@Article{cmes.2020.09632,
AUTHOR = {Baoyin Sun, Yantai Zhang, Caigui Huang},
TITLE = {Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {2},
PAGES = {755--776},
URL = {http://www.techscience.com/CMES/v125n2/40322},
ISSN = {1526-1506},
ABSTRACT = {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, a machine 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.},
DOI = {10.32604/cmes.2020.09632}
}