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Toward variability characterization and statistic models' constitution for the prediction of exponentially graded plates' static response

2018-10-13Journal article Open access
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dc.contributor.authorRosa, Rafael da Silva Batista
dc.contributor.authorLoja, M.A.R.
dc.contributor.authorCarvalho, Alda
dc.date.accessioned2021-07-08T12:48:00Z
dc.date.available2021-07-08T12:48:00Z
dc.date.issued2018-10-13
dc.description.abstractFunctionally graded composite materials may constitute an advantageous alternative to engineering applications, allying a customized tailoring capability to its inherent continuous properties transition. However, these attractive characteristics must account for the uncertainty that affects these materials and their structures' physical quantities. Therefore, it is important to analyze how this uncertainty will modify the foreseen deterministic response of a structure that is built with these materials, identifying which of the parameters are responsible for a greater impact. To pursue this main objective, the material and geometrical parameters that characterize a plate made of an exponentially graded material are generated according to a random multivariate normal distribution, using the Latin hypercube sampling technique. Then, a set of finite element analyses based on the first-order shear deformation theory are performed to characterize the linear static responses of these plates, which are further correlated to the input parameters. This work also considers the constitution of statistic models in order to allow their use as alternative prediction models. The results show that for the plates that were analyzed, the uncertainty associated with the elasticity modulus of both phases is mainly responsible for the maximum transverse deflection variability. The effectiveness of the statistical models that are built are also shown.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationROSA, Rafael da Silva Batista; LOJA, Maria Amélia Ramos; CARVALHO, Alda Cristina Jesus Valentim Nunes de – Toward variability characterization and statistic models' constitution for the prediction of exponentially graded plates' static response. Journal of Composites Science. ISSN 2504-477X. Vol. 2, N.º 4 (2018), pp. 1-19pt_PT
dc.identifier.doi10.3390/jcs2040059pt_PT
dc.identifier.issn2504-477X
dc.identifier.urihttp://hdl.handle.net/10400.21/13529
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.subjectFunctionally graded materialspt_PT
dc.subjectExponential lawpt_PT
dc.subjectStatic analysispt_PT
dc.subjectParametric uncertainty characterizationpt_PT
dc.subjectResponse variabilitypt_PT
dc.subjectMultiple linear regression modelspt_PT
dc.titleToward variability characterization and statistic models' constitution for the prediction of exponentially graded plates' static responsept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMulti%2F00491%2F2013/PT
oaire.citation.endPage19pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJournal of Composites Sciencept_PT
oaire.citation.volume2pt_PT
oaire.fundingStream5876
oaire.fundingStream5876
person.familyNameLoja
person.familyNameCarvalho
person.givenNameAmélia
person.givenNameAlda
person.identifier.ciencia-idA016-59D1-2D00
person.identifier.ciencia-idFD18-CBDD-B7C7
person.identifier.orcid0000-0002-4452-5840
person.identifier.orcid0000-0003-2642-4947
person.identifier.ridI-7513-2015
person.identifier.scopus-author-id15741348500
person.identifier.scopus-author-id25027091800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication214fa21c-bca0-4769-99b0-5f1239d2ea41
relation.isAuthorOfPublication.latestForDiscovery9f43a546-1632-49c5-bd44-94aac2d7b648
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