Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 126, Issue -, Pages 227-247Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2019.02.015
Keywords
Aleatory uncertainty; Epistemic uncertainty; Imprecise probability; Subset simulation; High-dimensional model representation; Imprecise stochastic simulation; Uncertainty quantification; Failure probability; Sensitivity analysis
Categories
Funding
- Natural Science Basic Research Plan in Shaanxi Province of China [2017JQ1007]
- Aerospace Science and Technology Foundation of China
- Alexander von Humboldt Foundation of Germany
- Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University
- program of China Scholarships Council (CSC)
Ask authors/readers for more resources
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. (C) 2019 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available