Artificial neural networks for prediction compressive strength of geopolymers with seeded waste ashes

In the present work, compressive strength of inorganic polymers (geopolymers) made from seeded fly ash and rice husk bark ash has been predicted by artificial neural networks. Different specimens were subjected to compressive strength tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days, and the other sets were oven-cured for 36 h at the range of 40-90 A degrees C and then room cured until 7 and 28 days. A model based on artificial neural networks for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk bark ash in the ashes mixture, the percentage of coarse rice husk bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters, in the neural networks model, the compressive strength of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the compressive strength of the geopolymer specimens.