Algorithm Development for the Non-Destructive Testing of Structural Damage
Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support V...
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doaj-b8c94576e3a14e8b809376bc838e7b1b2020-11-25T00:45:57ZengMDPI AGApplied Sciences2076-34172019-07-01914281010.3390/app9142810app9142810Algorithm Development for the Non-Destructive Testing of Structural DamageAzadeh Noori Hoshyar0Maria Rashidi1Ranjith Liyanapathirana2Bijan Samali3Centre for Infrastructure Engineering (CIE), Western Sydney University, Kingswood, NSW 2747, AustraliaCentre for Infrastructure Engineering (CIE), Western Sydney University, Kingswood, NSW 2747, AustraliaSchool of Computing, Engineering and Mathematics, Western Sydney University, Kingswood, NSW 2747, AustraliaCentre for Infrastructure Engineering (CIE), Western Sydney University, Kingswood, NSW 2747, AustraliaMonitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.https://www.mdpi.com/2076-3417/9/14/2810non-destructive testingmachine learningartificial intelligenceimage processingmicrowave or millimeter wave imagingstructural damage |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Azadeh Noori Hoshyar Maria Rashidi Ranjith Liyanapathirana Bijan Samali |
spellingShingle |
Azadeh Noori Hoshyar Maria Rashidi Ranjith Liyanapathirana Bijan Samali Algorithm Development for the Non-Destructive Testing of Structural Damage Applied Sciences non-destructive testing machine learning artificial intelligence image processing microwave or millimeter wave imaging structural damage |
author_facet |
Azadeh Noori Hoshyar Maria Rashidi Ranjith Liyanapathirana Bijan Samali |
author_sort |
Azadeh Noori Hoshyar |
title |
Algorithm Development for the Non-Destructive Testing of Structural Damage |
title_short |
Algorithm Development for the Non-Destructive Testing of Structural Damage |
title_full |
Algorithm Development for the Non-Destructive Testing of Structural Damage |
title_fullStr |
Algorithm Development for the Non-Destructive Testing of Structural Damage |
title_full_unstemmed |
Algorithm Development for the Non-Destructive Testing of Structural Damage |
title_sort |
algorithm development for the non-destructive testing of structural damage |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-07-01 |
description |
Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering. |
topic |
non-destructive testing machine learning artificial intelligence image processing microwave or millimeter wave imaging structural damage |
url |
https://www.mdpi.com/2076-3417/9/14/2810 |
work_keys_str_mv |
AT azadehnoorihoshyar algorithmdevelopmentforthenondestructivetestingofstructuraldamage AT mariarashidi algorithmdevelopmentforthenondestructivetestingofstructuraldamage AT ranjithliyanapathirana algorithmdevelopmentforthenondestructivetestingofstructuraldamage AT bijansamali algorithmdevelopmentforthenondestructivetestingofstructuraldamage |
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