Predicting the Strength of Fly Ash–Slag–Gypsum-Based Backfill Materials Using Interpretable Machine Learning Modeling

Predicting unconfined compressive strength (UCS) is essential for the safety and stability of solid waste-based backfill materials, particularly due to the correlation between strength development and hazardous substance immobilization. This study developed a machine learning model to predict UCS and optimize mixtures using fly ash, slag, and desulfurized gypsum. A dataset with 14 input features—including composition, water content, and curing time—was analyzed using Recursive Feature Elimination (RFE) for feature selection. Random Forest, Bayesian, and Gray Wolf Optimizer (GWO)-enhanced models were compared. The GWO-GB model achieved superior accuracy (R2 = 0.9335), with curing time (27.99%), water content (22.16%), and sulfur trioxide (18.98%) identified as the most significant features. The model enables rapid, high-precision UCS prediction, reduces experimental workload, and offers insights for mix design optimization and feature interaction analysis.