# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 5 Experimental Evaluation
### Analysis and Discussion  
(1) Experimental Study on the Impact of Borehole Numbers  
The experimental results for the number of boreholes selected are shown in Table 4, and the corresponding line graph is shown in Figure 19.  
Figure 19 illustrates the relationship between the number of boreholes chosen and the quality of the model. Both models achieve their best performance with 10 boreholes, reaching accuracy rates of 92% and 95% on the test set. The line graph also shows that when the number of boreholes ranges from 7 to 13, the models demonstrate notable effectiveness, with accuracy exceeding 80%. According to the coding algorithm used, changes in the number of boreholes alter both sample dimensions and characteristics, which impacts the network model's ability to extract features and, consequently, its performance on the test set. Based on the experimental results for the coding algorithm proposed in this paper, the optimal value of \(k\) is 10.  
(2) Model Performance Comparison  
The performance comparison experiment results of different machine learning methods on the test set are shown in Table 5, and the histogram obtained from this table is shown in Figure 20.  
Figure 19: Results for different numbers of boreholes.  
Figure 18: Foundation pit earthwork process.  
Based on the experimental results, we can conclude that the network model proposed in this paper performs optimally across various datasets. Specifically, within the five soil layer datasets, SCNN-5 achieves the highest test accuracy, with other categories also demonstrating accuracies surpassing 80%. Among the models evaluated, the LSTM neural network follows SCNN-5 in performance, reaching an accuracy of 90%. For datasets containing ten soil layers, SCNN-10 exhibits superior performance; however, both the decision tree and random forest models show a decline in effectiveness. This discrepancy is likely attributed to the increase in sample feature complexity and dataset size. In contrast, SCNN-10 thrives under these conditions, suggesting that deep convolution operations are particularly effective for feature extraction in complex datasets. These findings highlight the robustness of the proposed SCNN model in handling varied and complex data environments.  
(3) Model Adaptation Algorithm Performance Verification  
The experimental results of model adaptation algorithm performance verification are shown in Table 6. The histogram obtained from this table is shown in Figure 21.  
As can be seen from the figure, after applying the model adaptation algorithm, the performance of the models SCNN5 and SCNN-10 is similar to the performance of the model corresponding to the category. Specifically, the SCNN-10 model outperforms eight classification models, validating the effectiveness of the proposed model adaptation algorithm, which shows promise for practical applications. Utilizing this method significantly reduces the need for designing and training new models. Only a small number of models are required to accurately represent various soil layer characteristics, thereby enhancing the adaptability and efficiency of the models.  
Figure 21: Model adaptation algorithm performance verification.  
Figure 20: Results of different models.  
(4) Real-world Application and Simulation  
Based on the proposed three-dimensional soil layer reconstruction algorithm, multiple high-risk soil zones within the construction area were accurately identified, allowing for targeted pre-reinforcement measures. After excavation, verification using the Cone Penetration Test confirmed the accuracy of the predicted soil classifications. Additionally, the actual volume of high-risk soil blocks deviated from the algorithm's predicted values by only 6.8%, significantly enhancing construction controllability.