Climate change could result in more extreme weather events and induce increasing challenges to the low-lying coastal bridges. The structural performance of box-girder bridges under wave impacts was seldom investigated. In this paper, a three-dimensional (3D) computational fluid dynamics (CFD) model is established to simulate the wave-bridge interactions, and laboratory experiments are conducted to investigate wave impacts on a bridge. The experiments and 3D CFD simulations are unique as compared with other relevant studies since they provide the complex wave-bridge interactions through dynamic and spatial analyses. Specifically, time histories of vertical and horizontal wave forces are measured through experiments. The time-dependent pressure distributions, wave-induced forces, and overturning moments on the bridge model are computed with the CFD model. The effects of different parameters on the maximum wave forces are discussed. Also, comparisons of the differences between two-dimensional (2D) and 3D CFD models are conducted. Then, the results are converted to a prototype scale to explore the effects of wave impacts, and prediction formulas are proposed accordingly. This study combining experimental and 3D numerical investigations could help improve understanding of the wave-bridge interaction mechanism, and further aid the optimal and robust designs of coastal bridges.

This paper proposes a comprehensive analysis framework, combining three-dimensional (3D) numerical modelling and metamodeling, to investigate the probabilistic performance of retrofit actions on coastal bridges subjected to extreme wave forces. Specifically, a 3D Computational Fluid Dynamics (CFD) model is developed to calculate extreme wave load on the bridge superstructure. The established 3D model is validated by laboratory experiments. The characteristics of wave forces are parametrically investigated, and an Artificial Neural Network (ANN) metamodel is utilised to quantify the loading effects with multiple surge and wave parameters. Such a numerical-based ANN metamodel could predict wave forces under variable scenarios accurately, and significantly reduce the high computational cost of the 3D numerical model. Based on the numerical and metamodeling results, the bridge fragility curve is derived by considering uncertainties associated with structural demand, capacity, and hurricane hazard. Long-term failure risk is assessed under different climate change scenarios. Furthermore, different retrofit methods to improve structural performance and reduce failure risk are examined according to the proposed framework, including inserting air venting holes, enhancing connection strengths, and elevating bridge structures. The proposed framework could facilitate the optimal and robust design and maintenance of coastal infrastructures under hurricane effects in a long-term time interval.

Simply supported bridges comprise the majority of bridge systems in coastal communities and are susceptible to severe damage from extreme waves induced by storms or tsunamis. However, the effects of oblique wave impacts have been less investigated due to the lack of appropriate numerical models. To address this issue, this study investigates the effects of wave incident angles on coastal bridge superstructures by developing an advanced computational fluid dynamics (CFD) model. Different wave scenarios, including wave height, relative clearance, incident angle, and wavelength are tested. It is found that the maximum wave forces in the horizontal and longitudinal directions could reach 1901 and 862 kN under extreme conditions, respectively, destroying bearing connections. Three surrogate models, i.e., the Gaussian Kriging surrogate model, the Artificial Neural Network (ANN), and the Polynomial Chaos Expansion (PCE), are established by correlating the wave parameters with the maximum wave forces. Through comparisons among the three surrogate models, it is found that the 3-order PCE model has better performance in predicting loads in vertical and horizontal directions, while the ANN model is more suitable for results in the longitudinal direction. This study contributes to the optimized design of coastal bridges and also offers an opportunity for future studies to investigate hazard damage-mitigation measures.