期刊
AEROSPACE SCIENCE AND TECHNOLOGY
卷 79, 期 -, 页码 364-372出版社
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ast.2018.05.050
关键词
Failure probability; Sensitivity analysis; Bayes' theorem; Importance sampling; Markov chain
资金
- National Natural Science Foundation of China [NSFC 51775439]
The failure-probability-based global sensitivity measure can detect the effect of input variables on the structural failure probability, which can provide useful information in reliability-based design. In this paper, a new efficient simulation method is proposed to estimate the failure-probability-based global sensitivity measure. The proposed method is based on the Bayes' theorem and importance sampling Markov chain simulation. The Bayes' theorem is used to provide a single-loop simulation method and the importance sampling Markov chain simulation is used to further reduce the computational cost. Compared to the traditional double-loop Monte Carlo simulation method, the proposed method requires only a single set of samples to estimate the failure-probability-based global sensitivity measure and its computational cost does not depend on the dimensionality of input variables. Finally, one numerical example and two engineering examples are presented to illustrate the accuracy and efficiency of the proposed method. (C) 2018 Elsevier Masson SAS. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据