Fragility Analysis of a Subway Station Based on Probability Artificial Neural Network

Abstract:

The exceedance probability of a limit state of subway station is deduced based on the novel probabilistic seismic demand model （PSDM） proposed in the present paper using the deep learning method. Principal component analysis （PCA） was used to orthogonalize IMs and reduce the dimension of IMs. The trend model to predict the seismic responses of structure was established based on the back propagation （BP） neural network， which avoids the limitation of the assumption of the traditional PSDM that the demand measure （DM） of structure has a linear relationship with the intensity measure （IM） of ground motion in the log-transformed space. The error model to describe the error between the statistics-based trend model and the physical mechanism-based numerical model was established using the probabilistic neural network， which can expand the limitation of the assumption that the residuals is normally distributed with homogeneous variance. Taking a two-story and three-span subway station in Shanghai as a case study， the fragility curves of the subway station were developed based on the proposed method. The results show that the trend model established based on deep learning well simulates the nonlinear change of the seismic response with the first principal component of IMs. The established error model accurately describes the nonhomogeneous variance of residuals of the seismic responses predicted by using the trend model.