# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 1 Introduction  
In the field of civil engineering, particularly during the construction of deep foundation pits, accurate and reliable soil layer information is crucial for reducing construction risks and preventing accidents [1, 2]. To obtain this information, engineers employ computer graphics-related algorithms to predict the spatial three-dimensional structures of different soil layers, subsequently performing solid modeling of these structures. This process is referred to as three-dimensional soil layer reconstruction. The 3D reconstruction of soil layers has great significance for the management of the underground construction process, as it enables the accurate calculation of earth pressure based on the actual soil layer conditions. This calculated earth pressure serves as the input load for the supporting structure calculation. There exist several methodologies for soil layer modeling, such as 3D soil layer modeling based on boundaries and interpolation of soil layer generation based on borehole information [3, 4]. It is also possible to perform 3D geological modeling by fusing multiple data sources or by utilizing a hybrid bounding box approach [5, 6]. In practical engineering, soil layer information is primarily obtained through borehole data. However, boreholes are typically sparse and unevenly distributed, making it impossible to reflect the stratigraphic distribution across the entire construction area directly [7]. Interpolationalgorithms have become indispensable tools to infer the stratigraphic structure of the entire region from limited borehole data [8]. At present, the most commonly used interpolation method is kriging interpolation [9; 10; 11]. [PERSON] et al. present a novel geostatistical approach for subsurface geological profile interpolation that uses a fractional kriging method enhanced by random forest regression to effectively capture complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks [12]. Further, the inverse distance weighted average interpolation method also plays an important role in the reconstruction of 3D soil layers. The scholars compiled a conceptual model of the geological environment and studied three spatial interpolation methods (nearest point, inverse distance weighting, and kriging) [13]. Some scholars conducted a comparative study of the inverse distance weight interpolation method and the natural neighborhood interpolation method and concluded that the inverse distance weight interpolation method is more suitable for strata. The lack of severe horizons can better preserve the conclusion on the characteristics of stratum loss. Additionally, methods integrating user-defined cross-sections with borehole data for soil layer modeling have also been proposed [14]. However, these algorithms often perform poorly on complex strata, such as those with faults and hiatuses. The traditional soil layer modeling methods face several limitations: (1) The algorithms lack robustness and adaptability, and they heavily depend on the quality of the input data. (2) There is a lack of effective methods for handling sparse data, especially when the number of borehole samples is small, rendering the algorithms less applicable. (3) Many algorithms require human-computer interaction and rely on human expertise, which can cause the calculation results to vary depending on the user's experience and capabilities [15]. Convolutional neural networks have demonstrated superior performance in computer vision and other fields. With the development of big data technology and the improvement of computer computing power, machine learning technology has been proven to have significant effects in the fields of image and audio [16; 17; 18]. In the study of 3D geological modeling, machine learning has also been gradually applied [19], such as through a vector potential field solution from the perspective of machine learning, which constructs a geological model using implicit modeling methods [20].