# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
### Digital Twins of Slopes
(a) When traversing each of the slope soil layers, if there was only one insertion point within a segment, as shown in Figure 7a, the higher y-coordinate of the control point P(i + 1) and the corresponding y-coordinate of the upper soil layer of the insertion point were compared, and the maximum value (i.e., the corresponding y-coordinate of the lower soil layer of the insertion point and the y-coordinate of the control point P(i) were compared, and the minimum value (i.e., the higher point) was taken as the upper limit of the insertion point y-coordinate. If there were multiple insertion points between two control points, as in Figure 7b, the y-coordinate for the first insertion point was obtained using the above steps. For subsequent insertion points, the higher y-coordinate of the already inserted points and the y-coordinate of the upper soil layer corresponding to the insertion point were compared, and the maximum value was taken as the lower limit of the insertion point y-coordinate. The y-coordinate of the lower soil layer corresponding to the insertion point and the y-coordinate of the lower control point P(i) were also compared, and the minimum value was taken as the upper limit of the insertion point y-coordinate. (b) However, in the above random point insertion process, only the upper and lower ordinates of the insertion points were considered to prevent the local soil layer from invading the lower soil layer, but the connecting line between the insertion points invading the upper layer was not considered, resulting in the situation shown in Figure 8. In this paper, self-correction methods were considered to solve such invasions. Figure 6: Insert random points between two control points. Figure 7: The insertion points of the soil layer line: (**a**) Only one insertion point; (**b**) More than one insertion point. For the generated soil layer, each point of the upper soil layer was checked; if the ordinate of the point of the upper soil layer was greater than that of the corresponding local layer line, it proved that there was soil layer intrusion, and the ordinate of the point was regenerated and then checked again for upper soil layer intrusion. If there was still soil layer intrusion, the above process was repeated until an insertion point without soil layer intrusion was found. If the above process was repeated more than 30 times and there was still soil intrusion, the insertion point could be moved down 5 m and then checked for soil intrusion. If there was still soil intrusion, the insertion point could be moved down another 5 m until there was no soil intrusion. In this paper, 30 typical slopes were collected, and 100 samples were generated for each slope. A total of 3000 slope samples were generated as experimental data. This sample generation method not only maintains the rationality of the generated slopes but also greatly increases the number of samples, which meets the requirements of deep learning for big data. The partially generated slopes are shown in Table 3.  
Figure 8: A self-correcting method for soil penetration. (**a**) The penetration point is located between the insertion point and the control point; (**b**) The penetration point is located between two insertion points.