Modeling of Compressive Strength of Metakaolin Based Geopolymers by The Use of Artificial Neural Network RESEARCH NOTE)


1 Ceramic, Merc

2 Dept. of Energy, Materials and Energy Research Centre


In order to study the effect of R2O/Al2O3 (where R=Na or K), SiO2/Al2O3, Na2O/K2O and H2O/R2O molar ratios on the compressive strength (CS) of Metakaolin base geopolymers, more than forty data were gathered from literature. To increase the number of data, some experiments were also designed. The resulted data were utilized to train and test the three layer artificial neural network (ANN). Bayesian regularization method and Early Stopping methods with back propagation algorithm were applied as training algorithm. Good validation for CS was resulted due to the inhibition of overfitting problems with the applied training algorithm. The results showed that optimized condition of SiO2/Al2O3, R2O/Al2O3, Na2O/K2O and H2O/R2O ratios to achieve high CS should be 3.6-3.8, 1.0-1.2, 0.6-1 and 10-11, respectively. These results are in agreement with probable mechanism of geopolymerization.