Safety assessment for functionally graded structures with material nonlinearity

Feng, Y; Wu, D; Liu, L; Gao, W; Tin-Loi, F

Safety assessment for functionally graded structures with material nonlinearity

Feng, Y Wu, D

Liu, L Gao, W Tin-Loi, F

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Structural Safety, 2020, 86
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Feng, Y
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Liu, L
dc.contributor.author	Gao, W
dc.contributor.author	Tin-Loi, F
dc.date.accessioned	2021-03-18T00:37:25Z
dc.date.available	2021-03-18T00:37:25Z
dc.date.issued	2020-09-01
dc.identifier.citation	Structural Safety, 2020, 86
dc.identifier.issn	0167-4730
dc.identifier.issn	0167-4730
dc.identifier.uri	http://hdl.handle.net/10453/147307
dc.description.abstract	© 2020 Elsevier Ltd A machine learning aided reliability assessment framework is presented for functionally graded material (FGM) structures under plane strain/stress conditions with the consideration of elastoplasticity. The material nonlinearity of the FGM is modelled through the implementation of the Tamura-Tomota-Ozawa (TTO) model. For safety evaluation of FGM structures, the volume fraction of FGM has been modelled through spatially dependent uncertainty as random field for the concerned composite. In order to solve the complex stochastic elastoplastic problem, a further developed machine learning aided technique called the extended support vector regression (X-SVR) with a generalized Dirichlet feature mapping function has been introduced and then, the corresponding probabilistic features, including the statistical moments, probability density functions (PDFs), and cumulative distribution functions (CDFs), of the concerned structural responses can be effectively established for assessing the reliability of FGM structures. Moreover, the proposed approach is competent to deliver critical information regarding the uncertain system inputs which can be beneficial for subsequent safety assessment and structural designs for the FGM. Two test functions and two numerical examples have been adopted to visualise the accuracy, stability and capability of the proposed safety assessment framework for FGM structures.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Structural Safety
dc.relation.isbasedon	10.1016/j.strusafe.2020.101974
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Civil Engineering
dc.title	Safety assessment for functionally graded structures with material nonlinearity
dc.type	Journal Article
utslib.citation.volume	86
utslib.for	0905 Civil Engineering
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.consider-herdc	false
dc.date.updated	2021-03-18T00:37:22Z
pubs.publication-status	Published
pubs.volume	86

Abstract:

© 2020 Elsevier Ltd A machine learning aided reliability assessment framework is presented for functionally graded material (FGM) structures under plane strain/stress conditions with the consideration of elastoplasticity. The material nonlinearity of the FGM is modelled through the implementation of the Tamura-Tomota-Ozawa (TTO) model. For safety evaluation of FGM structures, the volume fraction of FGM has been modelled through spatially dependent uncertainty as random field for the concerned composite. In order to solve the complex stochastic elastoplastic problem, a further developed machine learning aided technique called the extended support vector regression (X-SVR) with a generalized Dirichlet feature mapping function has been introduced and then, the corresponding probabilistic features, including the statistical moments, probability density functions (PDFs), and cumulative distribution functions (CDFs), of the concerned structural responses can be effectively established for assessing the reliability of FGM structures. Moreover, the proposed approach is competent to deliver critical information regarding the uncertain system inputs which can be beneficial for subsequent safety assessment and structural designs for the FGM. Two test functions and two numerical examples have been adopted to visualise the accuracy, stability and capability of the proposed safety assessment framework for FGM structures.

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http://hdl.handle.net/10453/147307