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

Currently, 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...

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Bibliographic Details
Published in:Acta scientiarum. Technology 2016-01, Vol.38 (1), p.65-70
Main Authors: Moretti, José Fernando, Minussi, Carlos Roberto, Akasaki, Jorge Luis, Fioriti, Cesar Fabiano, Melges, José Luis Pinheiro, Tashima, Mauro Mitsuuchi
Format: Article
Language:English
Subjects:
artificial intelligence
compressive strength
concrete
modulus of elasticity
neural networks
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Summary:Currently, 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.
ISSN:1806-2563
1807-8664
1806-2563
DOI:10.4025/actascitechnol.v38i1.27194