dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:32:27Z
dc.date.available2018-11-26T16:32:27Z
dc.date.created2018-11-26T16:32:27Z
dc.date.issued2016-01-01
dc.identifierActa Scientiarum-technology. Maringa: Univ Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao, v. 38, n. 1, p. 65-70, 2016.
dc.identifier1806-2563
dc.identifierhttp://hdl.handle.net/11449/161366
dc.identifier10.4025/actascitechnol.v38i1.27194
dc.identifierWOS:000373403900009
dc.identifier2644132857349338
dc.identifier8316729380117323
dc.identifier0000-0001-5461-4495
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.
dc.languageeng
dc.publisherUniv Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao
dc.relationActa Scientiarum-technology
dc.rightsAcesso 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 networks
dc.typeArtículos de revistas


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