Using the enzyme-induced carbonate precipitation (EICP) technique to solidify rubber and clay mixtures as lightweight backfill is a feasible way to reduce waste tire impacts and boost rubber recycling in geotech engineering. In this study, a comprehensive laboratory investigation, including triaxial compression, oedometer, permeability, and nuclear magnetic resonance (NMR) tests, was conducted on EICP-reinforced rubber particle solidified clay (hereafter referred to as EICP-RC solidified clay) to evaluate the effects of rubber particle content and size on the mechanical behavior of the improved soil under various solidification conditions and to elucidate the solidification mechanism. The results show that although rubber particles inhibit EICP, they significantly enhance the mechanical properties of the samples. The addition of 5% rubber particles (rubber A) increased cohesion by 11% and the internal friction angle by 18% compared to EICP-treated clay without rubber. Additionally, incorporating smaller-sized tire particles facilitated pore filling, resulting in lower compression and swelling indices and reduced permeability coefficients, making these materials suitable for use behind retaining walls and in embankment construction.

Microbially Induced Carbonate Precipitation (MICP) is an environmentally friendly technique for sandy soil stabilization. However, the cementation performance is governed by multiple coupled factors and complex experimental procedures, making accurate prediction challenging. In this study, a dual-objective XGBoost prediction model suitable for small-sample scenarios is developed from 77 sets of laboratory data to rapidly estimate unconfined compressive strength (UCS) and calcium carbonate content (CCC) separately. A mechanism-guided feature engineering strategy is adopted to construct three cross features, including urease activity coupled with curing time, calcium carbonate content combined with curing time, and urea-calcium concentration, together with five key influencing parameters. Five-fold cross-validation is used to ensure model stability. In the UCS model, soil particle size fraction (29.94%) and urease activity (20.85%) dominate, while in the CCC model, soil particle size fraction (22.47%) and urease activity (17.63%) prevail, both align well with fundamental MICP mechanisms. The CCC model achieves a coefficient of determination (R2) of 0.6342 and a mean absolute error (MAE) of 2.92%, showing reliable predictive ability. The UCS model achieved an R2 of 0.7991 and a MAE of 844.83 kPa. However, due to the mathematical amplification of relative error, a small portion of low-strength specimens produced abnormally high MAPE (116.60%), limiting the formal engineering design of the UCS model. SHAP (SHapley Additive exPlanations) analysis is further employed to enhance model interpretability and to quantitatively clarify the marginal contributions and interaction effects of the input features. The proposed framework offers a valuable reference for parameter analysis and mechanistic interpretation of MICP-treated soils. At the same time, the larger prediction deviation for UCS highlights the intrinsic uncertainty of strength evolution in such complex multi-factor systems.