Simulating excavation processes for large-scale underground geological models using dynamic Boolean operations with spatial hash indexing and multiscale point clouds

The emergence of digital twins and construction simulation in underground space engineering has driven the demand for efficient Boolean operations on geological models to quickly simulate real-world excavation processes. Therefore, this paper proposes an efficient dynamic Boolean operation framework for large-scale geological models. Firstly, geological models are divided into finite subspace models using spatial bucketing algorithm and efficiently manages spatial triangle data with the R-tree algorithm. Intersecting subspace triangles are then converted into point clouds, and Ball-tree and K-means algorithms are employed to search and remove points, completing the Boolean operation between excavation equipment and geological models. Experiments show that the proposed method achieves a 13-fold speed improvement at 1 cm precision. Furthermore, Boolean operation speeds for point clouds of 10-different scales were analyzed, revealing the relationship between precision and time to meet diverse scenario requirements. The framework exhibits robustness and versatility, making it suitable for large-scale excavation and drilling simulations, including underground spaces and other construction projects.