Free vibration of axially or transversely graded beams using finite-element and artificial intelligence

The effect of grading direction on the natural frequencies of heterogeneous isotropic beams is investigated and the artificial neural network approach is conducted to estimate the free vibration characteristics. The two-dimensional beam is graded in axial or transverse direction according to the pow...

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Autor principal:	Sefa Yildirim
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Free vibration Functionally-graded materials Beams Artificial neural network Finite-element method Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/16621524f2944ceab32dc7150ffb18a9
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id	oai:doaj.org-article:16621524f2944ceab32dc7150ffb18a9
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spelling	oai:doaj.org-article:16621524f2944ceab32dc7150ffb18a92021-12-02T04:59:38ZFree vibration of axially or transversely graded beams using finite-element and artificial intelligence1110-016810.1016/j.aej.2021.07.004https://doaj.org/article/16621524f2944ceab32dc7150ffb18a92022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004725https://doaj.org/toc/1110-0168The effect of grading direction on the natural frequencies of heterogeneous isotropic beams is investigated and the artificial neural network approach is conducted to estimate the free vibration characteristics. The two-dimensional beam is graded in axial or transverse direction according to the power-law form. An artificial neural network model has been developed to estimate relationship between material properties and model, grading direction, slenderness ratio as an input layer and natural frequencies obtained by Finite-Element method as an output layer. The Levenberg–Marquardt back-propagation method is used as a training algorithm. The novelty of this study is that it deals with the estimation of free vibration characteristics of beams made of functionally-graded material using aforementioned input layer for the first time. The proposed artificial neural network model can predict the natural frequencies without the need for a solution of any differential equation or time-consuming experimental processes. The results show that artificial intelligence techniques can be efficiently adopted to free vibration problems of functionally graded beams. The influence of grading direction on the natural frequency is also demonstrated.Sefa YildirimElsevierarticleFree vibrationFunctionally-graded materialsBeamsArtificial neural networkFinite-element methodEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 2220-2229 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Free vibration Functionally-graded materials Beams Artificial neural network Finite-element method Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Free vibration Functionally-graded materials Beams Artificial neural network Finite-element method Engineering (General). Civil engineering (General) TA1-2040 Sefa Yildirim Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
description	The effect of grading direction on the natural frequencies of heterogeneous isotropic beams is investigated and the artificial neural network approach is conducted to estimate the free vibration characteristics. The two-dimensional beam is graded in axial or transverse direction according to the power-law form. An artificial neural network model has been developed to estimate relationship between material properties and model, grading direction, slenderness ratio as an input layer and natural frequencies obtained by Finite-Element method as an output layer. The Levenberg–Marquardt back-propagation method is used as a training algorithm. The novelty of this study is that it deals with the estimation of free vibration characteristics of beams made of functionally-graded material using aforementioned input layer for the first time. The proposed artificial neural network model can predict the natural frequencies without the need for a solution of any differential equation or time-consuming experimental processes. The results show that artificial intelligence techniques can be efficiently adopted to free vibration problems of functionally graded beams. The influence of grading direction on the natural frequency is also demonstrated.
format	article
author	Sefa Yildirim
author_facet	Sefa Yildirim
author_sort	Sefa Yildirim
title	Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
title_short	Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
title_full	Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
title_fullStr	Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
title_full_unstemmed	Free vibration of axially or transversely graded beams using finite-element and artificial intelligence
title_sort	free vibration of axially or transversely graded beams using finite-element and artificial intelligence
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/16621524f2944ceab32dc7150ffb18a9
work_keys_str_mv	AT sefayildirim freevibrationofaxiallyortransverselygradedbeamsusingfiniteelementandartificialintelligence
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Free vibration of axially or transversely graded beams using finite-element and artificial intelligence

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