Prediction of compressive strength of geopolymer composites using an artificial neural network

Geopolymers are highly complex materials which involve many variables and make for which modelling the properties is very difficult. There is no systematic approach in mix design for geopolymers. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength, an ANN (artificial neural network) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of adapting ANN and artificial intelligence system for predicting the compressive strength have been studied. Consequently, a multilayer ANN by using back propagation architecture can be developed for geopolymer compressive strength prediction. In this study, the coefficient of determination (R2) has been used for investigating the proposed model accuracy. As a result, proposed ANN model can predict the compressive strength of geopolymer with R2 = 0.958.