Materiales de Construcción, Vol 53, No 270 (2003)

Application of an artificial neural network to ready-mixed concretes mix design


https://doi.org/10.3989/mc.2003.v53.i270.270

J. Setién
División de Ciencia e Ingeniería de los Materiales. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos. Universidad de Cantabria, Spain

A. Carrascal
División de Ciencia e Ingeniería de los Materiales. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos. Universidad de Cantabria, Spain

J. F. Figueroa
División de Ciencia e Ingeniería de los Materiales. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos. Universidad de Cantabria, Spain

J. A. Polanco
División de Ciencia e Ingeniería de los Materiales. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos. Universidad de Cantabria, Spain

Abstract


This paper presents the practical application of cm artificial neural network (ANN) to the problem of concrete mix in a factory. After a brief introduction to the complex problem of concrete mixes design and a quick review of the fundamental basis of neurocomputation, an optimal neural network model has been developed to cope with such a problem. For training the net, several control mixes have been fabricated recording in all cases both the characteristic 28 days compressive strength and the workability measured in terms of the slump of the Abrams' cone. After the training process of the net, the power of its predictive ability is checked by comparison of the results obtained with those corresponding to four reference mixes; in this way, it is shown that the considered approach can be used in multicriterial search for optimal concrete mixes.

Keywords


neurocomputation; neural networks; data processing; concrete mix; workability

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