Cover
Vol. 23 No. 2 (2023)

Published: December 31, 2023

Pages: 87-98

Original Article

Numerical Study for Damage Identification in Beams Using Continuous Wavelet Transformation and Convolution Neural Network

Eman R. Bustan 1 Jaafar K. Ali 1
1 Department of Mechanical Engineering, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: April 3, 2023
  • Revised: June 4, 2023
  • Accepted: June 12, 2023
  • Available Online: December 30, 2023
DOI

https://doi.org/10.33971/bjes.23.2.11

Abstract

The discovery and identification of damages in engineering structures is very important in the field of engineering maintenance, as it is a great challenge in presenting new methods in measuring vibrations and discovering damages with the development in the field of automation and high accuracy in discovering damages. In this study, natural frequencies and mode shapes of transverse vibration for damage detection in structures are investigated. The study is performed for various crack depth and crack location. And suggested a new technique based on Continuous Wavelet Transform (CWT) and Convolution Neural Network (CNN). The comparison will be done by simulating the oscillations of a cantilever steel beam with and without defect as a numerical case. The proposed new technique proved to outperform classical methods and has achieved a100% accuracy in the identification of defect position for the data studied.

Keywords

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