Classification of Multistates of Internal Damage in Concrete Based on Convolutional Neural Network Analysis of Time-Frequency Images
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 6
Abstract
Computer vision based on machine learning theory has been widely used in the surface damage detection of concrete structures, but the characterization of internal damage in concrete still remains a challenge for researchers. Aiming at this problem, we propose a nondestructive evaluation (NDE) method to classify diverse conditions of internal damage in concrete based on short-time Fourier transform (STFT) and convolutional neural networks (CNN). The STFT converts the self-resonant vibration signals into two-dimensional time-frequency images that can be used as the input data for the CNN. The training set is fed into the CNN for feature extraction and classification, and then the testing set is brought into the trained model for verification. Both a simple case of virgin state and damaged state, as well as a complicated case covering all four internal damage states were successfully classified with an excellent recognition rate of testing samples. The key CNN hyperparameters were optimized and the classification accuracy rate of spectrum images was as high as 98.8%. Optimal data set size was also found to balance the accuracy and efficiency. The findings in this work validate the feasibility of the CNN for the detection and differentiation of invisible damage in concrete nondestructively.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by National Natural Science Foundation of China (Grant No. 51978027) and National Key Research and Development Program of China (Grant No. 2018YFB1600200).
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 36 • Issue 6 • December 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 24, 2022
Accepted: Jul 8, 2022
Published online: Sep 20, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 20, 2023
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Concrete
- Damage (material)
- Damage (structural)
- Engineering fundamentals
- Engineering materials (by type)
- Forensic engineering
- Fourier analysis
- Frequency analysis
- Materials characterization
- Materials engineering
- Methodology (by type)
- Network analysis
- Neural networks
- Structural engineering
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