IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v243y2024ics0951832023007998.html
   My bibliography  Save this article

Line sampling for time-variant failure probability estimation using an adaptive combination approach

Author

Listed:
  • Yuan, Xiukai
  • Zheng, Weiming
  • Zhao, Chaofan
  • Valdebenito, Marcos A.
  • Faes, Matthias G.R.
  • Dong, Yiwei

Abstract

An efficient sampling approach ‘Adaptive Combined Line Sampling’ is proposed for evaluating the ‘time-variant failure probability function’ (TFPF) of structures. Line Sampling is implemented in an adaptive and iterative way, where each individual Line Sampling run is carried out based on adaptively selected important directions, in order to ensure a sufficiently precise estimation of the TFPF over the whole time interval of analysis. An adaptive strategy and an optimal combination algorithm are developed for the practical implementation of the Line Sampling process. The adaptive strategy allows to determine the optimal important direction which is then used in the next Line Sampling run. The combination strategy allows to collect all these adaptive sampling runs together in an optimal way, which aims at minimising the coefficient of variation (C.o.V.) of the TFPF estimate. Due to these strategies, the proposed approach can estimate the TFPF in a more efficient way than the traditional Line Sampling, while guaranteeing that the C.o.V. of the estimate remains below a prescribed threshold over the whole time of analysis. Thus it can be seen as an extended version of classical Line Sampling specially tailored for time-variant reliability analysis. Examples are given to illustrate the performance of the proposed approach.

Suggested Citation

  • Yuan, Xiukai & Zheng, Weiming & Zhao, Chaofan & Valdebenito, Marcos A. & Faes, Matthias G.R. & Dong, Yiwei, 2024. "Line sampling for time-variant failure probability estimation using an adaptive combination approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
  • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007998
    DOI: 10.1016/j.ress.2023.109885
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023007998
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109885?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.

    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:243:y:2024:i:c:s0951832023007998. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.