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Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableAssessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models.Fraehr, N ; Wang, QJ ; Wu, W ; Nathan, R (Elsevier BV, 2024-03-15)Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.
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ItemNo Preview AvailableThe influence of spatial arrangement and site conditions on the fate of infiltrated stormwaterPoozan, A ; Fletcher, TD ; Arora, M ; William Western, A ; James Burns, M (Elsevier BV, 2024-02-01)
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ItemNo Preview AvailableManaging underground legal boundaries in 3D-extending the CityGML standardSaeidian, B ; Rajabifard, A ; Atazadeh, B ; Kalantari, M (KEAI PUBLISHING LTD, 2024-02)
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ItemNo Preview AvailableComparative life cycle assessment of renewable energy storage systems for net-zero buildings with varying self-sufficient ratiosLe, ST ; Nguyen, TN ; Bui, D-K ; Teodosio, B ; Ngo, TD (PERGAMON-ELSEVIER SCIENCE LTD, 2024-03-01)
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ItemNo Preview AvailableAn efficient approach to tackle the challenges of LDFE modelling of plate anchor-chain system during installation processMaitra, S ; Tian, Y ; Cassidy, MJ (ELSEVIER SCI LTD, 2024-02)
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ItemNo Preview AvailableRecession constants are non-stationary: Impacts of multi-annual drought on catchment recession behaviour and storage dynamicsTrotter, L ; Saft, M ; Peel, MC ; Fowler, KJA (Elsevier BV, 2024-02-01)
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ItemNo Preview AvailableExploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modellingGao, Z ; Guo, D ; Ryu, D ; Western, AW (ELSEVIER, 2024-04-01)
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ItemNo Preview AvailableOptimized Bridge Maintenance Strategies: A System Reliability-Based Approach to Enhancing Road Network PerformanceChen, S ; Chen, D ; Li, L ; Miramini, S ; Zhang, L (ASCE-AMER SOC CIVIL ENGINEERS, 2024-03-01)
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ItemNo Preview AvailableBalancing observational data and experiential knowledge in environmental flows modelingMussehl, M ; Angus Webb, J ; Horne, A ; O'Shea, D (Elsevier BV, 2024-02-01)
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ItemNo Preview AvailableDeterministic failure prediction of toughened glass when impacted by iceCui, Y ; Lam, N ; Shi, S ; Lu, G ; Gad, E ; Zhang, L (PERGAMON-ELSEVIER SCIENCE LTD, 2024-03)