Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180 days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer. According to these input parameters, in the multilayer feed forward neural networks models are predicted the compressive strength values of concretes containing metakaolin and silica fume. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 1, 3, 7, 28, 56, 90 and 180 days compressive strength values of concretes containing metakaolin and silica fume.