Fast Prediction of Solitary Wave Forces on Box-Girder Bridges Using Artificial Neural Networks

The extreme shallow-water waves during a tropical cyclone are often simplified to solitary waves. Considering the lack of simulation tools to effectively and efficiently forecast wave forces on coastal box-girder bridges during tropical cyclones, this study investigates the impacts of solitary waves on box girders and accordingly develops a fast prediction model for solitary wave forces. Computational fluid dynamics (CFD) simulations are used to simulate the hydrodynamic forces on the bridge deck. A total of 368 cases are calculated for the parametric study by varying the submergence coefficients (C-s), relative wave heights (H/h) and deck aspect ratios (W/h). With the CFD simulation results as the training datasets, an artificial neural network (ANN) is trained utilizing the back-propagation algorithm. The maximum wave forces first increase and then decrease with the C-s, while they monotonically increase with H/h. For relatively large H/h and small C-s values, the relationship between the maximum wave forces and W/h presents strong nonlinearities. The observed correlation coefficients between the ANN predictions and the CFD results for the vertical and horizontal wave forces are 98.6% and 98.1%, respectively. The trained ANN-based model shows good prediction accuracy and could be used as an efficient model for the tropical cyclone risk analysis of coastal bridges.

Automated summary

This study investigates the impacts of solitary waves on box girders and develops a fast prediction model based on computational fluid dynamics (CFD) simulations and artificial neural network (ANN). The maximum wave forces show a non-linear relationship with the deck aspect ratio (W/h) for relatively large relative wave heights (H/h) and small submergence coefficients (C-s). The trained ANN-based model has a high prediction accuracy of 98.6% for vertical wave forces and 98.1% for horizontal wave forces.