## 5 Results and Discussion
### Sensitivity Analysis  
#### 5.2.1 Overall Feature Analysis  
To thoroughly understand the influence degree and mechanism of various input parameters in predicting compressive strength of MICP-improved sand, this study introduces the SHAP (SHapley Additive exPlanations) analysis method to perform interpretability analysis on the constructed EnML-LARO. This method originates from Shapley value theory in game theory and can be used to quantitatively evaluate the relative contributions of input variables to model outputs, helping to enhance the comprehensibility and engineering applicability of model results [41; 42; 43; 44; 45].  
Figure 9 presents the SHAP value distribution of the eight input variables' influence on model prediction results. The horizontal axis represents the magnitude and positive/negative direction of each feature's influence on strength prediction, while the vertical axis lists the eight main input parameters involved in modelling, including calcite content (F\({}_{\text{CaCO}_{3}}\)), urease activity (UA), median particle size (D\({}_{\text{50}}\)), bacterial optical density (OD\({}_{\text{600}}\)), initial void ratio (e\({}_{\text{0}}\)), urea concentration (Mu), uniformity coefficient (Cu), and calcium chloride concentration (M\({}_{\text{Ca}}\)). Different colours represent the high-low values of features, with colours transitioning from light yellow to deep blue, reflecting the variation range of feature values in the samples.  
From the figure, it can be clearly observed that calcite content represents the most significant factor influencing model prediction results, with its SHAP value distribution range being the broadest, extending to approximately 9-10 in the positive region, and the majority of data points distributed in the positive influence area. Notably, when F\({}_{\text{CaCO}_{3}}\) content is high (deep blue points), the corresponding SHAP values are significantly positive, indicating that increased calcite content can substantially improve predicted target values. This result aligns with the fundamental principles of MICP technology, where calcite serves as the primary cementation product and its content directly determines soil strengthening effectiveness.  
The influence degree of urease activity is relatively weaker, with SHAP values mainly concentrated in the \(-1\) to 3 range. This variable demonstrates varying positive and negative effects under different values, indicating that the effectiveness of urease activity possesses certain complexity and conditionality. The SHAP value distribution of median particle size presents distinct bidirectional characteristics, with both significant negative influences and positive contributions, with SHAP values ranging from approximately \(-1.5\) to \(1.8\). Colour variations show that smaller particle sizes (yellow) primarily contribute negative  
Figure 9: SHAP analysis of key variables influencing MICP-treated sand strength.  
effects, while larger particle sizes (blue) are associated with positive influences. In contrast, bacterial optical density, initial void ratio, urea concentration, uniformity coefficient, and calcium chloride concentration all exhibit limited SHAP value distribution ranges, mainly concentrated in small intervals near zero values, indicating that these factors have relatively weak dominant effects on model prediction results.