IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v240y2023ics0951832023004660.html
   My bibliography  Save this article

Stochastic collocation enhanced line sampling method for reliability analysis

Author

Listed:
  • Wei, Ning
  • Lu, Zhenzhou
  • Hu, Yingshi

Abstract

The stochastic collocation enhanced line sampling (SC-LS) method for reliability analysis is proposed in this paper, which is the point estimation strategy within the framework of line sampling. Instead of utilizing the direct random sampling strategy to estimate the integration of the one-dimensional conditional failure probability function in the traditional line sampling approach, the proposed SC-LS method evaluate the integration directly by Gaussian quadrature combined with the univariate multiplicative dimensionality reduction method (U-MDRM) or bivariate multiplicative dimensionality reduction method (B-MDRM), and the failure probability is estimated as the weighted sum of the one-dimensional conditional failure probabilities at the generated Gaussian quadrature grids. The superiority of proposed SC-LS method is demonstrated by several numerical examples and finite element examples, and the proposed method has two advantages, one is that it avoids the random sampling in one-dimension-reduced space, which is required by traditional line sampling approach, hence no variability exists in the estimate of failure probability. The other one is that it can be used for the small failure probability problems and is much less sensitive to the choice of importance direction.

Suggested Citation

  • Wei, Ning & Lu, Zhenzhou & Hu, Yingshi, 2023. "Stochastic collocation enhanced line sampling method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
  • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004660
    DOI: 10.1016/j.ress.2023.109552
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023004660
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109552?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:240:y:2023:i:c:s0951832023004660. 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.