Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks

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dc.contributor.authorMoretti, Jose Fernando [UNESP]
dc.contributor.authorMinussi, Carlos Roberto [UNESP]
dc.contributor.authorAkasaki, Jorge Luis [UNESP]
dc.contributor.authorFioriti, Cesar Fabiano [UNESP]
dc.contributor.authorPinheiro Melges, Jose Luiz [UNESP]
dc.contributor.authorTashima, Mauro Mitsuuchi [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:32:27Z
dc.date.available2018-11-26T16:32:27Z
dc.date.issued2016-01-01
dc.description.abstractCurrently, artificial neural networks are being widely used in various fields of science and engineering. Neural networks have the ability to learn through experience and existing examples, and then generate solutions and answers to new problems, involving even the effects of non-linearity in their variables. The aim of this study is to use a feed-forward neural network with back-propagation technique, to predict the values of compressive strength and modulus of elasticity, at 28 days, of different concrete mixtures prepared and tested in the laboratory. It demonstrates the ability of the neural networks to quantify the strength and the elastic modulus of concrete specimens prepared using different mix proportions.en
dc.description.affiliationUniv Estadual Paulista, Fac Engn Ilha Solteira, Ave Brasil 56, BR-15385000 Sao Paulo, Brazil
dc.description.affiliationUniv Estadual Paulista, Fac Ciencias & Tecnol Presidente Prudent, BR-15385000 Sao Paulo, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Fac Engn Ilha Solteira, Ave Brasil 56, BR-15385000 Sao Paulo, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Fac Ciencias & Tecnol Presidente Prudent, BR-15385000 Sao Paulo, Brazil
dc.format.extent65-70
dc.identifierhttp://dx.doi.org/10.4025/actascitechnol.v38i1.27194
dc.identifier.citationActa Scientiarum-technology. Maringa: Univ Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao, v. 38, n. 1, p. 65-70, 2016.
dc.identifier.doi10.4025/actascitechnol.v38i1.27194
dc.identifier.issn1806-2563
dc.identifier.lattes2644132857349338
dc.identifier.lattes8316729380117323
dc.identifier.orcid0000-0001-5461-4495
dc.identifier.urihttp://hdl.handle.net/11449/161366
dc.identifier.wosWOS:000373403900009
dc.language.isoeng
dc.publisherUniv Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao
dc.relation.ispartofActa Scientiarum-technology
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectmodulus of elasticity
dc.subjectcompressive strength
dc.subjectconcrete
dc.subjectneural networks
dc.subjectartificial intelligence
dc.titlePrediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networksen
dc.typeArtigo
dcterms.rightsHolderUniv Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao
unesp.author.lattes2644132857349338
unesp.author.lattes7166279400544764[2]
unesp.author.lattes8316729380117323[4]
unesp.author.orcid0000-0001-6428-4506[2]
unesp.author.orcid0000-0001-5461-4495[4]

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Artigos - Fitotecnia, Tecnologia de Alimentos e Sócio Economia - FEIS