## 4 Results and Discussion
### Optimum Solution for Mixed Ground  
As shown in Figure 9a-e, the parameter-optimized BPNN achieved the minimum error prediction in all five strategies, but due to the fact that the data processing scheme \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training Set (mm/min)**} & \multicolumn{2}{c}{**Test Set (mm/min)**} \\ \cline{3-6}  & & _MAE_ & _RMSE_ & _MAE_ & _RMSE_ \\ \hline \multirow{4}{*}{A} & MLR & 3.923 & 5.178 & 4.234 & 5.617 \\ \cline{2-6}  & KNN & 2.756 & 3.644 & 3.664 & 4.735 \\ \cline{2-6}  & SVR & 3.026 & 4.183 & 3.740 & 4.753 \\ \cline{2-6}  & CART & 2.754 & 3.639 & 3.590 & 4.663 \\ \cline{2-6}  & BPNN & **1.948** & **2.595** & **2.742** & **3.552** \\ \hline \multirow{4}{*}{B} & MLR & 3.923 & 5.177 & 4.232 & 5.613 \\ \cline{2-6}  & KNN & 2.702 & 3.571 & 3.548 & 4.609 \\ \cline{2-6}  & SVR & 2.984 & 4.206 & 3.722 & 4.822 \\ \cline{2-6}  & CART & 2.736 & 3.621 & 3.737 & 4.995 \\ \cline{2-6}  & BPNN & **1.768** & **2.480** & **2.411** & **3.249** \\ \hline \multirow{4}{*}{C} & MLR & 3.980 & 5.346 & 4.216 & 5.665 \\ \cline{2-6}  & KNN & 2.756 & 3.680 & 3.587 & 4.565 \\ \cline{2-6}  & SVR & 3.067 & 4.234 & 3.721 & 4.699 \\ \cline{2-6}  & CART & 2.433 & 3.343 & 3.754 & 4.885 \\ \cline{2-6}  & BPNN & **2.251** & **3.016** & **2.960** & **3.724** \\ \hline \multirow{4}{*}{D} & MLR & 3.980 & 5.346 & 4.212 & 5.669 \\ \cline{2-6}  & KNN & 2.744 & 3.672 & 3.596 & 4.583 \\ \cline{2-6}  & SVR & 3.059 & 4.214 & 3.714 & 4.689 \\ \cline{2-6}  & CART & **1.970** & 2.833 & 3.892 & 5.098 \\ \cline{2-6}  & BPNN & 2.097 & **2.814** & **2.853** & **3.618** \\ \hline \multirow{4}{*}{E} & MLR & 4.064 & 5.388 & 4.254 & 5.732 \\ \cline{2-6}  & KNN & 2.734 & 3.630 & 3.744 & 4.791 \\ \cline{1-1} \cline{2-6}  & SVR & 3.074 & 4.224 & 3.746 & 4.724 \\ \cline{1-1} \cline{2-6}  & CART & 2.818 & 3.682 & 3.793 & 4.884 \\ \cline{1-1} \cline{2-6}  & BPNN & **1.996** & **2.659** & **2.889** & **3.700** \\ \hline \hline \end{tabular} Note: Bold donates the lowest loss.  
\end{table} 
Table 5: _MAE_ and _RMSE_ values of each model in the five strategies. was different in each strategy, which can be seen in Section 2.7, it was necessary to further analyze the advance rate prediction model of the shield tunneling that was suitable for mixed ground. In Figure 9f, the prediction models trained by BPNN in each strategy were horizontally analyzed, and their \(R^{2}\) were compared. The closer \(R^{2}\) is to 1, the more accurate the model's prediction is. For the data of the training set, we can easily find that Strategy B achieved the best results both in the training set and the test set. For the training set, the accuracy of Strategy B was 1% higher than that of the second-best strategy, and for the test set, the accuracy of Strategy B was 2% higher than that of the second-best strategy, while Strategy A's \(R^{2}\) value was only 0.905, far less than the 0.920 of Strategy B. For a prediction model, if it can achieve high prediction accuracy on the training set, indicating that the model learns the data of the training set very thoroughly, but for the data of the test set that it has never been exposed to, it can better reflect the generalization of the model. The high accuracy of the training set with the low accuracy of the test set reflects the overfitting of the model. Fortunately, the errors obtained between the training set and the test set of the model in the five strategies were not much different, indicating that the models were well trained and there was no overfitting. By analyzing the reasons, it can be found that the difference between Strategy B and Strategy A was that Strategy B introduced the composite ratio parameter \(p\). This reflected the proportion of different strata in the mixed ground, and the difference between Strategy B, Strategy C, Strategy D, and Strategy E lied in the way they each deal with the geological parameters of the upper and lower layer.