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Subset Simulation of a reliability model for radioactive waste repository performance assessment

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  • Cadini, F.
  • Avram, D.
  • Pedroni, N.
  • Zio, E.

Abstract

In this paper, we show an original application of the Subset Simulation (SS) technique on a model for the performance assessment of a near surface radioactive waste repository. The logic of the protective barriers of the repository is represented by a reliability model. The SS approach is founded on the idea that a small failure probability can be expressed as a product of larger conditional probabilities of some intermediate events; with a proper choice of the conditional events, the conditional probabilities can be sufficiently large to allow accurate estimation with a small number of samples. In the application, the method allows improving the efficiency of the random sampling for estimating the repository containment failure probability. Moreover, the peculiar set-partitioning scheme of the SS method is exploited for performing the analysis of the sensitivity of the failure probability estimate to the uncertain model parameters.

Suggested Citation

  • Cadini, F. & Avram, D. & Pedroni, N. & Zio, E., 2012. "Subset Simulation of a reliability model for radioactive waste repository performance assessment," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 75-83.
    • Handle: RePEc:eee:reensy:v:100:y:2012:i:c:p:75-83
    DOI: 10.1016/j.ress.2011.12.012
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    References listed on IDEAS

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    1. Lefebvre, Geneviève & Steele, Russell & Vandal, Alain C., 2010. "A path sampling identity for computing the Kullback-Leibler and J divergences," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1719-1731, July.
    2. Cadini, F. & De Sanctis, J. & Girotti, T. & Zio, E. & Luce, A. & Taglioni, A., 2010. "Monte Carlo-based assessment of the safety performance of a radioactive waste repository," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 859-865.
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    Citations

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    Cited by:

    1. Jing, Zhao & Chen, Jianqiao & Li, Xu, 2019. "RBF-GA: An adaptive radial basis function metamodeling with genetic algorithm for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 42-57.
    2. Cadini, F. & Gioletta, A. & Zio, E., 2015. "Improved metamodel-based importance sampling for the performance assessment of radioactive waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 188-197.
    3. Villén-Altamirano, J., 2014. "Asymptotic optimality of RESTART estimators in highly dependable systems," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 115-124.
    4. Edoardo Tosoni & Ahti Salo & Enrico Zio, 2018. "Scenario Analysis for the Safety Assessment of Nuclear Waste Repositories: A Critical Review," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 755-776, April.
    5. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    6. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    7. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "Estimation of rare event probabilities in power transmission networks subject to cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 9-20.
    8. Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Lee, Seunggyu, 2021. "Monte Carlo simulation using support vector machine and kernel density for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    10. Li, Yuyin & Zhang, Yahui & Kennedy, David, 2018. "Reliability analysis of subsea pipelines under spatially varying ground motions by using subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 74-83.

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