Combined importance sampling and separable Monte Carlo: analytical variance estimator and applications to structural reliability

被引：0
|
作者：
Capasso G. [1 ,2 ]
Gogu C. [1 ]
Bès C. [1 ]
Navarro J.-P. [2 ]
Kempeneers M. [2 ]
机构：
[1] Université de Toulouse, CNRS, UPS, INSA, Mines Albi, ISAE-SUPAERO, Institut Clément Ader, Toulouse
[2] Airbus Operations SAS, Toulouse
来源：
International Journal of Reliability and Safety | 2023年 / 17卷 / 3-4期
关键词：
analytical variance estimator; importance sampling; Monte Carlo methods; reliability analysis; sampling methods; separable limit state; separable Monte Carlo; structural reliability;
D O I：
10.1504/IJRS.2023.135679
中图分类号：
学科分类号：
摘要：
In this paper, we derive a new analytical variance estimator for the probability of failure estimated by Separable Importance Sampling, allowing to analytically determine the number of samples required to reach a given coefficient of variation on the probability of failure. The proposed method can be applied in all reliability problems where response and capacity of a given system are independent. Numerical investigations have been conducted on two benchmark reliability problems. Thanks to this variance estimator we were able to carry out a large number of statistical simulations, allowing us to provide a comprehensive analysis of situations where Separable Importance Sampling would be most beneficial. © 2023 Inderscience Enterprises Ltd.. All rights reserved.
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页码：200 / 227
页数：27
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