IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v238y2024i2p401-416.html
   My bibliography  Save this article

An efficient approximate optimization algorithm and its application to non-probabilistic reliability importance measures

Author

Listed:
  • Rongyao Song
  • Tong Yan
  • Xiaoyi Wang
  • Wenxuan Wang

Abstract

There are inevitably a large number of uncertainties in the actual engineering structures. How to measure the degree of influence of the uncertainty of input variables on structural response is an important issue in structural design. Global sensitivity analysis is an effective means of addressing this problem, in which, the non-probabilistic reliability sensitivity analysis method has received more attention because it is not restricted by the distribution type of random variables. However, the non-probabilistic importance analysis method requires optimization analysis to obtain the extreme values of the performance function, resulting in its application in practical engineering problems being somewhat limited. To address this problem, this paper firstly proposed an efficient optimization method based on the high-dimensional model decomposition and Taylor expansion series combined with the quadratic function; Secondly, the non-probabilistic reliability importance analysis method is improved based on the proposed optimization method; Finally, two numerical cases are utilized to illustrate the accuracy and efficiency of the proposed method, and an engineering example is used to illustrate the engineering practicality of the proposed method. It was found that regardless of the value of the safety threshold, it affects only the non-probability reliability indicators and has little effect on the magnitude of the non-probability reliability importance indicators and the order of importance of the parameters.

Suggested Citation

  • Rongyao Song & Tong Yan & Xiaoyi Wang & Wenxuan Wang, 2024. "An efficient approximate optimization algorithm and its application to non-probabilistic reliability importance measures," Journal of Risk and Reliability, , vol. 238(2), pages 401-416, April.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:2:p:401-416
    DOI: 10.1177/1748006X221138132
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X221138132
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X221138132?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. Zhang, Leigang & Lu, Zhenzhou & Cheng, Lei & Fan, Chongqing, 2014. "A new method for evaluating Borgonovo moment-independent importance measure with its application in an aircraft structure," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 163-175.
    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. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    2. Cai, Zhiqiang & Si, Shubin & Sun, Shudong & Li, Caitao, 2016. "Optimization of linear consecutive-k-out-of-n system with a Birnbaum importance-based genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 248-258.
    3. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.
    4. Yun, Wanying & Lu, Zhenzhou & Jiang, Xian, 2019. "An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 174-182.
    5. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    6. Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2021. "Simultaneous estimation of complementary moment independent and reliability-oriented sensitivity measures," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 721-737.
    7. Wei, Pengfei & Song, Jingwen & Lu, Zhenzhou & Yue, Zhufeng, 2016. "Time-dependent reliability sensitivity analysis of motion mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 107-120.
    8. Xing Pan & Lunhu Hu & Ziling Xin & Shenghan Zhou & Yanmei Lin & Yong Wu, 2018. "Risk Scenario Generation Based on Importance Measure Analysis," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    9. Yicheng Zhou & Zhenzhou Lu & Yan Shi & Kai Cheng, 2019. "A vine copula–based method for analyzing the moment-independent importance measure of the multivariate output," Journal of Risk and Reliability, , vol. 233(3), pages 338-354, June.
    10. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    11. Zhao, Yan-Gang & Zhang, Xuan-Yi & Lu, Zhao-Hui, 2018. "A flexible distribution and its application in reliability engineering," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 1-12.
    12. Liu, Jie & Hu, Yifeng & Xu, Can & Jiang, Chao & Han, Xu, 2016. "Probability assessments of identified parameters for stochastic structures using point estimation method," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 51-58.
    13. Weng, Ye-Yao & Liu, Teng & Zhang, Xuan-Yi & Zhao, Yan-Gang, 2025. "Probability density estimation of polynomial chaos and its application in structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    14. Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2019. "A nonparametric importance sampling estimator for moment independent importance measures," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 3-16.

    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:238:y:2024:i:2:p:401-416. 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.