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Structural and Multidisciplinary Optimization
Published in: Structural and Multidisciplinary Optimization 1/2020

05-08-2019 | Research Paper

Line sampling-based local and global reliability sensitivity analysis

Authors: Xiaobo Zhang, Zhenzhou Lu, Wanying Yun, Kaixuan Feng, Yanping Wang

Published in: Structural and Multidisciplinary Optimization | Issue 1/2020

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Abstract

Local reliability sensitivity (RS) and global RS can provide useful information in reliability-based design optimization, but the algorithm for solving them is still a challenge, especially in case of small failure probability and high dimensionality. In this paper, a novel method by combining Monte Carlo simulation (MCS) with line sampling (LS), an efficient method for estimating small failure probability in case of the high dimensionality, is proposed to evaluate local RS and global RS simultaneously. Since the proposed method employs LS samples to approximately screen out the failure samples from the MCS sample set, the proposed method possesses both the efficiency of the LS and the accuracy of the MCS. One numerical example and two engineering examples illustrate the accuracy and the efficiency of the proposed method.
previous article Determination of sample size for input variables in RBDO through bi-objective confidence-based design optimization under input model uncertainty
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Appendix
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Metadata
Title
Line sampling-based local and global reliability sensitivity analysis
Authors
Xiaobo Zhang
Zhenzhou Lu
Wanying Yun
Kaixuan Feng
Yanping Wang
Publication date
05-08-2019
Publisher
Springer Berlin Heidelberg
Published in
Structural and Multidisciplinary Optimization / Issue 1/2020
Print ISSN: 1615-147X
Electronic ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-019-02358-9

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