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

The increasing demand for sustainable and high-performance construction materials has created a need for reliable predictive modeling tools for geopolymer concrete systems. Conventional experimental methods for determining compressive strength are expensive, time-consuming, and often insufficient for capturing the complex nonlinear interactions among fiber reinforcement, precursor materials, and alkaline activator chemistry. This study introduces a novel explainable ensemble machine learning framework for predicting the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag (GGBS). The novelty of this work lies in the integration of multi-model boosting algorithms with interpretability techniques, including SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) analysis, to simultaneously achieve high predictive accuracy and physical insight into geopolymerization behavior. A comprehensive database of 370 geopolymer mix designs was used to develop a robust predictive framework for compressive strength estimation. Seven machine learning models, including XGBoost, LightGBM, Gradient Boosting, AdaBoost, CatBoost, Decision Tree, and K-Nearest Neighbors, were evaluated using multiple performance metrices. The LightGBM model achieved the best performance with an R