# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 4 Results
### Model Development and Results  
In this case study, four feedforward ANN models were developed, testing various architectures (layers, neurons, learning rates), batch sizes, epochs, and training/validation checks. The dataset, though small, was split 80% for training and 20% for testing, ensuring the model had no prior knowledge of the test data. Normalization was performed before the split to maintain accurate scaling, with maximum values derived from the training data. After experimenting with multiple architectures, the final network (Figure 5) featured 16 input neurons with ReLU activation, two hidden layers (32 neurons in the first, 16 in the second), and one output neuron with linear activation to estimate maximum surface \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Geometrical Parameters** & **Min** & **Max** & **Mean** & **Std.** \\ \hline Overburden (m) & 12.87 & 15.48 & 14.31 & 0.77 \\ \hline Distance to water table (tunnel invert to WT) (m) & \(-\)20.6 & \(-\)17.2 & \(-\)19.1 & 1.0 \\ \hline Distance from shaft (m) & 0.0 & 283.5 & 131.7 & 83.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description table of TBM geometrical parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Geotechnical Parameters** & **Min** & **Max** & **Mean** & **Std.** \\ \hline Cohesion c in face area (c\_e) (kN) & 52.4 & 74.8 & 62.1 & 5.6 \\ \hline Cohesion c in overburden (c\_o) (kN) & 76.1 & 94.8 & 85.3 & 4.8 \\ \hline Angle of friction \(\varphi\) in face area (f\_e) & 35.2 & 41.6 & 36.1 & 0.8 \\ \hline Angle of friction \(\varphi\) in overburden (f\_o) & 36.1 & 40.3 & 37.8 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description table of TBM geotechnical parameters. settlement (16 \(\times\) 32 \(\times\) 16 \(\times\) 1). The Adam optimizer with an initial learning rate of 0.001 was used. To enhance convergence and prevent overfitting, a \"Reduce Learning Rate on Plateau\" strategy adjusted the learning rate when performance stopped improving, and early stopping halted training when validation performance plateaued, ensuring efficient resource use. Four ANN models were developed to assess feature impacts, applying a simplified SHAP approach as follows:  
* ATV (all the variables): model with all features. * WST (without stop time): model excluding the stop time parameter. * WGV (without grout volume): model excluding the grout volume parameter. * WES (without excavated soil): model excluding the excavated soil parameter. Models were trained using the mean square error (MSE), with results also presented as the root mean square error (RMSE) and average prediction accuracy for comparison (Table 5). All models performed well, achieving an average prediction accuracy above 79%, highlighting their potential for estimating surface settlements and providing tunneling engineers with a valuable decision-making tool. The ATV and WST models emerged as the top performers, but their comparative effectiveness for predicting maximum settlements requires closer examination. Figure 6 illustrates this comparison with the gray line x = y representing perfect alignment between predicted and actual values. Points closer to this line indicate higher prediction accuracy. Overall, the WST model (aqua) slightly outperforms ATV, particularly in the mid-to-lower range of maximum settlements, where both models perform well. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**MSE (mm)**} & \multicolumn{2}{c}{**RMSE (mm)**} & \multicolumn{2}{c}{**Average Accuracy**} \\ \hline  & **Train** & **Test** & **Train** & **Test** & **Test** \\ \hline “ATV” & 0.004 & 0.037 & 0.020 & 0.193 & 80.10\% \\ \hline “WST” & 0.010 & 0.018 & 0.100 & 0.135 & 80.70\% \\ \hline “WGV” & 0.005 & 0.038 & 0.070 & 0.196 & 80.02\% \\ \hline “WES” & 0.012 & 0.023 & 0.109 & 0.152 & 79.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MSE and RMSE training and test model results along with the avg. accuracy of the test results.