Traditional concrete faces challenges such as low energy absorption, brittleness and major environmental impacts, attributed to its dependence on natural resources. Integrating natural fibers with recycled coarse aggregates into concrete presents a promising method of enhancing concrete’s sustainability and mechanical performance. Still, accurately predicting the mechanical properties of these innovative concrete mixes remains complex. This research investigates the predictive abilities of two machine learning (ML) models, classification and regression trees (CART) and stepwise polynomial regression (SPR), for estimating the compressive and splitting tensile strengths of NF-reinforced concrete containing recycled coarse aggregates. The CART model showed greater predictive accuracy, reaching R2 = 0.91 for compressive strength and R2 = 0.89 for splitting tensile strength. Additionally, the model demonstrated consistently lower error metrics (RMSE, MAD, MAPE, MSE) than comparable approaches. For compressive strength, CART achieved R2 = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R2 = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These results emphasize the significant potential of ML, particularly CART, to optimize the design of sustainable concrete mixtures, enabling more accurate and effective strength predictions and finally contributing to more resilient and sustainable infrastructure.
Zghair et al. (Tue,) studied this question.