IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i5d10.1007_s13198-023-02020-0.html
   My bibliography  Save this article

Machine learning models for damage detection in steel beams

Author

Listed:
  • Shree Harsha

    (Siddaganga Institute of Technology)

  • Siddesha Hanumanthappa

    (Siddaganga Institute of Technology)

  • Sreedhara B. Marulasiddappa

    (Siddaganga Institute of Technology)

  • Sujay Raghavendra Naganna

    (Manipal Academy of Higher Education)

Abstract

Early-stage damage detection in structural elements such as beams, columns, and slabs subjected to various loads will aid in planning retrofitting operations before the occurrence of failure. The retrofitting of structural elements significantly improves their load carrying capacity and life span. It is therefore necessary to monitor/locate the damage and its extent in various parts of the structure. Machine learning algorithms, namely the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used in the present study to identify the location of the damages in the cantilever and fixed beams. The natural beam frequencies obtained through experimentation and finite element analysis were provided as input parameters for machine learning models. The input parameters used to predict the Frequency (Hz) were relative crack position, relative crack depth and mode number. ANN and ANFIS techniques were implemented to comparatively evaluate their simulation efficiencies in damage detection within cantilever and fixed beams. The ANFIS models were found to be capable of detecting beam damage with great precision.

Suggested Citation

  • Shree Harsha & Siddesha Hanumanthappa & Sreedhara B. Marulasiddappa & Sujay Raghavendra Naganna, 2023. "Machine learning models for damage detection in steel beams," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1898-1911, October.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:5:d:10.1007_s13198-023-02020-0
    DOI: 10.1007/s13198-023-02020-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-023-02020-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-023-02020-0?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:ijsaem:v:14:y:2023:i:5:d:10.1007_s13198-023-02020-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.