Fragility Analysis for Subway Station Using Artificial Neural Network

ABSTRACT

To avoid the limitations of the assumptions for the traditional probabilistic seismic demand model (PSDM), this study proposed a novel PSDM for conducting fragility analysis of subway stations based on artificial neural network (ANN). The proposed ANN-based PSDM consists of an ANN-based trend model and a probabilistic-neural-network (PNN)-based error model. A two-story and three-span subway station in Shanghai was taken as a case. The results show that compared with the traditional PSDM, the proposed ANN-based PSDM can better predict the seismic responses of structures and describes the nonhomogeneous variance of residuals of the predicted seismic responses.

KEYWORDS:

• Artificial neural network
• fragility analysis
• subway station
• probabilistic neural network
• seismic responses

Highlights

• A novel deep-learning-based probabilistic seismic demand model (PSDM) was proposed.

• The proposed error model is developed based on the probabilistic neural network (PNN).

• The PNN-based error mode gives the inhomogeneous variance.

• The fragility curves for subway stations were obtained based on the proposed PSDM.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 51778464). All supports are gratefully acknowledged.

Declaration of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [number 51778464].