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A subset simulation based approach with modified conditional sampling and estimator for loss exceedance curve computation

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  • Bansal, Sahil
  • Cheung, Sai Hung

Abstract

A new stochastic simulation-based approach for the evaluation of loss exceedance curve without repeated reliability analyses, and the generation of samples of input random variables and any function of them conditioned on different levels of loss exceedance is proposed for a comprehensive risk and loss analysis, and investigation. The proposed approach involves the modification of the simulation algorithms in the Subset Simulation and the development of new estimators. It allows for a more comprehensive characterization of the probability distribution of the loss including the tail parts due to combinations of scenarios which can lead to extreme and catastrophic consequences. The approach is robust to the number of random variables involved. The effectiveness and efficiency of the proposed method are shown by an illustrative example involving a seismic loss analysis of a multi-story inelastic structure. A stochastic ground motion model coupled with a stochastic nonlinear dynamic model, and probabilistic fragility and loss functions are considered.

Suggested Citation

  • Bansal, Sahil & Cheung, Sai Hung, 2018. "A subset simulation based approach with modified conditional sampling and estimator for loss exceedance curve computation," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 94-107.
    • Handle: RePEc:eee:reensy:v:177:y:2018:i:c:p:94-107
    DOI: 10.1016/j.ress.2018.05.003
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    References listed on IDEAS

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    1. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
    2. Bansal, Sahil & Cheung, Sai Hung, 2017. "On the evaluation of multiple failure probability curves in reliability analysis with multiple performance functions," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 583-594.
    3. Cadini, F. & Gioletta, A., 2016. "A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 15-27.
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