Machine learning aided stochastic structural free vibration analysis for functionally graded bar-type structures

Wang, Q; Wu, D; Tin-Loi, F; Gao, W

Machine learning aided stochastic structural free vibration analysis for functionally graded bar-type structures

Wang, Q Wu, D

Tin-Loi, F Gao, W

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Publication Type:: Journal Article
Citation:: Thin-Walled Structures, 2019, 144
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Wang, Q	en_US
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024	en_US
dc.contributor.author	Tin-Loi, F	en_US
dc.contributor.author	Gao, W	en_US
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	Thin-Walled Structures, 2019, 144	en_US
dc.identifier.issn	0263-8231	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138885
dc.description.abstract	© 2019 Elsevier Ltd This paper presents a machine learning aided stochastic free vibration analysis for functionally graded (FG) bar-type structures through finite element method (FEM). The considered system uncertainties including the constituent material properties, the dimensions of structural members, and the degree of the gradation of the FGM are incorporated. A novel kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is presented to estimate the governing relationship between the uncertain system parameters and the structural natural frequencies. Subsequently, by applying the Monte-Carlo Simulation (MCS) through the established regression model, various types of statistical characteristics (i.e., mean, standard deviation, probability density function or PDF, and cumulative distribution function or CDF) of structural natural frequencies can be effectively established. Four numerical examples including test functions and practically stimulated engineering structures are thoroughly investigated herein to demonstrate the accuracy, applicability, and computational efficiency of the proposed approach.	en_US
dc.relation.ispartof	Thin-Walled Structures	en_US
dc.relation.isbasedon	10.1016/j.tws.2019.106315	en_US
dc.subject.classification	Civil Engineering	en_US
dc.title	Machine learning aided stochastic structural free vibration analysis for functionally graded bar-type structures	en_US
dc.type	Journal Article
utslib.citation.volume	144	en_US
utslib.for	0901 Aerospace Engineering	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	144	en_US

Abstract:

© 2019 Elsevier Ltd This paper presents a machine learning aided stochastic free vibration analysis for functionally graded (FG) bar-type structures through finite element method (FEM). The considered system uncertainties including the constituent material properties, the dimensions of structural members, and the degree of the gradation of the FGM are incorporated. A novel kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is presented to estimate the governing relationship between the uncertain system parameters and the structural natural frequencies. Subsequently, by applying the Monte-Carlo Simulation (MCS) through the established regression model, various types of statistical characteristics (i.e., mean, standard deviation, probability density function or PDF, and cumulative distribution function or CDF) of structural natural frequencies can be effectively established. Four numerical examples including test functions and practically stimulated engineering structures are thoroughly investigated herein to demonstrate the accuracy, applicability, and computational efficiency of the proposed approach.

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http://hdl.handle.net/10453/138885