This paper seeks to develop an interpretable Machine Learning (ML) model for predicting the unconfined compressive strength (UCS) of cohesive soils stabilized with geopolymer at 28 days. Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. The database consists of 282 samples collected from the literature with three different types of cohesive soil stabilized with three geopolymer categories including Slag-based geopolymer cement, alkali-activated fly ash geopolymer and slag/fly ash-based geopolymer cement. The optimal model is selected by comparing their performances with each other. The values of hyperparameters are tuned by Particle Swarm Optimization (PSO) algorithm and K-Fold Cross Validation. Statistical indicators show the superior performance of the ANN model with three metrics performance such as coefficient of determination R2 = 0.9808, Root Mean Square Error RMSE = 0.8808 MPa and Mean Absolute Error MAE = 0.6344 MPa. In addition, a sensitivity analysis was performed to determine the influence of different input parameters on the UCS of cohesive soils stabilized with geopolymer. The order of feature effect can be ordered in descending order using the Shapley additive explanations (SHAP) value as follows: Ground granulated blast slag content (GGBFS) > Liquid limit (LL) > Alkali/Binder ratio (A/B) > Molarity (M) > Fly ash content (FA) > Na/Al > Si/Al. The ANN model can obtain the best accuracy using these seven inputs. LL has a negative correlation with the growth of unconfined compressive strength, whereas GGBFS has a positive correlation.

This study tests relationships between, on the one hand, field-saturated infiltration rate into the uppermost sloping soil layer (KfJ and field-saturated hydraulic conductivity in soil horizons (Kfsh) and, on the other hand, in situ determined macropore sizes in soil profile descriptions. It was carried out at six locations along a transect on'a slope with loamy soils. The macropores were classified into representative size indices based on pore diameters (Ptd) and pore areas (P,,). A strong relationship was found between Kf,i and P,,, and between Kfsh and Ptd. The approach is promising and the methodology could be further developed with the aim of generalizing the functional relationships, so that Kf, can be estimated in areas where soil survey databases contain information on pore size abundance but lack Kf, values.