Seismic response prediction of a train-bridge coupled system based on a LSTM neural network

AbstractHigh complexity and randomness in high-speed train-bridge interactive dynamic analysis under earthquake lead to massive calculations in high-speed railway seismic design. To address this challenge, this paper introduces a deep learning-based method at enhancing the computational efficiency of the train-bridge coupled (TBC) system's seismic response prediction. Accordingly, a deep learning framework for predicting stochastic seismic response is established by using a long short-term memory (LSTM) neural network. Meanwhile, a feasible training strategy for the LSTM network is proposed. A traditional TBC theory and the LSTM neural network are used to perform the seismic response analysis as illustrative examples. The comparative analysis demonstrates the LSTM network's remarkable accuracy and efficiency in predicting the TBC system's seismic response. The prediction performance and extrapolation capability of the LSTM network is evaluated to be good enough to meet the requirement of engineering applications. In contrast to conventional methods like TBC theory or finite element analysis, the LSTM network significantly improves computational efficiency. Furthermore, the computed response data and the evolution characteristics of the probability density function are in good agreement with the conventional method. Therefore, the established LSTM network serves as an effective surrogate model for predicting the TBC system's seismic response.Keywords: Deep learninglong short-term memory (LSTM) neural networkseismic response predictiontrain-bridge coupled (TBC) systemearthquakehigh-speed railway (HSR) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research work was jointly supported by the National Natural Science Foundation of China (Grant No. 11972379), Hunan Science Fund for Distinguished Young Scholars (2021JJ10061), and the Key R&D Program of Hunan Province (2020SK2060).