Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach

of about 0.79 and RMSE of 9.74 MPa, indicating reliable prediction capability for heterogeneous fiber-reinforced geopolymer systems. Parametric analysis showed that the water-solid ratio, activator Ca/Na ratio, and additive-binder ratio were the most influential parameters, contributing the majority of strength variation. The proposed framework offers a data-driven approach to reduce experimental effort while supporting sustainable geopolymer mix optimization.