# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 6 Conclusions  
This paper introduces a three-dimensional soil layer reconstruction method grounded in machine learning, characterized by its robust performance and adaptability to various terrains, especially on complex strata. The key conclusions are summarized as follows:  
1. Data Augmentation Algorithm: The proposed data augmentation algorithm is capable of generating a substantial amount of virtual borehole data that closely mirror real-world borehole data, effectively enhancing the scale of the training dataset. 2. Feature Encoding Algorithm: The feature encoding algorithm designed for borehole data successfully produces borehole information feature maps. This encoding method bridges the gap between sparse borehole data and machine learning model requirements. 3. Soil Layer Prediction Model: Employing the soil layer prediction model developed in this study, an accuracy rate of 95% is achieved on simple soil layer test sets, while over 90% accuracy is attained on complex soil layer test sets. 4. Model Adaptation: The model adaptation algorithm aligns borehole data from different formations with their corresponding predictive models, enhancing the model's adaptability. This approach eliminates the need for training separate models for each soil layer configuration, significantly reducing computational resource requirements. 5. Visualization and Practical Application: Utilizing the soil labels generated by the model, we achieve visual modeling of soil layers through a three-dimensional voxel aggregation algorithm. This enables the creation of high-fidelity soil layer models that closely resemble real-world conditions, providing a robust foundation for subsequent engineering calculations and analyses. Our findings indicate that the designed models exhibit high adaptability. Compared to traditional interpolation algorithms, machine learning models offer superior performance in modeling complex soil layers. By incorporating a data augmentation algorithm to procure ample training data, we have streamlined the process from data generation, preprocessing, and model training to the creation of soil layer models. This approach ensures that the models trained on actual engineering borehole data exhibit remarkable fidelity to real soil layers, making them highly applicable in practical engineering scenarios. The proposed framework lays a solid foundation for advancing soil layer reconstruction techniques and their applications in geotechnical engineering. Additionally, although the accuracy of the proposed algorithm has been validated through dataset testing and practical engineering applications, there is still room for improvement. The uncertainty of the algorithm is influenced by sampling density, the distribution of sampling boreholes, and the geological complexity of the study area. This issue becomes even more pronounced when dealing with sparse and spatially heterogeneous data. Compared to large-scale 3D soil layer reconstruction in natural geographic studies, foundation pit construction introduces additional uncertainty due to the presence of artificial fill. The composition of artificial fill is often heterogeneous, making it difficult to quantify and analyze its soil parameters. This increases the risk of uneven settlement during construction. If the site or its surroundings have previously undergone construction or other infrastructure development, artificial fill may further interfere with soil stability. In future research, we will devote more time and effort to improving the algorithm, such as optimizing intelligent sampling design and developing dynamic learning systems. Additionally, we plan to introduce engineering-oriented quantitative evaluation criteria to assess the algorithm's performance. Data curation: N.L. and M.L. ; methodology: C.Z. ; funding acquisition: N.L., X.D. and M.L. ; investigation and analysis: C.Z. and N.L. ; writing--original draft: [PERSON]; writing--review and editing: [PERSON], X.D. and N.L. ; supervision: [PERSON] and B.L.