Open Access
ARTICLE
Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames
Baoyin Sun1, 2, Yantai Zhang3, Caigui Huang4, *
1 Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, 150080, China
2 Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China
3 College of Civil Engineering, Nanjing Forestry University, Nanjing, 210037, China
4 School of Architecture and Civil Engineering, Xiamen University, Xiamen, 361005, China
* Corresponding Author: Caigui Huang. Email:
(This article belongs to the Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Computer Modeling in Engineering & Sciences 2020, 125(2), 755-776. https://doi.org/10.32604/cmes.2020.09632
Received 18 January 2020; Accepted 28 June 2020; Issue published 12 October 2020
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.
Keywords
Cite This Article
Sun, B., Zhang, Y., Huang, C. (2020). Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames.
CMES-Computer Modeling in Engineering & Sciences, 125(2), 755–776. https://doi.org/10.32604/cmes.2020.09632
Citations