Damage detection in simply supported beams using machine learning

Authors

  • Alexandra-Teodora AMAN PhD. Stud. Eng., Babeș-Bolyai University, Faculty of Engineering, Piaţa Traian Vuia, nr. 1-4, 320085, Reşiţa, Romania, aman.alexandra@yahoo.com
  • Cristian TUFISI Lect. Dr. Eng., Babeș-Bolyai University, Faculty of Engineering, Piaţa Traian Vuia, nr. 1-4, 320085, Reşiţa, Romania, cristian.tufisi@ubbcluj.ro (*corresponding author) https://orcid.org/0000-0002-0567-6072
  • Gilbert-Rainer GILLICH Prof. Dr. Eng., Babeș-Bolyai University, Faculty of Engineering, Piaţa Traian Vuia, nr. 1-4, 320085, Reşiţa, Romania, gilbert.gillich@ubbcluj.ro https://orcid.org/0000-0003-4962-2567

DOI:

https://doi.org/10.24193/subbeng.2022.1.1

Keywords:

damage detection, machine learning, natural frequen-cy, structural health monitoring.

Abstract

The more our infrastructure is aging, the risk of structural failure is higher, making the detection of damage using modal parameters a very important factor that can be applied in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods. In the current paper, a modal approach using a machine learning technique by training a feedforward backpropa-gation neural network for detecting transverse damages in simple supported beam-like structures is presented. A method for analyti-cal determination of the training data is used and the obtained da-taset values are employed for training an ANN that will be used to locate and evaluate the severity of transverse cracks in cantilever beams. The output from the ANN model is compared by plotting the calculated error for each case in comparison with FEM results us-ing the SolidWorks simulation software.

References

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STUDIA UBB ENGINEERING, Volume 67 (LXVII), No. 1, November 2022

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Published

2022-11-11

How to Cite

AMAN, A.-T., TUFISI, C., & GILLICH, G.-R. (2022). Damage detection in simply supported beams using machine learning. Studia Universitatis Babeș-Bolyai Engineering, 67(1), 7–15. https://doi.org/10.24193/subbeng.2022.1.1

Issue

Volume 67, No. 1, November 2022

Section

Articles

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Copyright (c) 2022 Studia Universitatis Babeș-Bolyai Engineering

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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