Infrastructure Engineering - Research Publications
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ItemApplication of a fish habitat model to assess habitat fragmentation using high flow and sediment transport in the Rumei Dam in Lancang River (China)(Wiley, 2023)Dam construction and operation can result in serious disturbances to the downstream flow regime and fluvial process, river morphology and the river's ecological condition worldwide. To understand the effects of discharge and sedimentation on fish habitats and ecosystems, an ecohydraulic approach was applied to one of the mega hydropower schemes in the downstream Lancang River (Tibet). The approach comprised a dam operation module with high flow and sediment transport and the application of a dynamic fish habitat model for assessing habitat fragmentation of two targeted fish species: Schizothorax prenanti (S. prenanti) and Schizothorax davidi (S. davidi). The hydrodynamics of the river system and the assessed fish habitat show a significant impact of dam construction and operation on the downstream riverine ecosystem, in which fish habitats were found rapidly deteriorated with the destruction of feeding and reproductive grounds. To improve fish habitat after dam construction, the sediment supplement was applied to the river and shown to be a useful restoration strategy, which recovers the habitat fragmentation of target fish. Our model not only is useful to predict dam operation impacts on the Lancang River's ecological status but also shows great potential in mitigating hydropower-induced environmental impacts and developing river conservation guidelines worldwide.
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ItemComplexity of hydrology, sewage and industries in distribution and migration pathways of heavy metals at spatial scale of China's brownfields(Wiley, 2023)Hydrologic dynamics, sewage and industries determine the distribution and migration pathways of heavy metals in the natural environments including soils across the urbanized area. In this study, 323 stratified soil samples from a brownfield in Jiangsu Province, China, were collected to assess the heavy metals (Cu, Ni, Pb, Cd, As and Hg) contaminations. Contamination factor (Cfi), Nemerow pollution index (PIN) and enrichment factor (EFi) were evaluated to assess the heavy metal pollution, while sources of pollution were identified in combination with geo-statistical, correlations and principal components analysis. Moreover, transport of Ni in soil profiles over the next 30 years was simulated using HYDRUS. The vertical distribution revealed that the soil surface (0–10 cm) had the highest concentration of heavy metal contamination. ICP-MS measurements showed that the soil in the brownfield was enriched with Cu, Ni, Pb, Cd, As and Hg, where Ni was the most severe and prevalent contaminant. The results of source apportionment analysis showed that Ni, Cd, Pb and Cu were mainly derived from building materials and sewage discharge, while As and Hg may come from fossil fuel combustion and agricultural discharges from upstream river catchment. The migration of Ni was largely driven by the combination of hydrological variability including the flow and solute contaminant gradients in soils. Our work highlights the need for a comprehensive understanding of the complexity of hydrology, and sewage discharge in heavy metal dynamics and migration pathways in China's brownfield soil at regional and national scales.
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ItemNo Preview AvailableIntroducing HyPeak: An international network on hydropeaking research, practice, and policy(WILEY, 2023-03)Abstract An increase in the demand for renewable energy is driving hydropower development and its integration with variable renewable energy sources. When hydropower is produced flexibly from hydropower plants, it causes rapid and frequent artificial flow fluctuations in rivers, a phenomenon known as hydropeaking. Hydropeaking and associated hydrological alterations cause multiple impacts on riverine habitats with cascading effects on ecosystem functioning and structure. Given the significance of its ecological and socio‐economic implications, mitigation of hydropeaking requires an inter‐ and transdisciplinary approach. An interdisciplinary network called HyPeak has been conceived to enrich international research initiatives and support hydropower planning and policy. HyPeak has been founded based on exchange and networking activities linking scientists from several countries where hydropeaking has been widespread for decades and numerous studies dedicated to the topic have been carried out. HyPeak aims to integrate members from other countries and continents in which hydropower production plays a relevant role, and grow to be a reference group that provides expert advice on the topic to policy‐makers, as well as researchers, stakeholders, and practitioners in the field of hydropeaking.
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ItemNo Preview AvailableProbabilistic failure envelopes of strip foundations on soils with non-stationary characteristics of undrained shear strength(ICE Publishing, 2023-08-01)This paper investigates probabilistic failure envelopes of strip foundations on spatially variable soils with profiles of undrained shear strength, su, linearly increasing with depth using lower-bound random finite-element limit analysis. The spatially variable su is characterised by a non-stationary random field with linearly increasing mean and constant coefficient of variation (COV) with depth. The deterministic uniaxial capacities and failure envelopes are first derived to validate the numerical models and provide a reference for the subsequent probabilistic analysis. Results indicate that the random field parameters COVsu (the COV of su) and Δ(dimensionless autocorrelation distance) have a considerable effect on the probabilistic normalised uniaxial capacities, which alters the size of the probabilistic failure envelopes. However, an insignificant effect of COVsu and Δon the shape of probabilistic failure envelopes is observed in the V-H, V-M and H-M loading spaces, such that failure envelopes for different soil variabilities can be simply scaled by the uniaxial capacities. In contrast to COVsu and Δ, the soil strength heterogeneity index κ = μkB/μsu0 has the lowest effect on the probabilistic normalised uniaxial capacity factors, but the highest effect on the shape of the probabilistic failure envelopes. A series of expressions is proposed to describe the shape of deterministic and probabilistic failure envelopes for strip foundations under combined vertical, horizontal and moment (V-H-M) loading.
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ItemNo Preview AvailableA macro-element model for predicting the combined load behaviour of spudcan foundations in clay overlying sand(Thomas Telford Ltd., 2021-10-26)A macro-element model for predicting the load–displacement behaviour of a spudcan foundation in clay overlying sand when subjected to combined vertical, horizontal and moment loading is introduced. Observations from detailed drum centrifuge tests that measured the effect of the underlying sand layer on the foundation behaviour are combined with finite-element results and theoretical developments to derive the components of the model. The yield surface defined by the centrifuge test results suggests that as the spudcan nears the underlying sand layer, the absolute horizontal capacity remains relatively constant, while the vertical and moment capacities increase at approximately the same normalised rate. The model is demonstrated to accurately predict foundation behaviour by retrospectively simulating the experimental results. This macro-element model has the advantage that it can be integrated into the structural analyses of jack-up platforms required for site-specific assessments.
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ItemNo Preview AvailableExcavator 3D pose estimation using deep learning and hybrid datasets(ELSEVIER SCI LTD, 2023-01)
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ItemNo Preview AvailableThe significance of octane numbers to hybrid electric vehicles with turbocharged direct injection engines(ELSEVIER SCI LTD, 2023-02-15)
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ItemLightweight traffic anomaly detection: A case study with SCATS volume data of Melbourne(Australasian Transport Research Forum, 2023)In this paper, we evaluate performance of an anomaly detection framework with real traffic count data collected by SCATS (Sydney Coordinated Adaptive Traffic System) loop detectors in Melbourne. The goal is to detect anomalous daily volume profiles within temporally large historical traffic data utilizing a lightweight and parameter-free approach and use it for live applications. To achieve this, daily volume profiles are first compressed into two dimensions benefiting from the Principal Component analysis (PCA). Then, a parameter-free version of DBSCAN is applied to the data with unique days of the week. Results from more than 20 different locations in Melbourne are fully visualized and the advantages and disadvantages of the method are discussed. We found that, with this approach, anomalous volume profiles can be accurately detected in a wide range of spatiotemporal data without any pre-training, parameter setting, or using complex learning methods.
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ItemTesting request prioritization strategies to improve the quality of a shared autonomous vehicles service: A Melbourne case study(Australasian Transport Research Forum, 2023)Shared autonomous vehicles (SAVs) have the potential to revolutionize urban transport by offering a mobility service that combines the benefits of autonomous vehicles and ride-hailing systems. However, one of the main limitations of these services is the handling of all request types in a singular way, which can increase biases towards a fixed type of passengers who have different properties and accessibility-based constraints. This study proposes a prioritization approach based on different request properties and the urgency of the request. The study uses the MATSim simulator to evaluate the prioritization schemes and implements five different scenarios to assess the collective and individual effects of the schemes. The case study focuses on Melbourne Metropolitan Area, and the prioritization is done based on the existing public transport (PT) service and pre-calculated SAV demand of the network. The study considers different performance measures, such as service efficiency, externalities, and provision equity, to estimate the benefits of the prioritization. The results indicate that prioritization can improve overall equity by spreading wait times evenly across the network. The prioritization approach improves the service with more riders finding the service attractive, resulting in more served rides with lower average vehicle kilometers traveled per ride. In addition, the study shows that the PT mode share is increased in multiple scenarios, demonstrating the positive effect of considering accessibility when prioritizing requests.
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ItemWhat influences passenger’s arrival rate at stops in Melbourne: WASEA-LSTM: a novel deep learning model leveraging multi-source data fusion(ATRF, 2023)Public transportation demand plays a crucial role in service planning and operation. Accurate prediction of passenger arrival rates at transit stops allows transportation planners and operators to optimize resources and improve service efficiency. Current methodologies primarily focus on weather's impact in the aviation industry, supply dynamics, and arrival time prediction, while overlooking its influence on public transport demand variation. This study addresses these gaps by designing a deep neural network model that can predict public transit demand, using large-scale datasets from multiple sources in Melbourne, Australia. We propose a novel deep learning architecture called Wasea-Lstm (Weather-Aware Smart Exponential Activation LSTM) that captures spatial, temporal, and external correlations for passenger arrival rate prediction at tram stops. The model is trained and tested on integrated datasets from automatic fare collection (AFC), automatic passenger count (APC), and weather data over a period of three months. Results show that the Wasea-Lstm model significantly outperforms benchmark models, including gradient boosting machine (GBMR) and multi-layer perceptron (MLP) regression by 15% and 6% in R2 metric, respectively. The feature importance ranking reveals that stop location, time of the day, temperature, and humidity are the key influencers of passenger arrival behaviour in Melbourne. Overall, this study contributes to the development of a model that accounts for multi-dimensional, high-resolution determinants of passenger demand using large-scale datasets from real world. The proposed Wasea-Lstm architecture shows exceptional performance in precisely forecasting stop-level demand for one of Melbourne's largest tram routes. Moreover, its applicability extends seamlessly to all routes within the network.