Effects of random winds and waves on a long-span cross-sea bridge using Bayesian regularized back propagation neural network

Abstract

Uncertainties from random multiple factors bring great challenges for assessing response of long-span bridges. This study proposes a dynamic analysis framework by integrating the wind–wave–bridge system with Bayesian regularized back propagation neural network, and then investigates stochastic response of a cross-sea cable-stayed bridge under extreme wind and wave parameters. The wind–wave–bridge system involving wind–bridge and wave–bridge interactions is constructed to calculate dynamic response of the bridge by Newmark-β method, considering stationary fluctuating wind fields and multidirectional random wave fields. To reduce the calculation cost, a Bayesian regularized back propagation neural network model is introduced, and the model evaluation is carried out to illustrate its accuracy and efficiency. After performing small-scale finite element analyses, the response statistics are obtained and later used as the known samples for training the neural network. The power spectrum analyses of the deterministic results are performed to investigate the contribution mechanism of the wind and wave. Finally, the correlation between the bridge response and the single wind–wave parameter is given by uncertainty analysis of the Bayesian regularized back propagation neural network model. The results show that the proposed framework is capable of capturing the nonlinear bridge response resulting from nonlinear wind and wave loads, which, however, is significant different from that under wind alone. The bridge response receives significant contribution from wind and waves relative to the vibration characteristic of the bridge at smaller wind and wave loads. The contribution from vibration characteristic of the bridge becomes significant at larger wind and wave loads. The uncertainty analyses illustrate the significant effects of four wind and wave parameters on girder, tower, and submerged structure.