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Simulating excavation processes for large-scale underground geological models using dynamic Boolean operations with spatial hash indexing and multiscale point clouds
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.autcon.2025.105966
Penglu Chen, Wen Yi, Dong Su, Yi Tan, Jinwei Zhou, Xiangsheng Chen

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.

中文翻译:


使用具有空间哈希索引和多尺度点云的动态布尔运算模拟大规模地下地质模型的挖掘过程



数字孪生和施工仿真在地下空间工程中的出现推动了对地质模型进行高效布尔运算的需求,以快速仿真真实世界的挖掘过程。因此,本文提出了一种用于大规模地质模型的高效动态布尔运算框架。首先,利用空间分桶算法将地质模型划分为有限子空间模型,并利用 R 树算法对空间三角形数据进行高效管理;然后将相交的子空间三角形转换为点云,并采用 Ball-tree 和 K-means 算法来搜索和删除点,完成挖掘设备和地质模型之间的布尔运算。实验表明,所提出的方法在 1 cm 的精度下实现了 13 倍的速度提升。此外,还分析了 10 种不同尺度的点云的布尔运算速度,揭示了精度与时间之间的关系,以满足不同的场景要求。该框架具有稳健性和多功能性,适用于大规模挖掘和钻孔模拟,包括地下空间和其他建筑项目。
更新日期:2025-01-18
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