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.
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Data transparency. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Software application or custom code. The MATLAB codes applied in the current study are available from the corresponding author on reasonable request.
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Shree Harsha (SH) and SBM conceived of the presented idea; SH developed the models and performed the computations; SH wrote the manuscript with support from SRN and SBM; SRN and SBM guided SH in modeling. SBM verified the results; Siddesha Hanumanthappa supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.
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Harsha, S., Hanumanthappa, S., Marulasiddappa, S.B. et al. Machine learning models for damage detection in steel beams. Int J Syst Assur Eng Manag 14, 1898–1911 (2023). https://doi.org/10.1007/s13198-023-02020-0
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DOI: https://doi.org/10.1007/s13198-023-02020-0