IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v228y2014i5p529-540.html
   My bibliography  Save this article

Saddlepoint approximation-based reliability analysis method for structural systems with parameter uncertainties

Author

Listed:
  • Ning-Cong Xiao
  • Yan-Feng Li
  • Le Yu
  • Zhonglai Wang
  • Hong-Zhong Huang

Abstract

Due to epistemic uncertainty, precisely determining parameters of all distribution is impossible in engineering practice. In this article, a novel reliability analysis method based on the saddlepoint approximation is proposed for structural systems with parameter uncertainties. The proposed method includes four main steps: (1) sampling for random and probability-box variables, (2) approximating the cumulant generating functions for systems under the best and worst cases, (3) calculating saddlepoints for the best and worst cases, and (4) calculating the lower and upper bounds of the probability of failure. The proposed method is effective because it does not require a large sample size or solving complicated integrals. Furthermore, the proposed method provides results that have the same accuracy as the existing interval Monte Carlo simulation method, but with significantly reduced computational effort. The effectiveness of the proposed method is demonstrated with three examples that are compared against with the interval Monte Carlo simulation method.

Suggested Citation

  • Ning-Cong Xiao & Yan-Feng Li & Le Yu & Zhonglai Wang & Hong-Zhong Huang, 2014. "Saddlepoint approximation-based reliability analysis method for structural systems with parameter uncertainties," Journal of Risk and Reliability, , vol. 228(5), pages 529-540, October.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:5:p:529-540
    DOI: 10.1177/1748006X14537619
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X14537619
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X14537619?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. N-C Xiao & H-Z Huang & Z Wang & Y Li & Y Liu, 2012. "Reliability analysis of series systems with multiple failure modes under epistemic and aleatory uncertainties," Journal of Risk and Reliability, , vol. 226(3), pages 295-304, June.
    3. Beiqing Huang & Xiaoping Du & Ramaprasad E. Lakshminarayana, 2006. "A saddlepoint approximation based simulation method for uncertainty analysis," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 1(1/2), pages 206-224.
    4. Ferson, Scott & Troy Tucker, W., 2006. "Sensitivity analysis using probability bounding," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1435-1442.
    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. Ying-Kui Gu & Chao-Jun Fan & Ling-Qiang Liang & Jun Zhang, 2022. "Reliability calculation method based on the Copula function for mechanical systems with dependent failure," Annals of Operations Research, Springer, vol. 311(1), pages 99-116, April.
    2. Ning-Cong Xiao & Hong-Zhong Huang & Yan-Feng Li & Zhonglai Wang & Xiao-Ling Zhang, 2013. "Non-probabilistic reliability sensitivity analysis of the model of structural systems with interval variables whose state of dependence is determined by constraints," Journal of Risk and Reliability, , vol. 227(5), pages 491-498, October.
    3. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
    5. 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.
    6. 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.
    7. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    8. 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.
    9. Flage, Roger & Aven, Terje & Berner, Christine L., 2018. "A comparison between a probability bounds analysis and a subjective probability approach to express epistemic uncertainties in a risk assessment context – A simple illustrative example," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 1-10.
    10. Schöbi, Roland & Sudret, Bruno, 2019. "Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 129-141.
    11. Hongjie Tang & Shicheng Zhang & Jinhui Li & Lingwei Kong & Baoqiang Zhang & Fei Xing & Huageng Luo, 2023. "Imprecise P-Box Sensitivity Analysis of an Aero-Engine Combustor Performance Simulation Model Considering Correlated Variables," Energies, MDPI, vol. 16(5), pages 1-22, March.
    12. Zhou, Di & Pan, Ershun & Zhang, Xufang & Zhang, Yimin, 2020. "Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    13. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    14. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    15. Huang, Xianzhen & Jin, Sujun & He, Xuefeng & He, David, 2019. "Reliability analysis of coherent systems subject to internal failures and external shocks," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 75-83.
    16. SakallI, Ümit Sami & Baykoç, Ömer Faruk, 2011. "An optimization approach for brass casting blending problem under aletory and epistemic uncertainties," International Journal of Production Economics, Elsevier, vol. 133(2), pages 708-718, October.
    17. Xianzhen Huang & Yimin Zhang, 2014. "A probability estimation method for reliability analysis using mapped Gegenbauer polynomials," Journal of Risk and Reliability, , vol. 228(1), pages 72-82, February.
    18. Jérémy Rohmer & Gonéri Cozannet & Jean-Charles Manceau, 2019. "Addressing ambiguity in probabilistic assessments of future coastal flooding using possibility distributions," Climatic Change, Springer, vol. 155(1), pages 95-109, July.
    19. Xiao, Ning-Cong & Zuo, Ming J. & Zhou, Chengning, 2018. "A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 330-338.
    20. 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.

    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:228:y:2014:i:5:p:529-540. 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.