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Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks
Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an autom...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177575/ https://www.ncbi.nlm.nih.gov/pubmed/32230967 http://dx.doi.org/10.3390/ma13071557 |
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author | Słoński, Marek Schabowicz, Krzysztof Krawczyk, Ewa |
author_facet | Słoński, Marek Schabowicz, Krzysztof Krawczyk, Ewa |
author_sort | Słoński, Marek |
collection | PubMed |
description | Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements. |
format | Online Article Text |
id | pubmed-7177575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71775752020-04-28 Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks Słoński, Marek Schabowicz, Krzysztof Krawczyk, Ewa Materials (Basel) Article Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements. MDPI 2020-03-27 /pmc/articles/PMC7177575/ /pubmed/32230967 http://dx.doi.org/10.3390/ma13071557 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Słoński, Marek Schabowicz, Krzysztof Krawczyk, Ewa Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title | Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title_full | Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title_fullStr | Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title_full_unstemmed | Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title_short | Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks |
title_sort | detection of flaws in concrete using ultrasonic tomography and convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177575/ https://www.ncbi.nlm.nih.gov/pubmed/32230967 http://dx.doi.org/10.3390/ma13071557 |
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