# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 4 Results
### SHapley Additive exPlanations (SHAP) in Penetration Rate Prediction  
The SHAP library was applied to both models, using Tree Explainer for XGBR and Deep Explainer for ANN to enhance interpretability and compare model complexities. This comparison provides a deeper understanding of how each model processes information and makes predictions.  
The SHAP summary plots (Figure 13) reveal differences in how the models rank impactful parameters and their associated mean SHAP values. These variations are minor and align with theoretical engineering insights, reinforcing the validity of both models.  
Figure 11: Distribution plots for the two models in comparison to the actual.  
Figure 10: Effort to transform predicted distributions to actual.  
Both summary plots highlight the importance of features like AD. PERCENTAGE, RING PERCENTAGE, and STOP PERCENTAGE, emphasizing the correlation between PR delays and time-related factors.  
Both models also rank grout-related features (e.g., TOTAL GROUT B [L], A/B VOLUME, PRESSURE_GI) highly, following the consistently top-ranked EXCAVATING feature. Grout pressure is critical for stabilizing soil, reducing friction, and maintaining tunnel face balance to prevent collapse. This alignment between ML findings and traditional engineering principles underscores the robustness and explanatory power of both fields.  
The decision plots in Figure 14 reflect the key concepts from the summary plots. In the ANN plot (left), the high variability in impacts mirrors the summary plot, where multiple parameters significantly influence predictions. In contrast, the XGB plot (right) shows a more consistent impact, with fewer dramatic changes after the initial high values.  
Comparing features like EXCAVATING or RING PERCENTAGE from the top five highlights these differences. On both sides of the gray line (representing the model's output), the ANN model demonstrates greater sensitivity to these parameters, while the XGBR model responds more steadily. This variation stems from the models' training methods; XGBR's hierarchical tree structure produces smoother responses, whereas ANN's complex weight adjustments increase sensitivity to specific parameters.  
In the beeswarm plot (Figure 15), note that the \(x\)-axis ranges differ between the ANN (left) and XGBR (right) models. Thus, a high value on one plot does not directly correspond to the same high value on the other. However, the overall parameter behavior remains  
Figure 12: A generalization of the ANN (orange) and XGB (green) model—test dataset.  
Figure 13: SHAP summary plots on the average impact on the model’s output for the ANN model (**left**) and the XGBR model (**right**).  
consistent. For instance, in both plots, low values of the EXCAVATING feature consistently have a low-to-medium negative impact on the model's output.  
An interesting distinction lies in how features with minimal contributions are presented. In the XGB plot, these features appear after the top five, while in the ANN plot, they emerge later, after the eighth feature. This reflects the models' differing sensitivities to certain parameters during training.