Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Localization and investigation of damage extension in reinforced concrete beams using signal processing and machine learning

Document Type : Best papers - IRAST2018

Authors
Farhad Jedari Zare Zadeh 1
Masoud Farzam 2
Mehran Dadashzadeh 1
1 Ph.D. candidate, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2 Associate Prof., Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
10.22065/jsce.2023.392857.3082
Abstract
The propagation of incremental cracks in the main components of the structures is an important quandary that can cause irreparable damage in the structures over time. This study proposes a statistical method based on signal processing and machine learning to investigate crack location and propagation in reinforced concrete beams. The assumed beam with different boundary conditions is modeled in finite element software (ABAQUS) and incremental cracks are defined according to Extended Finite Element Method (XFEM) in two default locations along the length of the beam. Then, the incremental distributed load is assigned on the beam and the structure response is obtained through the loading time interval. The responses of the beam are used as a database to process with Continuous Wavelet Transform (CWT) and the wavelet coefficients are extracted in the same nodes that structure responses were. The distribution of wavelet coefficients has an effective and valuable implication to investigate the crack location and propagation. In order to consider the variation of wavelet coefficients during crack propagation, Principal Component Analysis (PCA) is used as a machine learning method. The study indicates that the first few components are highly correlated with the location of the damage. The results show that the proposed algorithm has recognized the crack propagation and localized it with high accuracy.
Keywords
Damage detection and localization
Incremental cracks
Extended finite element method
Wavelet transform
Principal component analysis

Subjects

 
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  • Receive Date 30 April 2023
  • Revise Date 30 July 2023
  • Accept Date 08 August 2023