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

• Autor(a) principal: Moretti, José Fernando
• Data de Publicação: 2016
• Outros Autores: Minussi, Carlos Roberto, Akasaki, Jorge Luis, Fioriti, Cesar Fabiano, Melges, José Luis Pinheiro, Tashima, Mauro Mitsuuchi
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta scientiarum. Technology (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/27194
• topic: modulus of elasticity; compressive strength; concrete; neural networks; artificial intelligence; Engenharia Civil

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.