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A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

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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.
Author(s)
하비브 엠디 아라파트주나예드 엠디김종면
Issued Date
2021
Type
Article
Keyword
Acoustic beamsClassification algorithmsConcreteConcrete crack characterizationcontinuous wavelet transformationContinuous wavelet transformsconvolutional neural networkFeature extractionMachine learning algorithmsSignal processing algorithmsSqueezeNet
DOI
10.1109/ACCESS.2021.3099124
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9159
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_1109_ACCESS_2021_3099124&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Lightweight%20Deep%20Learning-Based%20Approach%20for%20Concrete%20Crack%20Characterization%20Using%20Acoustic%20Emission%20Signals&offset=0&pcAvailability=true
Publisher
IEEE ACCESS
Location
미국
Language
영어
ISSN
2169-3536
Citation Volume
9
Citation Number
1
Citation Start Page
104029
Citation End Page
104050
Appears in Collections:
Engineering > IT Convergence
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