Predicting the ingredients of self compacting concrete using artificial neural network

Abstract

Self compacting concrete (SCC) is a highly flowable type of concrete that spreads into form without the need of mechanical vibration. This paper presents a comparative study between two methodologies which have been applied on two different data sets of SCC mixtures, which were gathered from the literature, using artificial neural network (ANN). The two methodologies aim to get the best prediction accuracy for the SCC ingredients using the 28-day compressive strength and slump flow diameters as inputs of the ANN. In the first methodology, the ANN model is constructed as a multi input – multi output neural network with the six ingredients as outputs. In the second methodology, the ANN model is constructed as a multi input – single output neural network where the six ingredient outputs are predicted separately from six different neural networks of multi input – single output type. Also, the influence of the mixes homogeneity on the prediction accuracy is investigated through the second data set. The results demonstrate the superiority of the second methodology in terms of accuracy of the predicted outputs. Moreover, the uniformity of the training data assures the accuracy of the predicted ingredients. Keywords: Self compacting concrete (SCC), Artificial neural network (ANN), Concrete mix design, Concrete compressive strength, Slump flow