second case, when the control line is located below the focus point, by using Poincare mapping method, flip bifurcation theory, and vector field analysis techniques, we find that the solution of the controlled system either globally converges to the boundary equilibrium, order-1 periodic solution, or order-2 periodic solution under certain conditions. When the control line is located above the focus point, the solution of the controlled system either globally converges to the focus point, order-1 or order-2 periodic solution. Finally, we use examples to verify the correctness and validity of the theoretical results.
Lim, S-M, Indraratna, B, Heitor, A, Yao, K, Jin, D, Albadri, WM & Liu, X 2022, 'Influence of matric suction on resilient modulus and CBR of compacted Ballina clay', Construction and Building Materials, vol. 359, pp. 129482-129482.
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Liu, L, Ji, J, Li, B, Miao, Z & Zhou, J 2022, 'Distributed Stochastic Consensus of Networked Nonholonomic Mobile Robots and Its Formation Application', Journal of Dynamic Systems, Measurement, and Control, vol. 144, no. 11.
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Abstract This paper proposes a novel distributed stochastic consensus seeking algorithm for networked nonholonomic wheeled mobile robots (NNWMRs) and its application to consensus-based formation. Time-varying delays and noisy measurement are incorporated into the dynamic model to represent two key issues inherently appearing in the communication and information exchange process among robots. Based on backstepping technique and sliding mode approach, the proposed consensus algorithm integrates kinematic controller and dynamic torque controller into the control protocol. A key feature of the proposed consensus algorithm is the introduction of the consensus gains, which characterizes the effects of time delays and noisy measurement. A unified methodology is provided for the convergence analysis of the networked system by using the generalized stochastic delayed Halanay inequality. It is shown that time delays and noisy measurement can play crucial roles in distributed consensus seeking in collaborative multirobot systems. Illustrative examples and simulations are provided to demonstrate and validate the theoretical results.
Lloret-Cabot, M & Sheng, D 2022, 'Assessing the accuracy and efficiency of different order implicit and explicit integration schemes', Computers and Geotechnics, vol. 141, pp. 104531-104531.
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A first order accurate fully implicit integration scheme and four different order explicit substepping integration schemes with automatic error control are used in this paper to integrate the constitutive relations of a critical state model for saturated soils. Their respective computational performance in terms of accuracy and efficiency is assessed in order to provide practical guidance for deciding which of the five is most suitable for solving numerical problems in geotechnical engineering involving critical state models. Even though existing literature of integration schemes applied to geotechnical problems has traditionally been focussed on the first order accurate implicit backward Euler and on the second order accurate explicit modified Euler with substepping almost exclusively, the findings of this paper suggest that the little extra work required in the implementation of an explicit third order Runge-Kutta substepping scheme is worth the effort, especially in terms of computational cost.
Lou, B, Barbieri, DM, Passavanti, M, Hui, C, Gupta, A, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Fang, K, Lam, L, Maharaj, B, Ghasemi, N, Qiao, Y, Adomako, S, Foroutan Mirhosseini, A, Naik, B, Banerjee, A, Wang, F, Tucker, A, Liu, Z, Wijayaratna, K, Naseri, S, Yu, L, Chen, H, Shu, B, Goswami, S, Peprah, P, Hessami, A, Abbas, M & Agarwal, N 2022, 'Air pollution perception in ten countries during the COVID-19 pandemic', Ambio, vol. 51, no. 3, pp. 531-545.
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AbstractAs largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
Luo, Z, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Nano/micromechanical characterisation and image analysis on the properties and heterogeneity of ITZs in geopolymer concrete', Cement and Concrete Research, vol. 152, pp. 106677-106677.
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Heterogeneity of interfacial transition zones (ITZs) is a key factor for the properties and failure mechanism of geopolymer concrete. The nano/microscale properties and heterogeneity of the ITZs (the top, bottom and lateral interfaces) prepared by encompassing polished aggregates in the modelled fly ash-based geopolymer concrete were statistically investigated in this study. The nanoindentation and nanoscratch results show that the nano/micromechanical properties of the gel-related phases of ITZs at the top and bottom boundaries are higher than the corresponding ones at the lateral boundaries and bulk paste. The mechanism of the better properties of ITZs at the top and bottom boundaries is unveiled based on quantitative image analysis of the amount, diameter and proportion distribution of fly ash particles. A strategy of controlling heterogeneity of ITZs and using polished aggregates, rapid scratch and statistical analysis is proposed to investigate more complicated ITZs within acceptable testing duration.
Ma, B, Teng, J, Li, H, Zhang, S, Cai, G & Sheng, D 2022, 'A New Strength Criterion for Frozen Soil Considering Pore Ice Content', International Journal of Geomechanics, vol. 22, no. 7, p. 04022107.
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Pore ice content is crucial in evaluating the mechanical properties of frozen soils. Existing strength criterion models are usually empirical and ignore the influence of pore ice content. By assuming that the critical state shear stress ratio of soil is a function of the stress level, a critical state line of frozen soil is proposed to consider pore ice content. By combining the Mohr-Coulomb (M-C) and Drucker-Prager strength criteria to describe the failure shape characteristics on the deviatoric plane, a new strength criterion is established for complex stress conditions. The proposed model is validated against existing models and measured data in the literature. In addition, the proposed model can uniformly describe the CSL of different types of geotechnical materials and has a clear physical meaning, which may provide a theoretical basis for constitutive models.
Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Data encryption aims to protect the confidentiality of data at storage, during transmission, or while in processing. However, it is not always the optimum choice as attackers know the existence of the ciphertext. Hence, they can exploit various weaknesses in the implementation of encryption algorithms and can thus decrypt or guess the related cryptographic primitives. Moreover, in the case of proprietary applications such as online social networks, users are at the mercy of the vendor's security measures. Therefore, users are vulnerable to various security and privacy threats. Contrary to this, covert communication techniques hide the existence of communication and thus achieve security through obscurity and hidden communication channels. Over the period, there has been a significant advancement in this field. However, existing literature fails to encompass all the aspects of covert communications in a single document. This survey thus endeavors to highlight the latest trends in covert communication techniques, related challenges, and future directions.
Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Most of the existing science and technology parks resort to various conventional ways to attract different stakeholders to the park. Some of these traditional measures include business support, workspaces, laboratories, networking events, accommodation, and essential commodities. Besides, with rampantly changing multidisciplinary technologies and increased data-oriented business models, the classic science and technology park value-creation strategies may not be instrumental in the near future. Hence, we foresee that future science and a technology parks should be fully integrated, sustainable, and innovative living science cities. Where park tenants can actively interact and contribute to emerging technologies. Therefore, this paper carries out an in-depth study of world s best practices in smart cities and science and technology parks, their characteristics, and value-added contributions that excite the prospective tenants. Developing on the detailed survey, we propose a unique feature of Autonomous Systems as a Service to bestow a futuristic look to the science and technology parks. It is envisaged that autonomous systems will not only provide value-added services to the park tenants but will also provide an infrastructure for testing new technologies within park premises. Furthermore, this study evaluates security and privacy challenges associated with autonomous systems and data-oriented services and recommends appropriate security measures. The role of universities in the success of a science and technology park is also delineated. Finally, the components deemed essential for the attainment of science and technology parks objectives are highlighted.
Malik, K, Kumar, D, Perissin, D & Pradhan, B 2022, 'Estimation of ground subsidence of New Delhi, India using PS-InSAR technique and Multi-sensor Radar data', Advances in Space Research, vol. 69, no. 4, pp. 1863-1882.
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Globally the land subsidence is a significant problem of the rapidly growing urban area. The factor responsible for the land subsidence caused by over-exploitation of the underground fluid such as water, petroleum, and gas respectively. In present study we present the result of detail investigation of active ground subsidance in New Delhi, National Capital Region (NCR). This area indicates a high rate of urban growth during the past decades. To analyze the land subsidence, we used multiple SAR sensor data and exploited the PS-InSAR technique. The data used for this study are Cosmo-skymed acquired between 08/06/2011 to 15/11/2017, Sentinel-1A-B (18-12-2014 to 27-11-2018), and ALOS PALSAR acquired between 19/01/2007 and 20/01/2011. These radar sensors operate in X, C, and L-band, which covers over ten years, from 2007 to 2018. The PSI results of Cosmo-skymed reveals that the Delhi NCR region has undergone an average deformation ± 15 mm/y, a maximum surface deformation observed from ALOS-PALSAR is 10 to 18 mm/y and the ground displacement observed from the SENTINAL-1A data is −2 to 16 mm/y. Groundwater level data also collected for the same period and a ground water level depletion compared with the subsidence. Monitoring land subsidence with ground-based conventional technology is time-consuming and can be carried out in a limited area due to the financial implication. PS-InSAR is an established method to detect the surface movement using the SAR sensor's time-series data. The result shows that a twenty centimeter of land subsidence is visible in some areas, validated with the collected ground evidence. The affected area is also showing resemblance to the groundwater depleting condition in those areas. This study also establish that multiple sensor data can be used to monitor the long term land subsidence.
Matin, SS & Pradhan, B 2022, 'Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review', Geocarto International, vol. 37, no. 21, pp. 6186-6212.
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Medawela, S, Indraratna, B, Athuraliya, S, Lugg, G & Nghiem, LD 2022, 'Monitoring the performance of permeable reactive barriers constructed in acid sulfate soils', Engineering Geology, vol. 296, pp. 106465-106465.
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Two pilot-scale permeable reactive barriers (PRBs) were installed in an acidic terrain to treat contaminated groundwater with low pH and high concentrations of Al and Fe. The first pilot-scale barrier (PRB-1) was installed in 2006 using recycled concrete aggregates (RCA) as the reactive material, and the second barrier (PRB-2) was installed in late 2019 using limestone aggregates (LA) as the reactive material. Although the initial material cost of the recycled concrete aggregates is low, laboratory trials conducted before the field applications deduced that limestone is capable of more reliable and efficient pH neutralisation in the long term, reducing frequent maintenance or material replacement in the PRB. The performance of PRB-1 has been monitored continuously over the past 14 years. In particular, both internal (within PRB) and external (upgradient and downgradient) variations in acidity (pH), ion concentrations, and the flow conditions, including the piezometric heads, have been analysed. These decade long field observations have resulted in a comprehensive understanding of the temporal variations of treatment by RCA along the groundwater flow path through the alkaline granular mass and its biogeochemical clogging. For instance, acid neutralisation at the entrance of PRB-1 decreased by 31% over 14 years, whereas the corresponding reduction at the outlet is only 6%. The non-homogeneous biogeochemical clogging in different PRB zones was evident by a 48% reduction in hydraulic conductivity at the inlet and a 34% reduction at the outlet.
Mehrabi, N & Khabbaz, H 2022, 'A trustful transition zone for high-speed rail using stone columns', Australian Journal of Civil Engineering, vol. 20, no. 1, pp. 56-66.
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The high-speed railway projects have encountered several geotechnical challenges. One of the most important challenges is the differential settlement control in transition zones. Cement-treated soil is a common method to prevent the differential settlement at transition zones. An alternative method uses stone columns for controlling the differential settlement in approaching embankment of bridges. In this study, numerical modelling using PLAXIS 2D is selected for the assessment of stone columns in the reduction of total and differential settlements. One of the overpass bridges of the track constructed for the Tehran–Isfahan railway, the first high-speed railway in the country, is chosen as the case study. Three models are created based on the properties of the selected case study. The first one is a typical approaching embankment. The second one is the bridge abutment section, and the last one is a typical reinforced approaching embankment with stone columns.
Mittal, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2022, 'A new method for detection and prediction of occluded text in natural scene images', Signal Processing: Image Communication, vol. 100, pp. 116512-116512.
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Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.
Mukund Deshpande, N, Gite, S, Pradhan, B & Ebraheem Assiri, M 2022, 'Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review', Computer Modeling in Engineering & Sciences, vol. 133, no. 3, pp. 843-872.
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Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A lot of emphasis has recently been paid to the development of methods for visualizing, explaining, and interpreting deep learning models. The situation is substantially different in safety-critical applications. The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case. Significantly, when single bad decisions can endanger human life and health (e.g., autonomous driving, medical domain) or result in significant monetary losses (e.g., algorithmic trading), depending on an unintelligible data-driven system may not be an option. This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer, e-commerce, or entertainment industries. Explainability is the term introduced in the preceding years. The AI model’s black box nature will become explainable with these frameworks. Especially in the medical domain, diagnosing a particular disease through AI techniques would be less adapted for commercial use. These models’ explainable natures will help them commercially in diagnosis decisions in the medical field. This paper explores the different frameworks for the explainability of AI models in the medical field. The available frameworks are compared with other parameters, and their suitability fo...
Ngo, T, Indraratna, B & Ferreira, F 2022, 'Influence of synthetic inclusions on the degradation and deformation of ballast under heavy-haul cyclic loading', International Journal of Rail Transportation, vol. 10, no. 4, pp. 413-435.
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This study investigates the benefits of artificial inclusions placed underneath the ballast layer. A series of large-scale cyclic triaxial tests were carried out on ballast with and without these inclusions under 25-tonne and 35-tonne axle loads and frequencies of f = 15 Hz and 25 Hz, using a Process Simulation Prismoidal Triaxial Apparatus. The laboratory results show that a geogrid installed between the ballast and capping layer decreases both deformation and degradation of the aggregates, which can be attributed to enhanced internal confinement and restricted particle movement. Laboratory tests also showed that placing a rubber mat underneath the ballast layer significantly reduced ballast breakage. A numerical model using the discrete element method (DEM) was developed and validated against the experimental observations. The DEM model was utilized to explore the contact forces that developed across the granular assemblies, and to study the interaction between aggregates and the synthetic inclusions from a particle-level perspective.
Nguyen, TN, Sanchez, LFM, Li, J, Fournier, B & Sirivivatnanon, V 2022, 'Correlating alkali-silica reaction (ASR) induced expansion from short-term laboratory testings to long-term field performance: A semi-empirical model', Cement and Concrete Composites, vol. 134, pp. 104817-104817.
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Correlating short-term expansion of concrete specimens in the laboratory and long-term expansion of concrete in the field is crucial to evaluate the reliability of laboratory test methods and essential for the prognosis of alkali-silica reaction (ASR) in concrete infrastructures. In this study, a novel semi-empirical approach is proposed for forecasting ASR-induced expansion of unrestrained concrete in the field using laboratory measurements data. In addition to the use of short-term laboratory expansion data, the model accounts for the effects of alkali leaching, alkali contribution from aggregates, and environmental conditions (i.e., temperature and relative humidity). A comprehensive database from the literature was gathered for the development and calibration of the proposed model. Finally, the model was used for various concrete blocks incorporating different reactive aggregates and exposed to three outdoor conditions in Canada and the USA. Model outcomes show that it is highly promising for forecasting the induced expansion of concrete in the field from the accelerated laboratory tests data. Analysing the modelling results also highlights the importance of alkali leaching and environmental conditions on the correlation between laboratory and field performance.
Nguyen, TT & Indraratna, B 2022, 'Fluidization of soil under increasing seepage flow: an energy perspective through CFD-DEM coupling', Granular Matter, vol. 24, no. 3.
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AbstractIncreasing seepage flow causes soil particles to migrate, i.e., from local piping to complete fluidization, resulting in reduced effectives stress and degraded shear stiffness of the soil foundation. This process has received considerable attention in the past years, however, majority of them concentrate on macro-aspects such as the internal erosion and soil deformation, while there is a lack of fundamental studies addressing the energy transport at micro-scale of fluid-soil systems during soil approaching fluidization. In this regard, the current study presents an assessment of the energy evolution in soil fluidization based on the discrete element method (DEM) coupled with computation fluid dynamics (CFD). In this paper, an upward seepage flow of fluid is modelled by CFD based on the modified Navier–Stokes equations, while soil particles are governed by DEM with their mutual interactions being computed through fluid-particle force models. The energy transformation from the potential state to kinetic forms during fluid flowing is discussed with respect to numerical (CFD-DEM) results and the energy conservation concepts. The results show that majority of the potential energy induced by fluid flows has lost due to frictional mechanisms, while only a small amount of energy is needed to cause the soil to fluidize completely. The contribution of rotational and translational components to the total kinetic energy of particles, and their changing roles during soil fluidization is also presented. The effect of boundary condition on the energy transformation and fluidization of soil is also investigated and discussed. Graphical abstract
Nguyen, TT & Indraratna, B 2022, 'Rail track degradation under mud pumping evaluated through site and laboratory investigations', International Journal of Rail Transportation, vol. 10, no. 1, pp. 44-71.
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This paper presents the results of field and laboratory studies of slurry tracks along the South Coast rail line in NSW, Australia. Site investigations on fouled tracks were followed by a series of laboratory tests to determine the properties of mud fines, and how they can reduce track performance. This study reveals two distinctly different ways of forming slurry tracks, i.e., non-subgrade and subgrade mud pumping, resulting in different characteristics of degraded tracks. More cohesive the fouling materials are, the greater the reduction in hydraulic conductivity (kb) and shear strength (Sb) of the contaminated ballast. When the fouling index FI > 30%, kb drops severely, causing insufficient drainage capacity of the track while the loss of Sb can exceed 22%. Different types of fouling index are also discussed with reference to the field and laboratory data, followed by proposed empirical equations to estimate the values of kb and Sb.
Nguyen, TT, Indraratna, B & Leroueil, S 2022, 'Localized behaviour of fluidized subgrade soil subjected to cyclic loading', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1844-1849.
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Recent investigations have shown that under adverse cyclic triaxial loading, the upper part of soil specimens can turn into a fluid-like state with increased water content (i.e., fluidization), whereas the lower layers can maintain a relatively high stiffness. This paper aims to gain further insight into this behaviour by monitoring the development in excess pore water pressure (EPWP) at the top and bottom of the test specimens, followed by post-analysis of water content distribution along the specimen. The results show that the EPWP at the uppermost part of the specimen develops rapidly and approaches the zero-effective stress level, whereas the EPWP at the bottom part of the specimen tends to stabilize while undergoing densification. Accompanied with this process is a redistribution of the water content along the specimen height where the water content at the upper soil layer increases to approach the liquid limit while increasing the void ratio.
Nguyen, TT, Indraratna, B & Rujikiatkamjorn, C 2022, 'A numerical approach to modelling biodegradable vertical drains', Environmental Geotechnics, vol. 9, no. 8, pp. 515-523.
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Because of their distinct features such as biodegradability and favourable engineering properties, naturally occurring materials including jute and coconut fibres have been used increasingly in numerous geoengineering applications in recent years. However, these materials can sometimes decompose rapidly when subjected to adverse environmental conditions, resulting in severe degradation of their engineering characteristics and consequently causing damage to the design target. This paper presents a numerical approach where the finite-element method (FEM) is used to estimate the influence that the degradation of natural fibre drains can have on soil consolidation. A subroutine which can describe the reduction in drain discharge capacity over time is incorporated into the FEM model. Different cases including those varying the rate and time-dependent form of biodegradation are examined in this paper. The results of this investigation indicate that the dissipation of excess pore pressure can be hampered significantly if drains decay too early and speedily, particularly when the discharge capacity falls below 0·03 m3/d. Different rates of decay can impose different consolidation responses in the surrounding soft soil. Application of the proposed FEM to compare with laboratory data indicates an acceptable agreement between the predictions and the measurements.
Ni, Q, Ji, JC, Feng, K & Halkon, B 2022, 'A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis', Mechanical Systems and Signal Processing, vol. 164, pp. 108216-108216.
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Being an effective methodology to adaptatively decompose a multi-component signal into a series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited bandwidth, the variational mode decomposition (VMD) has received increasing attention in the diagnosis of rolling element bearings. In implementing VMD, an optimal determination of decomposition parameters, including the mode number and bandwidth control parameter, is the pivotal starting point. However, in practical engineering, heavy background noise, abnormal impulses and vibration interferences from other internal components, often bring great challenges in selecting mode number and bandwidth control parameter. These issues may lead to the performance degradation of VMD for bearing fault diagnosis. Therefore, a fault information-guided VMD (FIVMD) method is proposed in this paper for extracting the weak bearing repetitive transient. To minimize the effects of background noise and/or interferences from other components, two nested statistical models based on the fault cyclic information, incorporated with the statistical threshold at a specific significance level, are used to approximately determine the mode number. Then the ratio of fault characteristic amplitude (RFCA) is defined and utilized to identify the optimal bandwidth control parameter, through which the maximum fault information is extracted. Finally, comparisons with the original VMD, empirical mode decomposition (EMD) and local mean decomposition (LMD) are conducted using both simulation and experimental datasets. Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
Omar, KR, Fatahi, B & Nguyen, LD 2022, 'Impacts of Pre-contamination Moisture Content on Mechanical Properties of High-Plasticity Clay Contaminated with Used Engine Oil', Journal of Testing and Evaluation, vol. 50, no. 6, pp. 3001-3027.
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Abstract The oil contamination of soils and the remediation techniques to enhance the engineering properties of the ground have been an emerging challenge in the geoenvironmental field. While several studies were conducted to examine the behavior of the contaminated granular soils, little is known about the mechanical properties of the oil-contaminated clays. This paper investigates the impacts of the in situ pre-contamination moisture content (PMC) on the behavior of fine-grained soil contaminated with various levels of used engine oil. Extensive laboratory experiments were performed on sandy clay with different initial moisture conditions and various amounts of used engine oil varying from 0 to 16 %. The experimental results, including the Atterberg limits, linear shrinkage (LS), unconfined compressive strength, shear strength, and small-strain shear modulus in conjunction with microstructural image analysis, were reported and discussed. It is observed that when oil content was increased, both LS and plastic limit (PL) increased while the liquid limit decreased in the contaminated soil. Moreover, the inclusion of engine oil contributed to the reduction in the plasticity index, which was also impacted by the PMC of the soil. An increment in the PL was correlated with a significant decrease in shear strength, shear modulus, and other associated parameters such as friction angle and cohesion. In agreement with the results, a broader range of elasticity and improved stability at the microstructure level was associated with a lower pre-contamination water content (PMC). Overall, this paper shows that knowledge of site moisture levels before contamination is essential to evaluate the implications of contamination by used engine oil.
Ouchchen, M, Boutaleb, S, Abia, EH, El Azzab, D, Miftah, A, Dadi, B, Echogdali, FZ, Mamouch, Y, Pradhan, B, Santosh, M & Abioui, M 2022, 'Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)', Ore Geology Reviews, vol. 143, pp. 104762-104762.
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Peellage, WH, Fatahi, B & Rasekh, H 2022, 'Experimental investigation for vibration characteristics of jointed rocks using cyclic triaxial tests', Soil Dynamics and Earthquake Engineering, vol. 160, pp. 107377-107377.
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Peng, M, Tian, Y, Gaudin, C, Zhang, L & Sheng, D 2022, 'Application of a coupled hydro‐mechanical interface model in simulating uplifting problems', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 46, no. 17, pp. 3256-3280.
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AbstractThis paper presents the detailed formulation of a coupled hydro‐mechanical structure‐soil interface and demonstrates its application in simulating uplifting problems. This interface features real‐time prediction of the pore pressure generation and structure‐soil separation, and thus rate dependency and ‘breakaway’ can be modeled without user intervention. Constitutive relations of this interface were derived by considering the coupling between soil skeleton and fluid along the interface. A complete finite element formulation and numerical implementation of the interface is provided based on an eight‐node element. The performance of this interface is demonstrated by simulating lifting a surface footing at varying rates (spanning across undrained, partially drained and drained conditions), compared with existing theoretical solutions, numerical results and experimental data. The good agreement achieved indicates that this interface is capable of modelling uplift at varying rates, which is an extremely challenging topic in offshore engineering. Sensitivity studies were conducted to investigate the parameters affecting uplifting behaviour. A unified backbone curve was established correspondingly, which is shown to be different from existing studies in compression, due to the difference in the mechanism between the two cases.
Pradhan, B, Jena, R, Talukdar, D, Mohanty, M, Sahu, BK, Raul, AK & Abdul Maulud, KN 2022, 'A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model', Remote Sensing, vol. 14, no. 18, pp. 4486-4486.
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Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrou...
Punetha, P & Nimbalkar, S 2022, 'Geotechnical rheological modeling of ballasted railway tracks considering the effect of principal stress rotation', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1793-1818.
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The rotation of principal stress direction experienced by the soil elements in a railway track substructure during a train passage influences the magnitude of accumulated settlement. However, the existing methods to evaluate the track response under repeated train loads disregard the influence of principal stress rotation (PSR). This article presents a novel approach for assessing the behavior of ballasted railway tracks incorporating the contribution of PSR on track deformation. The proposed technique employs a geotechnical rheological model to evaluate the track behavior, in which the material plasticity is captured through plastic slider elements. The influence of PSR is accounted for by extending an existing constitutive relationship for the slider elements for the substructure layers, which is successfully validated against experimental data reported in the literature. The results reveal that PSR causes significant cumulative deformation in the substructure layers, and disregarding it in the analysis leads to inaccurate predictions. The proposed approach is then applied to an open track-bridge transition with heterogeneous support conditions, in which the differential settlement is found to be largely influenced by PSR. The findings from this study highlight the importance of including the effect of PSR in predictive models for a reliable evaluation of track performance.
Punetha, P & Nimbalkar, S 2022, 'Performance improvement of ballasted railway tracks using three-dimensional cellular geoinclusions', Geotextiles and Geomembranes, vol. 50, no. 6, pp. 1061-1082.
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Qi, Y & Indraratna, B 2022, 'Influence of Rubber Inclusion on the Dynamic Response of Rail Track', Journal of Materials in Civil Engineering, vol. 34, no. 2.
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Quevedo, RP, Maciel, DA, Uehara, TDT, Vojtek, M, Rennó, CD, Pradhan, B, Vojteková, J & Pham, QB 2022, 'Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model', Geocarto International, vol. 37, no. 25, pp. 8190-8213.
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Most previous studies of landslide susceptibility mapping (LSM) have not contemplated spatial heterogeneity and the commonly used models for LSM are aspatial, which could reduce model performance. Therefore, aiming to evaluate the applicability of spatial algorithms to predict landslide susceptibility, the performance of geographical random forest (GRF) was evaluated, in comparison to random forest (RF) and extreme gradient boosting (XGBoost). Based on the results, GRF presented the better performance (AUC = 0.876), followed by RF (AUC = 0.748) and XGBoost (AUC = 0.745). GRF also provided the most suitable susceptibility map. While RF and XGBoost presented almost 50% of the study area as susceptible, the GRF presented more concentrated susceptibility areas spatially, with a reasonable area for moderate (15.55%), high (8.73%) and very-high (2.59%) susceptibility classes. Finally, it can be inferred that spatial assessment may improve model performance, and that spatial models have a great potential for LSM.
Rao, P, Ouyang, P, Wu, J, Li, P, Nimbalkar, S & Chen, Q 2022, 'Seismic Stability of Heterogeneous Slopes with Tensile Strength Cutoff Using Discrete-Kinematic Mechanism and a Pseudostatic Approach', International Journal of Geomechanics, vol. 22, no. 12.
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Rao, P, Xiang, Y, Ouyang, P, Nimbalkar, S & Chen, Q 2022, 'Finite Element Analysis of Electro-Thermal Coupling of Sandstone Under Lightning Currents', Geotechnical and Geological Engineering, vol. 40, no. 5, pp. 2593-2604.
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Rao, P-P, Ouyang, P-H, Nimbalkar, S, Chen, Q-S, Wu, Z-L & Cui, J-F 2022, 'Analytical modelling of the mechanical damage of soil induced by lightning strikes capturing electro-thermal, thermo-osmotic, and electro-osmotic effects', Journal of Mountain Science, vol. 19, no. 7, pp. 2027-2043.
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Rasouli, H & Fatahi, B 2022, 'Liquefaction and post-liquefaction resistance of sand reinforced with recycled geofibre', Geotextiles and Geomembranes, vol. 50, no. 1, pp. 69-81.
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The present study provides an insight into the effect of recycled carpet fibre on the mechanical response of clean sand as backfill material subjected to monotonic loading and cyclic loading as well as post-liquefaction resistance of both unreinforced and carpet fibre reinforced soils. To achieve these goals, a series of multi-stage soil element tests under cyclic loading event resulting in liquefaction followed by undrained monotonic shearing without excess pore water pressure dissipation as well as a series of monotonic undrained shear test is conducted. All the specimens are isotropically consolidated under a constant effective confining stress of 100 kPa by considering the effect of cyclic stress ratio and carpet fibre content ranging from 0.25% to 0.75%. The obtained results revealed the efficiency of carpet fibre inclusion in increasing the secant shear modulus and ductility of clean sand under monotonic shearing without previous loading history. The impact of carpet fibre inclusion on the trend of cyclic excess pore water pressure generation and cyclic stiffness degradation was minimal. However, adding carpet fibre significantly improved both liquefaction and post-liquefaction resistances of clean sand. The liquefaction resistance of clean sand, at a constant 15 loading cycles, improved by 26.3% when the soil was reinforced with 0.75% recycled carpet fibre. In addition, the initial shear modulus of the liquefied specimen significantly increased by adding recycled carpet fibre.
Rasouli, H, Fatahi, B & Nimbalkar, S 2022, 'Re-liquefaction resistance of lightly cemented sands', Canadian Geotechnical Journal, vol. 59, no. 12, pp. 2085-2101.
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The re-liquefaction resistance of cemented sands under multiple liquefaction events such as pre-shock, main-shock, and after-shock earthquakes is a complex phenomenon because the response may alter due to bond breakage. A series of multistage liquefaction–re-consolidation soil element tests under undrained stress-controlled cyclic loading condition using cyclic triaxial were carried out to assess the liquefaction and re-liquefaction resistance of cemented sands with varying degrees of cementation. Lightly cemented specimens were reconstituted using Sydney sand and high early strength Portland cement with cement content ranging from 0.25% to 1% and unconfined compression strength from 15 to 80 kPa. The results showed that the re-liquefaction resistance of cemented sands with different amounts of cement decreased after the first liquefaction event and then increased for succeeding liquefaction events. While the trend of residual excess pore water pressure ratio and cyclic stiffness degradation index of untreated sand under successive liquefaction events remained consistent, the corresponding responses for cemented sands altered for the second to the fifth liquefaction events. In fact, the residual excess pore water pressure ratio and cyclic stiffness of cemented sand increased and degraded faster during the early cycles of loading for the second to fifth liquefaction events.
Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.
Rozali, S, Abd Latif, Z, Adnan, NA, Hussin, Y, Blackburn, A & Pradhan, B 2022, 'Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique', Geocarto International, vol. 37, no. 11, pp. 3247-3264.
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Sabetamal, H, Sheng, D & Carter, JP 2022, 'Coupled hydro-mechanical modelling of unsaturated soils; numerical implementation and application to large deformation problems', Computers and Geotechnics, vol. 152, pp. 105044-105044.
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This paper presents coupled hydro-mechanical modelling of unsaturated soil problems by incorporating some advanced numerical and constitutive models in a general-purpose commercial software package, Abaqus. Two different strategies for the interpretation of unsaturated soil behaviour with respect to the soil volume change are considered and the relevant constitutive models are implemented through user-defined subroutines. The first approach that is commonly used in geomechanics considers suction as an additional variable and uses a constitutive model in the space of effective stress–suction assuming that soil compressibility is a function of suction. The second approach treats the constitutive model in the space of effective stress and degree of saturation assuming that the soil compressibility is a function of the degree of saturation. The hydro-mechanical behaviour regarding the change in the degree of saturation caused by both suction and net stress changes is also considered, together with the effect of hydraulic hysteresis. Validation of the implemented algorithms is presented through several benchmark problems. Finally, the application and utility of the implemented procedures are illustrated by simulations of two challenging problems of unsaturated geomechanics, including a slope failure due to seepage and rainfall infiltration as well as cone penetration tests in unsaturated soil. A suitable mesh optimisation scheme is also incorporated to handle finite deformations.
Sadeghian, F, Jahandari, S, Haddad, A, Rasekh, H & Li, J 2022, 'Effects of variations of voltage and pH value on the shear strength of soil and durability of different electrodes and piles during electrokinetic phenomenon', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 2, pp. 625-636.
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Saha, S, Gayen, A, Gogoi, P, Kundu, B, Paul, GC & Pradhan, B 2022, 'Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India', Geocarto International, vol. 37, no. 25, pp. 8004-8035.
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Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability.
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.
Sakti, AD, Fauzi, AI, Takeuchi, W, Pradhan, B, Yarime, M, Vega-Garcia, C, Agustina, E, Wibisono, D, Anggraini, TS, Theodora, MO, Ramadhanti, D, Muhammad, MF, Aufaristama, M, Perdana, AMP & Wikantika, K 2022, 'Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests', Remote Sensing, vol. 14, no. 3, pp. 543-543.
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Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.
Sakti, AD, Rahadianto, MAE, Pradhan, B, Muhammad, HN, Andani, IGA, Sarli, PW, Abdillah, MR, Anggraini, TS, Purnomo, AD, Ridwana, R, Yulianto, F, Manessa, MDM, Fauziyyah, AN, Yayusman, LF & Wikantika, K 2022, 'School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education', ISPRS International Journal of Geo-Information, vol. 11, no. 1, pp. 12-12.
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This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current numbe...
Sansom, TM, Oberst, S, Richter, A, Lai, JCS, Saadatfar, M, Nowotny, M & Evans, TA 2022, 'Low radiodensity μCT scans to reveal detailed morphology of the termite leg and its subgenual organ', Arthropod Structure & Development, vol. 70, pp. 101191-101191.
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Termites sense tiny substrate-borne vibrations through subgenual organs (SGOs) located within their legs' tibiae. Little is known about the SGOs' structure and physical properties. We applied high-resolution (voxel size 0.45 μm) micro-computed tomography (μCT) to Australian termites, Coptotermes lacteus and Nasutitermes exitiosus (Hill) to test two staining techniques. We compared the effectiveness of a single stain of Lugol's iodine solution (LS) to LS followed by Phosphotungstic acid (PTA) solutions (1% and 2%). We then present results of a soldier of Nasutitermes exitiosus combining μCT with LS + 2%PTS stains and scanning electron microscopy to exemplify the visualisation of their SGOs. The termite's SGO due to its approximately oval shape was shown to have a maximum diameter of 60 μm and a minimum of 48 μm, covering 60 ± 4% of the leg's cross-section and 90.4 ± 5% of the residual haemolymph channel. Additionally, the leg and residual haemolymph channel cross-sectional area decreased around the SGO by 33% and 73%, respectively. We hypothesise that this change in cross-sectional area amplifies the vibrations for the SGO. Since SGOs are directly connected to the cuticle, their mechanical properties and the geometric details identified here may enable new approaches to determine how termites sense micro-vibrations.
Senanayake, S & Pradhan, B 2022, 'Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka', Journal of Environmental Management, vol. 308, pp. 114589-114589.
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Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.
Senanayake, S, Pradhan, B, Alamri, A & Park, H-J 2022, 'A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction', Science of The Total Environment, vol. 845, pp. 157220-157220.
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Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.
Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2022, 'Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka', Science of The Total Environment, vol. 806, no. Pt 2, pp. 150405-150405.
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The spatial variation of soil erosion is essential for farming system management and resilience development, specifically in the high climate hazard vulnerable tropical countries like Sri Lanka. This study aimed to investigate climate and human-induced soil erosion through spatial modeling. Remote sensing was used for spatial modeling to detect soil erosion, crop diversity, and rainfall variation. The study employed a time-series analysis of several variables such as rainfall, land-use land-cover (LULC) and crop diversity to detect the spatial variability of soil erosion in farming systems. Rain-use efficiency (RUE) and residual trend analysis (RESTREND) combined with a regression approach were applied to partition the soil erosion due to human and climate-induced land degradation. Results showed that soil erosion has increased from 9.08 Mg/ha/yr to 11.08 Mg/ha/yr from 2000 to 2019 in the Central Highlands of Sri Lanka. The average annual rainfall has increased in the western part of the Central Highlands, and soil erosion hazards such as landslides incidence also increased during this period. However, crop diversity has been decreasing in farming systems, namely wet zone low country (WL1a) and wet zone mid-country (WM1a), in the western part of the Central Highlands. The RUE and RESTREND analyses reveal climate-induced soil erosion is responsible for land degradation in these farming systems and is a threat to sustainable food production in the farming systems of the Central Highlands.
Sepehrirahnama, S & Oberst, S 2022, 'Acoustic Radiation Force and Torque Acting on Asymmetric Objects in Acoustic Bessel Beam of Zeroth Order Within Rayleigh Scattering Limit', Frontiers in Physics, vol. 10.
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Acoustic momentum exchange between objects and the surrounding fluid can be quantified in terms of acoustic radiation force and torque, and depends on several factors including the objects’ geometries. For a one-dimensional plane wave type, the induced torque on the objects with arbitrary shape becomes a function of both, direct polarization and Willis coupling, as a result of shape asymmetry, and has only in-plane components. Here, we investigate, in the Rayleigh scattering limit, the momentum transfer to objects in the non-planar pressure field of an acoustic Bessel beam with axisymmetric wave front. This type of beam is selected since it can be practically realized by an array of transducers that are cylindrically arranged and tilted at the cone angle β which is a proportionality index of the momentum distribution in the transverse and axial propagation directions. The analytical expressions of the radiation force and torque are derived for both symmetric and asymmetric objects. We show the dependence of radiation force and torque on the characteristic parameters β and radial distance from the beam axis. By comparing against the case of a plane travelling plane wave, zero β angle, we demonstrated that the non-planar wavefront of a zeroth order Bessel beam causes an additional radial force and axial torque. We also show that, due to Willis coupling, an asymmetric object experiences greater torques in the θ direction, by minimum of one order of magnitude compared to a plane travelling wave. Further, the components of the partial torques owing to direct polarization and Willis coupling act in the same direction, except for a certain range of cone angle β. Our findings show that a non-planar wavefront, which is quantified by β in the case of a zeroth-order Bessel beam, can be used to con...
Sepehrirahnama, S, Oberst, S, Chiang, YK & Powell, DA 2022, 'Willis Coupling-Induced Acoustic Radiation Force and Torque Reversal', Physical Review Letters, vol. 129, no. 17, p. 174501.
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Acoustic meta-atoms serve as the building blocks of metamaterials, with linear properties designed to achieve functions such as beam steering, cloaking, and focusing. They have also been used to shape the characteristics of incident acoustic fields, which led to the manipulation of acoustic radiation force and torque for development of acoustic tweezers with improved spatial resolution. However, acoustic radiation force and torque also depend on the shape of the object, which strongly affects its scattering properties. We show that by designing linear properties of an object using metamaterial concepts, the nonlinear acoustic effects of radiation force and torque can be controlled. Trapped objects are typically small compared with the wavelength, and are described as particles, inducing monopole and dipole scattering. We extend such models to a polarizability tensor including Willis coupling terms, as a measure of asymmetry, capturing the significance of geometrical features. We apply our model to a three-dimensional, subwavelength meta-atom with maximal Willis coupling, demonstrating that the force and the torque can be reversed relative to an equivalent symmetrical particle. By considering shape asymmetry in the acoustic radiation force and torque, Gorkov's fundamental theory of acoustophoresis is thereby extended. Asymmetrical shapes influence the acoustic fields by shifting the stable trapping location, highlighting a potential for tunable, shape-dependent particle sorting.
Sepehrirahnama, S, Ray Mohapatra, A, Oberst, S, Chiang, YK, Powell, DA & Lim, K-M 2022, 'Acoustofluidics 24: theory and experimental measurements of acoustic interaction force', Lab on a Chip, vol. 22, no. 18, pp. 3290-3313.
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This tutorial review covers theoretical and experimental aspects of acoustic interaction force, as one of the driving forces of acoustophoresis. The non-reciprocity, rotational coupling, viscosity effects, and particle agglomeration are discussed.
Serbouti, I, Raji, M, Hakdaoui, M, El Kamel, F, Pradhan, B, Gite, S, Alamri, A, Maulud, KNA & Dikshit, A 2022, 'Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers', Remote Sensing, vol. 14, no. 21, pp. 5498-5498.
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In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.
Shafaghat, A & Khabbaz, H 2022, 'Recent advances and past discoveries on tapered pile foundations: a review', Geomechanics and Geoengineering, vol. 17, no. 2, pp. 455-484.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The growing tendency to study the behaviour of tapered piles in the last two decades has made it necessary to gain a deeper insight into this specific kind of deep foundation. Tapered piles have been investigated through analytical, experimental, and numerical studies. These piles have revealed different behaviour under various loading conditions. Hence, reviewing and assessing these efforts to comprehend their response can be of great significance. In this paper firstly, it is attempted to go over experimental studies, conducted on tapered piles. Then, the proposed mathematical and numerical solutions, employed to calculate the bearing capacity of single tapered piles, are compared to have a better vision of how these piles behave. In the third section, the optimum tapering angles of tapered piles in loose, medium, and dense sand are discussed. All the efforts are investigated technically to find the advantages, disadvantages, and the research gaps for this specific kind of piles. In addition, another section entitled the directions and ideas for future research on tapered piles is provided comprising the most recent achievements in this area. Moreover, the implementation of tapered piles in a significant project as a case study is discussed.
Shafaghat, A, Khabbaz, H & Fatahi, B 2022, 'Axial and Lateral Efficiency of Tapered Pile Groups in Sand Using Mathematical and Three-Dimensional Numerical Analyses', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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This study presents a new mathematical equation for calculating the pile group efficiency in cohesionless soil under combined axial and lateral loading conditions, considering the tapering angle effect. Based on the mathematical definition of the pile group efficiency, analytical correlations are developed. The tapering effect is considered by developing a new geometry coefficient for efficiency associated with the shaft vertical bearing component of tapered piles. In addition, a simplified mathematical equation is developed for predicting the group interaction factor as a function of pile spacing, number of piles in the group, diameter of the cylindrical reference pile, tapering angle, and pile slenderness ratio. On the other hand, an array of three-dimensional numerical analyses is performed for modeling same-volume single bored piles and pile groups with various arrangements to capture the accuracy of the proposed mathematical equation. The hardening soil constitutive model is adopted for the modeling of piles in loose sand. Subsequently, the load-displacement diagrams of single piles, as well as pile groups, are obtained. The bearing capacities of straight-sided and tapered bored piles are then calculated and compared using a definite settlement criterion. By computing the various bearing-capacity components, group efficiencies can be attained from both numerical and mathematical analyses. The results indicate an acceptable agreement between both analyses. Finally, the developed equation can predict the pile group efficiency incorporating the tapering angle and other influencing parameters as a novel and simple relationship under simultaneous axial and lateral loading conditions.
Shafapourtehrany, M, Yariyan, P, Özener, H, Pradhan, B & Shabani, F 2022, 'Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey', International Journal of Disaster Risk Reduction, vol. 79, pp. 103154-103154.
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Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone to earthquakes, however, there have been no earthquakes in those areas recently. This research proposes and tests the ability of the k-mean clustering method to create the training dataset for earthquake vulnerability analysis. Subsequently, the derived sample dataset was used in four state-of-the-art models i.e. Decision Tree (DT), Support Vector Machine (SVM), Self-Organizing Map (SOM) and Logistic Regression (LR) for assessing earthquake vulnerability in Istanbul, Turkey. The multicollinearity among the variables was determined using tolerance (TOL) and variance inflation factor (VIF) which revealed no multicollinearity among the variables. The highest VIF belonged to the “distance to faults” factor. Vulnerability related variables were classified, weighed and using k-mean clustering, a training database was constructed. Then, the standardized variables were keyed in as input alongside the training site maps into DT, SVM, SOM and LR to construct an Earthquake Vulnerability Map (EVM). EVMs were created for all the four samples and graded as very-low, relatively-low, moderate, high, or extremely-high. Several statistical metrics such as Area under the ROC curve (AUC), sensitivity (SST), specificity (SPF), root-mean-squared-errors (RMSE), positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the accuracy of the resultant maps. The highest and lowest AUC prediction rates were 0.962 and 0.912 from the K-means-SOM and K-means-LR models, respectively. The lowest RSME results using the testing dataset (0.329) belonged to K-means-SVM model. The region's most prone vulnerability ...
Shan, F, He, X, Jahed Armaghani, D, Zhang, P & Sheng, D 2022, 'Success and challenges in predicting TBM penetration rate using recurrent neural networks', Tunnelling and Underground Space Technology, vol. 130, pp. 104728-104728.
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Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.
Shanableh, A, Al-Ruzouq, R, Hamad, K, Gibril, MBA, Khalil, MA, Khalifa, I, El Traboulsi, Y, Pradhan, B, Jena, R, Alani, S, Alhosani, M, Stietiya, MH, Al Bardan, M & AL-Mansoori, S 2022, 'Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations', Remote Sensing Applications: Society and Environment, vol. 26, pp. 100757-100757.
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The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO2, O3, SO2, PM10, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO2 (up to 22%), SO2 (up to 17%), and AOD (up to 40%) with small changes in O3 (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO2 (49% - 57%) and PM10 (19% - 64%); however, the SO2 and O3 levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO2, PM10, and AOD. The data also demonstrated significant correlations between the mobility and NO2 and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.
Sharari, N, Fatahi, B, Hokmabadi, A & Xu, R 2022, 'Seismic resilience of extra-large LNG tank built on liquefiable soil deposit capturing soil-pile-structure interaction', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3385-3441.
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AbstractAssessment of seismic resilience of critical infrastructure such as liquefied natural gas (LNG) storage tanks, is essential to ensure availability and security of services during and after occurrence of large earthquakes. In many projects, it is preferred to build energy storage facilities in coastal areas for the ease of sea transportation, where weak soils such as soft clay and loose sand with liquefaction potential may be present. In this study, three-dimensional finite element model is implemented to examine the seismic response of a 160,000 m3full containment LNG tank supported by 289 reinforced concrete piles constructed on liquefiable soil overlaying the soft clay deposit. The seismic soil-structure interaction analysis was conducted through direct method in the time domain subjected to the 1999 Chi-Chi and the 1968 Hachinohe earthquakes, scaled to Safe Shutdown Earthquake hazard level for design of LNG tanks. The analyses considered different thicknesses of the liquified soil deposit varying from zero (no liquefaction) to 15 m measured from the ground surface. The key design parameters inspected for the LNG tank include the acceleration profile for both inner and outer tanks, the axial, hoop and shear forces as well as the von Mises stresses in the inner tank wall containing the LNG, in addition to the pile response in terms of lateral displacements, shear forces and bending moments. The results show that the seismic forces generated in the superstructure decreased with increasing the liquefied soil depth. In particular, the von Mises stresses in the inner steel tank exceeded the yield stress for non-liquefied soil deposit, and the elastic–plastic buckling was initiated in the upper section of the tank where plastic deformations were detected as a result of excessive von Mises stresses. However, when soil liquefaction occurred, although von Mises stresses in the inner tank shell remai...
Sharari, N, Fatahi, B, Hokmabadi, AS & Xu, R 2022, 'Impacts of Pile Foundation Arrangement on Seismic Response of LNG Tanks Considering Soil–Foundation–Structure Interaction', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2022, 'A Survey on Object Instance Segmentation', SN Computer Science, vol. 3, no. 6, p. 499.
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AbstractIn recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
Shirmard, H, Farahbakhsh, E, Heidari, E, Beiranvand Pour, A, Pradhan, B, Müller, D & Chandra, R 2022, 'A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data', Remote Sensing, vol. 14, no. 4, pp. 819-819.
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Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
Shivakumara, P, Chowdhury, PN, Pal, U, Doermann, D, Ramachandra, R, Lu, T & Blumenstein, M 2022, 'A Knowledge Enforcement Network-Based Approach for Classifying a Photographer’s Images', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 15, p. 2250046.
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Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%–10% on average. The dataset created for the experimental purpose will be made available upon publication.
Shivakumara, P, Das, A, Raghunandan, KS, Pal, U & Blumenstein, M 2022, 'New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 09.
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Document age estimation using handwritten text line images is useful for several pattern recognition and artificial intelligence applications such as forged signature verification, writer identification, gender identification, personality traits identification, and fraudulent document identification. This paper presents a novel method for document age classification at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classification of text line images of different ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at different wavelet decomposition levels. The specific advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classification of text lines of different ages by feeding features and the original image as input. To demonstrate the efficacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classification. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classification in terms of average classification rate.
Siddiq, A, Shukla, N & Pradhan, B 2022, 'Spatio-temporal modelling of dengue fever cases in Saudi Arabia using socio-economic, climatic and environmental factors', Geocarto International, vol. 37, no. 26, pp. 12867-12891.
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Silva, IN, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2022, 'Shear behaviour of subgrade soil with reference to varying initial shear stress and plasticity index', Acta Geotechnica, vol. 17, no. 9, pp. 4207-4216.
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AbstractThe influence of stress anisotropy (K) (i.e. the ratio between effective horizontal and vertical stresses) on the shear behaviour of soils has received significant attention in past studies, but how its influence depends on different values of the plasticity index (PI) has not been properly quantified. In this study, the results of a series of undrained triaxial tests on anisotropically consolidated soil at different values of K are reported, and together with past experimental data, the interactive roles of K and PI on the shear behaviour of soil are rigorously interpreted. The findings indicate that the peak shear strength increases with higher brittleness, whereas the peak excess pore pressure diminishes when the value of K decreases. Moreover, increasing the value of PI up to 35 tends to increase the peak shear strength, but beyond that the influence of PI seems marginal. Based on the findings of this study, empirical equations incorporating PI and K to estimate the undrained shear strength are proposed with acceptable accuracy.
Singh, SK, Taylor, RW, Pradhan, B, Shirzadi, A & Pham, BT 2022, 'Predicting sustainable arsenic mitigation using machine learning techniques', Ecotoxicology and Environmental Safety, vol. 232, pp. 113271-113271.
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This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce.
Song, Z, Ji, J, Zhang, R & Cao, L 2022, 'Development of a test equipment for rating front to rear-end collisions based on C-NCAP-2018', International Journal of Crashworthiness, vol. 27, no. 2, pp. 522-532.
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Autonomous emergency braking (AEB) systems play an important role in reducing front to rear-end collisions. To evaluate the performance of AEB systems, different countries have recently published their own new car assessment programs (NCAPs). This study firstly develops a set of test equipment to evaluate the performance of AEB systems in field tests according to the China New Car Assessment Program (C-NCAP-2018). Then, the test accuracy of the AEB test equipment is debugged and verified by comparing the test results with those from IIHS. Finally, field tests are performed to evaluate the AEB systems performance on collision avoidance speeds in CCRs test scenarios and actuation time of warning function and braking of five vehicles using the developed test equipment. Additionally, some underlying causes are discussed for the trade-off between driving comfort and braking effectiveness. The field tests confirm that the developed equipment can effectively evaluate the performance of AEB systems and thus improve the active safety technology for vehicles.
Soomro, MHAA, Indraratna, B & Karekal, S 2022, 'Critical shear strain and sliding potential of rock joint under cyclic loading', Transportation Geotechnics, vol. 32, pp. 100708-100708.
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A new concept of critical shear strain ετcritical of rock joint under cyclic loading is presented, and the role of ετcritical in evaluating the sliding potential of rock joint is highlighted. A series of cyclic triaxial tests was conducted on a cylindrical rock joint specimen with a replicated rough surface representing a joint roughness coefficient JRC value of 12.6 oriented at 60° with respect to the horizontal plane. The experimental results indicate that the onset of instability of rock joint is suppressed with increase in confining pressure and number of loading cycles N until the normalized shear deformation increases beyond a threshold value of ετcritical. Generally, the critical strain of most rock types is considered in the proximity of 1% under small strain conditions [36–37], however, in this study, the critical strain concept is extended to the domain of rock joints, and a semi-empirical model to more rigorously quantify the critical shear strain (ετcritical) of rock joint is suggested considering the effect of joint roughness coefficient JRC, cyclic loading amplitude, and the number of loading cycles N. Also, a rational classification of Joint Sliding Potential (JSP) based on the ετcritical and normalized total shear strain εθN of rock joint is proposed to characterize the cyclic loading induced sliding instability of a rock discontinuity.
Tao, G, Ouyang, Q, Lei, D, Chen, Q, Nimbalkar, S, Bai, L & Zhu, Z 2022, 'NMR-Based Measurement of AWRC and Prediction of Shear Strength of Unsaturated Soils', International Journal of Geomechanics, vol. 22, no. 9.
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Tian, Z, Li, Y, Li, S, Vute, S & Ji, J 2022, 'Influence of particle morphology and concentration on the piezoresistivity of cement-based sensors with magneto-aligned nickel fillers', Measurement, vol. 187, pp. 110194-110194.
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Cement-based sensors with magneto-aligned nickel fillers have the proven capability to significantly enhance piezoresistivity compared with the sensors with randomized fillers. In this paper, the influence of particle morphology and concentration of nickel particles on the piezoresistive and mechanical properties of cement-based sensors, treated with and without magnetic field intervention, are investigated experimentally. Five categories of nickel particles with different average diameters are type N50 (50 nm), N500 (0.5 μm), F(1 μm × 20 μm flake), T (5 μm) and U (25 μm). The obtained results indicate that the application of magnetic field enhances most of the piezoresistive performance and yields best piezoresistivity for the samples with type T nickel powder. Anisotropic piezoresistivity can be achieved under a very low filler content (0.1 vol%) in N50 nano-scale nickel powder and cement composite, followed by the N500 and T nickel particles in 5 vol% content. Small particles with lower content have similar piezoresistive performance to the samples with large particles and higher concentration. One half of the samples can achieve high giant gauge factor (GF) of over 500, two-thirds of which are aligned by magnetic field with anisotropic piezoresistive property. Samples with 5 vol% type T nickel content has the highest GF value, followed by the sample with 5 vol% type F nickel flakes and 10 vol% type U nickel powder. It is also found that mechanical strength decreases with the increase of particle concentration.
Tong, C-X, Dong, Z-L, Sun, Q, Zhang, S, Zheng, J-X & Sheng, D 2022, 'On compression behavior and particle breakage of carbonate silty sands', Engineering Geology, vol. 297, pp. 106492-106492.
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Tong, C-X, Zhai, M-Y, Li, H-C, Zhang, S & Sheng, D 2022, 'Particle breakage of granular soils: changing critical state line and constitutive modelling', Acta Geotechnica, vol. 17, no. 3, pp. 755-768.
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Tucho, A, Indraratna, B & Ngo, T 2022, 'Stress-deformation analysis of rail substructure under moving wheel load', Transportation Geotechnics, vol. 36, pp. 100805-100805.
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Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2022, 'Low-profile dual-band pixelated defected ground antenna for multistandard IoT devices', Scientific Reports, vol. 12, no. 1, p. 11479.
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AbstractA low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The propos...
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J, Esselle, KP & Shariati, N 2022, 'A Review on Antenna Technologies for Ambient RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications', IEEE Access, vol. 10, pp. 17231-17267.
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Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna's performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.
Van, CN, Tran Thanh, H, Nguyen, TN & Li, J 2022, 'Numerical investigation of the influence of casting techniques on fiber orientation distribution in ECC', Frontiers of Structural and Civil Engineering, vol. 16, no. 11, pp. 1424-1435.
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AbstractEngineered cementitious composites (ECC), also known as bendable concrete, were developed based on engineering the interactions between fibers and cementitious matrix. The orientation of fibers, in this regard, is one of the major factors influencing the ductile behavior of this material. In this study, fiber orientation distributions in ECC beams influenced by different casting techniques are evaluated via numerical modeling of the casting process. Two casting directions and two casting positions of the funnel outlet with beam specimens are modeled using a particle-based smoothed particle hydrodynamics (SPH) method. In this SPH approach, fresh mortar and fiber are discretized by separated mortar and fiber particles, which smoothly interact in the computational domain of SPH. The movement of fiber particles is monitored during the casting simulation. Then, the fiber orientations at different sections of specimens are determined after the fresh ECC stops flowing in the formwork. The simulation results show a significant impact of the casting direction on fiber orientation distributions along the longitudinal wall of beams, which eventually influence the flexural strength of beams. In addition, casting positions show negligible influences on the orientation distribution of fibers in the short ECC beam, except under the pouring position.
Vayghan, SS, Salmani, M, Ghasemkhani, N, Pradhan, B & Alamri, A 2022, 'Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data', Geocarto International, vol. 37, no. 10, pp. 2967-2995.
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Wang, C, Ji, J, Miao, Z & Zhou, J 2022, 'Udwadia-Kalaba approach based distributed consensus control for multi-mobile robot systems with communication delays', Journal of the Franklin Institute, vol. 359, no. 14, pp. 7283-7306.
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In this paper, a distributed consensus algorithm for multi-mobile robot systems (MMRSs) with communication delays is proposed based on the Udwadia-Kalaba (UK) approach. The key feature of the proposed algorithm is that the consensus requirement is configured as a second-order constraint, and then a concise and explicit equation of motion for the constrained mechanical systems is formulated. Furthermore, the necessary and sufficient conditions for achieving the consensus of MMRSs with or without communication delays are developed under the network topology possessing a directed spanning tree. Finally, some numerical simulations are performed to verify the validity of the proposed consensus algorithm.
Xu, B-H, Indraratna, B, Rujikiatkamjorn, C & Trung Nguyen, T 2022, 'A large-strain radial consolidation model incorporating soil destructuration and isotache concept', Computers and Geotechnics, vol. 147, pp. 104761-104761.
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Xu, Z, Khabbaz, H, Fatahi, B & Wu, D 2022, 'Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 5, pp. 1609-1625.
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An emerging real-time ground compaction and quality control, known as intelligent compaction (IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional (3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed. The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.
Yang, Z, Pan, J, Chen, J, Zi, Y, Oberst, S, Schwingshackl, CW & Hoffmann, N 2022, 'A novel unknown-input and single-output approach to extract vibration patterns via a roving continuous random excitation', ISA Transactions, vol. 129, pp. 675-686.
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Ye, K & Ji, JC 2022, 'An origami inspired quasi-zero stiffness vibration isolator using a novel truss-spring based stack Miura-ori structure', Mechanical Systems and Signal Processing, vol. 165, pp. 108383-108383.
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In this paper, an origami-inspired vibration isolator is proposed and numerically investigated to achieve a quasi-zero-stiffness (QZS) property. A truss-spring based stack Miura-ori (TS-SMO) structure is introduced in the vibration isolation system to provide a desired stiffness for high-static-low-dynamic requirement. The proposed TS-SMO structure is different from the traditional origami structures which include rigid facets and deformable creases. It uses coil spring sets to replace all the creases, to improve the physical realization in engineering applications. Nonlinear force response and the QZS feature can be achieved through the geometric nonlinearity and its unique Poisson's ratio profile. The static force and stiffness characteristics of the developed TS-SMO structure are numerically discussed to meet specific feature requirements. Then a QZS vibration isolator is presented under specific parameter design. The force–displacement response and stiffness diagram are obtained to verify the static performance as an isolation system. Furthermore, the displacement transmissibility is derived through dynamic analysis by employing both harmonic balance method (HBM) and numerical simulations. The isolation performance under variable viscous damping is also discussed to examine the effects of the system damping.
Yousefi, M, Tabatabaei, SH, Rikhtehgaran, R, Pour, AB & Pradhan, B 2022, 'Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran', Geocarto International, vol. 37, no. 25, pp. 9788-9816.
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Youssef, AM, Pradhan, B, Dikshit, A & Mahdi, AM 2022, 'Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt', Geocarto International, vol. 37, no. 26, pp. 11088-11115.
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Geohazard risk is high in Arab countries due to ineffective disaster preparedness measures, mismanagement, lack of public awareness, inadequate funding and lack of stakeholder support. One such country is Egypt, which is hit by floods every year that cost lives and bring the economy to a standstill. Moreover, not much has been done to map flood-prone areas. In this paper, flood susceptibility modelling was evaluated in the Ras Gharib region of Egypt using two effective techniques machine learning technique-MLT (Support Vector Machine (SVM)) and deep learning method-DL (Convolutional Neural Networks (CNN)). Thirteen flood related factors and flood inventory layer were prepared to construct these models. Validation was performed with 30% of the flood locations where receiver operating characteristic (ROC) curves showed that the deep learning technique (CNN) gave a prediction accuracy of 86.5% (high performance), while the MLTs (SVM) gave 71.6% (medium performance). The results show that CNN provides 17% better than SVM which indicates a powerful and accurate model in flood susceptibility mapping. Results were confirmed using the Astro Digital images shortly after the 2016 flood, in which the CNN model provides a good agreement.
Youssef, AM, Pradhan, B, Dikshit, A, Al-Katheri, MM, Matar, SS & Mahdi, AM 2022, 'Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA', Bulletin of Engineering Geology and the Environment, vol. 81, no. 4.
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Yu, Y, Liang, S, Samali, B, Nguyen, TN, Zhai, C, Li, J & Xie, X 2022, 'Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network', Engineering Structures, vol. 273, pp. 115066-115066.
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Zhang, H, Nguyen, H, Bui, X-N, Pradhan, B, Asteris, PG, Costache, R & Aryal, J 2022, 'A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm', Engineering with Computers, vol. 38, no. S5, pp. 3901-3914.
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In landslide susceptibility mapping or evaluating slope stability, the shear strength parameters of rocks and soils and their effectiveness are undeniable. However, they have not been studied for all-natural materials, as well as different locations. Therefore, this paper proposes a novel generalized artificial intelligence model for estimating the friction angle of clays from different areas/locations for evaluating slope stability or landslide susceptibility mapping, including the datasets from the UK, New Zealand, Indonesia, Venezuela, USA, Japan, and Italy. The robustness and consistency of the model’s prediction were checked by testing with various datasets having different geological and geomorphological setups. Accordingly, 162 observations from different areas/locations were collected from the locations and regions above for this aim. Subsequently, deep learning techniques were applied to develop the multiple layer perceptron (MLP) neural network model (i.e., DMLP model) with the goal of error reduction of the MLP model. Next, Harris Hawks optimization (HHO) algorithm was applied to boost the optimization of the DMLP model for predicting friction angle of clays aiming to get a better accuracy than those of the DMLP model, called HHO–DMLP model. A DMLP neural network without optimization of the HHO algorithm and two other conventional models (i.e., SVM and RF) were also employed to compare with the proposed HHO–DMLP model. The results showed that the proposed HHO–DMLP model predicted the friction angle of clays better than those of the other models. It can reflect the friction angle of clays with acceptable accuracy from different locations and regions (i.e., MSE = 12.042; RMSE = 3.470; R2 = 0.796; MAPE = 0.182; and VAF = 78.806). The DMLP model without optimization of the HHO algorithm provided slightly lower accuracy (i.e., MSE = 15.151; RMSE = 3.892; R2 = 0.738; MAPE = 0.202; and VAF = 73.431). Besides, two other conventional models (i.e., SVM and RF) p...
Zhang, S, Lan, P, Li, H-C, Tong, C-X & Sheng, D 2022, 'Physics-informed neural networks for consolidation of soils', Engineering Computations, vol. 39, no. 7, pp. 2845-2865.
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PurposePrediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.Design/methodology/approachA typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.FindingsThe overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.Originality/valueThis study presents the potential application of PINNs for the consolidation of soils. S...
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2022, 'Exploring Pairwise Relationships Adaptively From Linguistic Context in Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 3101-3113.
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For image captioning, recent works start to focus on exploring visual relationships for generating high-quality interactive words (i.e. verbs and prepositions). However, many existing works only focus on semantic level by analysing the feature similarity between objects in the visual domain but ignore the linguistic context included in the caption decoder. When captioning is being carried out, the entity words can be inferred based on visual information of objects. The interactive words representing the relationships between entity words can only be inferred based on high-level language meaning generated in the process of captioning decoding. Such high-level language meaning is called linguistic context, which refers to the relational context between words or phrases in the caption sentences. The linguistic context can be used as strong guidance to explore related visual relationships between different objects effectively. To achieve this, we propose a novel context-adaptive attention module that is strongly driven by the linguistic context from the caption decoder. In this module, a novel design of visual relationship attention is proposed based on a bilinear self-attention model to explore related visual relationships and encode more discriminative features under the linguistic context. It works parallelly with visual region attention. To achieve the adaptive process of attending to related visual relationships for generating interactive words or related visual objects for entity words, an attention modulator is integrated as an attention channel controller responding to the changing linguistic context of the caption decoder dynamically. To take full advantage of the linguistic context in the caption, an additional interaction dataset is extracted from the COCO caption datasets and COCO Entities dataset to supervise the training of the proposed context-adaptive attention module explicitly. Demonstrated by experiments on MSCOCO caption dataset, it is e...
Zhao, F, Cao, S, Luo, Q, Li, L & Ji, J 2022, 'Practical design of the QZS isolator with one pair of oblique bars by considering pre-compression and low-dynamic stiffness', Nonlinear Dynamics, vol. 108, no. 4, pp. 3313-3330.
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Various quasi-zero stiffness (QZS) vibration isolators have been developed by using the structures of oblique springs and bars. Towards a practical design, this paper further theoretically and experimentally studies the static and dynamic force of the QZS isolator with one pair of oblique bars by considering pre-compression of horizontal springs and producing an extremely low-dynamic stiffness. By designing the new parameter configuration, two simple formulations are derived on the basis of two QZS conditions to design an improved QZS isolator with a constant low-dynamic stiffness in a wide region around the static equilibrium position. A detailed comparison between the proposed and the existing isolators is made to show the significant improvement on isolation performance. On the basis of the derived formulations, a prototype is fabricated and tested to verify the theoretical formulations and constant low-dynamic stiffness. The experimental results show that the designed QZS isolator can achieve a much wider QZS region to isolate vibration in a larger frequency band and demonstrate a lower displacement transmissibility for the external excitation.
Zhao, Y, Chen, J, Zhang, J, Wu, D, Blumenstein, M & Yu, S 2022, 'Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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SummaryIn the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.
Zhong, D, Shivakumara, P, Nandanwar, L, Pal, U, Blumenstein, M & Lu, Y 2022, 'Local Resultant Gradient Vector Difference and Inpainting for 3D Text Detection in the Wild', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 08, p. 2253005.
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Three-dimensional (3D) text appearing in natural scene images is common due to 3D cameras and the capture of text from different angles, which presents new problems for text detection. This is because of the presence of depth information, shadows, and decorative characters in the images. In this work, we consider those images where 3D text appears with depth, as well as shadow information for text detection. We propose a novel method based on local resultant gradient vector difference (LRGVD), inpainting and a deep learning model for detecting 3D as well as two-dimensional (2D) texts in natural scene images. The boundary of components that are invariant to the above challenges is detected by exploring LRGVD. The LRGVD uses gradient magnitude and direction in a novel way for detecting the boundary of the components. Further, we propose an inpainting method in a new way for restoring the character background information using boundaries. For a given region and the input image, the inpainting method divides the whole image into planes and then propagates the values in the planes into the missing region based on posterior probabilities and neighboring information. This results in text regions with false positives. Then, the differential binarization network (DB-Net) is proposed for detecting text irrespective of orientation, background, 3D or 2D, etc. Experiments conducted on our 3D text images and standard datasets of natural scene text images, namely ICDAR 2019 MLT, ICDAR 2019 ArT, DAST1500, Total-Text and SCUT-CTW1500, show that the proposed method is effective in detecting 3D and 2D texts in the images.
Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2022, 'Minute-wise frost prediction: An approach of recurrent neural networks', Array, vol. 14, pp. 100158-100158.
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Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.
Bhandari, S, Fatahi, B, Khabbaz, H, Lee, J, Xu, Z & Zhong, J 1970, 'Evaluating the Influence of Soil Plasticity on the Vibratory Roller—Soil Interaction for Intelligent Compaction', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 247-260.
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Use of intelligent compaction (IC) is a growing technique for compaction in the field of construction. It provides an efficient way of evaluating the soil compaction level with a higher degree of certainty than traditional quality control methods. IC involves the interpretation of measured values received through the accelerometer and other sensors attached to the roller. The key objective of this paper is to analyse the dynamic roller–soil interaction via a three-dimensional nonlinear finite element model, capturing soil nonlinear response and damping in both small and large strain ranges as a result of dynamic load applied via the vibratory roller. In particular, the impact of soil plasticity index (PI) on the response of a typical vibratory roller is assessed. Indeed, the soil plasticity impacts stiffness degradation with shear strain influencing the soil stiffness during compaction and the roller response. The numerical predictions exhibit that the soil plasticity can significantly influence the response of the roller and the ground settlement level; hence, practising engineers can consider the soil plasticity index as an influencing factor to interpret the intelligent compaction results and optimize the compaction process.
Bourke, MA & Wijayaratna, KDS 1970, 'THE WHITE TRIANGLE: A PATH TOWARDS EFFICIENT AND INTEGRATED LIGHT RAIL SYSTEMS', Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022, International Conference of Hong Kong Society for Transportation Studies, Hong Kong, pp. 382-391.
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Australian light rail networks include significant lengths of on-street running, where frequent intersections increase passenger journey times. This paper reviews the White Triangle signals used on Sydney's light rail network. These signals allow drivers to continue up to a red signal at speed, knowing that it will change in their favour. Whilst similar systems are used elsewhere around the world, there exists limited guidance to aid signal designers and support the use of these signals. This paper presents a framework to optimise White Triangle display times to reduce intersection delays. This was found to provide a theoretical time saving of 3 to 30 seconds per intersection. Whilst this is only partially achievable in practice, the paper demonstrates that White Triangles can be used to reduce LRV phase lengths and maximise Transit Signal Priority effectiveness. The signals thus offer potential reductions in passenger journey times and cost savings to network operators.
Chemalamarri, VD, Abolhasan, M & Braun, R 1970, 'An agent-based approach to disintegrate and modularise Software Defined Networks controller', 2022 IEEE 47th Conference on Local Computer Networks (LCN), 2022 IEEE 47th Conference on Local Computer Networks (LCN), IEEE, Edmonton, CANADA, pp. 407-413.
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The Software Defined Network paradigm deviates from traditional networks by logically centralising and physically separating the control plane from the data plane. In this work, we present the idea of a modular, agent-based SDN controller. We first highlight issues with current SDN controller designs, followed by a description of the proposed framework. We present a prototype for our design to demonstrate the controller in action using a few common use-cases. We continue the discussion by highlighting areas that require further research.
Cullen, M, Ji, J & Zhao, S 1970, 'Acoustic based GMAW penetration depth identification using droplet transfer monitoring', 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 2369-2374.
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Process monitoring and quality control for industrial robotic Gas Metal Arc Welding (GMAW) systems are key components in ensuring the reliability of the produced products. While being a widely used process, there is still a lack of a robust, plug and play monitoring solution. In particular, weld bead penetration depth is a crucial factor in many fabrication applications, where substantial bonding strength is crucial. This paper introduces a new penetration depth estimation method using the emitted acoustic signal to monitor the droplet transfer process. By monitoring the droplet transfer, an estimation of the welding energy transferred to the base material can be obtained while accounting for variations in the welding process. Using this method, the penetration depth is able to be measured within an error of +15%, proving to be a promising solution for online monitoring in robotic welding applications.
Dang, CC, Dang, LC & Khabbaz, H 1970, 'Predicting the Stability of Riverbank Slope Reinforced with Columns Under Various River Water Conditions', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 513-523.
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A numerical analysis on the stability of soil–cement column-reinforced riverbank along a river delta region in Viet Nam is presented in this paper. The numerical analyses based on the limit equilibrium method (LEM) were performed to assess the safety factor of the column-reinforced riverbank system under various river water level (RWL) conditions. Several factors influencing the riverbank slope stability including the position, length, quantity of soil–cement columns, and RWL changes were investigated. The simulated results showed that the riverbank stability is improved with an increase in the column quantity and the column length when subjected to a constant RWL. Moreover, the predicted results by LEM indicated that the column location and the RWL change significantly influence the stability of riverbank with column reinforcement. The column location between the middle and the slope toe had a significant improvement of the riverbank slope stability, where an initial drawdown of RWL resulted in a notable reduction of the riverbank slope safety factor. These factors should be taken into consideration in the design of a riverbank slope, reinforced with columns, under variable RWL. It is worth mentioning that the use of soil–cement column-reinforced riverbank could be a practical and possible engineering countermeasure to prevent a steep riverbank slope under RWL variations from sliding failure.
Dang, L & Khabbaz, H 1970, 'Numerical Investigation on the Boiling Stability of Sheet Piles Supported Excavations in Cohesionless Soil', Lecture Notes in Civil Engineering, 6th International Conference on Geotechnics, Civil Engineering and Structures (CIGOS), Springer Nature Singapore, Hanoi, VIETNAM, pp. 401-410.
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This paper presents the findings of numerical investigations on the boiling stability against seepage failure of a sheet piled cofferdam supported excavation in cohesionless soil. A numerical analysis based on the finite element method using both plane strain and three-dimensional model was conducted to investigate the influence of seepage force on the stability of supported excavation. The results of this numerical analysis were validated with the report data of a case study on seepage force induced boiling failure inside a sheet pile cofferdam in support of deep excavations. Subsequently, a parametric study was undertaken to evaluate further the influence of different design parameters, including size of excavations against excavated level and penetration depths, on the boiling stability by seepage force of sheet piled-cofferdam supported excavations in sand. The numerical results demonstrated that the cofferdam stability against seepage failure significantly improved with an increase in the cofferdam size. Meanwhile, shallower sheet-pile penetration and deeper excavation level in the cofferdam base were found to have a substantial influence on the excavation base stability when the size effect of cofferdam was taken into consideration. Consequently, possible and practical solutions to improve the boiling stability of sheet pile-supported excavations are also proposed in this investigation.
Dang, LC & Khabbaz, H 1970, 'A Practical Application Using Industrial Waste for Enhancing the Mechanical Properties of Expansive Soil', Lecture Notes in Civil Engineering, Springer Singapore, pp. 80-88.
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In this study, a series of laboratory tests was conducted to investigate the possibility of enhancing the mechanical properties of expansive soil using bagasse fibre (BF, a waste by-product of sugar industry) integrated without or with lime stabilisation as a novel, practical application of reuse of industrial waste materials for sustainability. Soil samples reinforced with three different contents of bagasse fibre ranging from 0% to 2% without or with lime combination in a range of 0–6%, were systematically prepared to assess their effect on improved engineering mechanism of expansive soil. The results revealed that BF reinforcement produced the shear strength development of reinforced soils. Moreover, a lime-BF combination provided better improvement in the shrink-swell behaviour and the compressibility of reinforced soils as compared to soils treated with lime or bagasse fibre alone. The findings also indicated that adding BF into lime-soil mixtures reduced the compressible properties of lime-treated soils. Meanwhile, excessively increasing bagasse fibre content greater than 1% caused a minor decrease in the compressibility improvement of reinforced soils. Hence, an appropriate combination of lime and BF should be determined and used as an environmental-friendly, cost-effective and green solution for stabilisation of expansive soil to facilitate sustainable civil infrastructure development.
Ding, Y, Wang, L, Liang, B, Liang, S, Wang, Y & Chen, F 1970, 'Domain Generalization by Learning and Removing Domain-specific Features', Advances in Neural Information Processing Systems.
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Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods. Code is available at https://github.com/yulearningg/LRDG.
Doan, S, Fatahi, B, Khabbaz, H & Rasekh, H 1970, 'Analytical Solution for Plane Strain Consolidation of Soft Soil Stabilised by Stone Columns', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 753-767.
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This paper presents an analytical solution for free strain consolidation of a stone column-stabilised soft soil under instantly applied loading and two-dimensional plane strain conditions. Both horizontal and vertical flows of water were integrated into the mathematical model of the problem, while the total vertical stresses induced by the external load were assumed to distribute uniformly within each column and soil region. By utilising the separation of variables method, an exact series solution was obtained to predict the variation of excess pore water pressure and settlement with time for any point in the model. The achieved solution can capture the drain resistance effect due to the inclusion of permeability and size of the stone column in the mathematical model. A worked example investigating the dissipation of excess pore water pressure was conducted to exhibit the capabilities of the obtained analytical solution. The correctness of the solution was verified against a finite element modelling with good agreements. Besides, a parametric study to inspect the influence of consolidation parameters of soil on performance objectives (e.g. average degree of consolidation and average differential settlement) was also reported in this study. The results from the parametric analysis show that an increase in permeability of soil sped up considerably the consolidation and differential settlement. Furthermore, an increase in soil stiffness accelerated the consolidation and reduced the average differential settlement between stone column and soft soil significantly. Eventually, the proposed analytical solution is also feasible to predict the consolidation of soft soil with the inclusion of prefabricated vertical drains or pervious columns by adopting appropriate consolidation parameters and stress concentration ratio.
Dohuee, M, Khosravi, V, Shirazi, A, Shirazy, A, Nazerian, H, Pour, AB, Hezarkhani, A & Pradhan, B 1970, 'Alteration Detections Using ASTER Remote Sensing data and Fractal Geometry for Mineral prospecting in Hemich Area, NE Iran', IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE.
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Fattoruso, V, Sepehrirahnama, S, Tofigh, F, Lai, JCS, Nowotny, M & Oberst, S 1970, 'CONSIDERATION ON HOW TO IMPROVE GROUND REACTION FORCE MEASUREMENTS IN SMALL WALKING INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration, Singapore.
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Micro-vibrations caused by the motion of insects, provide a content-rich signal that may be perceived by nestmates, competitors or predators. Knowing the ground reaction forces of a single leg impacting the surface can provide quantitative information about the interaction with the substrate, the substrate itself, physiological and behavioural state of an individual, through mechanistic constraints and the diversity of the gait. Micro-force plates have been used for measuring the ground reaction forces in the order of micro-Newton, using highly sensitive strain gauges attached to compliant load-bearing parts of an underlying mechanical structure. However, their calibration and signal-to-noise-ratio are some of the main challenges of designing these highly sensitive systems. For fine movement analysis, the micro-force plates need to be coupled to high speed video recording systems; the synchronisation of the camera and force plate represents another challenge. For an existing micro-force plate designed for ant measurements, which showed linear signal response in the calibrated force with a lower limit of 120 μN, the linearity of force measurement and sensitivity of the device are investigated in a lower force range, extending the opportunity to study also insects with a lighter footfall. We take into account the difficulties of adapting such devices to the insects' needs related to the environment (i.e. temperature, light...) and morphology (i.e. dimension, weight...). Based on the experiments of the force plate, we consider how to design an experimental setup that overcomes many of the behavioural and technical challenges, to enable more efficient and accurate measurements for insects with body weights less than 5 mg.
Guo, Z, Halkon, B & Clemon, L 1970, 'Effects of infill parameters on the vibration characteristics of additively manufactured specimens', International Conference on Noise and Vibration Engineering, Leuven, Belgium.
Halkon, B, Perrin, R & Guo, Z 1970, 'INVESTIGATING THE VIBROACOUSTICS OF INDIAN ELEPHANT BELLS', Proceedings of the International Congress on Sound and Vibration, Singapore.
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The geometry of a handmade, 13-tine Indian elephant bell replica, captured with manual measurements and updated using a contemporary and accessible, smartphone-based approach, has been used to generate a simple finite element model. Mode shapes, presented using a novel approach, are compared with those derived from group theory predictions for this bell's symmetries and show excellent agreement for the first two singlets and five of the first six doublets. Natural frequencies are compared with those obtained from an experimental modal analysis campaign using a scanning laser Doppler vibrometer and an automatic modal impact hammer. Again, reasonable agreement is observed for the mode shapes of interest. Results are also qualitatively similar, with appropriate adjustments, to those previously reported in the literature for a 16-tine bell of different design obtained using Electronic Speckle Pattern Interferometry. The results allow us to compare and contrast the effectiveness of these two non-contact optical vibration measurement methods in work of this type on 3D structures.
Hayati, H, Eager, D & Oberst, S 1970, 'Recurrence Plot Qualification Analysis of the Greyhound Rotary Gallop Gait', Springer International Publishing, Sapienza University of Rome, Italy (online), pp. 331-341.
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Holmewood, R, Halkon, B & Darwish, A 1970, 'Towards real-time vibro-acoustic classification, verification and tracking of in-flight UAVs', International Conference on Noise and Vibration Engineering, Leuven, Belgium.
Indraratna, B, Ngo, T, Qi, Y & Rujikiatkamjorn, C 1970, 'Track Geomechanics for Future Railways: Use of Artificial Inclusions', Advances in Transportation Geotechnics IV Proceedings of the 4th International Conference on Transportation Geotechnics Volume 2, 4th International Conference on Transportation Geotechnics (ICGT2020), Springer International Publishing, Chicago, Illinois, USA, pp. 139-154.
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This volume presents selected papers presented during the 4th International Conference on Transportation Geotechnics.
Indraratna, B, Qi, Y, Tawk, M & Rujikiatkamjorn, C 1970, 'Mining Waste Materials and Recycled Rubber Matrix for Rail Tracks under Cyclic Loading', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, pp. 239-248.
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Kelly, R, Indraratna, B, Powrie, W, Zapata, C, Kikuchi, Y, Tutumluer, E & Correia, AG 1970, 'State of the Art on Transport Geotechnics', Proceedings of 20th International Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 41-41.
Khoi Tran, N, Sabir, B, Babar, MA, Cui, N, Abolhasan, M & Lipman, J 1970, 'ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16th European Conference on Software Architecture (ECSA), Springer International Publishing, Prague, CZECH REPUBLIC, pp. 49-65.
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Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets’ provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants’ control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML’s security against a threat model of a distributed ML workflow.
Le, VA, Nimbalkar, S, Zobeiry, N & Malek, S 1970, 'MULTI-SCALE VISCOELASTIC BENDING ANALYSIS OF LAMINATED COMPOSITES WITH SOFT INTERFACES', ECCM 2022 - Proceedings of the 20th European Conference on Composite Materials: Composites Meet Sustainability, pp. 755-762.
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This study investigates the bending behaviour of the orthotropic elastic and viscoelastic multi-layered plates with resin rich inter-plies to improve our understanding of the effects of shear deformation and ply slippage on wrinkle formation. This is accomplished by employing a three-dimensional (3D) multi-scale modelling framework that incorporates analysis at different scales (micro-, meso-, and macro-scale). The variation of resin viscoelastic characteristics at the early stage of cure and its effect on the bending properties of the composite is investigated numerically. The results highlight the importance of considering the material's rate dependency in describing the bending behaviour of composite prepregs accurately. Moreover, the bending response of the thin uncured prepregs is found to be dominated by their ply bending stiffness rather than inter-ply friction.
Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 1970, 'A Two-Stage Self-adaptive Model for Passenger Flow Prediction on Schedule-Based Railway System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer International Publishing, SW Jiaotong Univ, Chengdu, PEOPLES R CHINA, pp. 147-160.
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Platform-level passenger flow prediction is crucial for addressing the overcrowding problem on platforms that endangered the passengers’ safety and experience in railway systems. Although some studies exist on this topic, it remains difficult to apply these methods in the real world e.g., the data deficiency in older railway systems, potential impacts of dynamic interchange passenger flows, real-time predictive ability. Thus, we propose a two-stage self-adaptive model for accurately and timely predicting platform-level flow. In the first stage, a self-attention-based prediction model is introduced to predict the next-day passenger flow based on the historical boarding record. The proposed decomposing components transferring the discrete boarding records into continuous patterns make the module able to deliver a robust minute-level prediction. In the second stage, a real-time fine-tuning model is developed to adjust the predicted flow based on the real-time emergencies in passenger flows. The combination of offline deep learning mechanism and real-time reallocation algorithm ensures the real-time response without loss of accuracy. The experiments show that our model can offer accurate predictions to trip planners for timetable design and provide timely decision support for controllers when emergencies happen, and our end-to-end framework has been applied to the railway system in Sydney, Australia.
Malisetty, RS, Indraratna, B & Jayan, V 1970, 'Application of multilaminate model to analyse the influence of principal stress rotation stress paths on permanent deformation of railway ballast', Indian Geotechnical Conference, Kochi.
Malisetty, RS, Indraratna, B, Ngo, T & Tucho, A 1970, 'Dynamic stress response of track layers under high speed trains', Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, 20th Internation Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 1673-1678.
Medawela, S, Indraratna, B & Athuraliya, S 1970, 'Acidic Flow-Induced Clogging of Permeable Reactive Barriers in Pyritic Terrain', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers.
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Milton, J, Halkon, B, Oberst, S, Chiang, YK & Powell, D 1970, 'SONAR-BASED BURIED OBJECT DETECTION VIA STATISTICS OF RECURRENCE PLOT QUANTIFICATION MEASURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Society of Acoustics, Singapore, Singapore, pp. 1-8.
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Active sonar has been successfully deployed for naval mine countermeasures (MCM) to detect, localise, and classify mines and mine-like objects (MLOs). One of the most challenging problems in MCM operations is the detection and classification of (partially) covered objects; traditional image-based sonar processing techniques cannot readily detect objects within the seabed. In this paper, a processing technique that utilises recurrence plot quantification analysis, a class of nonlinear time series analysis, is proposed for improved covered MLO detection in raw sonar signals. Recurrence plots are binary, graphical visualisations of the recurrence matrix generated from time series data. Following an embedding process to reconstruct a copy of the dynamics in phase space, recurrence plot quantification analysis measures can be extracted and further statistically analysed. Using computationally generated sonar signals extracted from simplified representations of real-world relevant scenarios, this study explores the application of such an approach and its sensitivity to the user-defined parameters for detecting the presence of an MLO, irrespective of the level of burial.
Nalamati, M, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Exploring Transformers for Intruder Detection in Complex Maritime Environment', Springer International Publishing, pp. 428-439.
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Nerse, C & Oberst, S 1970, 'NUMERICAL VIBRATION ANALYSIS OF HONEYBEE COMB STRUCTURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Since ancient times much has been written about the geometrical perfection of honeybee comb structures. The hexagonal shape, trademark of the comb cell, has been credited for auxetic mechanical properties and efficient storage of honey. More recent studies on Apis mellifera ligustica have shown that bees have complex nest-building practices through ecological and behavioural evolution. Although mostly dominated by hexagonal cells, the comb structure is shown to feature imperfections due to uneven distribution of worker and drone cells, as well as tilting and merging of cluster of cells. The shape and conditions of the substrate in which the hive is built upon also affects the expansion of the comb structure. Experimental studies have shown that the honeybee comb may have unusual physical properties of vibration amplification and phase reversal. However, the confined nature of these studies poses challenges in understanding the physical mechanisms. In this study, we examine the sensitivity of geometrical and viscoelastic material properties of a honeybee comb on structural vibration transmission. For this purpose, a finite element model of a comb has been developed to obtain modal and frequency response characteristics. The results have shown that lateral deflection of the walls may contribute to efficient vibration transmission at certain resonant frequencies of the cells. Findings might elucidate on why certain frequencies have been observed in experiments, irrespective of the shape and the boundary conditions of the overall honeycomb, and how bees may use this feature to communicate within the colony.
Nerse, C, Oberst, S, Moore, S & MacGillivray, I 1970, 'ASSESSMENT OF FLANKING TRANSMISSIONS IN MEASUREMENTS OF SOUND TRANSMISSION LOSS OF MULTILAYER PANELS', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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The sound transmission loss measurements of small-sized panels ideally require perfect sealing of the panel frame and a rigid construction of the filler wall that encloses the panels. In practice, suppression of flanking transmission is achieved by having a sufficient isolation between both the source and the receiver rooms and blocking the indirect transmission by installing additional elements on the surfaces of both rooms. At the outer edges of the panel, the frame is supported by acoustically reflective materials and insulations to reduce the energy propagating into the wall. The sound transmission loss of the panels can be improved by installing layers that contribute to additional or more efficient dissipation. These layers are installed in such a way that they are tightly bolted into the frame with a niche being introduced on sides to further secure the panel within the opening. However, for panels with alternating layers of solid and porous materials, or with acoustic cavities, the structural rigidity of the supporting frame and joints are the primary factors that cause the flanking transmission. In this study, we investigate the extent of this transmission, and identify the vibration transmission paths and assess their negligibility in measurement of the sound transmission loss of the multilayer panels. A source-path-receiver approach has been proposed for ranking the critical transmission paths for different panel configurations. For this purpose, a numerical framework has been developed to measure the acoustic response of the room and vibration response of the structural elements at operating conditions. A finite element model in COMSOL is set to validate the results and is compared with an in-house analytical solution which shows good agreements. Assessment of the vibration and acoustic signals at sub-structures reveals transmission paths that are significant for the performance evaluation of multilayer panels.
Ngo, T, Indraratna, B & Rujikiatkamjorn, C 1970, 'DEM Modelling on the Interface Behavior of Geogrid-Stabilized Sub-Ballast', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, pp. 486-495.
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This paper presents a study on the interface behavior of geogrids and sub-ballast (capping) using a series of large-scale direct shear tests and discrete element modelling (DEM). Direct shear tests were carried out on sub-ballast with and without geogrid inclusions. The laboratory test data show that the interface shear strength is governed by normal stress and types of geogrid. The three-dimensional DEM was used to study the interface shear behavior of the sub-ballast subjected to direct shearing loads. Irregular-shaped particles of capping aggregates were modelled by clumping of many balls together in appropriate sizes and positions. Different types of geogrids were modelled by bonding small spheres together to form the desired grid geometry and apertures. The DEM model was then used to investigate the evolutions of contact force distributions and fabric anisotropy during the shear tests and the role of geogrid in micro-mechanical perspective.
Nguyen, T, Indraratna, B, Rujikiatkamjorn, C & Xu, B-H 1970, 'Evaluation on the performance of field embankment testing biodegradable drains based on spectral method analysis', 20th International Conference of Soil Mechanics and Geotechnical Engineering (ICSMGE), Sydney, pp. 3031-3036.
Nguyen, TT, Indraratna, B, Rujikiatkamjorn, C, Singh, M, Korkitsuntornsan, W & Novais Silva, I 1970, 'Effects of Plastic Properties on the Fluidization Behaviour of Subgrade Soil under Heavy Haul Rail Load', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, Charlotte, US, pp. 204-213.
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Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 1970, 'Efficient Dual-Band Single-Port Rectifier for RF Energy Harvesting at FM and GSM Bands', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE, Bordeaux, France, pp. 141-145.
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This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of26% and 22% at -20dBm, and also 58% and 51% at -10dBm with a maximum amount of 69% and 65% at -2.5dBm and -5dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.
Perrin, R, Halkon, B & Guo, Z 1970, 'Sacred Geometry and Axial Symmetry in the Modern Hand Bell', Acoustical Society of New Zealand (ASNZ) Conference, Acoustical Society of New Zealand (ASNZ) Conference, Wellington, New Zealand.
Punetha, P & Nimbalkar, S 1970, 'Mathematical Modeling of the Short-Term Performance of Railway Track Under Train-Induced Loading', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 15-24.
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The accurate prediction of the track deformation under train-induced repetitive loading is inevitable to assess the efficiency of a railway track. This paper presents an analytical technique to calculate the transient deformations in a railway track subjected to train-induced loading. The method considers the track substructure as multilayered media in which the behavior of an individual track layer is simulated using a mass-spring-dashpot model. Unlike existing approaches to model the track substructure as an equivalent single or double layer, the proposed analytical approach considers all the three layers of the ballasted track (i.e., ballast, capping or subballast and subgrade). The accuracy of the proposed technique is investigated by comparing the predicted values of track displacement with the published data available in the literature. The predicted results are found to be in good agreement with past studies. A parametric study on the substructure behavior revealed that the elastic modulus of track layers significantly influences the track response.
Richmond, J & Halkon, B 1970, 'COVERT COLLECTION AND AUTOMATED ANALYSIS OF VIBROACOUSTIC INTELLIGENCE FROM DRONE MOUNTED LASER DOPPLER VIBROMETERS', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Society of Acoustics, Singapore, pp. 1-8.
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The synthesis of Laser Doppler Vibrometers (LDVs) with autonomous or remotely piloted vehicles such as drones has the potential to enable highly sensitive, non-invasive and discrete vibroacoustic intelligence gathering processes in hostile environments without risk to human life. This work builds upon a previously developed vibroacoustic noise reduction and speaker diarisation system by exploring the effect of feature extraction parameters on diarisation performance. By tuning the Mel Frequency Cepstral Coefficients (MFCC) and x-vector windowing parameters - how many samples are used to produce a single feature vector - the optimal combination was determined to be 0.305 and 0.5 seconds, respectively, resulting in an error of approximately 5%. This work also presents a live or'online' vibroacoustic intelligence processing and analysis system by utilising an open-set clustering algorithm - Real-Time Exponential Filter Clustering (RTEFC). Similarly, the effect of the similarity threshold D and the exponential filter parameter α on diarisation performance was explored. The most effective combination was 0.96 and 0.75, respectively, resulting in an error of approximately 10%. Furthermore, a live transcription stage has also been included using the Microsoft Azure Speech-to-Text API, automating another important intelligence analysis process.
Rocha, CGD, Wijayaratna, K & Koskela, L 1970, 'Why Is Flow Not Flowing in the Construction Industry?', Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC), 30th Annual Conference of the International Group for Lean Construction (IGLC), International Group for Lean Construction, Edmonton.
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Roy, P, Ghosh, S, Bhattacharya, S, Pal, U & Blumenstein, M 1970, 'Scene Aware Person Image Generation through Global Contextual Conditioning', 2022 26th International Conference on Pattern Recognition (ICPR), 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, pp. 2764-2770.
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Roy, P, Ghosh, S, Bhattacharya, S, Pal, U & Blumenstein, M 1970, 'TIPS: Text-Induced Pose Synthesis', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 161-178.
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In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.
Rujikiatkamjorn, C, Indraratna, B, Arivalagan, J & Dzaklo, C 1970, 'Innovative ground improvement techniques to stabilize unstable subgrade', Proceedings of 16th International Conference on Geotechnical Engineering, Pakistan Geotechnical Society, Lahore, Pakistan, pp. 55-63.
Rujikiatkamjorn, C, Indraratna, B, Yin, JH, Baral, P & Leroueil, S 1970, 'Use of isotache for long term radial consolidation analysis', Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 3079-3083.
Sansom, T, Sepehrirahnama, S, Halkon, B, Lai, JCS & Oberst, S 1970, 'LASER INTENSITY-INDUCED DAMAGE EFFECTS ON DYNAMIC CHARACTERISATION OF WINGS OF THE EUROPEAN HONEYBEE (APIS MELLIFERA)', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Micromechanical and mesoscopic structures including biological tissue, insect appendages or hearing organs can be dynamically characterised through laser Doppler vibrometry (LDV). LDV measures surface vibrations with high spatial resolution, and high dynamic and frequency ranges without causing obvious damage to the specimens. Generally speaking, higher laser intensities lead to higher signal-to-noise ratios, desirable for accurate vibration measurements. However, for certain wavelengths and too high intensity values, the LDV, though only 1 mW output, may damage organic tissue. We aim to illustrate LDV measurements by studying the vibration characteristics of forewings (N= 5) of the European honeybee (Apis mellifera, Hymenoptera). We qualify the level of damage caused by a laser vibrometer with a Helium-Neon laser (532 nm) of a microsystems analyser using a white light-microscope. We monitor the change in the first three eigenfrequencies and the non-damaging intensity level at which the forced-vibration response (FRF) of the wings can still be measured. The first three frequencies at 0.48±0.04 kHz, 1.05±0.06 kHz, and 1.55±0.12 kHz, and their mode shapes of damaged wings are compared against those reported in literature and show ca. 15% frequency deviation. Assuming the stiff element hypothesis, the wing's first bending mode is expected to be at higher frequencies (485±37 Hz) than the approximate wing-beat frequency (234±13.9 Hz). Implementing a finite element model of the wing using a reinforced membrane geometry approach, the measurement results of the undamaged wings are verified. Our results indicate that the intensity levels in LDV measurements on bee wings need to be carefully monitored. The established experimental methodology based on non-damaging laser intensity can also be used for studies of other insects' filigree structures such as their appendages and their vibration and acoustic sensing organs.
Schuhmann, AH, Kleinfeller, N, Sepehrirahnama, S, Oberst, S, Adams, C & Melz, T 1970, 'Numerical analysis on defect detection using structural intensity in solid bodies', Proceedings of the International Congress on Acoustics.
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Analysing structural intensity (SI) offers the possibility to assess the transmission of wave energy within a structure. Measurement of SI has been mainly focused on thin shells and beams. In this work a measurement method is presented to evaluate SI within solid, homogeneous, and isotropic bodies. The method is based on the reciprocity principle, a fundamental assumption in linear vibroacoustics. It allows the reconstruction of the structural intensity field within the bulk of a solid body from the measured surface velocities on the exterior boundaries. From the preliminary results, we demonstrate the capability of this method in approximating the spatial variation of the reconstructed stress and velocity fields using finite element simulation results. Inspired by the reciprocity-based method, we also demonstrate a cavity detection technique using the structural intensity measured along a closed path on a surface of a solid block. Despite some discrepancies in the estimate of the magnitude, the method works well in principle for the benchmark problem of a rectangular cube and it can be verified using our recently set up experimental test. Our proposed method provides an alternative energy-based SI detection technique that may perform as well as those exploiting velocity/acceleration or strain/stress.
Sepehrirahnama, S, McManus, H & Oberst, S 1970, 'ACOUSTIC LEVITATOR-TWEEZER USING PRE-PROGRAMMED ACOUSTIC HOLOGRAMS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration 2022, Singapore.
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Objects in an acoustic field are subjected to acoustic radiation forces, which depend on the objects' scattering behaviour and becomes comparable to the objects' weight for sizes smaller than a few millimeters. This led to manipulation techniques with ultrasonic waves in fluids. In current acoustic levitators, naturally asymmetric objects undergo unwanted spin and rigid-body oscillations. We developed a design of an acoustic manipulator with the ability to levitate and tweeze in vertical and horizontal directions, respectively. This is realised, using three separate transducer arrays and a discretized, reflective floor, inspired by the MIT inForm machine. The floor is made of nine movable pins to change the surface topography and, consequently, manipulate the acoustic field. In this study, we implemented square, staircase, and flat surface configurations to apply pre-defined acoustic holograms for manipulating levitated objects. The two side arrays generate a strong horizontal trap for holding the objects stably at a point where the acoustic radiation force is near zero. The top array and the adjustable floor generate a radiation force as large as an object's weight at the point of levitation, indicated by its levitation height. The object responds to the change of pins by altering its original position in the chamber. Preliminary results obtained at a transducer driving frequency of 40 kHz indicate that an asymmetric object such as a Bee's wing can be levitated stably for more than half an hour with minimal response to external disturbances, and without using phased-array technique. Owing to acoustic radiation force, the measurements are contactless and potentially non-invasive or minimally invasive, dependent on the object. The suggested device design can be potentially employed in the study of delicate biological samples including insects' appendages, such as wings, legs or other filigree structures such as electronic components, wires or MEMS with d...
Sharari, N, Fatahi, B, Hokmabadi, A & Xu, R 1970, 'Impacts of Steel LNG Tank Aspect Ratio on Seismic Vulnerability Subjected to Near-Field Earthquakes', Springer Nature Singapore, pp. 941-956.
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Talaei, S, Zhu, X & Li, J 1970, 'Transfer Learning based Condition assessment for Bridges', International Conference on Structural Engineering Research, Western Sydney University, Australia.
Tang, W, Long, G, Liu, L, Zhou, T, Blumenstein, M & Jiang, J 1970, 'OMNI-SCALE CNNS: A SIMPLE AND EFFECTIVE KERNEL SIZE CONFIGURATION FOR TIME SERIES CLASSIFICATION', ICLR 2022 - 10th International Conference on Learning Representations.
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The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dataset. In this paper, we propose an Omni-Scale block (OS-block) for 1D-CNNs, where the kernel sizes are decided by a simple and universal rule. Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series. The experiment result shows that models with the OS-block can achieve a similar performance as models with the searched optimal RF size and due to the strong optimal RF size capture ability, simple 1D-CNN models with OS-block achieves the state-of-the-art performance on four time series benchmarks, including both univariate and multivariate data from multiple domains. Comprehensive analysis and discussions shed light on why the OS-block can capture optimal RF sizes across different datasets. Code available here.
Tofigh, F, Sepehrirahnama, S, Lai, JCS & Oberst, S 1970, 'CHARACTERISING AND CALIBRATING PIEZO ACTUATORS FOR MICRO-EXCITATION FOR VIBRATION PLAYBACK IN BI-OASSAYS OF INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th Intenational Congress on Sound and Vibration, Singapore.
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Micro-vibration signals in bioassays under controlled environmental conditions in biotremology require a device that can generate a similar level of vibration response as caused by the insect. Since bioassays often need to be run in environmental cabinets, the space available is limited, and structures to be excited should not be mass loaded. Considering the properties of piezo actuators in generating very short strokes with high frequency and fast response times, stacked arrangements were found suitable for micro-excitation based on a given approximation of a Dirac delta impulse, approximating in the first instance the impact signal of a walking insect. However, at below the current limit of miniaturised force and displacement actuators, it is essential to characterise and calibrate the piezo actuators to ensure they are producing the desired signal at the point of contact on a given structure. Here we established a methodology for driving piezo actuators at the order of μm/s to generate low-amplitude impulsive excitations. The methodology includes finding the transfer function of the piezo actuator and an aluminum and a wood beam (Pinus radiata) of 20x10mm2 cross section and 200mm length. The reaction force from the piezo actuator was measured from about 40mN down to 2mΝ for travel ranges between 1.2μm and 11μm. The results showed that the force varies linearly from 5-19μm for the ceramic, and 0.6μm to 1.4μm for the PI and the MTK actuators with an input voltage ranging from 2-10V. The measurement setup improved using an anechoic chamber to reduce the noise level by one order of magnitude, compared to reported results in literature, and ensure excitation amplitudes as low as ±10nm/s can be measured. The presented methodology allows developing affordable micro-excitors in the future for playback bioassays in confined spaces which cause minimal mass loading on the test specimen.
Vizcarra, GC, Muniz, L, Gonçalves, T & Nimbalkar, S 1970, 'Railway Subgrade Characterization Through Repeated Loading Triaxial Testing', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 327-335.
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Currently, the improvement of means of transportation is a great challenge. Brazil has a large ore production, which will continue in the next decades, and seeks to reduce the transportation times between production and export centers, as well as reduce the emission of contaminants to the environment. In this sense, railways are a more efficient and environmentally friendly means of land transportation, and their proper conservation and operability affect the net gains that Brazil receives from the export of commodities. The implementation of this program proposed in the engineering practice would allow taking more precise decisions regarding the activities of maintenance of railroads, generating significant savings. The first step of the research is the analysis and interpretation of results of repeated load triaxial tests carried out in Brazil on railway subgrade soils. An engineering methodology is presented considering the geotechnical properties of the foundation soil obtained through field and laboratory tests for performing of geotechnical analysis. To ensure the railway stability, criteria of bearing capacity, elastic deflection and permanent deformation for the railway substructure must be met. A prediction model of permanent deformation is used, as well as the influence of moisture on the behavior of the foundation soil. This study aims to contribute to the finding of a comprehensive methodology for evaluating the useful service life of the track substructure so that the most appropriate material can be selected for use as a railroad formation material in order to limit stresses on the railway subgrade, which in turn cause progressive loss of geometric profile of the railway, and to maintain a safe operation of the trains. This will allow significant savings in the periodic maintenance of the substructure, which are one of the activities to restore the track geometry of railways.
Wang, Y, Peng, X, Clarke, A, Schlegel, C & Jiang, J 1970, 'Machine Teaching-Based Efficient Labelling for Cross-unit Healthcare Data Modelling', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 320-331.
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A data custodian of a big organization (such as a Commonwealth Data Integrating Authority), namely teacher, can easily build an intelligent model which is well trained by comprehensive data collected from multiple sources. However, due to information security and privacy-related regulation requirements, full access to the well-trained intelligent model and the comprehensive training data is usually limited to the teacher only and not available to any unit (or branch) of that organization. Therefore, if a unit, namely student, needs an intelligent function similar to the trained intelligent model, the student has to train a similar model from scratch using the student’s own dataset. Such a dataset is usually unlabelled, requiring a big workload on labelling. Inspired by the Iterative Machine Teaching, we propose a novel collaboration pipeline. It enables the teacher to iteratively guide the student to select samples that are most worth labelling from the student’s own dataset, which significantly reduces the requirement for human labelling and, at the same time, prevents regulation and information security breaches. The effectiveness and efficiency of the proposed pipeline is empirically demonstrated on two publicly available healthcare datasets in comparison with baseline methods. This work has broad implications for the healthcare sector to facilitate data modelling in instances where the large labelled datasets are not accessible to each unit.
Xiao, T, Halkon, B, Oberst, S, Wang, S & Qiu, X 1970, 'SOUND FIELD MEASUREMENT AT AN ENCLOSURE OPENING USING REFRACTO-VIBROMETRY', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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A sound field can be measured by an array of microphones distributed across the area of interest or by moving a smaller number of microphones sequentially. Such procedures can be time-consuming and expensive when high spatial resolution is required. Furthermore, the presence of physical microphones might disturb the sound field. Refracto-vibrometry is based on the acousto-optic effect. It can serve as an alternative method to measure sound pressure at all the points of interest without disturbing the sound field. In this paper, three methods, the filtered back-projection, the truncated singular value decomposition and the Tikhonov regularisation methods, are used to evaluate the sound field at an enclosure opening. Comparison with a microphone array shows that the Tikhonov regularisation method yields the best result.
Xie, M, Jiang, J, Shen, T, Wang, Y, Gerrard, L & Clarke, A 1970, 'A Green Pipeline for Out-of-Domain Public Sentiment Analysis', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 190-202.
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In the changing social and economic environment, organisations are keen to act promptly and appropriately to changes. Sentiment analysis can be applied to social media data to capture timely information of new events and the corresponding public opinions. However, currently both the social topics and trending words are changing just as rapidly as the target topics and domains that organisations are interested in investigating. Therefore, there is a need for a well-trained sentiment analysis model able to handle out-of-domain input. Current solutions mainly focus on using domain adaptation techniques, but these solutions require domain-specific data and inevitably introduce extra overheads. To tackle this challenge, we propose a green Artificial Intelligence (AI) solution for a sentiment analysis pipeline (GreenSAP) to gain a better understanding of the changing public opinions on social media. Specifically, we propose to leverage the expressively powerful capability of the pre-trained Transformer encoder, and make use of several publicly-available sentiment analysis datasets from various domains and scenarios to develop a pipeline model. A sarcasm detection model is also included to eliminate false positive predictions. In experiments, this model significantly outperforms its competitors on three public benchmark datasets and on two of our labelled out-of-domain datasets for real-world applications.
Xu, Z, Khabbaz, H, Fatahi, B, Lee, J & Bhandari, S 1970, 'Numerical Assessment of Impacts of Vibrating Roller Characteristics on Acceleration Response of Drum Used for Intelligent Compaction', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 231-245.
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Intelligent compaction (IC) is an emerging technology for efficient and optimized ground compaction. IC combines the roller-integrated measurements with the Global Positioning System (GPS), which performs the real-time quality control and assurance during the compaction work. Indeed, IC technology is proven to be capable of providing a detailed control system for compaction process with real-time feedback and adjustment on full-area of compaction. Although roller manufacturers offer typical recommended settings for rollers in various soils, there are still some areas needing further improvement, particularly on the selection of vibration frequency and amplitude of the roller in soils experiencing significant nonlinearity and plasticity during compaction. In this paper, the interaction between the road subgrade and the vibrating roller is simulated, using the three-dimensional finite element method capturing the dynamic responses of the soil and the roller. The developed numerical model is able to simulate the nonlinear behavior of soil subjected to dynamic loading, particularly variations of soil stiffness and damping with the cyclic shear strain induced by the applied load. In this study, the dynamic load of the roller is explicitly applied to the simulated cylindrical roller drum. Besides, the impact of the frequency and amplitude on the level of subgrade compaction is discussed based on the detailed numerical analysis. The adopted constitutive model allows to assess the progressive settlement of ground subjected to cyclic loading. The results based on the numerical modeling reveal that the roller vibration characteristics can impact the influence depth as well as the level of soil compaction and its variations with depth. The results of this study can be used as a potential guidance by practicing engineers and construction teams on selecting the best choice of roller vibration frequency and amplitude to achieve high-quality compaction.
Ye, K, Ji, JC & Hu, D 1970, 'Dynamic Analysis of a Novel Zero-Stiffness Vibration Isolator by Considering Frictional Force Involved', Lecture Notes in Electrical Engineering, Springer Singapore, pp. 544-553.
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This study proposes a novel zero-stiffness vibration isolator and investigates its dynamic responses under micro-oscillation with a friction consideration. The novel vibration isolator is based on the mechanism of a cam-roller Quasi-Zero-Stiffness (QZS) system while with improvement by reducing its system components. The proposed vibration isolator consists of a designed bearing, which can provide stiffness responses in the radial direction, and an inserted rod with curved surface. Without the precise cooperation between the positive and negative stiffness systems required in a typical QZS isolator, the designed single stiffness system can provide the high-static-low-dynamic stiffness characteristic directly. The static characteristics of the stiffness performance are numerically confirmed, and then the dynamic responses with friction consideration at the contact surfaces are discussed. The displacement transmissibility in low frequency range is numerically validated when applying harmonic excitation on the base. The analysis results of this study reveal a unique vibration isolating performance of the zero-stiffness system under frication consideration.
Yousefi, M, Tabatabaei, SH, Pour, AB & Pradhan, B 1970, 'DPSB model-based clustering algorithm for mineral mapping in hyperspectral imagery', IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE.
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Zhang, X, Zhu, X & Li, J 1970, 'Knowledge transfer for structural damage detection using fine-tuning based on FRF', Proceedings of the 3rd International Conference on Structural Engineering Research, the 3rd International Conference on Structural Engineering Research, Sydney, Australia, pp. 184-189.
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The majority of data-driven structural damage detection techniques are created based on the assumption that comprehensive labelled data is available, and the underlying distribution of the training and test sets is the same, which is hard to conduct in real engineering applications. In this work, we propose an approach for structural health monitoring (SHM) based on a convolutional neural network (CNN) that uses frequency response functions (FRF) obtained from the structural response to detect structural damage. This method is capable to identify structural damage based on both severity and localisations in the real structure. Specifically, a numerical model was simulated firstly. The acceleration response was collected to process to the FRF and trained in the designed CNN model. Thus, a complete CNN model was created using the numerical data, and then the pre-trained CNN model was fine-tuned using the limited experimental data to adapt its feature distribution on the fully connected layer for the experimental data. Finally, the performance of this fine-tuned CNN was compared with the original CNN to validate the effectiveness of the proposed methods on damage detection. In addition, when limited damage data are available, this method can be used by utilising the damage information, having studied other structures to detect the damage to the structure. Besides, visualising the characteristics from the last layer can provide a physical understanding of how the network recognises various damage scenarios with different damage severities. It was found that the network extracted feature can represent some physical features of structural behaviours to some extent based on the damage scenarios, implying the accurate results to reveal that the proposed structural detection method outperforms the current method.
Zhou, S, Eager, D, Halkon, B, Walker, P, Covey, K & Braiden, S 1970, 'INVESTIGATION AND COMPARISON OF THE SOUND QUALITY OF THE LURES USED FOR GREYHOUND RACING', Proceedings of the International Congress on Sound and Vibration.
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This study investigates and compares the acoustic signatures of a traditional wire-cable pulled lure system and two novel alternative battery-operated lure systems which were developed to eliminate the hazardous steel-wire cable and make the sport of greyhound racing safer for greyhounds, participants, and spectators. The acoustical measurements of these three lure systems were conducted at the Murray Bridge greyhound racing track in South Australia with high-frequency B&K Type 4191 microphones. The microphones were positioned within the starting box and on the track adjacent to the starting boxes, at both the straight track and bending track. The measurements captured the sounds that the greyhounds hear before and after the opening of the starting box gate. The sound quality analysis was conducted to compare the lure sounds. It was found when the battery-lure was installed with all nylon rollers, it presented less sound energy than the traditional wire-cable pulled lure. When two of the nylon rollers were replaced with steel rollers, the battery-operated lure emitted a louder sound than the traditional wire-cable-pulled lure. The different acoustic characteristics of these lure systems suggest future research is warranted on the reaction of greyhounds to different lure sounds, particularly their excitement level within the starting box as the lure approaches. This initial research also suggests some greyhounds may not clearly hear the battery-operated lure with all nylon rollers approaching the starting boxes and the timing of these greyhounds to jump may be delayed, particularly during high wind conditions.
Zhou, S, Eager, D, Halkon, B, Walker, P, Covey, K & Braiden, S 1970, 'Investigation and Comparison of the Sound Quality the Wirecable Lure and Battery-Operated Lure used for Greyhound Racing', International Congress on Sound and Vibration, Singapore.
