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Sensitivity estimation of failure probability applying line sampling

Author

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  • Valdebenito, M.A.
  • Jensen, H.A.
  • Hernández, H.B.
  • Mehrez, L.

Abstract

This contribution presents a framework for calculating a sensitivity measure for problems of computational stochastic mechanics. More specifically, the sensitivity measure considered is the derivative of the failure probability with respect to parameters of the probability distributions (e.g. mean value, standard deviation) associated with the random input quantities of a system’s model. The proposed framework is formulated as a post-processing step of Line Sampling, which is a simulation-based method for estimating small failure probabilities. In particular, the proposed framework comprises two different approaches for estimating the sought sensitivity. The application of the proposed framework and comparison of the two aforementioned approaches is discussed through a number of numerical examples. The results obtained indicate that both approaches allow estimating the sought sensitivity measure.

Suggested Citation

  • Valdebenito, M.A. & Jensen, H.A. & Hernández, H.B. & Mehrez, L., 2018. "Sensitivity estimation of failure probability applying line sampling," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 99-111.
    • Handle: RePEc:eee:reensy:v:171:y:2018:i:c:p:99-111
    DOI: 10.1016/j.ress.2017.11.010
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    Citations

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    Cited by:

    1. Yuan, Xiukai & Qian, Yugeng & Chen, Jingqiang & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2023. "Global failure probability function estimation based on an adaptive strategy and combination algorithm," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Mahdi Shadab Far & Hongwei Huang, 2019. "Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    3. Teng, Da & Feng, Yun-Wen & Chen, Jun-Yu & Liu, Jia-Qi & Lu, Cheng, 2024. "Multi-polynomial chaos Kriging-based adaptive moving strategy for comprehensive reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    5. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    6. Xiaobo Zhang & Zhenzhou Lu & Kai Cheng & Yanping Wang, 2020. "A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation," Journal of Risk and Reliability, , vol. 234(4), pages 622-635, August.
    7. González, I.V. & Valdebenito, M.A. & Correa, J.I. & Jensen, H.A., 2019. "Calculation of second order statistics of uncertain linear systems applying reduced order models," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    8. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    9. Zhang, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Keshtegar, Behrooz & Chakraborty, Souvik, 2018. "Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 69-83.
    11. Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.

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