Moretti, Jose Fernando [UNESP]Minussi, Carlos Roberto [UNESP]Akasaki, Jorge Luis [UNESP]Fioriti, Cesar Fabiano [UNESP]Pinheiro Melges, Jose Luiz [UNESP]Tashima, Mauro Mitsuuchi [UNESP]2018-11-262018-11-262016-01-01Acta Scientiarum-technology. Maringa: Univ Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao, v. 38, n. 1, p. 65-70, 2016.1806-2563http://hdl.handle.net/11449/161366Currently, 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.65-70engmodulus of elasticitycompressive strengthconcreteneural networksartificial intelligenceArtigo10.4025/actascitechnol.v38i1.27194WOS:000373403900009Acesso restrito264413285734933883167293801173230000-0001-5461-4495