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Article Dans Une Revue International Journal of Reliability and Safety Année : 2023

Combined Importance Sampling and Separable Monte Carlo: Analytical Variance Estimator and applications to Structural Reliability

Résumé

This paper considers reliability analysis problems where the limit state function, characterizing the failure domain, can be expressed in terms of two independent components: response and capacity. An approach called Separable Importance Sampling has been proposed in the literature as an extension of Importance Sampling, allowing improved sampling efficiency due to separable sampling of response and capacity. 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. 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 numerical experiments, allowing us to provide a comprehensive analysis of situations where Separable Importance Sampling would be most beneficial.
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hal-04138222 , version 1 (22-06-2023)

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Gabriele Capasso, Christian Gogu, Christian Bes, Jean-Philippe Navarro, Martin Kempeneers. Combined Importance Sampling and Separable Monte Carlo: Analytical Variance Estimator and applications to Structural Reliability. International Journal of Reliability and Safety, 2023, 17 (3-4), ⟨10.1504/IJRS.2023.135679⟩. ⟨hal-04138222⟩
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