4.6 Article

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

Journal

WATER
Volume 15, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/w15101963

Keywords

solitary wave; hydrodynamic force; coastal bridge; artificial neural networks

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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.
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.

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