Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools

Abstract Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy ( R 2 = 0.975). SHapley Additive exPlanations analysis further identified cement and sand content as the most influential variables affecting CS. The findings were integrated into a graphical user interface for practical, real-time strength estimation. This tool can support engineers and material designers in developing sustainable RM mixes with improved performance and reduced reliance on extensive laboratory testing.