Synergistic Regulation of Waste Glass Powder and Aggregate Packing Structure for Sustainable Cement Mortar: Microstructural Mechanisms and XGBoost-PSO Intelligent Mix Design Optimization

Waste glass powder (WGP) is a promising recycled constituent for sustainable cementitious materials, yet its effectiveness depends strongly on the packing characteristics of the fine-aggregate skeleton. This study investigates the coupled effects of WGP replacement ratio and fine-aggregate gradation on the mechanical performance and microstructural evolution of cement mortar. Five aggregate gradations and four WGP replacement levels (0%, 8%, 10%, and 12% by mass) were evaluated through compressive and flexural strength tests at 7, 14, and 28 days, yielding 180 prism specimens in total. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) were used to identify the governing microstructural mechanisms. Four regression models—linear regression (LR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were compared for compressive strength prediction under identical data partitioning. XGBoost achieved the highest accuracy (R2 = 0.9346, RMSE = 0.9860 MPa, MAE = 0.6943 MPa), outperforming RF (R2 = 0.8896), LR (R2 = 0.8845), and SVR (R2 = 0.8612), and was subsequently coupled with particle swarm optimization (PSO) for constrained mixture design. Appropriate aggregate packing substantially improved WGP utilization by combining micro-filling, secondary pozzolanic reaction, pore refinement, and interfacial transition-zone densification. The best experimental combination (GCS10-M3) increased 28-day compressive and flexural strengths by 14.0% and 42.9%, respectively, relative to the reference mortar with the same gradation. The XGBoost-PSO framework identified a theoretical optimum containing 14.86% WGP, beyond the tested range, in a fines-rich bimodal aggregate gradation, with predicted 28-day compressive and flexural strengths of 41.10 MPa and 8.23 MPa, respectively. The results establish a mechanism-informed, data-driven route for designing resource-efficient mortar for building engineering applications.