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

Gabriele Capasso, Christian Gogu, Christian Bès, Jean-Philippe Navarro and Martin Kempeneers

International Journal of Reliability and Safety, 2023, vol. 17, issue 3/4, 200-227

Abstract: 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.

Keywords: reliability analysis; structural reliability; separable limit state; Monte Carlo methods; separable Monte Carlo; importance sampling; sampling methods; analytical variance estimator. (search for similar items in EconPapers)
Date: 2023
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