Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks

In the present work, compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nano alumina, and nano silica has been predicted by artificial neural networks. The neural network models were constructed by 12 input parameters including the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO2 nanoparticles content, Al2O3 nanoparticles content, oven curing temperature, oven curing time, and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated, and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.