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Classification of cracking sources of different engineering media via machine learning
  • +4
  • Jie Huang,
  • Qianting Hu,
  • Zhenlong Song,
  • Gongheng Zhang,
  • Chaozhong Qin,
  • Mingyang Wu,
  • Xiaodong Wang
Jie Huang
Chongqing University
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Qianting Hu
Chongqing University
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Zhenlong Song
Chongqing University
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Gongheng Zhang
Southern University of Science and Technology
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Chaozhong Qin
Chongqing University
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Mingyang Wu
Chongqing University
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Xiaodong Wang
Chongqing University
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Abstract

Complex civil structures require the cooperation of many building materials. However, it is difficult to accurately monitor and evaluate the inner damage states of various material systems. Based on a convolutional neural network (CNN) and the acoustic emission (AE) time-frequency diagram, we used the transfer learning method for classifying the AE signals of different materials under external loads. The results show the CNN model can accurately classify cracks that come from different materials based on AE signals. The recognition accuracy can reach 90% just by re-training the full connection layer of the pre-trained model, and its accuracy can reach 97% after re-training the top 2 convolutional layers of this model. A realization of cracking source identification mainly depends on the differences in mineral particles in materials. This work highlights the great potential for real-time and quantitative monitoring of the health status of composite civil structures.

Peer review status:ACCEPTED

14 Apr 2021Submitted to Fatigue & Fracture of Engineering Materials & Structures
15 Apr 2021Submission Checks Completed
15 Apr 2021Assigned to Editor
16 Apr 2021Reviewer(s) Assigned
08 May 2021Review(s) Completed, Editorial Evaluation Pending
09 May 2021Editorial Decision: Revise Major
15 May 20211st Revision Received
17 May 2021Submission Checks Completed
17 May 2021Assigned to Editor
17 May 2021Reviewer(s) Assigned
31 May 2021Review(s) Completed, Editorial Evaluation Pending
05 Jun 2021Editorial Decision: Accept