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

Author

Listed:
  • Gabriele Capasso
  • Christian Gogu
  • Christian Bès
  • Jean-Philippe Navarro
  • Martin Kempeneers

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.

Suggested Citation

  • Gabriele Capasso & Christian Gogu & Christian Bès & Jean-Philippe Navarro & Martin Kempeneers, 2023. "Combined importance sampling and separable Monte Carlo: analytical variance estimator and applications to structural reliability," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 17(3/4), pages 200-227.
    • Handle: RePEc:ids:ijrsaf:v:17:y:2023:i:3/4:p:200-227
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