Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
2 files | 9.55 MB
Add to list
  1. Search results
  2. Feature extraction and classification...

Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network

Muhammad Irfan Shirazi (UGent) , Samir Khatir (UGent) , Djilali Boutchicha and Magd Abdel Wahab (UGent)
(2024) COMPOSITE STRUCTURES. 327.
Author
Organization
Abstract
Structures and components need to be constantly monitored to ensure good service life and avoid failures. Structural health monitoring is a wide-ranging concept that tries to ensure the safety of components and structures. Vibration responses of small and large structures contain information about these damages. In the present study, this information is extracted and used to classify the location and severity of cracks. For this purpose, an FE model of Glass Fibre Reinforced Plastic (GFRP) free-free beam is created for beams without any crack and beams with three crack locations and three crack severities. Finite Element (FE) model is validated using modal analysis and is used to generate vibration responses from simulated hammer strikes at different locations on the beam. The vibration data consists of responses of beams that have only one crack present at a time and when two cracks are present. A 1D-CNN network is trained on the generated vibrational responses to classify damage labels assigned for each crack location and crack severity. The network was tested using datasets containing only single crack instances and only dual crack instances. The trained networks were found to be 95% and 93% accurate in determining the damage class respectively. Furthermore, datasets of these two instances combined, containing responses when single cracks and dual cracks are present, were also investigated. These networks had an accuracy of 92%. Hence, in all these instances, the 1D-CNN network classifies the healthy and damaged conditions satisfactorily. There are two advantages of using such a technique. Firstly, this approach simplifies the two-step approach: one for feature extraction and another one for damage prediction into a single step. Secondly, the use of raw vibration data for damage prediction means that real-time damage detection is possible.
Keywords
Civil and Structural Engineering, Ceramics and Composites, Structural health monitoring, Glass fibre reinforced plastic, 1D-convolutional neural network, Damage classification, Raw vibration, data, Modal analysis, STRUCTURAL DAMAGE DETECTION, IDENTIFICATION

Downloads

  • Download
    (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 7.01 MB
  • Download
    (...).docx
    • full text (Accepted manuscript)
    • |
    • UGent only (changes to open access on 2025-05-05)
    • |
    • Word
    • |
    • 2.55 MB

Citation

Please use this url to cite or link to this publication:

MLA
Shirazi, Muhammad Irfan, et al. “Feature Extraction and Classification of Multiple Cracks from Raw Vibrational Responses of Composite Beams Using 1D-CNN Network.” COMPOSITE STRUCTURES, vol. 327, 2024, doi:10.1016/j.compstruct.2023.117701.
APA
Shirazi, M. I., Khatir, S., Boutchicha, D., & Abdel Wahab, M. (2024). Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network. COMPOSITE STRUCTURES, 327. https://doi.org/10.1016/j.compstruct.2023.117701
Chicago author-date
Shirazi, Muhammad Irfan, Samir Khatir, Djilali Boutchicha, and Magd Abdel Wahab. 2024. “Feature Extraction and Classification of Multiple Cracks from Raw Vibrational Responses of Composite Beams Using 1D-CNN Network.” COMPOSITE STRUCTURES 327. https://doi.org/10.1016/j.compstruct.2023.117701.
Chicago author-date (all authors)
Shirazi, Muhammad Irfan, Samir Khatir, Djilali Boutchicha, and Magd Abdel Wahab. 2024. “Feature Extraction and Classification of Multiple Cracks from Raw Vibrational Responses of Composite Beams Using 1D-CNN Network.” COMPOSITE STRUCTURES 327. doi:10.1016/j.compstruct.2023.117701.
Vancouver
1.
Shirazi MI, Khatir S, Boutchicha D, Abdel Wahab M. Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network. COMPOSITE STRUCTURES. 2024;327.
IEEE
[1]
M. I. Shirazi, S. Khatir, D. Boutchicha, and M. Abdel Wahab, “Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network,” COMPOSITE STRUCTURES, vol. 327, 2024.
@article{01HFBVJ2QAG82HE702264QYSBK,
  abstract     = {{Structures and components need to be constantly monitored to ensure good service life and avoid failures. Structural health monitoring is a wide-ranging concept that tries to ensure the safety of components and structures. Vibration responses of small and large structures contain information about these damages. In the present study, this information is extracted and used to classify the location and severity of cracks. For this purpose, an FE model of Glass Fibre Reinforced Plastic (GFRP) free-free beam is created for beams without any crack and beams with three crack locations and three crack severities. Finite Element (FE) model is validated using modal analysis and is used to generate vibration responses from simulated hammer strikes at different locations on the beam. The vibration data consists of responses of beams that have only one crack present at a time and when two cracks are present. A 1D-CNN network is trained on the generated vibrational responses to classify damage labels assigned for each crack location and crack severity. The network was tested using datasets containing only single crack instances and only dual crack instances. The trained networks were found to be 95% and 93% accurate in determining the damage class respectively. Furthermore, datasets of these two instances combined, containing responses when single cracks and dual cracks are present, were also investigated. These networks had an accuracy of 92%. Hence, in all these instances, the 1D-CNN network classifies the healthy and damaged conditions satisfactorily. There are two advantages of using such a technique. Firstly, this approach simplifies the two-step approach: one for feature extraction and another one for damage prediction into a single step. Secondly, the use of raw vibration data for damage prediction means that real-time damage detection is possible.}},
  articleno    = {{117701}},
  author       = {{Shirazi, Muhammad Irfan and Khatir, Samir and Boutchicha, Djilali and Abdel Wahab, Magd}},
  issn         = {{0263-8223}},
  journal      = {{COMPOSITE STRUCTURES}},
  keywords     = {{Civil and Structural Engineering,Ceramics and Composites,Structural health monitoring,Glass fibre reinforced plastic,1D-convolutional neural network,Damage classification,Raw vibration,data,Modal analysis,STRUCTURAL DAMAGE DETECTION,IDENTIFICATION}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network}},
  url          = {{http://doi.org/10.1016/j.compstruct.2023.117701}},
  volume       = {{327}},
  year         = {{2024}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: