Journal of Architectural Environment & Structural Engineering Research

Abstract: Machine learning is widely used to predict concrete compressive strength because it captures nonlinear interactions among binders, aggregates, water content, and chemical admixtures. Tree-based ensemble models such as Random Forest and XGBoost often achieve high numerical accuracy; however, their discrete decision splitting mechanisms inherently produce stepwise response trends that may not reflect the smooth and... More