A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals.

Habib, Md. Arafat; Hasan, Md. Junayed; Kim, Jong-Myon

Abstract

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate two-dimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections.

Citation

HABIB, M.A., HASAN, M.J. and KIM, J.-M. 2021. A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. IEEE access [online], 9, pages 104029-104050. Available from: https://doi.org/10.1109/ACCESS.2021.3099124

• Journal Article Type: Article
• Acceptance Date: Jul 18, 2021
• Online Publication Date: Jul 26, 2021
• Publication Date: Dec 31, 2021
• Deposit Date: May 26, 2022
• Publicly Available Date: May 26, 2022
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 104029-104050
• DOI: https://doi.org/10.1109/ACCESS.2021.3099124
• Keywords: Concrete crack characterization; Continuous wavelet transformation; Convolutional neural network; SqueezeNet
• Public URL: https://rgu-repository.worktribe.com/output/1664785