Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning - Kennedy C. Onyelowe; Viroon Kamchoom‬; Shadi Hanandeh; S. Anandha Kumar; Rolando Fabián Zabala Vizuete; Rodney Orlando Santillán Murillo; Susana Monserrat Zurita Polo; Rolando Marcel Torres Castillo; Ahmed M. Ebid; Paul O. Awoyera; Krishna Prakash Arunachalam (Scientific Reports, 2025)
of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model's superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.
Keywords: Innovative concrete reinforcement materials; Infrastructure Maintenance and Monitoring; Concrete Corrosion and Durability