Using Artificial Neural Networks for the Prediction of the Compressive Strength of Geopolymer Fly Ash

Abstract

Geopolymers are promising cement replacement materials as their use results in a considerable reduction of CO2 emissions. Geopolymer Fly ash (GF) is a widely used geopolymer due to its low cost and waste management achievement. The compressive strength of GF depends on variables such as curing time, curing temperature, NaOH molarity, the ratio of sodium silicate to sodium hydroxide, the ratio of fly ash to alkaline solution, etc. Artificial Neural Networks are employed to predict the strength of GF due to their accurate prediction capability as well as saving time and cost of experimental work. The obtained Root Mean Square Error (RMSE) and correction coefficient (R2) values were 4.47 and 0.972 respectively. The results illustrate the ability of the ANN model to be used as an efficient tool in predicting the compressive strength and determining the optimal values of GF parameters. The maximum strength of GF was observed for 2 hours curing time at 100°C, molarity of 10, fly ash to alkaline solution ratio of 3, and sodium silicate to sodium hydroxide ratio of 1.