Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens ex...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Kian K. Sepahvand
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	damage detection supervised learning nonlinear support vector machine modal analysis fiber-reinforced composites Technology T Science Q
Acceso en línea:	https://doaj.org/article/3d403de8715643ef81d011b5ed9ae26f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3d403de8715643ef81d011b5ed9ae26f
record_format	dspace
spelling	oai:doaj.org-article:3d403de8715643ef81d011b5ed9ae26f2021-11-25T18:03:18ZStructural Damage Detection Using Supervised Nonlinear Support Vector Machine10.3390/jcs51103032504-477Xhttps://doaj.org/article/3d403de8715643ef81d011b5ed9ae26f2021-11-01T00:00:00Zhttps://www.mdpi.com/2504-477X/5/11/303https://doaj.org/toc/2504-477XDamage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.Kian K. SepahvandMDPI AGarticledamage detectionsupervised learningnonlinear support vector machinemodal analysisfiber-reinforced compositesTechnologyTScienceQENJournal of Composites Science, Vol 5, Iss 303, p 303 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	damage detection supervised learning nonlinear support vector machine modal analysis fiber-reinforced composites Technology T Science Q
spellingShingle	damage detection supervised learning nonlinear support vector machine modal analysis fiber-reinforced composites Technology T Science Q Kian K. Sepahvand Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
description	Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.
format	article
author	Kian K. Sepahvand
author_facet	Kian K. Sepahvand
author_sort	Kian K. Sepahvand
title	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
title_short	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
title_full	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
title_fullStr	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
title_full_unstemmed	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
title_sort	structural damage detection using supervised nonlinear support vector machine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3d403de8715643ef81d011b5ed9ae26f
work_keys_str_mv	AT kianksepahvand structuraldamagedetectionusingsupervisednonlinearsupportvectormachine
_version_	1718411673711149056

Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

Ejemplares similares