3D Numerical Modeling and Quantification of Oblique Wave Forces on Coastal Bridge Superstructures

Abstract

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.

Predictive capabilities of data-driven machine learning techniques on wave-bridge interactions

“…Scientists and engineers tried to establish full-scale numerical models to compute approximations for various hazard scenarios, while it is often prohibitive to form a sufficiently large database for its unbearable computational cost, especially for the reliability and life-cycle analysis involving large-scale computations (Chorzepa et al 2016;Dong and Frangopol 2016;Jia et al 2022;Xiao and Huang 2008). To overcome this limitation, machine learning (ML) methods have been utilized to establish the relationship between input hazard intensities and output structural responses.…”

Safety Assessment of Coastal Bridge Superstructures with Box Girders under Potential Landslide Tsunamis

“…The frequent occurrence of extreme events such as debris flows, storm surges, landslide tsunamis, hurricanes, and earthquake tsunamis [1,2] pose a severe threat to coastal structures, such as floating docks [3][4][5][6] and bridge superstructures [7,8]. Among these hazards, the risk of catastrophic landslide tsunamis should be include as they occur suddenly and are extremely destructive.…”