Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of...

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Autores principales:	Pham-The Hien, Ic-Pyo Hong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	non-destructive testing thickness classification support vector machine deep neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/4e95503b993248928f28f22825081e60
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spelling	oai:doaj.org-article:4e95503b993248928f28f22825081e602021-11-25T16:35:07ZMaterial Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning10.3390/app1122106822076-3417https://doaj.org/article/4e95503b993248928f28f22825081e602021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10682https://doaj.org/toc/2076-3417Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.Pham-The HienIc-Pyo HongMDPI AGarticlenon-destructive testingthickness classificationsupport vector machinedeep neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10682, p 10682 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	non-destructive testing thickness classification support vector machine deep neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	non-destructive testing thickness classification support vector machine deep neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Pham-The Hien Ic-Pyo Hong Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
description	Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.
format	article
author	Pham-The Hien Ic-Pyo Hong
author_facet	Pham-The Hien Ic-Pyo Hong
author_sort	Pham-The Hien
title	Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
title_short	Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
title_full	Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
title_fullStr	Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
title_full_unstemmed	Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning
title_sort	material thickness classification using scattering parameters, dielectric constants, and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4e95503b993248928f28f22825081e60
work_keys_str_mv	AT phamthehien materialthicknessclassificationusingscatteringparametersdielectricconstantsandmachinelearning AT icpyohong materialthicknessclassificationusingscatteringparametersdielectricconstantsandmachinelearning
_version_	1718413073252876288

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

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