Open Access

ARTICLE

Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

Baoyin Sun^{1, 2}, Yantai Zhang³, Caigui Huang^{4, *}

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

---

Citations