Published April 2, 2025 | Version v1
Journal article Open

A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications

Authors/Creators

  • 1. Department of Structural Engineering and Building Materials, Ghent University, Belgium
  • 2. Faculty of Civil Engineering Universitas Islam Indonesia
  • 3. Department of Civil Engineering, University of Engineering and Technology Peshawar Pakistan
  • 4. Institute of Green Civil Engineering University of Natural Resources and Life Sciences, Vienna
  • 5. Department of Rural Sociology, University of Agriculture, Faisalabad Pakistan
  • 6. Department of Civil Engineering Abasyn University Peshawar Pakistan
  • 7. Department of Physics Government College University Faisalabad, Pakistan
  • 8. Department of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture China
  • 9. Department of Civil Engineering, The University of Lahore, Pakistan

Description

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of civil infrastructure. Among various SHM techniques, vibration-based damage detection has gained significant attention due to its non-destructive nature and effectiveness in identifying structural anomalies. Traditional methods, including modal analysis, frequency-based approaches, and wavelet transform techniques, have been widely employed for detecting structural damage. However, these approaches often struggle with high computational costs, sensitivity to environmental variations, and limited accuracy in complex structures. Recent advancements in artificial intelligence (AI) have revolutionized vibration-based damage detection by introducing machine learning (ML) and deep learning (DL) techniques. These AI-driven approaches enable automated feature extraction, improved damage classification, and enhanced predictive capabilities.

 

Files

GSARJMS782025 FT Gelary script.pdf

Files (901.4 kB)

Name Size Download all
md5:9e422510680a4d1a743e1abd28669c02
901.4 kB Preview Download