| Summary: | This study investigates the effect of nano-zeolite and lime on the resistance of reconstituted soil using an integrated experimental and explainable machine learning framework. Soil samples were prepared with varying proportions of nano-zeolite, lime, and fines, and cured under controlled temperature and time conditions. Soil resistance (q) was measured to evaluate the mechanical performance of each mixture. Eight machine learning models, including artificial neural networks (ANN), random forest (RF), random tree (RT), random committee–random tree (RC-RT), M5Rules, KStar, RBFS, and additive regression–decision stump (AR-DS), were developed using Weka 3.8.6 to predict soil resistance based on the input parameters. Model performance was assessed using SSE, MAE, MSE, RMSE, Error %, Accuracy %, R<sup>2</sup>, correlation coefficient, Willmott Index, Nash–Sutcliffe Efficiency, Kling–Gupta Efficiency, and SMAPE. ANN and RF achieved superior accuracy (R<sup>2</sup> ≥ 0.98) with minimal prediction error, effectively capturing the nonlinear interactions between stabilizer content, curing time, and environmental conditions. Sensitivity analyses using the analysis index and SHAP values revealed that nano-zeolite, lime, and curing time were the dominant factors influencing soil resistance, while fines content and curing temperature had secondary effects. The results demonstrate that nano-zeolite and lime significantly enhance soil resistance and that explainable machine learning models can reliably predict and interpret soil performance, providing a data-driven framework for optimized soil stabilization in geotechnical engineering applications.
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