Shiva Kumar Govindaraju

This study investigates the stabilization of lateritic soil through partial replacement of cement with flue gas desulfurization (FGD) gypsum, aiming to enhance its engineering properties for pavement subgrade applications. Lateritic soils are known for their high plasticity and low strength, which limit their utility in infrastructure. To address these challenges, soil specimens were treated with varying cement contents (3%, 6%, 9%) and FGD gypsum additions (1%–6%). Laboratory tests were conducted to evaluate plasticity, compaction, permeability, unconfined compressive strength (UCS), California Bearing Ratio (CBR), and fatigue behaviour. The optimal mix 6% cement with 3% FGD gypsum demonstrated significant improvements: UCS increased by over 110% after 28 days, permeability reduced by 26%, and soaked CBR improved by 56% compared to untreated soil. Additionally, fatigue life showed remarkable enhancement under cyclic loading, indicating increased durability for high-traffic applications. To support predictive insights, machine learning models including Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) were trained on 168 data samples. The MLP and Random Forest models achieved high prediction accuracy (R² ≈ 0.98), effectively capturing the non-linear interactions between mix proportions and UCS. SHAP (SHapley Additive exPlanations) analysis identified curing duration as the most influential factor affecting strength development. This integrated experimental–computational approach not only validates the feasibility of using FGD gypsum in sustainable soil stabilization but also demonstrates the effectiveness of machine learning in predicting key geotechnical parameters, reducing reliance on extensive laboratory testing and promoting data-driven pavement design.