# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 5 Experimental Evaluation
### Experimental Results  
(1) Experimental Study on the Impact of Borehole Numbers  
During the development of the coding algorithm, we selected multiple boreholes surrounding a sample point for data encoding. Recognizing that the number of these boreholes significantly influences model quality, we conducted a series of experiments aimed at identifying the impact of different borehole numbers on model quality. In our study, we utilized SCNN-5 and SCNN-10 network models. Our methodology involved selecting the borehole closest to the sample point, encoding its data using the specified algorithm, and subsequently evaluating the accuracy of our test set through neural network analysis. The outcomes of our experiments are summarized in Table 4, showing the impact of varying borehole numbers on model performance. (2) Model Performance Comparison  
To test the performance of the model, several classic classification algorithms were selected for comparison, including the LSTM neural network, random forest, decision tree, and SVM classifier. Each algorithm was tested using both 5-category and 10-category datasets. The results are shown in Table 5.  
(3) Model Adaptation Algorithm Performance Verification  
In this part, to enhance the adaptability of the model, a model adaptation algorithm was designed. This approach utilized two models to address classification problems spanning 1 to 10 different categories. Missing soil layer data were handled by performing zero-padding. Within this section, classification models for scenarios 3, 4, 7, and 8 were developed. Regarding the missing layer borehole data, the performance of the classification models for scenarios 3 and 4 was compared against SCNN-5. Similarly, the performance of the classification models for scenarios 7 and 8 was compared with SCNN-10. The experimental results are summarized in Table 6.  
(4) Real-world Application and Simulation Results  
To further validate the practical applicability of our proposed method, we applied the algorithm to a real-world foundation pit project. The project is located in an urban area of a city in China. The geological conditions within the foundation pit site are complex, with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **3** & **4** & **7** & **8** \\ \hline Corresponding model & 86\% & 89\% & 78\% & 90\% \\ SCNN-5 & 84\% & 89\% & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model adaptation algorithm performance verification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline SCNN-5 & 87\% & 87\% & 89\% & 92\% & 92\% & 88\% & 84\% \\ SCNN-10 & 86\% & 93\% & 94\% & 95\% & 92\% & 90\% & 89\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different numbers of boreholes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Categories** & **SCNN-5** & **SCNN-10** & **Decision Tree** & **Random Forest** & **SVM** & **LSTM** \\ \hline
5 categories & 92\% & / & 82\% & 85\% & 87\% & 90\% \\
10 categories & / & 95\% & 79\% & 82\% & 86\% & 91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of different models. soil layers composed of artificial fill, silty sand, silt, and clay. The excavation has an overall hexagonal shape, measuring approximately 75 m in the east-west direction and 119 m in the north-south direction, with a perimeter of 378 m and a depth of about 19.45 m.  
Additionally, a main building structure with a basement is situated approximately 35 m east of the excavation site. To the south, a subway tunnel runs nearby, with a minimum distance of about 15 m from the excavation edge. The tunnel has an inner diameter of 8.5 m and is buried approximately 5.3 m below the excavation bottom. These factors significantly increase the construction difficulty and risks. To enhance risk assessment and optimize the support system design, soil layer reconstruction is essential for such engineering projects.