IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v231y2017i1p3-10.html
   My bibliography  Save this article

An approach for reliability-based sensitivity analysis based on saddlepoint approximation

Author

Listed:
  • Hao Lu
  • Gang Shen
  • Zhencai Zhu

Abstract

Reliability-based sensitivity analysis quantifies the effects of input random variables on model outputs. It is an integral part of reliability-based design and robust optimal design. An efficient reliability sensitivity method is developed in this article by combining the stochastic perturbation technique and the moment-based saddlepoint approximation. The main advantage of the presented method is that only the first three moments of the structural response are needed and the cumulant generating functions of input random variables are not strictly required. Several examples are illustrated, and the analysis procedure shows that the method is convenient to be applied to parameter sensitivity analysis of structural reliability.

Suggested Citation

  • Hao Lu & Gang Shen & Zhencai Zhu, 2017. "An approach for reliability-based sensitivity analysis based on saddlepoint approximation," Journal of Risk and Reliability, , vol. 231(1), pages 3-10, February.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:1:p:3-10
    DOI: 10.1177/1748006X16660489
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X16660489
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X16660489?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Huang, Beiqing & Du, Xiaoping, 2008. "Probabilistic uncertainty analysis by mean-value first order Saddlepoint Approximation," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 325-336.
    2. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    2. 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).
    3. 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).
    4. Bakeer, Tammam, 2023. "General partial safety factor theory for the assessment of the reliability of nonlinear structural systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Wenxuan Wang & Hangshan Gao & Pengfei Wei & Changcong Zhou, 2017. "Extending first-passage method to reliability sensitivity analysis of motion mechanisms," Journal of Risk and Reliability, , vol. 231(5), pages 573-586, October.
    6. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    7. Ling, Chunyan & Lu, Zhenzhou & Zhu, Xianming, 2019. "Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 23-35.
    8. Yuan, Xiukai & Lu, Zhenzhou, 2014. "Efficient approach for reliability-based optimization based on weighted importance sampling approach," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 107-114.
    9. Rong Yuan & Debiao Meng & Haiqing Li, 2016. "Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis," Journal of Risk and Reliability, , vol. 230(6), pages 570-578, December.
    10. 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.
    11. Au, Siu-Kui & Patelli, Edoardo, 2016. "Rare event simulation in finite-infinite dimensional space," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 67-77.
    12. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    13. El Masri, Maxime & Morio, Jérôme & Simatos, Florian, 2021. "Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    15. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    16. Song, Shufang & Bai, Zhiwei & Kucherenko, Sergei & Wang, Lu & Yang, Caiqiong, 2021. "Quantile sensitivity measures based on subset simulation importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    17. Papaioannou, Iason & Straub, Daniel, 2021. "Variance-based reliability sensitivity analysis and the FORM α-factors," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    18. Zhao, Enyong & Wang, Qihan & Alamdari, Mehrisadat Makki & Gao, Wei, 2023. "Advanced virtual model assisted most probable point capturing method for engineering structures," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    19. Yiwei Wang & Christian Gogu & Nicolas Binaud & Christian Bes & Raphael T Haftka & Nam-Ho Kim, 2018. "Predictive airframe maintenance strategies using model-based prognostics," Journal of Risk and Reliability, , vol. 232(6), pages 690-709, December.
    20. Yi, Jiaxiang & Cheng, Yuansheng & Liu, Jun, 2022. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:231:y:2017:i:1:p:3-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.