The Prediction of Abrasion Resistance of Mortars Modified with Granite Powder and Fly Ash Using Artificial Neural Networks

Abstract

This paper predicts the abrasion resistance of a cementitious composite containing granite powder and fly ash replacing up to 30% of the cement weight. For this purpose, intelligent artificial neural network (ANN) models were used and compared. A database was built based on mix composition, curing time, and curing method. The model developed to predict the abrasion resistance of the cementitious composites containing granite powder and fly ash was shown to be accurate. It was proved by the very high values of the accuracy parameters that were above 0.93 in the case of the coefficient of the determination R2 and very low values of the errors, which were about 10% in the case of mean average percentage error. This method can be used especially for designing cement mortars with granite powder and fly ash additives replacing cement in a range from 0 to 30% of its weight. These mortars can be used for floors in industrial buildings.

Keywords

• abrasion resistance
• neural networks
• cementitious composites
• green concrete