Modeling of resistence to the compression of concrete mediante redes neuronal artificiales

• Autores: Acuña Pinaud, Leoncio Luis, Torre Carrillo, Ana Victoria, Moromi Nakata, Isabel, Espinoza Haro, Pedro Celino, García Fernández, Francisco
• Formato: artículo
• Fecha de Publicación: 2013
• Institución: Universidad Nacional de Ingeniería
• Repositorio: Revistas - Universidad Nacional de Ingeniería
• Lenguaje: español
• OAI Identifier: oai:oai:revistas.uni.edu.pe:article/71
• Enlace del recurso: https://revistas.uni.edu.pe/index.php/tecnia/article/view/71
• Nivel de acceso: acceso abierto
• Materia: concreto; resistencia a la compresión; redes neuronales artificiales; concrete; compression strength; artificial neural networks

The use of concrete as a structural element increases year by year. However, this product needs very stringent control of its mechanical properties in order to be uses as structural element. This type of control requires to have very large testing equipment with a load capacity of up to 3.000KN. Production control would benefit greatly from the use of a highly reliable alternative method that would enable the mechanical properties to be found through more easily obtained physical and mechanical properties. The high capacity of artificial neural networks (ANN) to model a broad range of industrial processes makes them a very useful instrument in the concrete industry. In this study, one neural network was developed to obtain the properties of compressive strength. This property was then modeled though the composition of concrete and manufacturing parameters. The network designed, a multilayer perceptron, allowed the compression strength to be obtained with a regression coefficient of 0,97. This demonstrates the effectiveness of ANN for obtaining the mechanical properties of compression strength of concrete.