Strength Prediction and Mixture Optimization of Cement–Industrial-Solid-Waste-Stabilized Waste Soil Using Projection Pursuit Regression

Waste soil generated by building and underground construction is commonly characterized by high water content and low strength, while conventional cement stabilization entails high cement consumption. This study develops two low-cement binders incorporating ordinary Portland cement (P), carbide slag (CS), ground granulated blast-furnace slag (GGBS), and fly ash (FA) and establishes projection pursuit regression (PPR) models for predicting the unconfined compressive strength (UCS) of stabilized soil. Orthogonal and supplementary tests were conducted by varying total binder content, curing age, solid-waste replacement ratio, and compaction pressure. Separate PPR models were calibrated for the P–CS–GGBS (PC) and P–CS–GGBS–FA (PF) systems using 12 training and 10 within-domain validation mixtures. The mean relative errors for the training and validation sets were 3.46% and 5.68% for PC and 3.09% and 4.87% for PF, respectively. For both systems, the solid-waste replacement ratio was the dominant variable, followed by curing age, binder content, and compaction pressure. Model-based optimization identified binder compositions containing 75% solid waste for PC and 60% for PF, with recommended internal proportions of 25% P–18.75% CS–56.25% GGBS and 40% P–15% CS–22.5% GGBS–22.5% FA, respectively. The proposed framework provides a practical tool for strength prediction and mixture design of low-cement-stabilized waste soil.