Rapid peak seismic response prediction of two-story and three-span subway stations using deep learning method

A deep learning-based rapid peak seismic response prediction model for the most common two-story and three-span subway stations is proposed in this study. The established model predicts the peak seismic responses of subway stations with a data-driven fashion and using limited information. The prediction model extracts the features of ground motions using one-dimensional convolutional neural network (1D-CNN) and then integrates the information of subway stations (i.e., the seismic fortification intensity, buried depth, and shear wave velocity) through a fully connected neural network for regression, resulting in peak seismic responses, namely the peak floor acceleration (PFA) and maximum inter-story drift ratio (MIDR). The model is trained using 19,200 samples obtained from the nonlinear time-history analyses (NLTHAs) of the designed 48 typical subway station structures. Furthermore, the external model verification was performed on 960 additional samples. For the predictions of PFA and MIDR, the coefficient of determination (R²) values are 0.967 and 0.986, respectively, and the damage states of subway stations are further evaluated, achieving an accuracy of 95.0%. These indicates that the model has good predictive performance and generalization ability. Moreover, the prediction model demonstrates a significantly higher computational efficiency compared to numerical simulation methods.