Fragility Analysis for Subway Station Using Artificial Neural Network
Pengfei Huang, Zhiyi Chen
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.
Topics & Concepts
FragilityArtificial neural networkProbabilistic logicProbabilistic neural networkEngineeringComputer scienceArtificial intelligenceTime delay neural networkChemistryPhysical chemistryGeotechnical Engineering and Underground StructuresInfrastructure Maintenance and MonitoringGeotechnical Engineering and Analysis