# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning  
[PERSON]  
1 School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China; [EMAIL_ADDRESS] (X.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.T)  
[PERSON]  
1  
[PERSON]  
1 School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China; [EMAIL_ADDRESS] (X.K. ); [EMAIL_ADDRESS] (M.L. ); [EMAIL_ADDRESS] (S.T)  
[PERSON]  
1  
[PERSON]  
2 School of Civil Engineering, Guangzhou University, Guangzhou 510006, China  
1  
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criterion. The LEMs mainly include the [PERSON] method, the [PERSON] method, the Bishop method, and the unbalanced thrust method. The LEMs are easy to implement and have been tested in engineering, so they are still the most widely used methods. [PERSON] et al. [17] proposed a simplified three-dimensional slope stability analysis method based on LEMs. [PERSON] et al. [18] established a rigorous limit equilibrium column method considering the intercolumn force based on six equilibrium conditions. [PERSON] et al. [19] established the stability diagram of the homogeneous isotropic slope by using the LEMs and the strength reduction method. [PERSON] et al. [20] proposed a method to evaluate the stability of reinforced slopes based on LEMs. However, the traditional LEMs of calculating the slope safety factor require engineers to draw slope profiles (including the geometric parameters of each soil layer) according to field exploration data and then input the slope profiles and the material parameters of the soil layers into engineering software to calculate the slope safety factor. Or, in the finite element software, the strength reduction method can be used to calculate the slope safety factor. However, the above methods require in-depth professional skills and knowledge for slope inspectors. With the development of artificial intelligence (AI), ML is becoming popular in slope stability analysis. [PERSON] et al. [21] established various artificial neural networks (ANNs) to predict the slope safety factor. Compared with support vector machines (SVM) and genetic programming (GP), ANNs are more efficient. [PERSON] et al. [22] used ANN and multiple regression (MR) models to predict the critical safety factor of homogeneous slopes subjected to earthquake forces. [PERSON] et al. [23] used ANNs to predict the safety factor of dump slopes in a coal mine. [PERSON] et al. [24] used functional networks (FNs), multivariate adaptive regression splines (MARS), and multigene genetic programming (MGGP) to predict the safety factor of slopes and give the prediction model equation. [PERSON] et al. [25] used multiple linear regression (MLR) and ANNs to predict the stability of slopes. [PERSON] et al. [26] intended to use optimized ANNs to design pure, cohesive slopes. [PERSON] et al. [27] predicted the safety factor of slopes by using six ML techniques. However, most of the above studies take the internal friction angle \(\varphi\), the cohesion \(c\) of the slope soil, and the slope height and angle as the input parameters of each slope but do not consider the slope shape or the geometric and material parameters of each soil layer, so they are only applicable to homogeneous slopes with simple shapes. However, actual slopes are more complex and cannot be expressed simply by slope height and angle, and slopes are always composed of more soil layers. Therefore, this paper proposes a digital model that can truly reflect the authentic profiles of an actual slope and accurately predict its stability.