AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar

Waste glass powder (WGP) faces challenges in recycling and regeneration, which is used as a partial substitute for concrete components, with its macro-mechanical properties being investigated. This study aims to elucidate the extent to which various variables affect the unconfined compressive strength (UCS) and alkali–silica reactivity (ASR) of waste glass incorporated concrete. Initially, in the experimental procedure, 291 data points for the UCS and 485 data points for the ASR were obtained from laboratory tests. Subsequently, four machine learning models were introduced, including Gradient Boosting Regressor, Random Forest, Hist Gradient Boosting Regressor, and XGBoost. Their performance was analyzed and compared based on evaluation indexes. The findings reveal that Gradient Boosting Regressor accurately models the actual data distribution, generating reliable synthetic data. Partial dependence plots (PDPs) were used to understand the impact of individual features on glass concrete UCS and ASR, and Shapley additive explanation (SHAP) values were used to analyze the predictive output influenced by the contribution of each feature. The feature interaction effects analyzed through PDP indicate that UCS is highest when WGP is 202.5 kg/m3, and ASR is maximized when WGP is 708.75 kg/m3. The SHAP value analysis results reveal that the “alkali” feature exerts the most pronounced influence on the UCS model predictions. Conversely, in the case of the ASR model, the “curing duration” feature emerges as the primary driver of its predictions.