IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v258y2025ics0951832025000948.html
   My bibliography  Save this article

A new probabilistic transformation technique for evidence-theory-based structural reliability analysis

Author

Listed:
  • Zhang, Dequan
  • Hao, Zhijie
  • Han, Xu
  • Dai, Shijie
  • Li, Qing

Abstract

Reliability analysis signifies an indispensable approach for ensuring the safety and functionality of engineering structures. Evidence-theory-based reliability analysis (ETRA) has been developed attributable to its superior ability to deal with various epistemic uncertainties presented in practice. However, ETRA could inevitably result in a high computational burden due to its repeated call for costly performance functions. To tackle such a computational efficiency problem, an effective reliability analysis method is proposed here to deal with epistemic uncertainty. First, a new probabilistic transformation technique is developed based on cubic spline interpolation (CSI). Second, with the help of CSI, the most probable focal element is located using an improved HL-RF algorithm accurately. Third, the actual performance function is replaced by a response surface model which is constructed by the central composite design around the most probable focal element. Finally, the belief measure and plausibility measure of the reliability problem are performed. The effectiveness of the proposed reliability analysis method is verified by three benchmarking numerical examples and an engineering case study on kinematic trajectory precision of an industrial robot. The results indicate that the proposed method has high efficiency with satisfactory computational accuracy.

Suggested Citation

  • Zhang, Dequan & Hao, Zhijie & Han, Xu & Dai, Shijie & Li, Qing, 2025. "A new probabilistic transformation technique for evidence-theory-based structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:reensy:v:258:y:2025:i:c:s0951832025000948
    DOI: 10.1016/j.ress.2025.110891
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025000948
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.110891?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Meng, Yuan & Zhang, Dequan & Shi, Baojun & Wang, Dapeng & Wang, Fang, 2024. "An active learning Kriging model with approximating parallel strategy for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    4. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Zhao, Wei & Fan, Feng & Wang, Wei, 2017. "Non-linear partial least squares response surface method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 69-77.
    6. Helton, Jon C. & Johnson, Jay D., 2011. "Quantification of margins and uncertainties: Alternative representations of epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1034-1052.
    7. Zhang, Z. & Jiang, C. & Wang, G.G. & Han, X., 2015. "First and second order approximate reliability analysis methods using evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 40-49.
    8. Riley, Matthew E., 2015. "Evidence-based quantification of uncertainties induced via simulation-based modeling," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 79-86.
    9. Zhang, Dequan & Shen, Shuoshuo & Wu, Jinhui & Wang, Fang & Han, Xu, 2023. "Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Fan Yang & Ming Liu & Lei Li & Hu Ren & Jianbo Wu, 2019. "Evidence-Based Multidisciplinary Design Optimization with the Active Global Kriging Model," Complexity, Hindawi, vol. 2019, pages 1-13, November.
    11. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.
    13. Xiao, Ning-Cong & Yuan, Kai & Zhan, Hongyou, 2022. "System reliability analysis based on dependent Kriging predictions and parallel learning strategy," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    14. Chen, Wen-Bin & Li, Xiao-Yang & Wu, Ji-Peng & Kang, Rui, 2024. "Uncertain random accelerated degradation modelling and statistical analysis with aleatory and epistemic uncertainties from multiple dimensions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Jiang, Hongrui & Ding, Long & Ji, Jie & Zhu, Jiping, 2024. "Building reliability of risk assessment of domino effects in chemical tank farm through an improved uncertainty analysis method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    16. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Shah, Harsheel & Hosder, Serhat & Winter, Tyler, 2015. "Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 59-72.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Di & Chen, Zhen & Chen, Zhaoxiang & Han, Jinrui & Pan, Ershun, 2025. "Dynamic reliability evaluation considering the stochastic evolving process based on extreme characteristics of system responses," Reliability Engineering and System Safety, Elsevier, vol. 260(C).

    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. Chen, Zequan & He, Jialong & Li, Guofa & Yang, Zhaojun & Wang, Tianzhe & Du, Xuejiao, 2024. "Fast convergence strategy for adaptive structural reliability analysis based on kriging believer criterion and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Zhao, Qiangqiang & Duan, Jinyan & Jia, Kang & Hong, Jun, 2025. "PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    3. Wu, Jinhui & Tao, Yourui & Han, Xu, 2023. "Polynomial chaos expansion approximation for dimension-reduction model-based reliability analysis method and application to industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.
    5. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2024. "A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Cai, Yu & Zhao, Wei & Wang, Xiaoping & Ou, Yanjun & Chen, Yangyang & Li, Xueyan, 2024. "A novel multiple linearization method for reliability analysis based on evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    7. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.
    8. Zhang, Dequan & Shen, Shuoshuo & Wu, Jinhui & Wang, Fang & Han, Xu, 2023. "Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Dong, Manman & Cheng, Yongbo & Wan, Liangqi, 2024. "A new adaptive multi-kernel relevance vector regression for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    10. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. 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.
    12. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    13. Li, Wenxiong & Geng, Rong & Chen, Suiyin, 2024. "CSP-free adaptive Kriging surrogate model method for reliability analysis with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    16. Mathpati, Yogesh Chandrakant & More, Kalpesh Sanjay & Tripura, Tapas & Nayek, Rajdip & Chakraborty, Souvik, 2023. "MAntRA: A framework for model agnostic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    17. Zhan, Hongyou & Xiao, Ning-Cong, 2025. "A new active learning surrogate model for time- and space-dependent system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    18. Zhan, Hongyou & Xiao, Ning-Cong & Ji, Yuxiang, 2022. "An adaptive parallel learning dependent Kriging model for small failure probability problems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    19. Menz, Morgane & Gogu, Christian & Dubreuil, Sylvain & Bartoli, Nathalie & Morio, Jérôme, 2020. "Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    20. He, Wanxin & Wang, Yiyuan & Li, Gang & Zhou, Jinhang, 2024. "A novel maximum entropy method based on the B-spline theory and the low-discrepancy sequence for complex probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 243(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:eee:reensy:v:258:y:2025:i:c:s0951832025000948. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.