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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Estadual de Maringá (UEM) |
| Repositorio: | Acta scientiarum. Technology (Online) |
| Idioma: | inglés |
| OAI Identifier: | oai:periodicos.uem.br/ojs:article/27194 |
| Acceso en línea: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/27194 |
| Access Level: | acceso abierto |
| Palabra clave: | modulus of elasticity compressive strength concrete neural networks artificial intelligence Engenharia Civil |
| Sumario: | 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. |
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