IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v210y2021ics0951832021000454.html
   My bibliography  Save this article

Efficient imprecise reliability analysis using the Augmented Space Integral

Author

Listed:
  • Yuan, Xiukai
  • Faes, Matthias G.R.
  • Liu, Shaolong
  • Valdebenito, Marcos A.
  • Beer, Michael

Abstract

This paper presents an efficient approach to compute the bounds on the reliability of a structure subjected to uncertain parameters described by means of imprecise probabilities. These imprecise probabilities arise from epistemic uncertainty in the definition of the hyper-parameters of a set of random variables that describe aleatory uncertainty in some of the structure’s properties. Typically, such calculation involves the solution of a so-called double-loop problem, where a crisp reliability problem is repeatedly solved to determine which realization of the epistemic uncertainties yields the worst or best case with respect to structural safety. The approach in this paper aims at decoupling this double loop by virtue of the Augmented Space Integral. The core idea of the method is to infer a functional relationship between the epistemically uncertain hyper-parameters and the probability of failure. Then, this functional relationship can be used to determine the best and worst case behavior with respect to the probability of failure. Three case studies are included to illustrate the effectiveness and efficiency of the developed methods.

Suggested Citation

  • Yuan, Xiukai & Faes, Matthias G.R. & Liu, Shaolong & Valdebenito, Marcos A. & Beer, Michael, 2021. "Efficient imprecise reliability analysis using the Augmented Space Integral," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000454
    DOI: 10.1016/j.ress.2021.107477
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021000454
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107477?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. 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.
    2. Mancuso, A. & Compare, M. & Salo, A. & Zio, E. & Laakso, T., 2016. "Risk-based optimization of pipe inspections in large underground networks with imprecise information," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 228-238.
    3. 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.
    4. Mullins, Joshua & Ling, You & Mahadevan, Sankaran & Sun, Lin & Strachan, Alejandro, 2016. "Separation of aleatory and epistemic uncertainty in probabilistic model validation," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 49-59.
    5. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
    6. Yin, Yi-Chao & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2017. "An imprecise statistical method for accelerated life testing using the power-Weibull model," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 158-167.
    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. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(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. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. Zheng, Xiaohu & Yao, Wen & Zhang, Yunyang & Zhang, Xiaoya, 2022. "Consistency regularization-based deep polynomial chaos neural network method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    3. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Chen, Thomas Ying-Jeh & Guikema, Seth David & Daly, Craig Michael, 2019. "Optimal pipe inspection paths considering inspection tool limitations," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 156-166.
    5. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.
    7. Cheng, Yao & Liao, Haitao & Huang, Zhiyi, 2021. "Optimal degradation-based hybrid double-stage acceptance sampling plan for a heterogeneous product," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    8. 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).
    9. Bi Liu & Yunlong Teng & Qi Huang, 2020. "RETRACTED: A novel imprecise reliability prediction method for incomplete lifetime data based on two-parameter Weibull distribution," Journal of Risk and Reliability, , vol. 234(1), pages 208-218, February.
    10. Morales-Torres, Adrián & Escuder-Bueno, Ignacio & Serrano-Lombillo, Armando & Castillo Rodríguez, Jesica T., 2019. "Dealing with epistemic uncertainty in risk-informed decision making for dam safety management," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    12. Wang, Chong & Matthies, Hermann G., 2019. "Novel model calibration method via non-probabilistic interval characterization and Bayesian theory," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 84-92.
    13. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Zhang, Qiang & Zhao, Yan-Gang & Kolozvari, Kristijan & Xu, Lei, 2022. "Reliability analysis of reinforced concrete structure against progressive collapse," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. Chen, Thomas Ying-Jeh & Riley, Connor Thomas & Van Hentenryck, Pascal & Guikema, Seth David, 2020. "Optimizing inspection routes in pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    16. Ehre, Max & Papaioannou, Iason & Straub, Daniel, 2020. "A framework for global reliability sensitivity analysis in the presence of multi-uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    17. Yao, Wen & Zheng, Xiaohu & Zhang, Jun & Wang, Ning & Tang, Guijian, 2023. "Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    18. Wang, Zhiheng & Hawi, Philippe & Masri, Sami & Aitharaju, Venkat & Ghanem, Roger, 2023. "Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Urbani, Michele & Brunelli, Matteo & Punkka, Antti, 2023. "An approach for bi-objective maintenance scheduling on a networked system with limited resources," European Journal of Operational Research, Elsevier, vol. 305(1), pages 101-113.
    20. Wang, Zhenqiang & Jia, Gaofeng, 2020. "Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 197(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:210:y:2021:i:c:s0951832021000454. 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.