Publications

Arachchige, CMK, Indraratna, B, Rujikiakamjorn, C, Qi, Y & Siddiqui, AR 2023, 'Use of waste rubber inclusions for ballasted railway construction – A real-life case study' in Smart Geotechnics for Smart Societies, CRC Press, pp. 2567-2570.
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Bragar, EP, Pronozin, YA, Zhussupbekov, AZ, Gerber, AD & Indraratna, B 2023, 'Evaluation of the strength parameters of clay loams during freezing–thawing cycles' in Smart Geotechnics for Smart Societies, CRC Press, pp. 2036-2041.
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Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Influence of microscale cohesive contacts on the macro-behaviour of soils through DEM investigation' in Smart Geotechnics for Smart Societies, CRC Press, pp. 229-243.
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Microscale contacts are inherent in most geotechnical failures such as soil liquefaction, landslides and internal instability, thus study of these failures based on microscale concepts can significantly enhance our intrinsic understanding as well as the quality of prediction and designs. Despite rapidly increasing investigation on micro-mechanisms of soil failures, especially based on Discrete Element Method (DEM), cohesive behaviours of particles are usually ignored and simplified, resulting in incomplete and/or inaccurate understanding. My study aims to overcome this imperative limitation and improve our modelling capability by investigating the influence of microscale cohesive contacts on fundamental soil behaviours such as the formation of angle of repose and direct shear. Different degrees of cohesion between particles are incorporated into DEM models and the results are validated against experimental data. The results show cohesive contacts can significantly affect soil behaviour and the predicted outcomes if they are not considered properly. The prediction capability of this DEM model can be further applied to study the cohesive behaviour of geomaterials in various geotechnical problems such as soil clogging and debris flow.

Indraratna, B, Qi, Y & Ngo, T 2023, 'Sustainable Transport Infrastructure Adopting Energy-Absorbing Waste Materials' in Springer Transactions in Civil and Environmental Engineering, Springer Nature Singapore, pp. 159-173.
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Indraratna, B, Qi, Y, Rujikiatkamjorn, C, Tawk, M, Mehmood, F, Navaratnarajah, SK, Neville, T & Grant, J 2023, 'Smart Solutions in a Circular Economy for Advancing Railroad Design and Construction Using Recycled Materials' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 1-32.
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Indraratna, B, Vinod, JS & Athukorala, R 2023, 'Chemically Treated Soils' in DSC/HISS Modeling Applications for Problems in Mechanics, Geomechanics, and Structural Mechanics, CRC Press, pp. 149-164.
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Larpruenrudee, P, Paul, G, Saha, SC, Husain, S, Mortazavy Beni, H, Lawrence, C, He, X, Gu, Y & Islam, MS 2023, 'Ultrafine particle transport to the lower airways: airway diameter reduction effects' in Digital Human Modeling and Medicine, Elsevier, pp. 253-274.
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Li, B, Guo, T, Wang, Y & Chen, F 2023, 'Data‐Driven Delay Analysis with Applications to Railway Networks' in Advances in Data Science and Analytics: Concepts and Paradigms, Wiley, USA, pp. 115-143.
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Reliability is one of the key evaluation criteria in railway service. Many factors contribute to the measure, such as delays spanning over spatiotemporal dimensions. A common method used to improve reliability is to design a better timetable to reduce the mutual influence between trains. Recently, machine learning shows great potential in improving the effectiveness and efficiency of decision-making to increase operational performance. For railway system management, this seems like an opportunity worth further exploration. In this chapter, we focus on analyzing railway disruptions and their impact on the traffic in the whole network. Specifically, we build three different data-driven models: (1) a conditional Bayes Net Model with the introduction of Markov property for delay propagation prediction, (2) a primary delay tracking back model, and (3) a dwell improvement evaluation model which can estimate the potential network benefits of the dwell improvement on single or multiple platforms. All the delay analysis models have been validated and deployed to the railway network in the Great Sydney area and the outcome of this application of the intelligent delay analysis technology significantly reduces delay-caused losses, increases the operation efficiency, and enables the train operating system to meet performance metrics and recover from incidents.

Li, B, Guo, T, Zhu, X, Wang, Y & Chen, F 2023, 'ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation' in Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, pp. 369-386.
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An essential weakness of existing personalized recommender systems is that the learning is biased and dominated by popular items and users. Existing methods, particularly graph-based approaches, primarily focus on the “heterogeneous interaction” between user-item, leading to a disproportionately large influence of popular nodes during the graph learning process. Recently, popularity debiasing models have been proposed to address this issue, but they excessively concentrate on considering cause-effect or re-weighting the item/user popularity. These approaches artificially alter the nature of the data, inadvertently downplaying the representation learning of popular items/users. Consequently, balancing the trade-off between global recommendation accuracy and unpopular items/users exposure is challenging. In this paper, we advocate the concept of “homogeneous effect” from both user and item perspectives to explore the intrinsic correlation and alleviate the bias effects. Our core theme is to simultaneously factorize user-item interactions, user-user similarity, and item-item correlation, thereby learning balanced representations for items and users. To pursue well-balanced representations, we propose a Consensus factorized Graph Convolution neural Network (ConGCN), which leverages graph-based nonlinear representation learning and manifold constraints to regulate the embedding learning. An inherent advantage of ConGCN is its consensus optimization nature, where item-item and user-user relationships ensure that unpopular items are well preserved in the embedding space. The experiments on four real-world datasets demonstrate that ConGCN outperforms existing single-task-oriented methods on two typical tasks with opposite goals (global recommendation and popularity debiasing recommendation), indicating that our model can perform a balanced recommendation with both higher global and debiasing recommendation accuracy with greater long-tail item/user exposure.

Liang, B, Li, Z, Tian, H, Liang, S, Wang, Y & Chen, F 2023, 'Optimizing Water Quality with Data Analytics and Machine Learning' in Advances in Data Science and Analytics: Concepts and Paradigms, Wiley, USA, pp. 39-65.
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It is critical to continuously maintain the high quality of drinking water in water distribution network management. However, it remains challenging to predict water demand and optimize dosage to ensure safe drinking water. This paper details solutions that utilize data analytics and machine learning to provide water demand forecasting and chemical dosing optimization. Key environmental factors are used to build a Bayesian linear model to predict the water demand for each supply zone. To ensure safe drinking water, chlorine residual is one of the most important indicators. However, as the water age becomes high, the chlorine residual decays very fast. Thus, we provide a data-driven solution to determine the chlorine set points and optimize the ammonia dosage for reservoirs. The optimization results can determine the set point of the dosage devices to be installed at reservoirs. Evaluation results demonstrate that the solutions achieve reasonably consistent results.

Samali, B, Yu, Y, Li, Y, Li, J & Li, H 2023, 'The Impact of Smart Materials on Structural Vibration Control' in Automation in Construction toward Resilience, CRC Press, pp. 57-72.
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The future development of smart materials in the areas, such as self-tuning vibrating energy harvesting devices, self-sustainable networks, and earthquake protection devices, is of significant importance. With the great potential to develop smart structures, smart material includes shape memory alloy, piezoelectric, magnetorheological (MR) and electrorheological (ER) materials, which possess stimulus responses. Accordingly, smart materials can be regarded as promising candidates for adaptive device development for structural vibration mitigation application. In this chapter, the smart material classification and potential applications are reviewed first. Then, a case study is conducted to comprehensively illustrate application of MR material-based semi-active control device in structural seismic protection. Finally, the challenges and future work on this topic are briefly analysed and discussed.

Abdollahi, A & Pradhan, B 2023, 'Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model', Science of The Total Environment, vol. 879, pp. 163004-163004.
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Abdollahi, A, Pradhan, B & Alamri, A 2023, 'Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach', International Journal on Smart Sensing and Intelligent Systems, vol. 16, no. 1.
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Abstract Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.

Abdollahi, A, Pradhan, B, Alamri, A & Lee, C-W 2023, 'Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights', Journal of Sensors, vol. 2023, pp. 1-14.
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The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat-8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt-and-pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat-8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat-8 data ensures that decision makers have the resources they need to track land cover throughout the year.

Abharian, S, Sarfarazi, V, Marji, MF, Rasekh, H & Sadrekarimi, A 2023, 'Effect of geogrid reinforcement on tensile failure of high-strength self-compacted concrete', Magazine of Concrete Research, vol. 75, no. 8, pp. 379-401.
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In this study, the tensile strength, failure mechanism and ductile behaviour of geogrid-reinforced high-strength self-compacting concrete discs subjected to both the Brazilian tensile strength test and a biaxial compressive test are studied. To determine the combined effects of geogrid layer numbers and inclination angle on the ultimate tensile strength of concrete samples, 21 experiments were conducted with up to three layers of geogrids inclined at angles of 0° to 90°, at increments of 15°. In addition, discrete-element numerical simulations were conducted using two-dimensional particle flow code to examine the failure behaviour of geogrid-reinforced high-strength self-compacting concrete discs. The numerical models were first calibrated by the experimental results and then the failure behaviour of models containing geogrids was investigated. Both experimental and numerical results demonstrate that augmenting the concrete discs with geogrids increases the ductility of specimens, especially after failure. As the number of geogrid layers increased, the tensile strength of specimens also increased, whereas the tensile strength and absorbed energy were the same for specimens with different numbers of geogrid layers and inclination angles of 75° and 90°. The specimen with three horizontal geogrid layers had the highest tensile strength, biaxial compression strength and ductility of all specimens tested.

Abraham, MT, Vaddapally, M, Satyam, N & Pradhan, B 2023, 'Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India', CATENA, vol. 223, pp. 106948-106948.
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Adnan Farooq, M & Nimbalkar, S 2023, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366, pp. 130260-130260.
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Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 2023, 'Acoustic radiation force-induced push-pull particle oscillations', Journal of the Acoustical Society of America.

Al-Najjar, HAH, Pradhan, B, Beydoun, G, Sarkar, R, Park, H-J & Alamri, A 2023, 'A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset', Gondwana Research, vol. 123, pp. 107-124.
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As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it is important to understand how decisions are made. Fairness, and transparency becomes ever more vital due to ethical concerns and trust. Despite the popularity of machine learning (ML) algorithms in landslide modeling, the explainability of these methods are often considered as black box. This paper aims to propose an explainable artificial intelligence (XAI) for landslide prediction using synthetic-aperture radar (SAR) time-series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives. We employed a Shapley Additive Explanations (SHAP) approach to understand how and what decisions ML-based models are making. 37 features were extracted from various sources such as ALOS-PALSAR (ALOS Phased Array type L-band Synthetic Aperture Radar), ALOS-2 (SAR), Landsat-8, topographic maps, and DEM for landslide susceptibility mapping in a landslide prone area in Chukha, Bhutan as a test site. The result was then compared using two standard ML methods: random forest (RF) and support vector machine (SVM). As per results, the RF model outperformed (0.914) the SVM. Moreover, the higher reliability of the RF model was proved by the area under the curve (AUC) of 0.941. XAI results revealed, features like altitude, aspect, NDVI-2014, NDVI-2017, and NDVI-2018 were the most effective features for landslide prediction by both models. Interestingly, among those features, NDVI-2014, aspect, and NDVI-2017 negatively correlated with the landslide prediction; whereas positively correlated when SVM was utilized. This interpretation ability indicates the advantages of XAI over the conventional methods as it measures the impact, interaction and correlation of conditioning factors within a model. The current research finding can provide more transparency and explainability when working with MLs ...

Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2023, 'RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1374-1387.
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Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$ $\times$ $4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.

Basak, S, Agrawal, H, Jena, S, Gite, S, Bachute, M, Pradhan, B & Assiri, M 2023, 'Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems', Computer Modeling in Engineering & Sciences, vol. 135, no. 2, pp. 1053-1089.
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Beena, KS, Sandeep, MN, Indraratna, B & Malisetty, RS 2023, 'Near-field vibrations in railway track on soft subgrades for semi high-speed trains', Transportation Engineering, vol. 12, pp. 100176-100176.
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Bi, Z, Zhang, L, He, X & Zhai, Y 2023, 'Effect of oblique incidence angle and frequency content of P and SV waves on the dynamic behavior of liquid tanks', Soil Dynamics and Earthquake Engineering, vol. 171, pp. 107929-107929.
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Bisui, S, Pradhan, B, Roy, S, Sengupta, D, Bhunia, GS & Shit, PK 2023, 'Estimating Forest-Based Livelihood Strategies Focused on Accessibility of Market Demand and Forest Proximity', Small-scale Forestry, vol. 22, no. 3, pp. 537-556.
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Biswas, K, Shivakumara, P, Pal, U, Lu, T, Blumenstein, M & Lladós, J 2023, 'Classification of aesthetic natural scene images using statistical and semantic features', Multimedia Tools and Applications, vol. 82, no. 9, pp. 13507-13532.
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Chen, J, Indraratna, B, Vinod, JS, Ngo, T & Liu, Y 2023, 'Discrete element modelling of the effects of particle angularity on the deformation and degradation behaviour of railway ballast', Transportation Geotechnics, vol. 43, pp. 101154-101154.
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Railroad ballast exhibits distinct morphological characteristics represented by shape irregularity, corner angularity, and surface texture. Upon repeated train loading, the morphology of ballast undergoes inevitable degradation, particularly in terms of its corner sharpness, which can affect track performance and even pose a substantial threat to operational safety. These aspects have rarely been captured insightfully in most DEM studies on ballast. In contrast, this study examines the influence of particle angularity on the deformation and degradation behaviour of railway ballast upon repeated loading using the discrete element method (DEM). The angularity of ballast particles is captured and quantified using the CT scanning technology in conjunction with an image-based processing strategy, after which the irregularly shaped particles are reconstructed in the DEM. In this numerical procedure, aggregates with varying angularities are created by incorporating a particle degradation subroutine to capture corner abrasion and surface attrition of ballast to mimick real-life field processes. The macro-response of a typical ballasted track subjected to cyclic rail loading is investigated, and the results show that as the angularity increases, the permanent deformation of the track corresponds to a lower permanent strain rate and a higher resilient modulus. However, the opposite behaviour is observed if excessive breakage of the aggregates occurs that reduces the angularity of the individual particles. In this study, detailed microscopic analysis based on DEM in terms of interparticle interaction and associated vibration velocity has also been performed. The results offer distinct clarity to the essential micro-mechanisms embracing particle angularity, and the accompanying influence on the deformation and degradation characteristics of ballast is elucidated with greater insight.

Chen, J, Vinod, JS, Indraratna, B, Ngo, T & Liu, Y 2023, 'DEM study on the dynamic responses of a ballasted track under moving loading', Computers and Geotechnics, vol. 153, pp. 105105-105105.
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This paper presents the discrete element modeling of the dynamic response of a ballasted track under moving loads. The DEM model, consisting of sleepers, ballast, and sub-ballast, has been calibrated using field and laboratory data. This model was further used to examine the dynamic responses of the ballasted track subjected to a series of moving traffic loading representing various train axle loads and speeds. The results show that the permanent settlement of the sleeper, the breakage of ballast, and the dynamic stresses in the track substructure increase with an increase in train axle load and speed. As the train moves, the magnitudes of dynamic stresses and the orientations of principal stress axes in the track change continuously, and a more pronounced principal stress rotation is observed at sleeper edges than those underneath sleepers. The capping layer is found to play a critical role in reducing train-induced stress and further alleviating the disturbance from the trains to the subgrade. The interparticle contacts and the vibration of ballast during the movement of the train including the influences of train axle load and speed on the dynamic responses of ballasted railway tracks are captured and analyzed from a micromechanical perspective.

Chen, K, He, X, Liang, F & Sheng, D 2023, 'Contribution of capillary pressure to effective stress for unsaturated soils: Role of wet area fraction and water retention curve', Computers and Geotechnics, vol. 154, pp. 105140-105140.
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Chen, Q, Xie, K, Tao, G, Nimbalkar, S, Peng, P & Rong, H 2023, 'Laboratory investigation of microstructure, strength and durability of cemented soil with Nano-SiO2 and basalt fibers in freshwater and seawater environments', Construction and Building Materials, vol. 392, pp. 132008-132008.
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Chen, Q, Yu, R, Gaoliang, T & Nimbalkar, S 2023, 'Microstructure, strength and durability of nano-cemented soils under different seawater conditions: laboratory study', Acta Geotechnica, vol. 18, no. 3, pp. 1607-1627.
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Chen, Q, Zhang, H, Ye, J, Tao, G & Nimbalkar, S 2023, 'Corrosion Resistance and Compressive Strength of Cemented Soil Mixed with Nano-Silica in Simulated Seawater Environment', KSCE Journal of Civil Engineering, vol. 27, no. 4, pp. 1535-1550.
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Cui, J, Rao, P, Li, J, Chen, Q & Nimbalkar, S 2023, 'Time-dependent evolution of bearing capacity of driven piles in clays', Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, vol. 176, no. 4, pp. 402-418.
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Analysis of the time-dependent variation in the axial capacity of driven piles is difficult yet critical for geotechnical engineers. In this work, to investigate the short-term evolution of the bearing capacity of driven piles, a two-dimensional finite-element (FE) model was developed using the Abaqus program. Pile installation, soil consolidation and loading were incorporated in an integrated FE model. Changes in the excess pore pressure and the void ratio of the surrounding soil were investigated to evaluate the consolidation mechanism. The findings revealed that excess pore water pressure dissipation was the primary cause of the short-term evolution of the pile's bearing capacity. The dissipation of excess pore water pressure lowered the void ratio and increased the strength and stiffness of the surrounding soil. The effect of the permeability coefficient was also assessed. The permeability coefficient was found to affect the rate of evolution but not its magnitude. A centrifuge model test was used to verify the numerical results. The findings of this study may serve as a guide for improved design and construction of driven piles.

da Rocha, CG, Wijayaratna, K, Jong, K & Haller, D 2023, 'Examining Off-Site Construction from a Flow Viewpoint', Journal of Construction Engineering and Management, vol. 149, no. 3.
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Darroch, MM, Cooper-Woolley, B & Halkon, BJ 2023, 'Design and development of SiteHive MEMS based system for real-time vibration monitoring', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A303-A303.
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Construction projects need to proactively manage their works that may cause vibration impacts to nearby structures and stakeholders. Risks of vibration include cosmetic and structural damage to buildings and threats to human comfort. The advent of MEMS accelerometers offers significant opportunities to improve on traditional vibration monitoring practices based on geophones. Geophones measure velocity which preclude acceleration based measurements and calculations like vibration dose value (the measure for human comfort), groundborne noise, and auto-levelling. The inability to capture these results means additional monitoring devices are required to capture all key measurements. MEMS-based vibration monitoring systems can be much cheaper, smaller, and more power efficient than traditional vibration monitoring systems. This enables easier installation, greater mobility, and more monitoring to be conducted. SiteHive has worked extensively with the National Measurement Institute (NMI) and the University of Technology Sydney (UTS) to test and validate the efficacy of the MEMS-based accelerometers and develop a calibration system for MEMS-based devices. This paper will outline the design research and findings that have gone into this development, results from field testing, and details on the value offered by this innovation.

Deng, Z, Li, W, Dong, W, Sun, Z, Kodikara, J & Sheng, D 2023, 'Multifunctional asphalt concrete pavement toward smart transport infrastructure: Design, performance and perspective', Composites Part B: Engineering, vol. 265, pp. 110937-110937.
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Ding, H & Ji, JC 2023, 'Vibration control of fluid-conveying pipes: a state-of-the-art review', Applied Mathematics and Mechanics, vol. 44, no. 9, pp. 1423-1456.
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AbstractFluid-conveying pipes are widely used to transfer bulk fluids from one point to another in many engineering applications. They are subject to various excitations from the conveying fluids, the supporting structures, and the working environment, and thus are prone to vibrations such as flow-induced vibrations and acoustic-induced vibrations. Vibrations can generate variable dynamic stress and large deformation on fluid-conveying pipes, leading to vibration-induced fatigue and damage on the pipes, or even leading to failure of the entire piping system and catastrophic accidents. Therefore, the vibration control of fluid-conveying pipes is essential to ensure the integrity and safety of pipeline systems, and has attracted considerable attention from both researchers and engineers. The present paper aims to provide an extensive review of the state-of-the-art research on the vibration control of fluid-conveying pipes. The vibration analysis of fluid-conveying pipes is briefly discussed to show some key issues involved in the vibration analysis. Then, the research progress on the vibration control of fluid-conveying pipes is reviewed from four aspects in terms of passive control, active vibration control, semi-active vibration control, and structural optimization design for vibration reduction. Furthermore, the main results of existing research on the vibration control of fluid-conveying pipes are summarized, and future promising research directions are recommended to address the current research gaps. This paper contributes to the understanding of vibration control of fluid-conveying pipes, and will help the research work on the vibration control of fluid-conveying pipes attract more attention.

Ding, L, Ji, J, Li, Y, Wang, S, Noman, K & Feng, K 2023, 'A Novel Weak Feature Extraction Method for Rotating Machinery: Link Dispersion Entropy', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-12.
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The entropy-based feature extraction is a promising tool for extracting weak features from rotating machinery. However, the existing research has paid little attention to the state transition process, which brings the problem of accuracy and comprehensiveness in complexity estimation. To address this issue, this paper proposes link dispersion entropy (LDE) based on the theory of the Markov chain for weak feature extraction. By calculating the transition probability of symbol patterns, the LDE can extract the fault information contained in the transition, enabling it to capture the early weak fault. Furthermore, LDE is extended to a multiscale analysis by combining it with the coarse-gaining process for comprehensive feature extraction, termed multiscale LDE (MLDE). Finally, three simulated signals and two different experimental data are utilized to verify the advantage of MLDE in extracting the weak fault features. Results demonstrate that MLDE has the best performance in fault diagnosis of rotating machinery compared with the existing five methods, namely sample entropy, fuzzy entropy, permutation entropy, dispersion entropy and symbolic dynamic entropy.

Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Interactive Role of Rolling Friction and Cohesion on the Angle of Repose through a Microscale Assessment', International Journal of Geomechanics, vol. 23, no. 1.
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Cohesion and rolling friction play key roles in governing the behavior of soil; however, only a limited number of studies have been able to assess the simultaneous contributions of these two microparameters on the macroproperties of soil. In this respect, the innovation of the current study includes an attempt to examine the interplay of these two primary parameters on the angle of repose (AoR) based on the discrete-element method (DEM). Lifting cylinder tests on cohesive wet sand have been carried out in DEM, while the cohesion and rolling friction are captured through proposed computational models. In this paper, macroparameters, such as the geometry and developmental stages of sand piles obtained in DEM simulation, are compared with experimental data, while their microevolution is quantified in detail. The results show that a large AoR can only be obtained when the cohesive and rotational frictional forces work in tandem. Increasing the cohesion and rolling friction results in smaller contact numbers, with increasing chain-like connections between particles and larger pore spaces to account for a larger AoR. For the first time, this study distinctly identifies three major stages that contribute to the AoR, based on the development of contact numbers and the transformation of energy. Accordingly, the linkage between macroscale AoR and the microstructural coordination number is formulated with varying levels of cohesion and rolling friction. The DEM results prove that the more cohesive the particles are, the greater the delay in the dissipation of kinetic energy.

Feng, K, Ji, JC & Ni, Q 2023, 'A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics', Structural Health Monitoring, vol. 22, no. 2, pp. 1027-1048.
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The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.

Feng, K, Ji, JC & Ni, Q 2023, 'A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems', International Journal of Fatigue, vol. 168, pp. 107459-107459.
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With the material degradation of gear over its service lifespan, the gearbox is prone to fatigue, especially under harsh working environments. The interaction between gear fatigue and gear dynamics often results in high complexity measurements. This poses significant challenges to developing effective vibration-based techniques to monitor the gear fatigue propagation and predict its remaining useful life (RUL). To address this issue, a novel transmission error-based indicator is proposed to assess the fatigue severity, and then it is utilized to predict the RUL of the gearbox. The effectiveness of the proposed prognostic methodology is validated using endurance tests.

Feng, K, Ji, JC, Ni, Q & Beer, M 2023, 'A review of vibration-based gear wear monitoring and prediction techniques', Mechanical Systems and Signal Processing, vol. 182, pp. 109605-109605.
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Gearbox plays a vital role in a wide range of mechanical power transmission systems in many industrial applications, including wind turbines, vehicles, mining and material handling equipment, oil and gas processing equipment, offshore vessels, and aircraft. As an inevitable phenomenon during gear service life, gear wear affects the durability of gear tooth and reduces the remaining useful life of the gear transmission system. The propagation of gear wear can lead to severe gear failures such as gear root crack, tooth spall, and tooth breakage, which can further cause unexpected equipment shutdown or hazardous incidents. Therefore, it is necessary to monitor gear wear propagation progression in order to perform predictive maintenance. Vibration analysis is a widely used and effective technique to monitor the operating condition of rotating machinery, especially for the diagnosis of localized failures such as gear root crack and tooth surface spalling. However, vibration-based techniques for gear wear analysis and monitoring are very limited, mainly due to the difficulties in identifying the complex vibration characteristics induced by gear wear propagation. Understanding the effect of gear wear on vibration characteristics is essential to develop vibration-based techniques for monitoring and tracking gear wear evolution. However, no research work has been previously published to summarize the research progress in vibration-based gear wear monitoring and prediction. To fill the research gap, this review paper aims to conduct a state-of-the-art comprehensive review on vibration-based gear wear monitoring, including studying the gear surface features caused by different gear wear mechanisms, investigating the relationships between gear surface features and vibration characteristics, and summarizing the current research progress of vibration-based gear wear monitoring. This review also makes some recommendations for future research work in this area. It is e...

Feng, K, Ji, JC, Ni, Q, Li, Y, Mao, W & Liu, L 2023, 'A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system', Wear, vol. 522, pp. 204697-204697.
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Gearbox has a compact structure, a stable transmission capability, and high transmission efficiency. Thus, it is widely applied and used as a critical transmission system in intelligent manufacturing systems, such as machine tools and robotics. The gearbox usually operates in harsh and non-stationary working environments, making the gear surface prone to wear. The progression of gear surface wear may lead to severe gear failures, such as gear tooth breakage and root crack, potentially damaging the whole gear transmission system. Therefore, it is essential to assess the gear surface wear progression and predict its remaining useful life (RUL) in order to ensure the reliable operation of the gear transmission system. To this end, this paper developed a novel gear wear prognostic scheme based on vibration analysis for gear health management. More specifically, a novel health indicator (HI) is first developed for gear wear monitoring in the proposed prognostic scheme. The novel HI, inferred from the cyclic correntropy and Wasserstein distance (WD), can accurately reflect the wear-induced cyclic correntropy spectra distribution change over time. Therefore, the novel HI can robustly evaluate the gear wear severity with high accuracy. With the developed HI, a network, namely the optimized gated recurrent unit (GRU), is applied for predicting the gear transmission system RUL during surface wear progression. As for the optimized GRU network, the genetic algorithm (GA) is applied to find the optimal hyperparameters adaptively, which can significantly improve the practicality of the developed prognostic scheme. To conclude, the developed prognostic scheme can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. A series of endurance tests are conducted to verify the effectiveness of the developed prognostic scheme for gear health management in surface wear progression.

Feng, K, Ji, JC, Ni, Q, Yun, H, Zheng, J & Liu, Z 2023, 'A novel vibration indicator to monitor gear natural fatigue pitting propagation', Structural Health Monitoring, vol. 22, no. 5, pp. 3126-3140.
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Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshing process, there is a close relationship between the tribological features of fatigue pitting and gear vibration cyclostationarity. Based on the vibration cyclostationarity, this paper develops a novel second-order cyclostationary (CS2) fatigue pitting monitoring indicator, which can accurately assess the degradation status of the gear system and benefit subsequent health management. The advantage of the developed cyclostationary indicator in evaluating and monitoring the process of fatigue pitting propagation is demonstrated with the natural fatigue pitting progression test, through comparisons with other conventional indicators.

Feng, K, Ji, JC, Zhang, Y, Ni, Q, Liu, Z & Beer, M 2023, 'Digital twin-driven intelligent assessment of gear surface degradation', Mechanical Systems and Signal Processing, vol. 186, pp. 109896-109896.
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Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.

Fu, J, Abharian, S, Sarfarazi, V, Haeri, H, Rasekh, H & Xu, L 2023, 'The rock fracturing in the jointed tunnel face ground with TBM: Experimental and numerical study', Theoretical and Applied Fracture Mechanics, vol. 125, pp. 103933-103933.
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Galpathage, SG, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2023, 'Pull-out behaviour of simulated tree roots embedded in compacted soil', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 176, no. 1, pp. 54-64.
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Vegetated ground is strengthened by the suction generated during evapotranspiration as well as physically and mechanically by the reinforcement effect induced by tree roots. When suction suddenly decreases during flooding or intense rainfall, the shear strength of the soil–plant system relies mainly on the physical root reinforcement. In this paper, the pull-out behaviour of simulated roots embedded in a compacted soil is investigated to assess how the soil–plant system is mechanically strengthened. An analytical framework is developed to estimate the pull-out force and it is validated using a series of pull-out tests. The tests are carried out on simulated roots embedded in a box of compacted soil having different water contents and equivalent dry unit weights. The results show that the pull-out capacity of this root system is mainly influenced by the initial (as-compacted) suction, and the length and diameter of the roots. The model predictions agree reasonably well with the experimental observations.

Ge, M, Pineda, J & Sheng, D 2023, 'Competing effects of wetting and volume change on G₀ in compacted loess', Géotechnique Letters, vol. 13, no. 4, pp. 182-190.
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This paper explores the relative contributions of wetting (suction reduction) and its associated volume change on the small-strain shear stiffness, G0, in compacted loess from Xi’an, China. Results from one-dimensional compression tests with measurements of the shear wave velocity upon wetting and loading paths are presented. The experimental results show that the softening caused by wetting compete with the densification caused by plastic deformation and their effects on G0 are strongly controlled by stress level applied prior to wetting. Below the compaction stress, suction effects are dominant and G0 reduces irrespective of the magnitude of the collapse strain. With the increase in the stress level, the reduction in G0 caused by saturation is compensated by the plastic deformation triggered by soil collapse. This behaviour is clearly observed when the soil is first loaded to the compaction stress, where the maximum collapse strain is measured upon wetting. Volume change is dominant once the compaction stress is exceeded so that G0 tends to increase upon wetting. A wetting-induced stiffness factor D is defined to demonstrate that the change in G0 varies linearly with the stress level and this behaviour is independent of the compaction conditions.

Gedela, R, Indraratna, B, Medawela, S & Nguyen, TT 2023, 'Effects Of Fines Content On The Strength And Stiffness Of Biopolymer Treated Low-Plasticity Soils', Australian Geomechanics Journal, vol. 58, no. 1, pp. 33-41.
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The use of biopolymers to enhance the engineering properties of soil has received increasing attention in recent years, however, the interactive role that biopolymers and the fines content of the soil play in governing the geotechnical parameters still requires insightful investigation, in relation to chemical soil treatment that can be ecologically detrimental. This paper examines the combined effects of Xanthan Gum (XG) derived from specific bacterial strains and the presence of clay fines content (kaolin) on the strength and stiffness of low plasticity soils, with special reference of cyclic traffic (road and rail) loading. In this study, fine sand is mixed with different contents of kaolin, whereby laboratory compression and tensile tests were conducted on natural (untreated) and XG-treated soil specimens. The results indicate that soil strength can be enhanced significantly when XG is added, however the effectiveness is a function of the kaolin content (KC). At an optimum XG content of 2% and a fines content increasing from 5% to 30%, split tensile strength increases from 230 to 750 kPa,while the unconfined compressive strength rises from 1.4 to 7.9 MPa, respectively. For XG content between 0.5% and 2%, the small strain stiffness of treated soil increases fourfold from 206 to 854 MPa.

Gedela, R, Indraratna, B, Nguyen, TT & Medawela, S 2023, 'The effect of biopolymer treatment on the potential instability of a soft soil under cyclic loading', Transportation Geotechnics, vol. 42, pp. 101102-101102.
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Ghezelbash, R, Maghsoudi, A, Shamekhi, M, Pradhan, B & Daviran, M 2023, 'Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity', Neural Computing and Applications, vol. 35, no. 1, pp. 719-733.
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Gholami, H, Mohammadifar, A, Golzari, S, Song, Y & Pradhan, B 2023, 'Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion', Science of The Total Environment, vol. 904, pp. 166960-166960.
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Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modell...

Hakim, WL, Fadhillah, MF, Park, S, Pradhan, B, Won, J-S & Lee, C-W 2023, 'InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression', Remote Sensing of Environment, vol. 287, pp. 113453-113453.
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Halkon, BJ, Darwish, A, Rothberg, S, Mohammadi, M & Oberst, S 2023, 'Correction of scanning laser Doppler vibrometer measurements when subjected to six degree-of-freedom base motion', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A74-A74.
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Scanning laser Doppler vibrometer (SLDV) measurements are affected by sensor head vibrations as though they are vibrations of the target surface itself. Previous work has established a fully general theoretical analysis which shows that the only measurement required for measurement correction is of the vibration velocity at the incident point on the final steering mirror in the direction of the outgoing laser beam. Two practical—but not quite perfect—options for measurement correction were presented (one more suitable to manufacturer implementation, one more applicable to the vibration engineer end user). In both cases, placement of the correction transducer is critical with correction working for totally arbitrary instrument vibration and scan angle. Experimental validation, employing frequency-domain based processing, has been completed for one degree-of-freedom, on-axis vibration. Simultaneously, advancements in the data processing approach have realised improved correction in practice, especially for lower frequencies and for transient, as opposed to statistically stationary, vibration. In this paper, extension of the experimental validation to six degree-of-freedom instrument vibration is presented for the first time. In combination with the latest data processing approaches, reductions in the measurement error of 29.4 and 28.2 dB for the frequency- and time-domain processing techniques, respectively, are realised.

Haq, S, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Hydromechanical state of soil fluidisation: a microscale perspective', Acta Geotechnica, vol. 18, no. 3, pp. 1149-1167.
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AbstractThis paper investigates soil fluidisation at the microscale using the discrete element method (DEM) in combination with the lattice Boltzmann method (LBM). Numerical simulations were carried out at varying hydraulic gradients across the granular assembly of soil. The development of local hydraulic gradients, the contact distribution, and the associated fabric changes were investigated. Microscale findings suggest that a critical hydromechanical state inducing fluid-like instability of a granular assembly can be defined by a substantial increase in grain slip associated with a rapid reduction in interparticle contacts. Based on these results, a new micromechanical criterion is proposed to characterise the transformation of granular soil from a hydromechanically stable to an unstable state. The constraint ratio (ratio of the number of constraints to the number of degrees of freedom) is introduced to portray the relative slippage between particles and the loss of interparticle contacts within the granular fabric. Its magnitude of unity corresponds to the condition of zero effective stress, representing the critical hydromechanical state. In practical terms, the results of this study reflect the phenomenon of subgrade mud pumping that occurs in railways when heavy-haul trains pass through at certain axle loads and speeds.

Haq, S, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Micromechanical Analysis of Internal Instability during Shearing', International Journal of Geomechanics, vol. 23, no. 6, p. 04023078.
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Internal instability means that finer particles pass through the constrictions of coarser particles at a hydraulic gradient well below that of heave or piping, rendering the soil ineffective for its intended purpose. The soil could make a transition from an internally stable state to an unstable state or vice versa due to shear-induced deformation. The discrete element method (DEM) is adopted in this study to examine and quantify soil behavior by simulating the quasi-static shear deformation of internally stable and unstable soils at the micro- and macroscales. The dense bimodal specimens were sheared under drained conditions following a constant mean stress path in order to investigate the influence of stress heterogeneity. At the macroscale, the peak deviatoric stress was found to be a function of the fines content and the initial void ratios of the specimens. The development of the average number of contacts per particle and the stress transfer to the finer fraction during shearing are discussed. The simulation results innovatively show that a dense specimen could undergo a transition from an internally stable to an unstable soil as it dilates during shear. These numerical results have significant implications on the importance of real-life situations, such as predicting mud pumping in railroad tracks.

Hasan, HA, Hacheem, ZA, Almurshedi, AD & Khabbaz, H 2023, 'The Influence of Styrene Butadiene Latex on Sandy Soil Reinforced by Soil Mixed Columns under Raft Foundation', Mathematical Modelling of Engineering Problems, vol. 10, no. 3, pp. 733-739.
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He, H, Teng, J, Zhang, S & Sheng, D 2023, 'Determining frost heave classification by using ratio of frost heave to square root of time', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 45, no. 12, pp. 2519-2528.
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The frost heave classification is the critical index for the design of foundation engineering in cold regions. At present, it is considered as a basic property of materials. Many criteria belong to empirical or semi-empirical methods and lack theoretical support. The frost heave tests are tedious and long time-consuming, and are not easily operated. To propose a rational and simple frost heave classification index, from the frost heave mechanism, an analytical model for unsaturated frozen soil is established and validated. Then a new frost heave classification index R (mm/h0.5), which is the ratio of frost heave to square root of time, is identified based on the proposed model. Through comparison with the large number of frost heave results, the value of R less than 0.21 indicates the low frost heave classification, that between 0.21 and 1.18 represents the medium heave classification, and that greater than 1.18 means the high frost heave classification. From a statistical probability perspective, the probability density distribution of the values of each classification index is analyzed, and their trends are also compared. It is found that the concentration and stability of the new index R are the highest during freezing process. The new index R has theoretical support and simultaneously couples the basic soil properties and freezing environmental factors. It breaks through the limitation of the existing indexes, and enriches the frost heave classification system, and provides theoretical support for the engineering design in cold regions.

He, X, Xu, H & Sheng, D 2023, 'Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs', Acta Geotechnica, vol. 18, no. 4, pp. 1681-1698.
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AbstractData-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of sample data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an example. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.

Horry, MJ, Chakraborty, S, Pradhan, B, Paul, M, Zhu, J, Barua, PD, Mir, HS, Chen, F, Zhou, J & Acharya, UR 2023, 'Full-Resolution Lung Nodule Localization From Chest X-Ray Images Using Residual Encoder-Decoder Networks', IEEE Access, vol. 11, pp. 143016-143036.
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Horry, MJ, Chakraborty, S, Pradhan, B, Shulka, N & Almazroui, M 2023, 'Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches', Earth Systems and Environment, vol. 7, no. 2, pp. 525-540.
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AbstractHigh-velocity data streams present a challenge to deep learning-based computer vision models due to the resources needed to retrain for new incremental data. This study presents a novel staggered training approach using an ensemble model comprising the following: (i) a resource-intensive high-accuracy vision transformer; and (ii) a fast training, but less accurate, low parameter-count convolutional neural network. The vision transformer provides a scalable and accurate base model. A convolutional neural network (CNN) quickly incorporates new data into the ensemble model. Incremental data are simulated by dividing the very large So2Sat LCZ42 satellite image dataset into four intervals. The CNN is trained every interval and the vision transformer trained every half interval. We call this combination of a complementary ensemble with staggered training a “two-speed” network. The novelty of this approach is in the use of a staggered training schedule that allows the ensemble model to efficiently incorporate new data by retraining the high-speed CNN in advance of the resource-intensive vision transformer, thereby allowing for stable continuous improvement of the ensemble. Additionally, the ensemble models for each data increment out-perform each of the component models, with best accuracy of 65% against a holdout test partition of the RGB version of the So2Sat dataset.

Huang, Z, Shivakumara, P, Kaljahi, MA, Kumar, A, Pal, U, Lu, T & Blumenstein, M 2023, 'Writer age estimation through handwriting', Multimedia Tools and Applications, vol. 82, no. 11, pp. 16033-16055.
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Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.

Hunt, H, Indraratna, B & Qi, Y 2023, 'Ductility and energy absorbing behaviour of coal wash – rubber crumb mixtures', International Journal of Rail Transportation, vol. 11, no. 4, pp. 508-528.
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The reuse of waste materials, such as coal wash (CW) and rubber crumbs (RC), is becoming increasingly popular in large-scale civil engineering applications, which is environmentally friendly and economically attractive. In this study, the ductility and strain energy density of CW-RC mixtures with different RC contents compacted to the same initial void ratio and subjected to triaxial shearing are evaluated. As expected, the ductility and energy absorbing capacity of the waste mixture are improved with RC addition. This makes the use of CW-RC mixtures in substructure applications a promising development for future rail design where loads are expected to increase. Furthermore, empirical models for the shear strength and strain energy density based on the RC content are proposed. These models may be used as a guide to approximate the sheacr strength and strain energy density of these compacted CW-RC mixtures prior to the undertaking of extensive triaxial tests.

Indraratna, B, Armaghani, DJ, Gomes Correia, A, Hunt, H & Ngo, T 2023, 'Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques', Transportation Geotechnics, vol. 38, pp. 100895-100895.
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The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles, the loading frequency, and the confining pressure. To do so, two non-linear predictive models, namely, the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), are used to predict the MR values under different loading conditions. To evaluate and predict MR, an experimental database with 196 data samples is considered in this study. A series of sensitivity analyses is carried out to investigate the most effective parameters in each non-linear model and also predict the highest performance model. Although the results from the primary validation phase are satisfactory for the ANN and ANFIS models, ANFIS proves better (i.e., the coefficient of determination = 0.709) at estimating the MR during the secondary validation phase, using an independent dataset. Hence, it can be used as a powerful and practical model for predicting the magnitude of MR. On the basis of the ANFIS model, this study also offers some design considerations in terms of MR of ballast under a practical range of cyclic loading parameters.

Jain, K, Pradhan, B & Mishra, V 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 304, pp. v-vi.

Jena, R, Pradhan, B, Almazroui, M, Assiri, M & Park, H-J 2023, 'Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques', Geoscience Frontiers, vol. 14, no. 1, pp. 101460-101460.
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Jena, R, Pradhan, B, Gite, S, Alamri, A & Park, H-J 2023, 'A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model', Gondwana Research, vol. 123, pp. 54-67.
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Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.

Jena, R, Shanableh, A, Al-Ruzouq, R, Pradhan, B, Gibril, MBA, Khalil, MA, Ghorbanzadeh, O & Ghamisi, P 2023, 'Earthquake spatial probability and hazard estimation using various explainable AI (XAI) models at the Arabian peninsula', Remote Sensing Applications: Society and Environment, vol. 31, pp. 101004-101004.
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Joshi, A, Pradhan, B, Chakraborty, S & Behera, MD 2023, 'Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm', Ecological Informatics, vol. 77, pp. 102194-102194.
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Kalhori, H, Rafiee, R, Ye, L, Halkon, B & Bahmanpour, M 2023, 'Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel', International Journal of Impact Engineering, vol. 176, pp. 104576-104576.
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Karbassiyazdi, E, Altaee, A, Ibrar, I, Razmjou, A, Alsaka, L, Ganbat, N, Malekizadeh, A, Ghobadi, R & Khabbaz, H 2023, 'Fabrication of carbon-based hydrogel membrane for landfill leachate wastewater treatment', Desalination, vol. 564, pp. 116783-116783.
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The challenge of effectively managing the discharge of metal ions into aquatic environments, which poses a significant risk to both human health and ecosystems, persists despite the availability of various analytical tools and techniques. There are limitations of existing separation technologies and the inefficacy of hydrogel materials in removing low molecular weight contaminants, such as metal ions, in aqueous solutions. This study added carbon powder to the hydrogel membrane to reduce the low-mechanical strength and drying problems and increase its capacity for adsorbing ionic and non-ionic substances. The study introduced a novel carbon-based aluminium hydroxide hydrogel for wastewater filtration. CG was characterized using various analytical techniques, including examining surface morphology, elemental analysis, surface functional groups, and surface charge. These analytical tools provided a comprehensive understanding of the properties and performance of the CG. The effects of different carbon-based hydrogel (CG) concentrations on water flux and ion rejection were evaluated in a gravity filtration setup. Experiments investigated the influence of different ion concentrations, activated carbon (AC) concentration, centrifugation, water flux, and rejection on removing heavy metals from synthetic and natural wastewater. The pure water flux of the hydrogel membrane was 120 LMH. The results indicated that an AC concentration of 4 g/L in the aqueous solution is optimal for heavy metals removal, with 99.9 % removal for Pb2+ and Cu2+, 84 % rejection for Ca2+, and 85 % rejection for Mg2+ in 10 mg/L of synthetic water. Besides, the 4 g/L AC hydrogel membrane removed 90 % of Ni, Zn, Pb, As, and Cu ions and 53 % of the total organic carbon from leachate wastewater.

Karbassiyazdi, E, Altaee, A, Razmjou, A, Samal, AK & Khabbaz, H 2023, 'Gravity-driven composite cellulose acetate/activated carbon aluminium-based hydrogel membrane for landfill wastewater treatment', Chemical Engineering Research and Design, vol. 200, pp. 682-692.
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Khabbaz, H, Rujikiatkamjorn, C & Parsa, A 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 325 LNCE, pp. v-vi.

Khade, S, Gite, S, D. Thepade, S, Pradhan, B & Alamri, A 2023, 'Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers', Computer Modeling in Engineering & Sciences, vol. 136, no. 1, pp. 323-345.
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Korkitsuntornsan, W, Indraratna, B, Rujikiatkamjorn, C & Nguyen, TT 2023, 'Depth-dependent soil fluidization under cyclic loading—an experimental investigation', Canadian Geotechnical Journal, vol. 60, no. 6, pp. 946-950.
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Past studies have shown that shallow subgrade soil can transform to a slurry (i.e., fluidization) under unfavourable cyclic loading. However, the depth-dependent behaviour of soil parameters during this process has not been properly understood. The current study utilised a large-scale cylindrical test rig, where instrumentation was installed to observe the soil behaviour along the depth of the test specimens under cyclic loading, to examine and quantify the onset of soil fluidization. The results show that excess pore water pressure tends to rise more at the upper layers causing zero-effective stress, while void ratio expands rapidly within the deteriorated soil fabric, making the water content approach the liquid limit of soil when internal moisture migration occurs from the bottom to the top of the specimen. The larger the cyclic load, the deeper the fluidized zone and the faster the fluidization. The study also suggests that the zero-effective stress condition alone cannot interpret the inception of soil fluidization; hence, the change in void ratio and the liquidity index during the application of cyclic loading should also be considered in tandem.

Kronowetter, F, Pretsch, L, Chiang, YK, Melnikov, A, Sepehrirahnama, S, Oberst, S, Powell, DA & Marburg, S 2023, 'Sound attenuation enhancement of acoustic meta-atoms via coupling', The Journal of the Acoustical Society of America, vol. 154, no. 2, pp. 842-851.
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Arrangements of acoustic meta-atoms, better known as acoustic metamaterials, are commonly applied in acoustic cloaking, for the attenuation of acoustic fields or for acoustic focusing. A precise design of single meta-atoms is required for these purposes. Understanding the details of their interaction allows improvement of the collective performance of the meta-atoms as a system, for example, in sound attenuation. Destructive interference of their scattered fields, for example, can be mitigated by adjusting the coupling or tuning of individual meta-atoms. Comprehensive numerical studies of various configurations of a resonator pair show that the coupling can lead to degenerate modes at periodic distances between the resonators. We show how the resonators' separation and relative orientation influence the coupling and thereby tunes the sound attenuation. The simulation results are supported by experiments using a two-dimensional parallel-plate waveguide. It is shown that coupling parameters like distance, orientation, detuning, and radiation loss provide additional degrees of freedom for efficient acoustic meta-atom tuning to achieve unprecedented interactions with excellent sound attenuation properties.

Lal Mohammadi, E, Khaksar Najafi, E, Zanganeh Ranjbar, P, Payan, M, Jamshidi Chenari, R & Fatahi, B 2023, 'Recycling industrial alkaline solutions for soil stabilization by low-concentrated fly ash-based alkali cements', Construction and Building Materials, vol. 393, pp. 132083-132083.
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Le Nguyen, K, Thi Trinh, H, Nguyen, TT & Nguyen, HD 2023, 'Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications', Expert Systems with Applications, vol. 230, pp. 120649-120649.
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Le, T, Desa, S & Khabbaz, H 2023, 'The Influence Of Bagasse Fly Ash Particle Size In Controlling Expansive Soils In Combination With Hydrated Lime', Australian Geomechanics Journal, vol. 58, no. 1, pp. 47-57.
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Sugarcane is the second largest export crop in Australia. Industrial production of sugar, from sugarcane, results in bagasse fly ash (BFA), a by-product from the cogeneration in sugar milling operations that generate electricity by steam. The chemical and physical properties of BFA highlight its potential as a promising pozzolan for the stabilization of expansive soils, due primarily to a high content and surface area of the amorphous silicate found in BFA. Silicate in bagasse fly ash reacts extensively with calcium hydrate in lime to produce hydrated products via pozzolanic reactions, this results in a hardening of the material to which BFA and lime have been added. This reaction has been studied to be a function of the size of BFA particles and conditions of the curing process. This study explored the variables that influence the reaction and evaluated shrinkage and compressive strength of the mixtures to which bagasse fly ash, in the form of different particle size distributions, and hydrated lime are added. The maximum BFA particles sizes considered within this study include 75, 150 and 425 μm; curing times of 7 and 28 days are also explored. A suite of testing, including Atterberg limits, linear shrinkage (LS), and unconfined compressive strength (UCS) tests were completed on the prepared mixtures. The findings indicate that bagasse fly ash with a maximum size of 425 μm yields a higher UCS and lower LS, compared to finer BFA particle mixtures. The ash with a maximum particle size of 425-μm also improves the ductility of treated soils and accelerates their strength gain, compared to soil- lime stabilized samples. The results of the study build towards a better understanding of BFA, and the ways in which such a material maybe engineered to replace concrete in road work projects and other applications involving expansive soils.

Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 2023, 'Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway Systems', IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14182-14196.
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Li, K, Li, X, Chen, Q & Nimbalkar, S 2023, 'Laboratory Analyses of Noncoaxiality and Anisotropy of Spherical Granular Media under True Triaxial State', International Journal of Geomechanics, vol. 23, no. 9.
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Li, L, Ju, N & Sheng, D 2023, 'Seismic performance and failure mechanism of interbedded slopes with steep rock layers', Engineering Geology, vol. 326, pp. 107312-107312.
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Numerous interbedded rock (IR) slopes fail during the Wenchuan earthquake in the mountainous region of western China. Landslides are also triggered in IR slopes with a 60° layer inclination, which are generally stable in gravity-dominant environments. This study examines the effect of seismic motion on the response characteristics and failure patterns of IR slopes with steep layers to develop a landslide hazard assessment tool for earthquake-prone regions. First, we use a centrifuge shaking table test to model the failure process and acceleration responses of two IR slope models with stratigraphic dips of 60° and 80°, respectively, under different seismic intensities. Next, we adopt the Particle Flow Code to examine the crack propagation features and peak ground acceleration amplification effects for the IR slopes. We find that the seismic failure pattern of IR slopes depends largely on rock layer inclination: buckling failure is triggered when rock layers are parallel or nearly parallel to the slope surface, while toppling failure is triggered when the rock layer inclination is significantly higher than that of the slope surface. Following seismic excitation, the damage is mainly observed in the weak rock layers, creating lateral stress on adjacent strong rocks, which undergoes deformation and ultimate macroscopic failure. Further, displacement of the IR slope is negatively correlated to rock layer inclination. Rock layer thickness has a major influence on the damaged area inside the slope mass, while rock layer stiffness mainly affects the deformation distribution near the slope shoulder.

Li, S, Ji, JC, Xu, Y, Sun, X, Feng, K, Sun, B, Wang, Y, Gu, F, Zhang, K & Ni, Q 2023, 'IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions', Reliability Engineering & System Safety, vol. 237, pp. 109387-109387.
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Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios.

Li, W, Ji, J, Huang, L & Cai, Z 2023, 'Periodic orbit analysis for a delayed model of malicious signal transmission in wireless sensor networks with discontinuous control', Mathematical Methods in the Applied Sciences, vol. 46, no. 5, pp. 5267-5285.
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This paper employs a discontinuous temporary immunity control to obtain the periodic orbit for a class of delayed malicious signal transmission model in wireless sensor networks under the framework of differential inclusion. The positivity and boundedness of the solution for the discontinuous system is proved first. Then, by using the Kakutani's fixed point theorem of set‐valued maps, the existence of a periodic orbit is obtained under some assumptions and constraints. Furthermore, the globally exponentially stable ‐periodic orbit is investigated using the Lyapunov functional method. The obtained results can help us better understand the dynamic characteristics of discontinuous delayed systems and have direct applications to the wireless sensor networks for guaranteeing fast response to malicious signals. Finally, the numerical simulations of three examples are given to validate the correctness of the theoretical results.

Li, W, Ji, J, Huang, L & Zhang, L 2023, 'Global dynamics and control of malicious signal transmission in wireless sensor networks', Nonlinear Analysis: Hybrid Systems, vol. 48, pp. 101324-101324.
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This paper studies the global dynamics of a discontinuous delayed model of malicious signal transmission in wireless sensor networks under the framework of differential inclusion. The local stability of two types of steady states are investigated for the discontinuous system by studying the corresponding characteristic equation. The sufficient conditions for the existence of two types of globally asymptotically stable steady states are obtained for the discontinuous system by using the comparison arguments method. Furthermore, the optimal control of the discontinuous system is investigated by using Pontryagin's maximum principle. Numerical simulations of two examples are carried out to illustrate the main theoretical results. The obtained results can help us to better control and predict the spread of malicious signal transmission in wireless sensor networks.

Li, W, Ji, J, Huang, L & Zhang, Y 2023, 'Complex dynamics and impulsive control of a chemostat model under the ratio threshold policy', Chaos, Solitons & Fractals, vol. 167, pp. 113077-113077.
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In this paper, we study the periodic solution and global stability of a chemostat model under impulsive control. First, we investigate the positivity and boundedness of the solution of the controlled system. Second, we find the periodic solution of the controlled system by employing the Poincare map and Brouwer's fixed-point theorem. Furthermore, we obtain a sufficient condition which allows the existence of orbitally stable order-k periodic solutions (k=1,2) by using the comparison method and the vector field analysis. We find that the controlled system exists a unique positive equilibrium point that is globally asymptotically stable (GAS) under some conditions. Finally, we provide two numerical examples to verify the correctness of the theoretical results.

Li, W, Zhang, Y, Ji, J & Huang, L 2023, 'Dynamics of a diffusion epidemic SIRI system in heterogeneous environment', Zeitschrift für angewandte Mathematik und Physik, vol. 74, no. 3, p. 104.
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This paper studies the dynamical behaviors of a diffusion epidemic SIRI system with distinct dispersal rates. The overall solution of the system is derived by using L p theory and the Young's inequality. The uniformly boundedness of the solution is obtained for the system. The asymptotic smoothness of the semi-flow and the existence of the global attractor are discussed. Moreover, the basic reproduction number is defined in a spatially uniform environment and the threshold dynamical behaviors are obtained for extinction or continuous persistence of disease. When the spread rate of the susceptible individuals or the infected individuals is close to zero, the asymptotic profiles of the system are studied. This can help us to better understand the dynamic characteristics of the model in a bounded space domain with zero flux boundary conditions.

Li, Y, Wang, X, Zheng, J, Feng, K & Ji, JC 2023, 'Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system', Measurement Science and Technology, vol. 34, no. 6, pp. 065011-065011.
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Abstract Multiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.

Li, Z, Gao, W, Kessissoglou, N, Oberst, S, Wang, MY & Luo, Z 2023, 'Multifunctional mechanical metamaterials with tunable double-negative isotropic properties', Materials & Design, vol. 232, pp. 112146-112146.
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Lin, D, Ji, J, Yu, C, Wang, X & Xu, N 2023, 'A non-linear model of screen panel for dynamics analysis of a flip-flow vibrating screen', Powder Technology, vol. 418, pp. 118312-118312.
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By taking advantage of periodic high-frequency flexure deformation of screen panels, flip-flow vibrating screens (FFVSs) can achieve outstanding sieving performance. As the amplitude of the relative displacement between the main frame and the floating frame of a FFVS exceeds the relaxing length of screen panel, the tensile stress generated from the deformation of screen panel can considerably affect the dynamics and the screening performance of the FFVS. However, there is a research gap in understanding the mechanical properties (especially the stiffness and damping) of screen panels. To address this research issue, the dynamic tests are first conducted to investigate the dynamic behavior of screen panels under harmonic excitations. Then the Kelvin-Voigt (KV) model is adopted to represent the hysteresis feature of the tension force. Furthermore, to characterize the mechanical properties of the screen panels under different stretching lengths, a nonlinear mechanical model is introduced and incorporated into the dynamic model of the FFVS. The effects of the stiffness, damping and relaxing length of screen panel, the shear springs and the eccentric mass moment on the vibration characteristics of the FFVS are numerically studied using a genetic algorithm and Newmark-β algorithm. The obtained results show that the panel tension force can induce the hardening nonlinearity in the relative displacement response of FFVS and the soft type of nonlinearity in the displacement response of the main screen frame in a certain frequency region. Furthermore, at the second-order resonance peak, a small change in frequency can cause a substantial increase in the vibration amplitude of the main frame and a significant decrease in the relative amplitude. This nonlinear phenomenon would induce a large alternating stress on the main frame structure and thus reduce the service life of the FFVS.

Lin, D, Ji, JC, Wang, X, Wang, Y, Xu, N, Ni, Q, Zhao, G & Feng, K 2023, 'A rigid-flexible coupled dynamic model of a flip-flow vibrating screen considering the effects of processed materials', Powder Technology, vol. 427, pp. 118753-118753.
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Flip-flow vibrating screens (FFVSs) are the critical screening equipment for classifying and dewatering wet materials in mining processing industry. During the screening process, the FFVSs can be regarded as a complex rigid-flexible coupled multi-body system where the screening operation and the dynamics of two screen frames interact. However, there exists no mechanical model that can describe the dynamics of FFVSs during the screening process. The lack of such a dynamic model causes the amplitudes of the main and the floating screen frames unpredictable after the processed materials are loaded on FFVSs, which affects the screening performance and the service life of FFVSs. To bridge this research gap, the loaded dynamic model of a FFVS is established in this paper. First, dynamic tests are performed to investigate the equivalent stiffness and the equivalent damping of the force along the screen surface which is induced by the processed materials. Then, the proposed model of the FFVS is verified qualitatively by existing experimental results, and the effects of the processed materials on the dynamics of the FFVS are explored by comparing the non-load dynamics of the FFVS. Finally, the sensitivities of the main parameters on the dynamic response are investigated based on Sobol's method of global sensitivity analysis. It is shown that the proposed rigid-flexible coupled multi-body dynamic model of the FFVS can not only effectively reveal the dynamic response of FFVS in the screening process, but can also provide a reference for modelling the dynamics of the screening process of other screening equipment.

Liu, CL, Yagi, Y, Kamiya, T, Blumenstein, M, Lu, H, Yang, W & Cho, SB 2023, 'Preface for ACPR 2023 Proceedings', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14406 LNCS, pp. v-vi.

Liu, K, Lyu, S, Shivakumara, P, Blumenstein, M & Lu, Y 2023, 'A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation', IEEE Signal Processing Letters, vol. 30, pp. 1607-1611.
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Lu, S & Oberst, S 2023, 'Recurrence-based reconstruction of dynamic pricing attractors', Nonlinear Dynamics, vol. 111, no. 16, pp. 15263-15278.
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AbstractDynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems.

Luo, L, Li, B, Fan, X, Wang, Y, Koprinska, I & Chen, F 2023, 'Dynamic customer segmentation via hierarchical fragmentation-coagulation processes', Machine Learning, vol. 112, no. 1, pp. 281-310.
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Understanding customer behavior is necessary to develop efficient marketing strategies or launch tailored programs with social value for the public. Customer segmentation is a critical task for understanding diverse and dynamic customer behavior. However, as the popularity of different products varies, building dynamic customer behavior models for products with few customers may overfit the data. In this paper, we propose a new Bayesian nonparametric model for dynamic customer segmentation—Hierarchical Fragmentation-Coagulation Processes (HFCP), which allows sharing behavior patterns across multiple products. We conduct comprehensive empirical evaluations using two real-world purchase datasets. Our results show that HFCP can: (i) determine the number of groups required to model diverse customer behavior automatically; (ii) capture the changes such as split and merge of customer groups over time; (iii) discover behavior patterns shared among products and identify products with similar or different purchase behavior impacted by promotion, brand choice and change of seasons; and (iv) overcome overfitting problems and outperform previous customer segmentation models on estimating behavior for unseen customers. Hence, HFCP is a flexible and accurate segmentation model that can be used by stakeholders to understand dynamic customer behavior and compare the purchase behavior for different products.

Makhdoom, I, Abolhasan, M, Franklin, DR, Lipman, J, Zimmermann, C, Piccardi, M & Shariati, N 2023, 'Detecting compromised IoT devices: Existing techniques, challenges, and a way forward.', Comput. Secur., vol. 132, pp. 103384-103384.
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Malisetty, RS, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2023, 'Shakedown response of recycled rubber–granular waste mixtures under cyclic loading', Géotechnique, vol. 73, no. 10, pp. 843-848.
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Understanding and quantifying the long-term deformation behaviour of granular materials under repeated loads is imperative for ensuring the longevity of railway tracks. One of the most relevant characteristics of granular materials under repeated cycles of loading and unloading is their ability to achieve a relatively stable state (shakedown) after being subjected to initial compression. The shakedown response of blended rubber–granular waste mixtures under triaxial test conditions has been investigated in past studies highlighting the influence of the rubber content, confining stress and cyclic loading amplitude. However, a clear methodology for estimating shakedown yield limits of these granular mixtures has not been discussed in detail. The current study highlights the influence of the peak shear strength of these mixtures under static loading on their shakedown response in cyclic loading conditions. It is observed that the variation of static shear strength with rubber contents and confining stresses is found to affect the shakedown response. A unified method of estimating the shakedown limit is proposed by analysing permanent axial strains with normalised cyclic stress ratio at different loading cycles. The proposed method is validated through two independent sets of drained cyclic triaxial test data on coal wash–rubber crumb mixtures and rail ballast.

Medawela, S, Armaghani, DJ, Indraratna, B, Kerry Rowe, R & Thamwattana, N 2023, 'Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain', Computers and Geotechnics, vol. 161, pp. 105557-105557.
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Medawela, S, Indraratna, B & Rowe, RK 2023, 'The reduction in porosity of permeable reactive barriers due to bio-geochemical clogging caused by acidic groundwater flow', Canadian Geotechnical Journal, vol. 60, no. 2, pp. 151-165.
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This study demonstrates the change in porosity of permeable reactive barrier (PRB) material when it reacts with acidic flow. The laboratory column test data obtained over 9 months prove that the porosity of a granular limestone assembly decreases significantly due to bio-geochemical clogging caused by a continuous flow of acidic groundwater. The variations in pH, the pressure measurements, ion concentrations, and the results from X-ray diffraction suggest that clogging at the outlet of the column is much less than at the inlet. About 57% of the total reduction in porosity of the column is attributed to chemical clogging, while the remainder is mainly due to biological clogging. In this paper, a mathematical approach is proposed to estimate the reduction of reactive surface area based on changes in the pore volume. These proposed equations suggest that at the end of experimentation, the dissolution of calcite and bio-geochemical clogging can reduce the total surface area of limestone aggregates by more than 70%. The rigorous approach presented in this paper to determine the dominant clogging component within a granular filter at a given time is vital in estimating the longevity of a PRB and for planning its maintenance.

Meilianda, E, Mauluddin, S, Pradhan, B & Sugianto, S 2023, 'Decadal shoreline changes and effectiveness of coastal protection measures post-tsunami on 26 December 2004', Applied Geomatics, vol. 15, no. 3, pp. 743-758.
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Mishra, AK, Singh, O, Kumar, A, Puthal, D, Sharma, PK & Pradhan, B 2023, 'Hybrid Mode of Operation Schemes for P2P Communication to Analyze End-Point Individual Behaviour in IoT', ACM Transactions on Sensor Networks, vol. 19, no. 2, pp. 1-23.
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The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of individuals using huge amounts of data collected from their activities. The behavioural data collection process from an individual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD.

Nguyen, TT & Indraratna, B 2023, 'Influence of varying water content on permanent deformation of mud-fouled ballast', Transportation Geotechnics, vol. 38, pp. 100919-100919.
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The contamination of ballast by mud pumping is known to cause considerable reduction in the shear resistance as well as increased settlement of railroad foundations. However, how varying water content (w) of fouled ballast can affect this deterioration has not been properly understood. The current study thus adopts a large-scale cyclic triaxial test to examine permanent (plastic) deformation of mud-fouled ballast collected from a site with a history of mud pumping with a consideration of different water contents. In these tests, fouling content varies from 5 to 30 %, while the water content changes from 0 to 40 %. The results show that while increasing content of fines causes larger permanent settlement of ballast, varying water content of fines can influence this behaviour significantly. A salient finding of this study is the critical threshold of water content near to the liquid limit (LL) of fine soil (finer than 0.425 mm) that can cause a swift increase in ballast settlement. The results show that the peak permanent strain can increase by about 26 % compared to the dry state when w of fines reaches the LL. On the other hand, permanent strain of fouled ballast can decrease at the optimum water content of fines, if a sufficient mass of fines (>20 % by weight) is provided to reinforce the granular assembly. An empirical method is provided to estimate the ultimate settlement of mud-fouled tracks considering the moisture state that would be most beneficial in real-life applications.

Ni, Q, Ji, JC & Feng, K 2023, 'Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1301-1311.
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The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.

Ni, Q, Ji, JC, Halkon, B, Feng, K & Nandi, AK 2023, 'Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics', Mechanical Systems and Signal Processing, vol. 200, pp. 110544-110544.
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Various deep learning methodologies have recently been developed for machine condition monitoring recently, and they have achieved impressive success in bearing fault diagnostics. Despite the capability of effectively diagnosing bearing faults, most deep learning methods are tremendously data-dependent, which is not always available in industrial applications. In practical engineering, bearings are usually installed in rotating machinery where speed and load variations frequently occur, resulting in difficulty in collecting large training datasets under all operating conditions. Additionally, physical information is usually ignored in most deep learning algorithms, which sometimes leads to the generated results of low compliance with the physical law. To tackle these challenges, a novel Physics-Informed Residual Network (PIResNet) is proposed for learning the underlying physics that is embedded in both training and testing data, thus providing a physical consistent solution for imperfect data. In the proposed method, a physical modal-property-dominant-generated layer is adopted at first to generate the modal-property-dominant feature. Then, a domain-conversion layer is constructed to enable the feasibility of extracting the discriminative bearing fault features under varying operating speed conditions. Lastly, a parallel bi-channel residual learning architecture that can automatically extract the bearing fault signatures is meticulously established to incorporate the bearing fault characteristics. Experimental datasets under variable operating speeds and loads, and time-varying operating speeds are utilized to demonstrate the superiority of the PIResNet under non-stationary operating conditions.

Nimbalkar, S & Basack, S 2023, 'Pile group in clay under cyclic lateral loading with emphasis on bending moment: Numerical modelling', Marine Georesources & Geotechnology, vol. 41, no. 3, pp. 269-284.
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Pile foundations supporting major structures are often founded in soft compressible clays. Apart from usual super-structural loading, these piles are subjected to cyclic lateral loads originating from actions of waves, ship impacts, winds or moving vehicles. Such repetitive loading induces stress reversal in adjacent soft clay initiating progressive degradation in soil strength and stiffness. This not only deteriorates the pile capacity with unacceptable displacements, the bending moments also increase. Although past studies investigated the response of single pile under lateral cyclic loading, a detailed study on pile group in clay under cyclic lateral loading with emphasis on bending moment is of immense practical interest. This paper focuses on detailed study of the response of pile group in clay under cyclic lateral loading, with emphasis on bending moment, through numerical modelling via a three-dimensional dynamic finite element (FE) approach and simplified boundary element modelling (BEM). Comparisons of computed results with available test data imply that the results obtained by 3 D dynamic FE model are better than the BEM. Extensive parametric studies with field data indicate that pile bending moment has been significantly influenced by cyclic loading parameters (number of cycles, frequency and amplitude). Relevant conclusions are drawn from the entire study.

Oberst, S & Sepehrirahnama, S 2023, 'A case study on generative learning approaches in a studio and flipped class-room setting for increased learning outcomes', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A59-A59.
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Teaching and learning during Covid-19 have been strongly affected by lockdowns, isolated online learning, and the sudden requirement to alternatively assess students while considering the effect internet-based information sources. Here, we present outcomes on the learning outcome of students returning from distance learning into the face-to-face mode, studying the subject “Embedded Mechatronic Systems” employing increasingly methods of Generative Learning Theory (GLT) in a flipped classroom environment, using studio and project-based learning approaches. By introducing a group project component, the formerly disconnected laboratory components become strongly connected with students being exposed to practical aspects and teamwork, generating reflected reports and videos of their practical work. To overcome the effects of Covi-d19, tighter assessments, and in-person engagements is emphasised. Viva-voces have been introduced and AI invigilated final exams have been altered to in-class room quizzes, while monitoring the cohort’s performance over 3 sessions. Our data indicate that face-to-face learning and hands-on practice with peers using self-testing and self-explaining strategies enacts higher outcomes, opposed to remote modes of teaching. Our results exemplify on how to move back into face-to-face teaching with future steps to increase learning outcomes using the flipped classroom, GLT, and a studio setting being discussed.

Ouyang, P, Rao, P, Wu, J, Cui, J, Nimbalkar, S & Chen, Q 2023, 'Hydromechanical Modeling of High-Voltage Electropulse-Assisted Fluid Injection for Rock Fracturing', Rock Mechanics and Rock Engineering, vol. 56, no. 6, pp. 3861-3886.
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Patil, G, Shivakumara, P, Gornale, SS, Pal, U & Blumenstein, M 2023, 'A new robust approach for altered handwritten text detection', Multimedia Tools and Applications, vol. 82, no. 14, pp. 20925-20949.
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Peellage, WH, Fatahi, B & Rasekh, H 2023, 'Assessment of cyclic deformation and critical stress amplitude of jointed rocks via cyclic triaxial testing', Journal of Rock Mechanics and Geotechnical Engineering, vol. 15, no. 6, pp. 1370-1390.
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Perrin, R, Halkon, BJ & Guo, Z 2023, '(Re-)exploring the normal modes of axisymmetric structures: An English church bell case study', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A74-A74.
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The normal modes of axisymmetric structures are of interest to structural engineers, physicists and musical acousticians. Previously, some of the present authors have made studies of church bells, hand bells, elephant bells, various gongs and rings. Group theoretical arguments have been used, with considerable success, in classifying the normal modes of these structures and in understanding how “beats” arise from split degenerate doublets. It is now pointed out that further information can be obtained from group theory using a variety of additional arguments. In particular, it infers a basic distinction between “in-extensional” and “extensional” types of modes. In the present work, we concentrate on the case of an English church bell, as an example axisymmetric structure, whose normal modes were previously measured in a frequency range of up to about 10 kHz. In that earlier work, the results were analyzed with what was then considered a state-of-the-art finite-element package. We now repeat this exercise with a modern finite-element package to explore the differences between the types of modes and validate the Group theory observations. The agreement with experiment is much improved and some new level of understanding of the spectrum of the bell is achieved.

Pradhan, B, Dikshit, A, Lee, S & Kim, H 2023, 'An explainable AI (XAI) model for landslide susceptibility modeling', Applied Soft Computing, vol. 142, pp. 110324-110324.
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Pradhan, B, Lee, S, Dikshit, A & Kim, H 2023, 'Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model', Geoscience Frontiers, vol. 14, no. 6, pp. 101625-101625.
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Pradhan, S, Qiu, X & Ji, J 2023, 'On Time–Frequency Domain Flexible Structure of Delayless Partitioned Block Adaptive Filtering Approach for Active Noise Control', Circuits, Systems, and Signal Processing, vol. 42, no. 12, pp. 7580-7595.
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Frequencydomain filtered-x least mean square algorithms can reduce the computational complexity of the time domain counterpart with long filters; however, they suffer from large block delay, additional quantization error due to large size transformations and implementation difficulties in existing DSP hardware. In this paper, a time–frequencydomain flexible structure is proposed using the partitioned block frequencydomain adaptive filtering technique, which has no signal path delay and is well suited for low-cost DSP implementation. The proposed structure divides the long filters into many equal partitions and carries out the control filter update in frequency domain while generating the control signal in both time and frequency domains, thereby eliminating the forward path delay completely while maintaining low computational complexity. The proposed structure has a potential benefit for controlling broadband noise, where the causality constraint is more important. The simulation results using the measured acoustic paths demonstrate that the proposed structure maintains similar control performance as that of the time domain algorithm but with much less computational complexity. Furthermore, the tracking performance of the proposed structure under different levels of measurement noise is investigated.

Punetha, P & Nimbalkar, S 2023, 'An innovative rheological approach for predicting the behaviour of critical zones in a railway track', Acta Geotechnica, vol. 18, no. 10, pp. 5457-5483.
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AbstractThe poor performance of critical zones along a railway line has long been a subject of concern for rail infrastructure managers. The rapid deterioration of track geometry in these zones is primarily ascribed to limited understanding of the underlying mechanism and scarcity of adequate tools to assess the severity of the potential issue. Therefore, a comprehensive evaluation of their behaviour is paramount to improve the design and ensure adequate service quality. With this objective, a novel methodology is introduced, which can predict the differential plastic deformations at the critical zones and assess the suitability of different countermeasures in improving the track performance. The proposed technique employs a three-dimensional geotechnical rheological track model that considers varied support conditions of the critical zone. The approach is successfully validated with published field data and predictions from finite element analysis. This methodology is then applied to a bridge-open track transition zone, where it is observed that an increase in axle load exacerbates the track geometry degradation problem. The results show that the performance of critical zones with weak subgrade can be improved by increasing the granular layer thickness. Interpretation of the predicted differential settlement for different countermeasures exemplifies the practical significance of the proposed methodology.

Punetha, P & Nimbalkar, S 2023, 'Numerical investigation on dynamic behaviour of critical zones in railway tracks under moving train loads', Transportation Geotechnics, vol. 41, pp. 101009-101009.
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Qi, Y & Indraratna, B 2023, 'Closure to “Influence of Rubber Inclusion on the Dynamic Response of Rail Track”', Journal of Materials in Civil Engineering, vol. 35, no. 8.
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Qi, Y & Indraratna, B 2023, 'The effect of adding rubber crumbs on the cyclic permanent deformation of waste mixtures containing coal wash and steel furnace slag', Géotechnique, vol. 73, no. 11, pp. 951-960.
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Among the numerous studies into the dynamic loading behaviour of rubber crumbs–soil/waste mixtures, the main focus is on how the content of rubber crumbs ([Formula: see text]) affects the damping ratio, shear modulus and total deformation. However, research into the influence of [Formula: see text] on the permanent strain rate ([Formula: see text]) and the deformation mechanism under repeated loading is very limited. In the current study, the cyclic deformation response for waste mixtures of steel furnace slag (SFS), coal wash (CW) and rubber crumbs (RC) is analysed and the test results reveal that [Formula: see text] has a significant influence on the initial [Formula: see text] and the slope of the permanent axial strain rate line, whereas cyclic deviator stress ([Formula: see text]) mainly affects the initial [Formula: see text]. The influence of [Formula: see text] and [Formula: see text] on  the [Formula: see text] value of the waste mixture is incorporated in an empirical model, which enables prediction of the permanent deformation mechanism of SFS + CW + RC mixtures with wider-ranging amounts of RC and higher cyclic deviator stresses.

Quyet Truong, D, Choo, Y, Akther, N, Roobavannan, S, Norouzi, A, Gupta, V, Blumenstein, M, Vinh Nguyen, T & Naidu, G 2023, 'Selective rubidium recovery from seawater with metal-organic framework incorporated potassium cobalt hexacyanoferrate nanomaterial', Chemical Engineering Journal, vol. 454, pp. 140107-140107.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12219-12233.
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Ren, Z, Ji, J, Zhu, Y, Hong, J & Feng, K 2023, 'Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-17.
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Saha, S, Kundu, B, Paul, GC & Pradhan, B 2023, 'Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models', Stochastic Environmental Research and Risk Assessment, vol. 37, no. 7, pp. 2513-2540.
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AbstractDrought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

Saha, S, Kundu, B, Saha, A, Mukherjee, K & Pradhan, B 2023, 'Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India', Theoretical and Applied Climatology, vol. 151, no. 1-2, pp. 891-913.
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Sajjad, MB, Indraratna, B, Ngo, T, Kelly, R & Rujikiatkamjorn, C 2023, 'A Computational Approach to Smoothen the Abrupt Stiffness Variation along Railway Transitions', Journal of Geotechnical and Geoenvironmental Engineering, vol. 149, no. 8.
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Sakhare, A, Punetha, P, Meena, NK, Nimbalkar, S & Dodagoudar, G-R 2023, 'Dynamic behaviour of integral abutment bridge transition under moving train loads', Transportation Geotechnics, vol. 40, pp. 100989-100989.
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Transition zones, such as bridge approaches, are discontinuities along a railway line that are highly prone to differential movement due to a rapid variation of support conditions along the track. The concrete approach slabs are often provided before and after the bridges to reduce this differential movement and provide a gradual variation in track stiffness. This paper provides insights into the dynamic behaviour of an integral abutment railway bridge (IARB) transition zone consisting of approach slab under moving train loads using finite element (FE) analyses. Firstly, the FE model is successfully validated against the published field data. Subsequently, the validated model is employed to investigate the influence of parameters such as approach slab geometry (length, thickness, inclination, and shape), backfill soil type, direction of train movement and train speed. Results show that the behaviour of IARB is sensitive to the length of the approach slab, backfill soil type and train speed. The findings of this study enhance the current understanding of the behaviour of IARBs subjected to moving train loading and identify the important parameters that influence their performance.

Sakti, AD, Anggraini, TS, Ihsan, KTN, Misra, P, Trang, NTQ, Pradhan, B, Wenten, IG, Hadi, PO & Wikantika, K 2023, 'Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products', Science of The Total Environment, vol. 854, pp. 158825-158825.
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Samadi-Boroujeni, H, Haghshenas-Adarmanabadi, A, Shayannejad, M & Khabbaz, H 2023, 'Comparison of Mohr-Coulomb and hardening soil constitutive models for simulation of settlements in the Karkheh earth dam', Australian Geomechanics Journal, vol. 58, no. 3, pp. 143-158.
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This paper presents the settlement behaviour of Karkheh earth dam during its construction and operation stages. Karkheh is one of the largest earth dams in the world in terms of its reservoir capacity and body volume. The settlement of such a large body of soil can affect the performance of the dam elements and endanger downstream areas; should a breach or failure occur in the dam, more than two million people will be affected. It is crucial to know the settlement behaviour of this structure and use the existing results to predict its future settlements and calibrate the existing stress-strain models. For anticipation of dam settlement the measured displacement from the portable probe anchor magnets installed in the dam body are compared to the results of numerical simulations. The available data cover a period of 12 years including construction, and two material impounding and operation periods of the dam. The numerical analysis is performed in 2D plane-strain conditions and two material models are used, including Mohr-Coulomb (MC) and Hardening Soil (HS) models. The comparison between the calculation results and the measured vertical deformations in the dam site reveals that the accuracy of model for the deformations in the middle levels of dam is better than those of the crest for both applied material models in construction and impounding stages. The maximum settlement differences between computed and observed values are 0.05 m for MC model and 0.01 m for HS model. For the operation stage, the error of calculated settlements for the MC model is smaller; hence the results of this model might be more reliable for prediction of future dam settlements. The similar trends, obtained from both material models, exhibit the suitability of the model parameters used in the simulations.

Samsuddin Sah, S, Abdul Maulud, KN, Sharil, S, A. Karim, O & Pradhan, B 2023, 'Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images', The Egyptian Journal of Remote Sensing and Space Sciences, vol. 26, no. 4, pp. 989-998.
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Shaharuddin, S, Abdul Maulud, KN, Syed Abdul Rahman, SAF, Che Ani, AI & Pradhan, B 2023, 'The role of IoT sensor in smart building context for indoor fire hazard scenario: A systematic review of interdisciplinary articles', Internet of Things, vol. 22, pp. 100803-100803.
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Shakeel, K, Wijayaratna, K, Barbieri, DM, Lou, B & Rashidi, TH 2023, 'Mobility perceptions regarding the COVID-19 pandemic from around the world', Travel Behaviour and Society, vol. 33, pp. 100631-100631.
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Shan, F, He, X, Armaghani, DJ & Sheng, D 2023, 'Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance', Journal of Rock Mechanics and Geotechnical Engineering.
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Shan, F, He, X, Armaghani, DJ, Zhang, P & Sheng, D 2023, 'Response to Discussion on “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Georg H. Erharter, Thomas Marcher', Tunnelling and Underground Space Technology, vol. 139, pp. 105064-105064.
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Siddiqui, AR, Indraratna, B, Ngo, T & Rujikiatkamjorn, C 2023, 'Laboratory assessment of rubber grid-reinforced ballast under impact testing', Géotechnique Letters, vol. 13, no. 2, pp. 118-128.
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This study presents the use of rubber grids (RGs) fabricated from end-of-life conveyor belts (i.e. discarded from the mining industry) to improve the performance of ballast tracks. The square apertures of these recycled rubber sheets were cast using a waterjet cutting process. A series of large-scale impact tests were performed on ballast specimens stabilised with three different grids of varied effective area ratios (KA.eff) to evaluate their effectiveness in mitigating the applied impact forces, in relation to both displacement and breakage of the ballast aggregates. Smart Ballast particles with motion-sensing capabilities were adopted to monitor the interaction between the grid and ballast assembly. The impact test results indicate that the inclusion of a RG decreases the deformation and breakage of ballast as well as reduces its vibrations. This study demonstrates that these recycled RGs with optimum effective area ratios can be more effective than conventional polymer geogrids, apart from the obvious environmental benefits.

Stone, RC, Farhangi, V, Fatahi, B & Karakouzian, M 2023, 'A novel short pile foundation system bonded to highly cemented layers for settlement control', Canadian Geotechnical Journal, vol. 60, no. 9, pp. 1332-1351.
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While design methods of deep foundations are mainly developed for homogenous soil deposits, the presence of highly cemented layers could lead to underestimation of resistance and overestimation of settlement of pile foundations. This study presents a novel approach using competent caliche layers bonded to the top and bottom of a continuous flight auger (CFA) pile as a new composite foundation system named caliche stiffened pile (CSP). The key objective is to optimize the required pile length in a cost-effective approach without ameliorating soil properties. Settlements of the CSP foundation for a high-rise building were monitored and full-scale tests were conducted to measure piles’ capacity. Finite element back analyses were performed to avoid adverse effect of sample disturbance in settlement calculations. A back calculation of a test fill embankment was performed to determine soil stiffness parameters by simulating an unscheduled imposed load to the structure. Impacts of the CSP on controlling the settlement of pile foundation and optimizing the required pile length are investigated using finite element analysis and a parametric study. The proposed CSP foundation can reduce the CFA pile settlement significantly in the presence of caliche layers with thickness equal or greater than a pile diameter at CFA pile head and toe, where the CSP is located.

Talaei, S, Zhu, X, Li, J, Yu, Y & Chan, THT 2023, 'Transfer learning based bridge damage detection: Leveraging time-frequency features', Structures, vol. 57, pp. 105052-105052.
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Tao, G, Guo, E, Yuan, J, Chen, Q & Nimbalkar, S 2023, 'Permeability and Cracking of Compacted Clay Liner Improved by Nano-SiO2 and Sisal Fiber', KSCE Journal of Civil Engineering, vol. 27, no. 12, pp. 5109-5122.
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Tao, G, Ouyang, Q, Lei, D, Chen, Q, Nimbalkar, S, Bai, L & Zhu, Z 2023, 'Erratum for “NMR-Based Measurement of AWRC and Prediction of Shear Strength of Unsaturated Soils”', International Journal of Geomechanics, vol. 23, no. 9.
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Tao, G, Peng, P, Chen, Q, Nimbalkar, S, Huang, Z, Peng, Y & Zhao, W 2023, 'A new fractal model for nonlinear seepage of saturated clay considering the initial hydraulic gradient of microscopic seepage channels', Journal of Hydrology, vol. 625, pp. 130055-130055.
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Teng, J, Liu, J, Zhang, S & Sheng, D 2023, 'Frost heave in coarse-grained soils: experimental evidence and numerical modelling', Géotechnique, vol. 73, no. 12, pp. 1100-1111.
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Frost heave in coarse-grained soils caused by vapour transfer has attracted much attention, but little experimental or numerical evidence has been reported thus far. A series of laboratory experiments is carried out by a frost heave apparatus and an X-ray micro-computed tomography instrument. The only water supply mechanism to the tested specimen is vapour transfer. The results indicate that considerable frost heave occurs in coarse-grained soil specimens with a zero fines content. The ratio of frost heave to the initial height can reach 13·8% and 25·1% at 14 days and 18 days, respectively. Ice crystals first grow in pores causing the soil particle to rotate and move, and the soil porosity to increase. With continued ice crystal growth, they eventually become connected and form an ice lens. If a constant temperature gradient is applied, only one horizontal ice lens is formed, which differs from the layered ice lenses observed in fine-graded soils. A new numerical model is developed to simulate ice formation and frost heave in coarse-grained soils, which considers the process of vapour transfer and desublimation. The predicted frost heave results agree well with the measured results. This study provides a novel explanation for the frost heave mechanism in coarse-grained soils.

Tran, T, Nerse, C, Oberst, S, Halkon, BJ, Sawalhi, N & Sepehrirahnama, S 2023, 'Vibrational timber characterization through the use of model updating', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A75-A75.
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The characterization of orthotropic materials has challenged the vibration and acoustics community for quite some time. Complex composite materials such as wooden structures require attention to factors including moisture, grain boundaries in addition to macroscopic features. Here we devise a basic model developed by measuring the vibrational response in two separate axes to determine the material characteristics of a timber dowel. A proposed benchtop procedure utilises vibrometers and accelerometers to gather data before the updating process, for which, FEMtools was used. Based on the input material parameters, uncovered by previous studies, provide a starting point for the model updating procedure where experimental mode shapes and frequency responses are correlated to the finite element model. With the focus on radiata pine, the results show radial and tangential values converge similar to previous literature but with variation in the longitudinal direction and shear planes. Overall, this study provides a solid foundation to the characterization process of orthotropic materials like timber which can be further expanded into fields of structural health monitoring, damage detection and potential use in digital twins. The authors acknowledge the support of the Australian Research Council Linkage Project LP200301196.

Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2023, 'Multiservice Compact Pixelated Stacked Antenna With Different Pixel Shapes for IoT Applications', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19883-19897.
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Wan, M, Shukla, N, Li, J & Pradhan, B 2023, 'Data-driven approaches to sustainable referral system design integrating the offline channel and the online channel', Journal of Cleaner Production, vol. 414, pp. 137691-137691.
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Wan, M, Shukla, N, Li, J & Pradhan, B 2023, 'Optimization of teleconsultation appointment scheduling in National Telemedicine Center of China', Computers & Industrial Engineering, vol. 183, pp. 109492-109492.
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Wang, QY, Teng, JD, Zhong, Y, Zhang, S & Sheng, DC 2023, 'Mesoscale simulation of pore ice formation in saturated frozen soil by using lattice Boltzmann method', Yantu Lixue/Rock and Soil Mechanics, vol. 44, no. 1, pp. 317-326.
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The frost heave of subgrade has an important effect on the operation of high-speed railway in cold regions, while the ice-water phase transition is the key to understanding the mechanism of frost heave. The lattice Boltzmann method is applied in this study, which is a mesoscale numerical method. The modified freezing temperature algorithm of pore water is combined with the enthalpy-based lattice Boltzmann phase transition model. Two freezing processes including the freezing of suspended droplets and the formation of pore water into ice in frozen soil are investigated, which aim to reveal the mesoscopic mechanism of the ice-water phase transition in free state and pore-bound state, respectively. The numerical results show that the process of ice crystals growing from the inside to the outside in the pores is completely opposite to the freezing process of droplets suspended in the air, and the pore water has a lower freezing temperature when it is closer to the surface of the soil particles. The soil freezing characteristic curves (SFCCs) differ obviously for the particles with the same size but in different particle arrangements. Meanwhile, the morphology of SFCC becomes steeper with increasing soil particle size, and the residual water content gradually decreases. The numerical results of the ice-water phase transition process are validated by measured data in the literature, which indicate that the lattice Boltzmann method can provide a new tool to study the water-gas migration and phase transformation process in porous media in mesoscale.

Wang, Z, Li, J, Teng, J, Zhang, S & Sheng, D 2023, 'THM coupled model for simulating frost heave based on a new water film pressure criterion', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 45, no. 5, pp. 997-1007.
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The frost heave and thaw settlement are the main frost damage in cold areas, which are the complex coupling process of water, temperature and stress fields. In this study, a coupled thermal-hydraulic-mechanical model is developed based on the water film theory, in which the temperature and void ratio of soils are the input variables. The novelty of this model is that the frozen water film pressure is used as the criterion for the generation of ice lens. The driving force of water migration is newly defined, and the frost heave includes the pristine frost heave and the amount of ice segregation. The fully coupled model is numerically solved based on the Matlab and COMSOL Multiphysics, generating the results of soil temperature, moisture, stress and the layered ice lens. The simulated results are then compared with those of the laboratory freezing tests, which shows that they match quite well and verify the validity of the proposed model. The simulation indicates that temperature gradient can promote the frost heave, and the overburden pressure can attract more water to the freezing front but decrease the amount of the frost heave. In addition, both the hydraulic conductivity and the compressive modulus have positive effects on the frost heave. The proposed model provides a new approach to understand the frost heave.

Wijayaratna, KP, Hossein Rashidi, T & Gardner, L 2023, 'Adapting to the Emergence of Generation Z in Tertiary Education: Application of Blended Learning Initiatives in Transport Engineering', Journal of Civil Engineering Education, vol. 149, no. 3.
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Xu, B-H, Indraratna, B, Rujikiatkamjorn, C, Nguyen, TT & He, N 2023, 'Nonlinear consolidation analysis of multilayered soil with coupled vertical-radial drainage using the spectral method', Acta Geotechnica, vol. 18, no. 4, pp. 1841-1861.
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AbstractThe nonlinear variation of soil compressibility and permeability with void ratio (i.e., e-log σ′ and e-log k) has been included in the consolidation theory to accurately predict the behavior of soft soil stabilized by vertical drains. However, most current nonlinear consolidation models incorporating the coupled radial-vertical flow are based on some simplified assumptions, while including some features such as the complex implementation of multilayered computations, time-dependent loading and stress distribution with depth. This study hence introduces a novel approach where the spectral method is used to analyze the nonlinear consolidation behavior of multilayered soil associated with coupled vertical-radial drainage. In addition, time- and depth-dependent stress and soil properties at each soil layer are incorporated into the proposed model. Subsequently, the solution is verified against experimental and field data with comparison to previous analytical solutions. The results show greater accuracy of the proposed method in predicting in-situ soil behavior. A parametric study based on the proposed solution indicates that the ratio between the compression and permeability indices (ω = Cc/Ck) has a great impact on the consolidation rate, i.e., the greater the ω, the smaller the consolidation rate. Increasing the load increment ratio and the absolute difference between unity and ω (i.e., |ω − 1|) can exacerbate prediction error if the conventional simplified methods are used.

Xu, H, He, X, Pradhan, B & Sheng, D 2023, 'A pre-trained deep-learning surrogate model for slope stability analysis with spatial variability', Soils and Foundations, vol. 63, no. 3, pp. 101321-101321.
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Xu, Y, Ji, JC, Ni, Q, Feng, K, Beer, M & Chen, H 2023, 'A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems', Mechanical Systems and Signal Processing, vol. 200, pp. 110609-110609.
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Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.

Xu, Z, Khabbaz, H, Fatahi, B & Wu, D 2023, 'Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction', Engineering Structures, vol. 274, pp. 115180-115180.
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Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.

YAMADA, K & JI, J 2023, 'Accuracy enhancement of modal analysis using higher-order residual terms', Mechanical Engineering Journal, vol. 10, no. 5.
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YAMADA, K & JI, J 2023, 'Substructure elimination method for evaluating bending vibration of beams', Mechanical Engineering Journal, vol. 10, no. 6.
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YAMADA, K & JI, J 2023, 'Substructure elimination method for vibration systems governed by a one-dimensional wave equation', Mechanical Engineering Journal, vol. 10, no. 5.
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Ye, K & Ji, JC 2023, 'A Novel Morphing Propeller System Inspired by Origami-Based Structure', Journal of Mechanisms and Robotics, vol. 15, no. 1.
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Abstract For a standard propeller system, the thrust output and the energy dissipation are proportionally dependent on its rotating speed, as its physical characteristics and working conditions are normally fixed during its operation. In order to improve the system performance and meet special application requirements, this paper presents the design of a novel two-stage propeller system with a morphing blade structure for higher thrust output and energy efficiency in operations. Based on the stacked Miura-ori (SMO) pattern, an origami-based structure is designed to enable a change in blade length for a propeller system and thus improve the system performance. The unique snap-through feature of the proposed origami structure is utilized to provide a two-stage working condition according to its rotating speed. The geometric parameter analysis of the SMO structure is first investigated, specifically focusing on the operating mechanism due to the snap-through behavior. Then, the implementation of the SMO structure into a rotating system is studied. The effects of design parameters on the critical transition points, which correspond to two operating states of the proposed propeller system, are numerically discussed. The simulation results confirm the performance improvement in the thrust output and energy-saving. The feasibility of using origami-based structures provides valuable insights into more applications in similar domains, such as fan system and wind turbine blades.

Ye, K & Ji, JC 2023, 'Dynamic analysis of the effects of self-weight induced structural and damping nonlinearity on the performance of an origami-inspired vibration isolator', Journal of Sound and Vibration, vol. 547, pp. 117538-117538.
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Origami-inspired structure has shown strong nonlinearity on its force response during morphing process between phases. When the origami-inspired structure is applied as vibration isolation system, the structural weight is rare to be considered and discussed in the modelling and analysis of vibration isolation system. The effects of the structural self-weight on the dynamic behaviour of the isolation system is not yet fully understood. Thus, this study aims to investigate the influence of the self-weight induced structural and damping nonlinearity on the dynamic performance of an origami-based vibration isolator. A three-mass body, which includes the payload mass, top facets’ mass and bottom facets’ mass, with multiple degree-of-freedom (DOF) motion is proposed to describe the vibration isolation system. First, a quasi-zero-stiffness feature is designed and its static performance is discussed for a set of specifically selected system parameters. Then, the equation of motion for such three-mass body with spring damping considered is derived by using the harmonic balance method (HBM) on its Lagrange's formulation, where the effects of strong nonlinearity on its dynamic performance can be investigated. The analytical expression is verified with the numerical solutions, which are obtained using the Newmark numerical integration method. The influences of each important system parameter on the dynamic nonlinearity are also discussed. It is expected that this study would provide valuable insights to the effects of structural self-weight in a quasi-zero-stiffness isolation system.

Ye, K, Ji, JC & Fitch, R 2023, 'Further investigation and experimental study of an origami structure-based quasi-zero-stiffness vibration isolator', International Journal of Non-Linear Mechanics, vol. 157, pp. 104554-104554.
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A quasi-zero stiffness (QZS) vibration isolator formed from a truss-spring stacked Miura-ori (TS-SMO) origami structure can provide a desired ultra-low dynamic stiffness for vibration isolation while remaining a high-static stiffness for load supporting capacity. This paper further investigates the design parameters and experimentally studies the dynamic performance of the proposed TS-SMO vibration isolation system. The effects of the spring parameters and the initial setup conditions on its static response are analysed. With the proper parameter selection, the resultant supporting force generated by the origami structure can be expressed as a polynomial containing the static force and dynamic force components which does not have the linear term. The displacement transmissibility of the proposed system is calculated to evaluate its isolation performance. Analytical and numerical results are in good agreement and both demonstrate an ultra-low resonance frequency for vibration isolation. The dynamic behaviour of the proposed system is also investigated under different conditions to enhance the vibration isolation performance. A proof-of-concept prototype is designed, fabricated and tested to verify both static and dynamic performances of the TS-SMO QZS isolator. The comparative experimental results between the corresponding linear isolation system and the proposed nonlinear QZS system validate the design of origami-inspired structure for vibration isolation and further confirm the effectiveness of the QZS vibration isolator. It is hoped that this research would provide a solid foundation for designing and modelling the TS-SMO structure adopted for vibration isolation in practical engineering applications.

Ye, S-Q, Ding, H, Wei, S, Ji, J-C & Chen, L-Q 2023, 'Nonlinear forced vibrations of a slightly curved pipe conveying supercritical fluid', Journal of Vibration and Control, vol. 29, no. 15-16, pp. 3634-3645.
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Vibrations of pipes caused by axially flowing fluids are very common in engineering applications. Due to material imperfections, guide misalignment, and improper supports, the installed pipes are prone to the initial curvature. Though small, the initial curvature can significantly change the dynamic characteristics of the slightly curved pipe system. This study investigates the non-linear forced vibration of a slightly curved pipe conveying supercritical fluid around the curved equilibrium, with the emphasis on amplitude–frequency responses around two asymmetric non-trivial equilibrium configurations. The governing equations for the forced vibration of a slightly curved pipe conveying supercritical fluids are derived using the generalized Hamilton principle. Then, the equations of motion are discretized into a set of coupled ordinary differential equations via the Galerkin truncation method and solved by the harmonic balance method combined with the pseudo-arc length technique. The approximate analytical results are verified by the numerical integration results. The obtained results demonstrate that the initial curvature has a significant effect on the dynamic characteristics of pipes conveying supercritical fluids, and can lead to significant differences in the dynamic response of the pipe system near different equilibrium configurations.

Yu, Y, Li, J, Li, J, Xia, Y, Ding, Z & Samali, B 2023, 'Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion', Developments in the Built Environment, vol. 14, pp. 100128-100128.
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A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.

Zeng, Y-C, Ding, H, Ji, J-C, Jing, X-J & Chen, L-Q 2023, 'A tristable nonlinear energy sink to suppress strong excitation vibration', Mechanical Systems and Signal Processing, vol. 202, pp. 110694-110694.
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As well known, the vibration reduction efficiency of the nonlinear energy sink (NES) is poor under strong excitation. In this paper, a tristable NES (TNES) is proposed. The TNES can degenerate into bistable and mono-stable NES by adjusting the geometric parameters of the springs. The governing equations of a linear oscillator coupled with the TNES under harmonic excitation are derived. The approximate analytical solution of the coupled system is obtained by using the harmonic balance method and verified numerically. The vibration suppression efficiency of TNES and NES is compared. The dynamic behavior of TNES under strong excitation is demonstrated. The results show that the nonlinear restoring force is softened due to the wide distribution of the three stable points of TNES. Therefore, compared with NES, the proposed TNES can suppress stronger excitation vibration. In addition, the low side barrier depth is conducive to TNES to perform chaotic inter-well oscillation, which can effectively suppress the strong excitation vibration and obtain good vibration reduction performance. As a result, the proposed TNES can eliminate the detached resonance curve and enlarge the effective range of the NES. Under relatively weak excitation, the vibration suppression efficiency of TNES is slightly lower than that of NES, although the TNES is also relatively significant. Therefore, this paper reveals the vibration suppression mechanism of TNES, and provides a way to effectively solve the problem of low vibration reduction efficiency of NES under strong excitation.

Zhang, S, Ding, L, Xie, M, He, X, Yang, R & Tong, C 2023, 'Reliability analysis of slope stability by neural network (NN), principal component analysis (PCA), and transfer learning (TL) techniques', Journal of Rock Mechanics and Geotechnical Engineering.
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Zhang, S, Zhang, T, Su, J & Sheng, D 2023, 'Mesoscopic theoretical and numerical study of particle migration in porous media', Journal of Railway Science and Engineering, vol. 20, no. 6, pp. 2103-2111.
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Particle migrations in porous media emerge in many engineering problems, and exert influence phenomena like structure changes of soil and pollutants transportation in soils. There are few theoretical analyses for these migration processes from the mesoscopic aspects of particle motion and porous media structure. Therefore, theoretical methods and simulation were implemented here to study this problem. In the theoretical aspect, the motion of the particle was treated as a random process, and the one-dimensional convection-diffusion equation was derived from the mesoscopic view. Diffusion coefficient and convection coefficient were expressed as functions of the parameters p and α, where p represents particle mesoscopic motion probability and α represents the blocking effect of porous media. In the aspect of simulation, the relation between α and connectivity of porous media gets studied by embedding random walk into percolation configuration. Theoretical results show that convection-diffusion equation can be used to describe the macroscopic transportation of particles, which mesoscopic motions are random. Simulation shows that, when connected probability P is larger than 0.5, convection-diffusion equation can well describe the process of particle migration, and there exists quadratic function relation between α and P. When P is less than 0.5, clogging phenomenon appears and theoretical result becomes not applicable gradually.

Zhang, W, Pradhan, B, Stuyts, B & Xu, C 2023, 'Application of artificial intelligence in geotechnical and geohazard investigations', Geological Journal, vol. 58, no. 6, pp. 2187-2194.
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The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.

Zhang, X, Peng, H, Zhang, J & Wang, Y 2023, 'A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification', Expert Systems with Applications, vol. 213, pp. 119073-119073.
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Zhang, Y, Feng, K, Ji, JC, Yu, K, Ren, Z & Liu, Z 2023, 'Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 2, pp. 1070-1080.
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Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.

Zhang, Y, Ji, JC, Ren, Z, Ni, Q & Wen, B 2023, 'Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions', Mechanical Systems and Signal Processing, vol. 191, pp. 110172-110172.
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Domain adaptation techniques have the proven ability to deal with fault diagnosis issues under variable operating conditions. They can achieve a superb diagnostic performance in single-sensor monitoring scenarios where the training and test data share the same label space. However, in practical engineering, fault modes are usually mixed with each other and new failure modes may appear during operation, which poses a challenge to the effectiveness of existing cross-domain fault diagnosis methods. Furthermore, with the increasing complexity of modern industrial systems, multi-sensor collaborative monitoring has been increasingly deployed for comprehensive measurement and detection of the complicated system. Unfortunately, there is less attention paid to multi-sensor cross-domain diagnosis in the current literature. To bridge this research gap, this paper aims to develop a novel multi-sensor open-set cross-domain fault diagnosis method. First, a convolutional neural network-based single-sensor feature extraction module and a Transformer-based multi-sensor feature fusion module are constructed for discriminative feature extraction and fusion. Second, a weighted adversarial learning scheme is built to conduct domain-invariant learning of the shared fault modes between the source and target domains. Then, a threshold-based supervised contrastive loss is defined to realize instance-level domain alignment, together with an entropy max–min loss to identify unknown class samples. The effectiveness and practicability of the proposed method are validated by a series of experiments conducted on two different types of gearboxes.

Zhang, Y, Ji, JC, Ren, Z, Ni, Q, Gu, F, Feng, K, Yu, K, Ge, J, Lei, Z & Liu, Z 2023, 'Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing', Reliability Engineering & System Safety, vol. 234, pp. 109186-109186.
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Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.

Zhang, Z, Wang, L, Wang, Y, Zhou, L, Zhang, J & Chen, F 2023, 'Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 247-263.
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Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel '`base-detector repository'' and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.

Zhao, F, Cao, S, Luo, Q & Ji, J 2023, 'Enhanced design of the quasi-zero stiffness vibration isolator with three pairs of oblique springs: Theory and experiment', Journal of Vibration and Control, vol. 29, no. 9-10, pp. 2049-2063.
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Quasi-zero stiffness vibration isolators have been extensively studied due to superior passive vibration isolation performance. As the quasi-zero stiffness region of the isolators is generally small, the research on their responses to the excitation with high amplitude is currently quite limited. This paper presents an improved design of the quasi-zero stiffness isolator with three pairs of oblique springs to increase the amplitude of the excitation. Theoretical formulations are derived for stiffness and force, and then the influences of three independent parameters on the quasi-zero stiffness region are studied to obtain optimal design parameters. A prototype is fabricated and tested for displacement excitations with amplitudes of 5 mm, 10 mm, and 15 mm in a frequency range of 1.5–10 Hz. The absolute displacement transmissibility of the enhanced quasi-zero stiffness isolator is theoretically and experimentally compared with that of the corresponding linear isolator and that of the previous isolators with three pairs of oblique springs using the same parameter conditions of the loaded mass, the horizontal length of oblique springs, and the vertical spring. The experimental results show that the enhanced design of the quasi-zero stiffness isolator with three pairs of oblique springs can achieve lower displacement transmissibility and deal with the displacement excitation with higher amplitude.

Zheng, B, Ji, J, Miao, Z & Zhou, J 2023, 'Achieving Distributed Consensus in Networked Flexible-joint Manipulator Systems via Energy-shaping Scheme', International Journal of Control, Automation and Systems, vol. 21, no. 7, pp. 2323-2337.
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This paper deals with the distributed consensus problem for networked flexible-joint manipulator systems which are formulated by underactuated Euler-Lagrange (EL) dynamics. Based on the energy-shaping scheme of passivity-based control (PBC) with interconnection and damping assignment, a novel decentralized controller is proposed to solve the leaderless and the leader-follower consensus problems. The main feature of the present work is the systematical integration of the energy of the systems composed of underactuated and actuated components and the energy of the controller as a total energy. Then the total energy is formulated as a suitable Lyapunov function to solve distributed consensus problems for the networked underactuated EL systems. The proposed consensus scheme without the need of velocity measurement possesses a relatively simple structure and good robustness. It is shown that interconnection pattern and damping assignment of the PBC are two key factors to affect the cooperative behavior of networked flexible-joint manipulator systems, which will be used to regulate or improve the cooperative performance of networked flexible-joint manipulator systems in practice. Finally, two numerical examples of networked six flexible-joint manipulator systems are presented to validate the correctness of the proposed algorithms.

Zheng, J, Ying, W, Pan, H, Tong, J, Liu, Q & Ji, J 2023, 'Improved Holo-Hilbert Spectrum Analysis-Based Fault Diagnosis Method for Rotating Machines', Journal of Mechanical Engineering, vol. 59, no. 1, pp. 162-162.
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Although the time-frequency analysis method can extract both the time and frequency domains information of vibration signal for the faulty equipment simultaneously, its use in reflecting the cross-scale coupling relationship between the amplitude-modulation and frequency-modulation characteristics of the nonlinear vibration signal has so far been hindered, and it is prone to be interfered by noises. On this base, the Holo-Hilbert spectral analysis (HHSA) method is innovatively introduced into mechanical fault diagnosis. The internal modulation characteristics of vibration signals can be completely described by the HHSA method with double-layer empirical mode decomposition (EMD) structure, making it an ideal tool for detecting the local faults of mechanical components. At the same time, to improve the diagnosis accuracy of HHSA and suppress the noise interference and the mode aliasing caused by EMD, an improved HHSA (IHHSA) method based on improved regenerated phase shifted sinusoidal assisted EMD (IRPSEMD) is proposed. The usefulness of the IHHSA method for local fault feature diagnosis are validated by the analysis of simulation signals. Finally, the IHHSA method is applied to the detection of gear crack fault and the diagnosis of rolling bearings with local fault. The results show that the internal modulation relationship of nonlinear fault vibration signal can be reflected by the proposed IHHSA method comprehensively, which shows a better fault identification ability.

Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2023, 'Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data', Environmental Modelling & Software, vol. 165, pp. 105724-105724.
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Adams, C & Oberst, S 1970, 'Modelling of noise due to impulsive excitation using nonlinear time series analysis', Noise and Vibrations Emerging Methods, Auckland, New Zealand.

Akbarzade, M, Oberst, S, Sepehrirahnama, S & Halkon, B 1970, 'Sensitivity and bifurcation analysis of an analytical model of a trapped object in an externally excited acoustic radiation force field', Proceedings of NOVEM23, Noise and Vibration Emerging Methods, Auckland, New Zealand, pp. 1-10.
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Acoustic radiation force (ARF) is a nonlinear acoustic phenomenon for which the acoustic field properties and, to an even greater extent, the explicit dynamics of the object, have received limited attention in the published literature to date. Any oscillations due to the flow field or external perturbations are thereby negligible while the particle is trapped in a stable position. By changing the viewpoint from the acoustic field to the dynamics of a levitated particle, the amplitude and frequency of external oscillation is non-negligible, we ask the question of how external excitation changes the dynamics of the object. We explicitly derive an analytical formulation of a trapped object in the form of a Duffing-like equation with its constants being defined by the object itself, the fluid, the acoustic wave, and the external vibration properties. In this case, the bifurcation behaviour is studied, and we show this together with a sensitivity analysis to represent correct dynamic behaviour in certain regimes of the bifurcation diagram.

Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 1970, 'Effectiveness of Geosynthetics at Preventing Subgrade Instability under Cyclic Loading', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers.
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Arnaz, A, Lipman, J & Abolhasan, M 1970, 'A Multi-objective Reinforcement Learning Solution for Handover Optimization in URLLC', 2023 28th Asia Pacific Conference on Communications (APCC), 2023 28th Asia Pacific Conference on Communications (APCC), IEEE.
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Bayat, A, Oberst, S & Lai, JCS 1970, 'NUMERICAL SIMULATION OF HEAT TRANSFER IN TERMITE MOUNDS', Proceeding of International Heat Transfer Conference 17, International Heat Transfer Conference 17, Begellhouse.
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Cooper-Woolley, B, Darroch, MM, Cong, J, Hendriks, X, Zhu, Q, Xiao, T, Zhao, S & Halkon, B 1970, 'Initial design, development & calibration of MEMS based sound level meter for real-time construction monitoring', 2022 Conference of the Acoustical Society of New Zealand, 2022 Conference of the Acoustical Society of New Zealand, Wellington, New Zealand.

Dzaklo, CK, Rujikiatkamjorn, C, Indraratna, B & Kelly, R 1970, 'Cyclic Wetting and Drying Behaviour of Coal Wash Treated Black Soil', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers.
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Gao, M, Zhu, X & Li, J 1970, 'Time-Varying Analysis of Vehicle-Bridge Interaction System for Bridge Health Monitoring Using Synchrosqueezing Transform', Proceedings of the 12th International Conference on Structural Health Monitoring of Intelligent Infrastructure, International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hangzhou, China, pp. 346-351.
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The passage of the vehicle over the bridge is a time-varying process. The bridge damage will cause a change in thetime-varying characteristics of the vehicle-bridge interaction (VBI) system. This paper presents a synchrosqueenzing transformbased method for extracting the time-varying characteristics from the dynamic response of the bridge subject to a moving vehicle.A numerical and experimental study has been conducted to show the performance of the proposed method. The bridge damage issimulated as a damage zone with three parameters representing the location, range and severity of the damage. The instantaneousfrequency is obtained by the edge detection from the time-frequency representation of bridge dynamic response. The results showthat the time-varying characteristics of the VBI system using the proposed method could be a good indicator of bridge damage

Halder, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 1970, 'A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video', Pattern Recognition, Asian Conference on Pattern Recognition, Springer Nature Switzerland, Kitakyushu, Japan, pp. 30-44.
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Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure.

Indraratna, B, Nguyen, TT, Arivalagan, J, Singh, M, Rujikiatkamjorn, C & Doan, T 1970, 'Undrained instability and fluidization of soft subgrade soils under cyclic rail loading', CRC Press, pp. 10-24.
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Indraratna, B, Qi, Y, Ngo, T & Rujikiatkamjorn, C 1970, 'Use of Synthetic Energy Absorbing Layer (SEAL) in Rail Substructure to Minimize Track Degradation', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 343-352.
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This paper presents novel solutions for increasing the stability and resiliency of track structures by developing a synthetic energy-absorbing layer (SEAL) using recycled rubber products based on large-scale laboratory tests (i.e., drop-hammer impact tests and cubic triaxial tests). This includes (1) installing recycled rubber mats under ballast, and (2) using SEAL composed of a mixture of rubber crumbs, steel furnace slag, and coal wash with changing amounts of rubber: 0%, 10%, 20%, 30%, and 40% (by weight) to replace traditional rockfill as subballast. The test results confirm that the inclusion of a recycled rubber mat underneath the ballast layer actively reduces ballast deformation and the propagation of impact loading within the depth of the substructure. Also, the SEAL mixture with 10% rubber reduces the track lateral dilation, reduces ballast breakage and load distribution, and also maintains an acceptable settlement.

Indraratna, B, Rujikiatkamjorn, C, Qi, Y & Kulappu Arachchige, CM 1970, 'Assessment of initial compaction characteristics of Rubber Intermixed Ballast System', Proceedings of the 14th Australia and New Zealand Conference on Geomechanics, Cairns, Australia.

Kulappu Arachchige, CM, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 1970, 'Experimental Study of Rubber Intermixed Ballast Stratum Subjected to Monotonic and Cyclic Loads', Geocongress 2023 Proceedings, ASCE library org, Los Angeles, California, pp. 565-574.

Kulappu Arachchige, CM, Indraratna, B, Qi, Y & Tawk, M 1970, 'A Sustainable Foundation: Recycling Waste Rubber Tyres in the Railway Track', Proceedings of the 9ICEG 9th International Congress on Environmental Geotechnics, Chania, Greece.
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Li, B, Guo, T, Zhu, X, Li, Q, Wang, Y & Chen, F 1970, 'SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM.
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Malisetty, RS, Indraratna, B & Kelly, R 1970, 'Displacement of weak rock joints under railway loading using numerical modelling', Australia and New Zealand Conference on Geomechanics,, Cairns, Australia.

Nerse, C, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Casacci, LP & Barbero, F 1970, 'PROPENSITY TO EFFICIENTLY TRANSMIT VIBRATIONS IN SNAPDRAGONS IN RESPONSE TO VIBROACOUSTIC SIGNALLING', Proceedings of the International Congress on Sound and Vibration, Proceedings of the 29th International Congress on Sound and Vibration, Prague, Czech Republic.
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The coevolution of angiosperms and pollinating insects has drawn a diverse repertoire of plant-pollinator interactions leading to different mechanisms of pollen transfer. Buzz pollination is a dynamic pollen removal process between plants and insects that is thought to have emerged due to ecological and evolutionary factors. The floral morphology in buzz-pollinated flowers restricts the pollen access to non-pollinating insects to increase the conditions that favour fertilisation. Efficient pollinators such as bumblebees use sonication to vibrate the anthers of the flower, thereby causing pollen grains to be expelled through the apical pores of the anthers. Recent studies have shown that the conditions in which buzz pollination occurs vary with respect to the floral morphology and vibration signals produced by the bees mainly determined by the duration, amplitude, and frequency of the vibration. The structural topology and material properties of the flower also induce resonance and damping behaviour, thus mediating the transmission of substrate-borne vibrations. Along with the best-studied mechanism of buzz pollination, it is increasingly clear that vibroacoustic (VA) signals may have played a role in co-evolutionary responses that significantly impact several aspects of plant and insect ecology. However, studies on the material properties in terms of VA transmission are still in their infancy. Therefore, in this study, we numerically investigate the sensitivity of the floral topology on transmission of vibrations from a source signal. For this purpose, stamen structures at different scales and loading conditions are modelled and analysed in a finite element software package. A representative VA signal is implemented as the excitation at locations impacting the flower during feeding events. The results demonstrate that natural frequencies and mode shapes of the stamen may influence the conditions in which the vibrational energy is scattered across the fl...

Ngo, T, Indraratna, B & Rujikiatkamjorn, C 1970, 'Modelling of Stone Columns Reinforces Railway Embankments: Coupled DEM-FDM Analysis', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 235-245.
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Stone columns have been increasingly adopted as an environmentally friendly and cost-effective method for stabilizing and reinforcing soft ground embankments. This paper presents a coupled modelling using the discrete element method and continuum modeling approach to study the load-deformation responses of stone columns. In the coupled discrete-continuum model, a soft soil domain under an embankment is simulated by the continuum method (i.e., finite difference method-FDM), while a stone column is simulated by the discrete element method (DEM). A series of interface elements are introduced to facilitate the force-displacement transmission of both domains. The DEM transfers moment and contact forces to the FDM, and then the FDM moves displacements back to the DEM. The model is calibrated and further validated by experimental data. Contact force distributions and shear stress contours developed in the stone column and surrounding clay are captured to provide a better understanding of the load-deformation responses of the stone column from a micromechanical perspective.

Nguyen, TN, Li, J, Sirivivatnanon, V & Sanchez, L 1970, 'Modeling the Alkali–Silica Reaction and Its Impact on the Load-Carrying Capacity of Reinforced Concrete Beams', Nanotechnology in Construction for Circular Economy, Springer Nature Singapore, pp. 365-372.
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AbstractThe alkali–silica reaction (ASR) is one of the most harmful distress mechanisms affecting concrete infrastructure worldwide. The reaction leads to cracking, loss of material integrity, and consequently compromises the serviceability and capacity of the affected structures. In this study, a modeling approach was proposed to simulate ASR-induced expansion considering three-dimensional stress/restraint conditions, and its impact on the structural capacity of reinforced concrete members. Both the losses in concrete mechanical properties and prestressing effects induced by the expansion under restraints are taken into account in the model. Validation of the developed model is conducted using reliable experimental datasets derived from different laboratory testings and field exposed sites. With the capability of modelling both ASR-induced expansion and its impact on structural capacity, the model provides valuable results to specify effective repair and/or mitigation strategies for concrete structures affected by ASR.

Nguyen, TT, Indraratna, B, Rujikiatkamjorn, C & Haq, S 1970, 'A Comparative Study on the Performance of CFD/LBM-DEM Coupling in Predicting Soil Fluidization', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 51-61.
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Coupling discrete element method (DEM) with computational fluid dynamics including Navier-Stokes theories (CFD-DEM) and Lattice Botlzmann method (LBM-DEM) has been used widely to model the response of soil foundation under increasing seepage flows; however, a comparison of these methods in predicting soil and fluid behaviours during fluidization has not been carried out in a rigorous way. The current paper will hence provide an evaluation on their performance by applying them to model a laboratory test where a sandy soil is subjected to fluidization process under increasing hydraulic gradient. A brief discussion about the differences in their theories and numerical algorithm is made before the DEM is used to simulate a representative soil element while NS-based CFD and LBM are used separately to model upward fluid flows. The mutual interactions between fluid and solid phases are carried out and update to each other through third-party platforms. The results show relatively similar hydraulic conductivity predicted by the two methods, which agrees well with the experimental data; however, the critical hydraulic gradient estimated by LBM-DEM coupling is found closer to the experimental value. The CFD-DEM coupling provides more stable computation through its averaged fluid variables, whereas LBM-DEM coupling can provide more detailed interactions between fluid and soil particles at micro-scale due to its high resolution. The study then suggests several conditions which can optimize the efficiency of using these methods in practical applications.

Oberst, S, Sepehrirahnama, S, Halkon, B, Lai, JCS, Atkinson, T & Evans, TAE 1970, 'A microactuator device for the detection of termite damage in timber poles', International Union of Forest Research Organisations, Cairns.

Oberst, S, Sepehrirahnama, S, Nerse, C, Brodzeli, Z, Lai, JCS, Mankowski, M, Atkinson, T, Arango, R, Kirker, G & Evans, T 1970, 'Towards a microactuator-sensing network for structural health monitoring of timber poles', Proceedings IRG Annual Meeting, International Research Group on Wood Protection, The International Research Group on Wood Protection, Cairns, Queensland, Australia, pp. 1-9.

Rujikiatkamjorn, C & Indraratna, B 1970, 'Application of vertical drains and vacuum preloading for stabilising soft ground for transport infrastructure', CRC Press, pp. 269-277.
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Sajjad, MB, Indraratna, B, Ngoc Ngo, T & Rujikiatkamjorn, C 1970, 'Modelling of Tracks at Transition Zones: Analytical and Numerical Modelling Approach', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 22-29.
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Rail track transitions are the discontinuities along the railway lines where conventional track adjoins the tunnels and bridges and are responsible for a sudden change in track structural properties. Frequent movement of fast passenger trains and heavy freights cause enhanced impact loads and differential settlements at these transitions that further lead to accelerated track degradation and increased maintenance costs. This paper presents a brief introduction to track transitions, the associated problems, and their causes and explains the occurrence of differential settlement at track transitions through analytical and numerical (2D FEM) modelling approaches. The stiffness effect on track settlement response under train loading is investigated, considering an Euler-Bernoulli beam on elastic foundation (BOEF). Vertical displacements of the track are calculated for various track stiffness values under train wheel loading (P = 10 tonnes). The results proved that the stiffer tracks undergo lesser settlements than the softer tracks. The effect of sudden stiffness variation is investigated by considering a one-step track transition where the stiffness suddenly changes from 80 MN/m (slab track) to 5 MN/m (ballast track). The results show that the settlement on ballasted track is almost 12 times greater than those on the slab track resulting in a large differential settlement at track transition.

Sepehrirahnama, S, Sansom, T, Lai, JCS & Oberst, S 1970, 'DESIGN OF A MINIATURISED MICRO-FORCE PLATE TO STUDY THE LOCOMOTION OF SMALL ARTHROPODS', Proceedings of the International Congress on Sound and Vibration, 29, International Institute of Acoustics and Vibrations (IIAV) - IIAV CZECH s.r.o., Prague, Czech Republic.
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Walking insects exhibit various types of locomotory gaits to adjust to their environment, for foraging activities, defence, building, courtship and communication. Among hexapods, the walking gaits of ants had been studied by measuring the ground reaction force from their steps with a Micro-Force Plate, which is a bespoke and sensitive thin plate mounted on a structure similar to the suspension system of a vehicle. Compared to ants, termites have quieter footsteps on the same substrate (peak velocity of 0.004 ms-1 compared to 0.4 ms-1 for ants). To study common gaits of small arthropods, we designed a miniaturised micro-force plate capable of measuring forces in the order of 1 μN in the z-direction (out-of-plane); an order of magnitude smaller than the one used for study of ants walking gaits. This is achieved by more compliant beams (halved width, at least 50% longer in the x and y-directions) from 3D-printed resin with minimum required curing; compared to the one for ants from the completed stereolithography. The improved design is assessed numerically by characterising its vibration response in terms of settling time due to an axial force excitation. Our micro-force plate can be used to increase the signal-to-noise ratio, as measured for a 3D-printed prototype, and better resolved force (approximately 1 μN without using any MEMS component). Results presented here indicate our micro-force plate is suitable for studying quiet tiny hexapods to provide insights for bio-inspired engineering of robotic locomotion systems.

Slack-Smith, D, De Silva Wijayaratna, K & Zeibots, M 1970, 'Enhancing the modelling of shared spaces: Evolution of the Social Force Model', Australasian Transport Research Forum 2023, Perth, Western Australia.

Wang, W, Shen, T, Blumenstein, M & Long, G 1970, 'Improving Open-Domain Answer Sentence Selection by Distributed Clients with Privacy Preservation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 15-29.
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Open-domain answer sentence selection (OD-AS2), as a practical branch of open-domain question answering (OD-QA), aims to respond to a query by a potential answer sentence from a large-scale collection. A dense retrieval model plays a significant role across different solution paradigms, while its success depends heavily on sufficient labeled positive QA pairs and diverse hard negative sampling in contrastive learning. However, it is hard to satisfy such dependencies in a privacy-preserving distributed scenario, where in each client, fewer in-domain pairs and a relatively small collection cannot support effective dense retriever training. To alleviate this, we propose a brand-new learning framework for Privacy-preserving Distributed OD-AS2, dubbed PDD-AS2. Built upon federated learning, it consists of a client-customized query encoding for better personalization and a cross-client negative sampling for learning effectiveness. To evaluate our learning framework, we first construct a new OD-AS2 dataset, called Fed-NewsQA, based on NewsQA to simulate distributed clients with different genre/domain data. Experiment results show that our learning framework can outperform its baselines and exhibit its personalization ability.

Zhang, X, Zhu, X, Yu, Y & Li, J 1970, 'Unsupervised Knowledge Transfer for Structural Damage Detection with Limited Data', The twelfth International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hangzhou, China.

Zhang, Y, Zhou, F, Li, Z, Wang, Y & Chen, F 1970, 'Fair Representation Learning with Unreliable Labels', Proceedings of Machine Learning Research, pp. 4655-4667.
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In learning with fairness, for every instance, its label can be systematically flipped to another class due to the practitioner's prejudice, namely, label bias. The existing well-studied fair representation learning methods focus on removing the dependency between the sensitive factors and the input data, but do not address how the representations retain useful information when the labels are unreliable. In fact, we find that the learned representations become random or degenerated when the instance is contaminated by label bias. To alleviate this issue, we investigate the problem of learning fair representations that are independent of the sensitive factors while retaining the task-relevant information given only access to unreliable labels. Our model disentangles the dependency between fair representations and sensitive factors in the latent space. To remove the reliance between the labels and sensitive factors, we incorporate an additional penalty based on mutual information. The learned purged fair representations can then be used in any downstream processing. We demonstrate the superiority of our method over previous works through multiple experiments on both synthetic and real-world datasets.

Halkon, B UTS 2023, Space Machines Co. SmallSat vibration testing research project – Final Report Phase II, UTS.

Arachchige, CMK, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2023, 'Experimental Study of Rubber Intermixed Ballast Stratum Subjected to Monotonic and Cyclic Loads'.

ashtarinakhaei, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'A Location-Based Multi-Hop Routing Protocol for Future Wireless Cellular Networks', Elsevier BV.
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Chakrabortty, R, Pal, SC, Ghosh, M, Arabameri, A, Saha, A, Roy, P, Pradhan, B, Mondal, A, Ngo, PTT, Chowdhuri, I, Yunus, AP, Sahana, M, Malik, S & Das, B 2023, 'Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India', Springer Science and Business Media LLC, pp. 11067-11068.
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Ding, Y, Wang, L, Liang, B, Liang, S, Wang, Y & Chen, F 2023, 'Domain Generalization by Learning and Removing Domain-specific Features'.
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Hertrampf, T, Oberst, S & Sepehrirahnama, S 2023, 'Recurrence rate spectrograms for impact localization in wood', Acoustical Society of America (ASA), pp. A142-A142.
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Characteristics like cellular grain structure, inhomogeneous density, aging, and altering environmental conditions (moisture, temperature) give wood highly anisotropic viscoelastic properties and non-linear vibrational wave propagation properties. Nonlinearity limits the use of linear methods, such as modal analysis and parameter identification via transfer functions. Acoustic localization of natural damage to wood, like crack growth, is of general interest in structural health monitoring of timber structures. Time-difference of arrival or energy attenuation is commonly used for localization, which are prone to boundary reflections or require the frequency response function. Recent advancements in machine learning-based classification of non-linear signals can achieve a much higher accuracy when recurrence rate-based spectrograms are used compared relative to conventional short-time Fourier transforms, especially in the presence of noise. Hence, in this work, multi-sensor measurements of impulse induced vibration in wood beams are classified by their distance to the excitation, based on their time series, avoiding a priori knowledge of a transfer function for the localization. The machine learning model is trained across various widths and thicknesses of samples, giving a localization estimate independent of beam dimensions. This research will contribute to early detection of damage in the field of vibration-based structural health monitoring of wood.

Horry, MJ, Chakraborty, S, Pradhan, B, Paul, M, Zhu, J, Barua, PD, Acharya, UR, Chen, F & Zhou, J 2023, 'Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks'.

Kronowetter, F, Maeder, M, Chiang, Y, Huang, L, Schmid, J, Oberst, S, Powell, D & Marburg, S 2023, 'Direct visualization of a Friedrich-Wintgen quasi-bound state in the continuum', Research Square Platform LLC.
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Mandal, S, Oberst, S & Lai, JCS 2023, 'Modelling termites’ tunnelling and decision-making behaviors', Cold Spring Harbor Laboratory.
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Phillips, L, Oberst, S & Sepehrirahnama, S 2023, 'Sensor fusion for simultaneous measurement of micro-vibrations', Acoustical Society of America (ASA), pp. A141-A142.
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Low-amplitude micro-vibrations are common in nature and engineering, throughout structural applications, and biology. The ability to accurately measure and analyse these vibrations in the presence of noise (unwanted signal content) has far reaching consequences across many fields of acoustics. Methodologies for the enhancement and improvement of such signals are, therefore, sought. We explore the capability of sensor fusion in combination with Kalman filtering (KF), using pairs of accelerometers to improve measurement of low-amplitude micro-vibrations. Prior research of pairing sensor fusion with machine learning approaches like KF, support vector machines, or coherence analysis reported up to 90% reductions in “ghost” detections. This research attempts to extend this success to micro-vibrations where broadband noise, and external perturbations can have dramatic impacts on measurability. A pair of accelerometers has been placed both parallel and perpendicular to the axis of an excitation in pine timber planks of varying dimensions and cuts. Simultaneous measurement of ∼5 N excitations with an automated hammer at varying distances are recorded and patterns observed in the time domain through preliminary analysis in MATLAB. Features identifiable from this data become clearer as compared to conventional approaches and have potential applications in non-invasive early predictive analysis of structures for timber pest control.

Pradhan, S, Zhang, G, Qiu, X, Ji, JC & Parnell, J 2023, 'Robust improved multiband-structured subband adaptive filter for active noise control with impulsive interference', Acoustical Society of America (ASA), pp. A121-A121.
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The feedforward filtered-x least mean square algorithm is extensively implemented for active control of broadband noise. However, the control performance is substantially deteriorated due to colored noise and the presence of uncorrelated disturbances near the reference and error sensors. To tackle these issues, in this paper, a robust improved multiband-structured subband adaptive filter based on logarithmic and total least squares method is proposed for active control. Unlike the conventional method of total least squares, the proposed algorithm adopts logarithmic and Rayleigh quotient functions. The closed loop implementation of the improved multiband-structured subband adaptive filter is adopted to meet the delayless requirement. The proposed algorithm is well-suited for environments where the reference signal is highly correlated and the residual noise is contaminated by impulsive noise. Furthermore, an affine combination of the proposed algorithm is developed to meet the complementary requirements of faster convergence and improved noise reduction. Eventually, the stability and computational complexity are studied. Simulation results using measured acoustic paths in an anechoic chamber and a normal room illustrate the effectiveness of the proposed algorithm for controlling broadband noise with impulsive interference. In addition, the tracking control performance is evaluated in a time-varying acoustic environment.

Sepehrirahnama, S, Oberst, S & Lai, JCS 2023, 'Acoustic black hole designs for circular beams under axial loading', Acoustical Society of America (ASA), pp. A58-A58.
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Acoustic Black Hole (ABH), as a non-reflecting wave effect, has been realised in beams and plates by locally changing their thickness. Geometrical ABH designs stem from their transverse-load bearing properties, assuming isotropic material properties associated with engineering materials (e.g., steels). The underlying physics is to manipulate stiffness locally for a transversely-loaded structure, leading to a change in the elastic wave speed. However, the same ABH would have limitations for axial loading, i.e., slender beams for which longitudinal waves dominate. Here, we present a wave propagation approach using wavefront tracking to identify a potential ABH for axially-loaded circular beams. We study wave propagation in three exponential designs of finite length, monitoring the wave-travel time. Indicative of an effective ABH in finite length sections, the wave-travel time increases compared to the case of a beam without ABH. By employing the wave front tracking method for the design of an ABH with axial loading, it is possible to verify the effectiveness of ABHs. Also, various material models, e.g., orthotropic materials such as wood, and different loading conditions can be considered, which opens a new avenue in applications of ABH phenomena beyond conventional vibro-acoustic control problems.