Data-Driven Insights into Concrete Flow and Strength: Advancing Smart Material Design Using Machine Learning Strategies

Concrete plays a pivotal role in modern methods of construction due to its enhanced strength, durability, and adaptability to advanced building technologies. Compressive strength (CS) and workability (flow) are two important performance measures of concrete, and this paper investigates how two evolutionary machine learning methods, gene expression programming (GEP) and multi-expression programming (MEP), might be used for this purpose. An experimental dataset with ten crucial input parameters was employed to develop and assess the models. While the GEP model demonstrated strong predictive capability (R2 = 0.910 for CS and 0.882 for flow), the MEP model exhibited superior precision, attaining R2 values of 0.951 for CS and 0.923 for flow. Model evaluation through statistical indices and correlation metrics further supported the robustness of the MEP approach. To enhance interpretability and material design insight, Shapley additive explanation (SHAP) analysis was conducted, identifying water-to-binder ratio and slag content as critical predictors for CS, and water and slag as dominant factors for flow. These results underscore the potential of MEP as a reliable decision-support tool in the sustainable design and optimization of concrete for advanced construction applications.