# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
## 4 Results
### Optimization of Non-Circular Slip Surface
#### 4.2.2 Comparison of CNN Models with Different Depths  
The CNN was composed of multiple convolutional layers, and the predictive performance of the network model composed of different numbers of convolutional layers was different. To find the most suitable network model for the experimental dataset, six different depth network models were compared. As shown in Table 10, the network structure of Model 1 - Model 5 was similar, and only one convolutional layer was increased in turn. As shown in Figure 18, Model 6 was a depth residual network. The hyperparameters of all the network models are shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Layer** & **Type** & **Kernel Num.** & **Kernel Size** & **Stride** & **Pad** & **Activation** \\ \hline \multirow{4}{*}{1} & 1 & Input & — & — & — & — & — \\  & 2 & Conv\_1\({}^{a)}\) & 32 & 2 \(\times\) 2 & [1,1] & 0 & Leaky ReLU \\  & 3 & FC\({}^{b)}\) & — & — & — & — & — \\  & 4 & Regression & — & — & — & — & — \\ \hline \multirow{4}{*}{2} & 1 & Input & — & — & — & — & — \\  & 2 & Conv\_1\({}^{a)}\) & 32 & 2 \(\times\) 2 & [1,1] & 0 & Leaky ReLU \\  & 3 & Conv\_2\({}^{a)}\) & 64 & 2 \(\times\) 2 & [2,2] & 0 & Leaky ReLU \\  & 4 & FC\({}^{b)}\) & — & — & — & — & — \\  & 5 & Regression & — & — & — & — & — \\ \hline \multirow{4}{*}{3} & 1 & Input & — & — & — & — \\  & 2 & Conv\_1\({}^{a)}\) & 32 & 2 \(\times\) 2 & [1,1] & 0 & Leaky ReLU \\  & 2 & Conv\_2\({}^{a)}\) & 64 & 2 \(\times\) 2 & [1,1] & 0 & Leaky ReLU \\  & 3 & Conv\_2\({}^{a)}\) & 64 & 2\(\times\) 2 & [1,1] & 0 & Leaky ReLU \\  & 5 & FC\({}^{b)}\) & — & — & — & — & — \\  & 6 & Regression & — & — & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Network structure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Iteration** & **Model** & **Non-Circular Slip Surfaces** & **Circular Slip Surfaces** \\ \hline
1 st & 0.0770 & 0.0770 & 0.0799 & 0.0782 \\
2 nd & 0.0776 & 0.0770 & 0.0759 & 0.0777 \\
3 rd & 0.0742 & 0.0742 & 0.0760 & 0.0767 \\
4 th & 0.0729 & 0.0725 & 0.0733 & 0.0716 \\
5 th & 0.0718 & 0.0686 & 0.0718 & 0.0668 \\
6 th & 0.0778 & 0.0779 & 0.0769 & 0.0771 \\
7 th & 0.0852 & 0.0826 & 0.0803 & 0.0768 \\
8 th & 0.0758 & 0.0727 & 0.0760 & 0.0719 \\
9 th & 0.0783 & 0.0783 & 0.0836 & 0.0797 \\
10 th & 0.0807 & 0.0785 & 0.0850 & 0.0843 \\ Average & 0.0771 & 0.0759 & 0.0779 & 0.0761 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: RMSE of the model under 10-fold cross-validation. The RMSE of network models with different depths is shown in Figure 19. As the number of network convolutional layers increased from three to six, the RMSE of the network model became smaller and smaller, and the prediction effect became better and better. However, when the number of convolutional layers was seven, the RMSE of the network model was significantly larger than that of the network model (Model 4) with six convolutional layers. The RMSE of the residual depth network model was the largest at 0.0696. Obviously, it was not the case that the deeper the number of network layers, the better the network prediction effect. Especially when the sample data volume is small, as the number of network layers increases, less effective information can be extracted for the deeper convolutional layer. Therefore, for this experimental dataset, the 2D CNN model with six convolutional layers has the best performance with an RMSE of 0.0666. The comparative results of the network hyperparameter optimization are shown in Appendix A.