Prediction of Sustainable Concrete Strength Incorporating Fly Ash and Recycled Aggregates Using Artificial Neural Networks

Abstract

Artificial Neural Networks (ANNs) have been used to predict the compressive strength of sustainable concrete as an alternative to large laboratory experiments․ Sustainable concrete mixes were designed by replacing natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) at varying percentages ranging from 0% to 100%․ Ordinary Portland cement was replaced with fly ash at the rate of 0%‚ 5%‚ 10%‚ 15%‚ 20%‚ 25%‚ 30%‚ 35% and 40% to obtain samples․ Twenty-five concrete mixes were tested for compressive strength at 3‚ 14 and 28 days․ In contrast‚ for moderate fly ash replacement levels (10-20 percent)‚ strength increased at later ages due to pozzolanic reactions․ At high fly ash replacement levels‚ strength development was delayed during the curing period․ Increasing RCA content decreased compressive strength because of the presence of unhydrated mortar‚ greater porosity‚ lower bonding between the aggregate and paste matrix‚ and greater water absorption compared to normal aggregate․ An ANN model has been developed to predict the concrete strength‚ using five parameters as input (NCA content‚ RCA content‚ cement content‚ fly ash content and curing age) and the concrete compressive strength as an output parameter․ Based on the seventy-five experimental results‚ the developed model is highly accurate‚ with R²-value of 0․9935 and RMSE (Root Mean Square Error) of 0․6465 MPa․ These results show that the ANN can successfully predict the sustainable concrete compressive strength․ The proposed model is very useful for mix proportion optimization with considerable reductions in the time‚ cost‚ and effort of experimental studies․