摘要
Steel frames equipped with buckling restrained braces(BRBs)have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces.Therefore,special attention has been paid to the seismic risk assessment(SRA)of such structures,e.g.,seismic fragility analysis.Conventional approaches,e.g.,nonlinear finite element simulation(NFES),are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures.In this study,amachine learning(ML)-based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions.An optimal artificial neural network model can be trained using calculated damage and intensity measures,a technique which will be used to compute the fragility curves of a steel BRB frame instead of employing NFES.Numerical results show that a highly efficient instantaneous failure probability assessment can be made with the proposed framework for realistic large-scale building structures.
基金
Financial support received from the Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2019EEEVL05
the National Key Research and Development Program of China under Grant No.2016YFC0701106
the National Natural Science Foundation of China under Grant No.51578473 are gratefully acknowledged.