Safety analysis of high-speed trains on bridges under earthquakes using a LSTM-RNN-based surrogate model

In this paper, a novel method is proposed to predict the nonlinear seismic response of train-bridge coupled (TBC) systems by utilizing a long short-term memory (LSTM) recurrent neural network (RNN) based surrogate model. The surrogate model employed in this paper adopts a unidirectional multi-layer stacked LSTM architecture and implements sliding time windows for recursive calculation. The evaluation metrics used to assess the model’s performance have been enhanced to account for sensitivity variations in response amplitudes and to mitigate the phase-sensitive issues encountered with traditional evaluation metrics. Furthermore, network hyperparameters are carefully selected and presented for reference, and the surrogate model’s generalization ability is examined under different seismic scenarios. The results demonstrate that the LSTM-RNN-based model exhibits excellent computational accuracy and robustness when confronted with various types of seismic waves and system parameters. This approach offers fresh insights in situations where conventional numerical methods face limitations, such as rapid seismic response predictions in urban areas and simplifications for seismic design of high-speed railways. Overall, this paper contributes to the state of the art by introducing a novel approach that effectively predicts the nonlinear seismic response of TBC systems, addressing the increasing complexity and demands for accuracy and efficiency.