Effects of Earthquake-Induced Hydrodynamic Force on Train-Bridge Interactions

High-speed trains running over sea-crossing railway bridges can be subjected to earthquake action in deepwater. In analyzing hydrodynamic-induced response on train-bridge interactions, the effect of earthquake-induced hydrodynamic pressure is a critical issue that still needs to be correctly modeled and understood. This study adopts a machine learning-based method for evaluating the earthquake-induced response on train-bridge interactions. A back-propagation neural network (BPNN) surrogate model is established by correlating the environmental parameters with the stochastic responses of train-bridge interactions to improve computational efficiency. Pintang's bridge, located in China, is selected as a case study. The results show that the proposed method is robust and accurate. The error is less than 1% when compared with the Monte Carlo method (MCM). Moreover, the consideration of earthquake increases the dynamic indices of the bridge girder to about 18%, compared to the case applying only hydrodynamic pressure. Furthermore, this study performed parametric analysis and found that the hydrodynamic pressure reached the maximum value under the action of -90 degrees of incident angle. These results will be helpful for the design of railway bridges in the coastal area.

Automated summary

This study utilizes a machine learning-based method to evaluate the response of train-bridge interactions induced by earthquakes and establishes a back-propagation neural network (BPNN) surrogate model. The results show that the proposed method is robust and accurate, which can be helpful for the design of railway bridges in coastal areas.