Machine Learning Prediction and Interpretability Analysis of the Unconfined Compressive Strength of Alkali-Activated Stabilized Soft Soil Under Coupled Wetting–Drying Cycles and Acid Attack

Predicting the unconfined compressive strength (UCS) of alkali-activated stabilized soft soil under coupled acid attack and wetting–drying cycles requires evaluation across correlated laboratory observations. This study analyzed 689 records from 53 complete mixtures, with seven material and environmental inputs, using four machine-learning models (DT, RF, SVM, and XGBoost) and four neural models (PINN, BPNN, CNN, and LSTM). Generalization to previously unseen mixtures was evaluated by five-fold outer GroupKFold with three-fold mixture-grouped tuning. Each original record contributed one out-of-fold prediction. The pooled grouped-CV R2 values ranged from 0.5043 to 0.7605, with RMSEs of 0.3579–0.5148 MPa. XGBoost attained the highest point estimate (R2=0.7605; RMSE = 0.3579 MPa), followed by SVM (R2=0.7362; RMSE = 0.3756 MPa). PINN and its architecture-matched BPNN produced identical grouped-CV predictions and no demonstrated incremental benefit. A prospective penalty-activation diagnostic confirmed that soft penalties can affect training trajectories but did not reconstruct the archived model-selection runs. A separately tuned, five-step, structure-matched ablation found no additional predictive gain from preceding design covariates in CNN and LSTM. Historical fixed-configuration random-CV comparisons were removed because the original hyperparameter-selection provenance could not be verified. SHAP and partial dependence analyses describe model-learned associations, and SEM offers qualitative morphological context. These models provide preliminary screening aids within the tested materials and factor levels, not engineering design tools; independent batch and field validation remains necessary.