# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
## 5 Conclusions  
In this paper, the DT method was used to build a slope stability prediction model based on big data and deep learning. Initially, the sample database of the slope was established by using the sample generation method, and the optimized safety factor of the non-circular slip surface was compared with the safety factor of the circular slip surface. Secondly, the CNN regression model is established, and the predictive effects of 1D and 2D network models and different depth network models are compared, respectively. The following conclusions are drawn:  
1. The DT slope models generated based on the LEMs greatly expand the slope sample dataset while maintaining the rationality of the slope samples, thus providing robust training samples for the big data-based deep learning method to predict slope stability.
2. Based on the circular slip surface, the safety factor of the non-circular slip surface is optimized, and the safety factor is smaller than that of the circular slip surface. The minimum safety factor of the non-circular slip surface decreases slightly as the number of control points on the slip surface increases, but the computation time increases significantly. The RMSE of the prediction model based on the non-circular slip surface is slightly lower than that based on the circular slip surface.
3. For this experimental dataset, the RMSE of 2D CNN was slightly lower than that of 1D CNN. The RSME of CNN first decreases and then increases as the number of network layers increases.
4. The prediction model of slope safety factor based on CNN had strong generalization ability, and the prediction error was within an acceptable range, providing a method for rapid detection of slope stability.  
In this paper, the DT slope stability prediction model based on CNN is built. The inspector only needs to input the exploration data into the slope stability prediction model to obtain the safety factor of the slope. The procedure is simple, does not require strong professional knowledge, and has great engineering application value. However, the input data may be redundant. In future studies, sensitivity analysis will be carried out, and pruning and other methods will be considered for clipping input data from slopes. In addition, this study only considers 2D slopes, and in a later stage, a 3D slope sample  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Slope Name** & **The Slope Shape** & **Soil Layer** & **c** & \(\boldsymbol{\varphi}\) & \(\boldsymbol{\gamma}\) & \(\boldsymbol{\gamma_{sat}}\) \\ \hline \multirow{4}{*}{Qingning Town landslide, Daxian County, Sichuan Province} & Quaternary sediment & 34 & 27 & 18.8 & 24 \\ \cline{2-6}  & Strongly & & & & \\ \cline{1-1}  & wearhered sandstone & 220.8 & 44 & 22.5 & 27.4 \\ \cline{1-1}  & Weakly & & & & \\ \cline{1-1}  & wearhered mudstone & 120.7 & 32 & 24 & 26 \\ \cline{1-1}  & Weakly & & & & \\ \cline{1-1}  & wearhered sandstone & 350.5 & 52 & 25.5 & 28.8 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Qingning Town landslide, Daxian County, Sichuan Province.  
database can be constructed by combining finite element analysis to achieve 3D slope stability prediction. Finally, this study only focuses on the study of slope stability prediction methods based on CNNs. In the future, a user-oriented intelligent detection platform can be developed to promote the real implementation of this research result in slope engineering practice.  
Methodology, G.C., M.L., S.T. and Z.L.; Software, X.K., M.L. and S.T.; Validation, G.C. and X.K.; Formal analysis, M.L.; Investigation, G.C., X.K., M.L. and S.T.; Resources, G.C. and Z.L.; Data curation, X.K.; Writing--original draft, X.K.; Writing--review & editing, S.T. and Z.L.; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.