Projection-Pursuit Regression-Based Optimization of Frost Resistance and Mechanical Performance in Alkali-Activated Slag Cement Pavements

In recent years, applying slag micro-powder as a substitute for cement in preparing alkali-activated slag cement stabilized sand (AASCSS) mixtures has become increasingly widespread. In the severe cold regions of Xinjiang, multi-objective optimization of the mechanical and frost resistance properties of AASCSS is particularly crucial. This paper adopts slag micro-powder to replace Portland cement, together with lime and desulfurization gypsum as activators, to explore the effects of activator type and dosage on the mechanical and frost-resistance properties of AASCSS. A prediction model for the mechanical and frost-resistance properties of AASCSS based on projection-pursuit regression (PPR) was proposed and established. Using the developed PPR model, contour plots of the comprehensive performance were calculated, simplifying the multi-objective problem into two single-objective problems focusing on mechanical and frost resistance properties for analysis. This method avoids subjective weighting and hypothesis-based modeling. By analyzing the contour plots of comprehensive performance, the optimal performance indices for mechanical and frost–thaw properties and the corresponding types and dosages of activators can be directly determined. When the required 7-day unconfined compressive strength in road engineering is 5.6 MPa, the optimal value of the freeze–thaw performance index (BDR) is 94.08%. At this point, the corresponding lime content is 2.1%, and the desulfurization gypsum content is 3.3%. The research results provide a reference for applying slag to road-based materials.

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