Performance Evaluation and Predictive Modelling of Fly Ash Concrete: An Experimental and Machine Learning Approach

Abstract

The increasing demand for sustainable construction materials has led to the selection of supplementary cementitious materials (SCMs) to minimize the environmental burden of Portland cement manufacturing. This paper experimentally investigates the effect of varied volume of fly ash replacement on some fresh, mechanical and physical properties of M25-grade concrete and also develops machine learning-based models to predict performance. In this study, examining the effect of different fly ash replacement levels (0%-50%) on hardened concrete properties, six distinct concrete mixes were designed with constant water-to-cementitious material ratio (0.48) and 144 cylindrical specimens were casted from all mixtures to determine compressive strength, splitting tensile strength, workability and unit weight at age of 7, 14 and 28 days respectively. Although increasing fly ash content improved workability, high replacement levels reduced compressive and splitting tensile strength because of cement dilution and the relatively slow early-age pozzolanic reaction of Class F fly ash. Containing approximately 20–30% replacement levels, the best all-round performance with moderate compressive and tensile strength alongside cement mass reduction. It also employs the Random Forest and Extreme Gradient Boosting (XGBoost) regression models to predict compressive strength of a mixture from its variables. The prediction performances of the XGBoost model were more accurately represented (R² = 0.951; RMSE=1.128 MPa; MAE=0.966 MPa). The findings demonstrate the potential of ensemble-learning models to support preliminary fly ash concrete mix evaluation, although validation using larger independent datasets is necessary.