## 4 Results and Discussion
### Analysis of Feature Importance  
RF is an ensemble learning model composed of multiple decision trees. Its feature importance is measured by calculating the reduction in impurity based on the decision trees. Impurity reduction refers to the decrease in impurity caused by using a specific feature for splitting when constructing the decision tree. For every feature, RF averages the impurity reduction across all decision trees and normalizes it to obtain the feature's importance. A higher feature importance indicates a greater contribution of that feature to predicting the target variable within the RF. The obtained feature importance is illustrated in Figure 8, with the characteristic importance of each characteristic factor of the validation set. Features such as D, WL, SS2, AF, and ACT demonstrate prominent importance in the feature ranking, suggesting their relatively significant roles in the predicting task. Factor D especially has the most significant impact in the case. In the construction process, under the comprehensive action of supporting the axial force, soil pressure, and other factors, the enclosures show a consistent deformation pattern over time, which is illustrated in Figure 3. This pattern is correlated to the depth of enclosure of the foundation pit. The WL plays a crucial role in influencing the saturation degree of the soil and the effective stress it experiences. The fluctuation in groundwater level directly impacts the drainage efficiency of the soil; a higher groundwater level may impede proper drainage, increasing the saturation degree and potentially impacting the horizontal displacement of enclosure structures. Additionally, the water level is closely tied to soil drainage, which further affects the enclosure's stability. AF directly reflects the deformation of the pit's support structure, serving as an indicator of the binding force between the support structure and the soil. The magnitude of the support axial force demonstrates the structural integrity of the support system. SS2 causes vertical compression of the soil, leading to lateral movement. This lateral movement exerts a force on the support structure, potentially resulting in horizontal displacement. Simultaneously, SS2 compresses the soil, reducing its volume and compromising its strength and stability. This reduction in soil strength increases the risk of horizontal displacement. The ACT factor, the timing of pouring the pit floor, is a critical consideration for minimizing the horizontal displacement of the enclosure structure. The pouring time directly influences the overall stability and deformation behavior of the pit support system, making it an essential engineering parameter to be carefully managed.  
Figure 8: Characteristic importance of each characteristic factor of the validation set.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Statistical Results of Significance** & \(\mathbf{R^{2}}\) & **MAE (mm)** & **RMSE (mm)** \\ \hline Random Forest Prediction Model & 0.9905 & 0.8572 & 1.9119 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of the RF prediction models on test data.  
Notably, the feature importance of factors like WBP and ST appears relatively weak, implying their minor impact on predicting the target variable. Civil engineering issues are often influenced by complex factors. Feature importance is a relative metric calculated based on the reduction in impurity from decision trees. When features are highly correlated, their importance might offset each other, resulting in lower importance values for specific features. Moreover, as the random forest is a nonlinear model, feature importance reflects the influence of nonlinear relationships. Even if a feature has low importance, it might still exhibit nonlinear relationships with the target variable.