Title: Least square support vector machine for structural reliability analysis

Authors: Changxing Zhu; Hongbo Zhao

Addresses: School of Civil Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China ' School of Civil Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China

Abstract: Monte-Carlo Simulation (MCS) is a powerful tool in solving reliability problems. However, it is time-consuming use for the complex structural engineering problems. Another commonly used method, First-Order Second Moment Method (FOSM) usually requires the values and derivatives of limit state function. This paper presents two types of Least Square Support Vector Machine (LS-SVM) based reliability analysis methods, i.e. LS-SVM-based MCS and LS-SVM-based FOSM. In the first method, LS-SVM is adopted to replace the limit state function and enhance the efficiency of computing. In the second method, LS-SVM is adopted to approximate the limit state function and its partial derivatives which FOSM requires. Thus, based on the LS-SVM, both methods are substantially improved in efficiency. To assess the validity of this methodology, three structural examples are studied and discussed. The results prove that the LS-SVM based new methods are effective in structural reliability analysis problems involving the implicit limit state function.

Keywords: structural engineering; reliability analysis; FOSM; first-order second moment; MCS; Monte Carlo simulation; LS-SVM; least squares; support vector machines; SVM; structural reliability.

DOI: 10.1504/IJCAT.2016.073610

International Journal of Computer Applications in Technology, 2016 Vol.53 No.1, pp.51 - 61

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 13 Dec 2015 *

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