IDEAS home Printed from https://ideas.repec.org/a/ids/ijrsaf/v8y2014i1p51-69.html
   My bibliography  Save this article

System reliability-based optimisation for truss structures using genetic algorithm and neural network

Author

Listed:
  • Yang Liu
  • Naiwei Lu
  • Mohammad Noori
  • Xinfeng Yin

Abstract

Optimum structures must have adequate resistance against external random loads. Since most truss structures involve a series of failure processes, it is necessary to develop system reliability analyses for the optimum design of truss structures. In this paper, a hybrid method of system reliability-based design optimisation (SRBDO) is proposed by combining genetic algorithms (GAs) and radial basis functions (RBFs) neural networks. The proposed method is applied to truss structures, and then the validity is demonstrated through two specific examples. Detailed discussions for the failure sequences such as buckling failure and bending failure are presented. It is concluded that the structural weight increases significantly with the increase of the target system reliability index or the coefficient of variation of design parameters. Results of two optimisation schemes of the steel truss girder show that the cross-sectional areas of the beams are decreased and those of web members are increased.

Suggested Citation

  • Yang Liu & Naiwei Lu & Mohammad Noori & Xinfeng Yin, 2014. "System reliability-based optimisation for truss structures using genetic algorithm and neural network," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 8(1), pages 51-69.
  • Handle: RePEc:ids:ijrsaf:v:8:y:2014:i:1:p:51-69
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=62640
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.

    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:ids:ijrsaf:v:8:y:2014:i:1:p:51-69. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=98 .

    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.