Prediction of compressive strength for HPC mixes containing different blends using ANN

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the compressive strength of High Performance Concrete (HPC) containing binary and quaternary blends. The investigations were done on 23 HPC mixes, and specimens were cast and tested after 7, 28 and 56 days curing. The obtained experimental datas of 7, 28 and 56 days are trained using ANN which consists of eight input parameters like cement, metakaolin, blast furnace slag and fly ash, fine aggregate, coarse aggregate, superplasticizer and water binder ratio. The corresponding output parameters are 7, 28 and 56 days compressive strengths. The predicted values obtained using ANN show a good correlation between the Experimental data. The performance of the 8-9-3-3 architecture was better than other architectures. It concluded that ANN tool is convenient and time saving for predicting compressive strength at different ages.