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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
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