Advanced regression approaches for predicting the mechanical behaviour of limestone-enhanced concrete

Abstract The use of limestone powder as a partial replacement for cement in concrete has gained significant attention due to its potential to enhance compressive strength and promote sustainability. This study investigates the mechanical behavior of limestone-modified concrete, focusing on strength development over various curing periods. Advanced machine learning techniques—Gradient Boosting (GB) and K-Nearest Neighbors (KNN)—are employed to optimize mix proportions and accurately predict compressive strength. The GB model achieved a high predictive accuracy with an R² value of 0.98, effectively capturing the complex nonlinear relationships between cement content, limestone dosage, and curing time. Meanwhile, the KNN model demonstrated strong performance with an R² of 0.965 by leveraging pattern similarities in experimental data. Both regression models align closely with experimental results, validating limestone’s positive impact on long-term concrete performance. This data-driven approach enhances mix design decisions, ensuring structural reliability and sustainability while reducing cement usage and its associated environmental footprint.