Details
| Original language | English |
|---|---|
| Pages (from-to) | 227-247 |
| Number of pages | 21 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 126 |
| Early online date | 22 Feb 2019 |
| Publication status | Published - 1 Jul 2019 |
Abstract
Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples.
Keywords
- Aleatory uncertainty, Epistemic uncertainty, Failure probability, High-dimensional model representation, Imprecise probability, Imprecise stochastic simulation, Sensitivity analysis, Subset simulation, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 126, 01.07.2019, p. 227-247.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Non-intrusive stochastic analysis with parameterized imprecise probability models
T2 - II. Reliability and rare events analysis
AU - Wei, Pengfei
AU - Song, Jingwen
AU - Bi, Sifeng
AU - Broggi, Matteo
AU - Beer, Michael
AU - Lu, Zhenzhou
AU - Yue, Zhufeng
N1 - Funding Information: This work is supported by the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2017JQ1007) and the Aerospace Science and Technology Foundation of China. The first and third authors are both supported by the Alexander von Humboldt Foundation of Germany. The first author is also supported by the Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University. The second author is supported by the program of China Scholarships Council (CSC). The authors are thankful for all these grants. The authors would also like to thank Prof. Hongshuang Li for providing the Matlab code of subset simulation, Dr. Xiaojing Wu for providing the wing flutter model and the two anonymous reviewers for helpful comments.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples.
AB - Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples.
KW - Aleatory uncertainty
KW - Epistemic uncertainty
KW - Failure probability
KW - High-dimensional model representation
KW - Imprecise probability
KW - Imprecise stochastic simulation
KW - Sensitivity analysis
KW - Subset simulation
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85061803582&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2019.02.015
DO - 10.1016/j.ymssp.2019.02.015
M3 - Article
AN - SCOPUS:85061803582
VL - 126
SP - 227
EP - 247
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
ER -