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Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network

Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of d...

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Autores principales: Hu, Tianyu, Zhao, Jinhui, Zheng, Ruifang, Wang, Pengfeng, Li, Xiaolu, Zhang, Qichun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444079/
https://www.ncbi.nlm.nih.gov/pubmed/34604513
http://dx.doi.org/10.7717/peerj-cs.635
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author Hu, Tianyu
Zhao, Jinhui
Zheng, Ruifang
Wang, Pengfeng
Li, Xiaolu
Zhang, Qichun
author_facet Hu, Tianyu
Zhao, Jinhui
Zheng, Ruifang
Wang, Pengfeng
Li, Xiaolu
Zhang, Qichun
author_sort Hu, Tianyu
collection PubMed
description Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency.
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spelling pubmed-84440792021-09-30 Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network Hu, Tianyu Zhao, Jinhui Zheng, Ruifang Wang, Pengfeng Li, Xiaolu Zhang, Qichun PeerJ Comput Sci Artificial Intelligence Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency. PeerJ Inc. 2021-08-31 /pmc/articles/PMC8444079/ /pubmed/34604513 http://dx.doi.org/10.7717/peerj-cs.635 Text en © 2021 Hu et al. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Hu, Tianyu
Zhao, Jinhui
Zheng, Ruifang
Wang, Pengfeng
Li, Xiaolu
Zhang, Qichun
Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title_full Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title_fullStr Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title_full_unstemmed Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title_short Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network
title_sort ultrasonic based concrete defects identification via wavelet packet transform and ga-bp neural network
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444079/
https://www.ncbi.nlm.nih.gov/pubmed/34604513
http://dx.doi.org/10.7717/peerj-cs.635
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