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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

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Abstract

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

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Acknowledgements

This experimental research was supported by research funds provided by Università Politecnica Delle Marche. The authors would like to express their gratitude to all the technicians and students who collaborated to develop the experimental research.

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Authors and Affiliations

  1. Structural Section DICEA, Polytechnic University of Marche, Ancona, 60131, Italy

    Abdelwahhab Khatir, Roberto Capozucca & Erica Magagnini

  2. Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, 700000, Vietnam

    Samir Khatir

Authors
  1. Abdelwahhab Khatir
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  2. Roberto Capozucca
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  3. Samir Khatir
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  4. Erica Magagnini
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Correspondence to Samir Khatir.

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Khatir, A., Capozucca, R., Khatir, S. et al. Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network. Front. Struct. Civ. Eng. 16, 976–989 (2022). https://doi.org/10.1007/s11709-022-0840-2

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  • DOI: https://doi.org/10.1007/s11709-022-0840-2

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