基于点云数据的高陡岩质边坡结构面识别与稳定性分析

奚望; 陈宜伟; 张文广; 边林松; 门林; 梁鹏飞; 周博

doi:10.19509/j.cnki.dzkq.tb20230667

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基于点云数据的高陡岩质边坡结构面识别与稳定性分析

工程地质与地质灾害 | 更新时间：2026-11-05

- 基于点云数据的高陡岩质边坡结构面识别与稳定性分析
- 基于点云数据的高陡岩质边坡结构面识别与稳定性分析
- 地质科技通报 2025年44卷第4期页码：330-339
- 作者机构：
  
  1. 中铁十九局集团广州工程有限公司
  2. 中国铁建投资集团有限公司
  3. 华中科技大学土木与水利工程学院
- 作者简介：
- 基金信息：
  
  中铁十九局集团有限公司项目“基于BIM数字孪生技术的岩溶地区桥梁桩基精细化设计与智慧施工”（0231242361）
- DOI：10.19509/j.cnki.dzkq.tb20230667
  中图分类号： TU457;P225.2
- 网络首发：2024-08-27，
  
  纸质出版：2025
- 稿件说明：
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奚望, 陈宜伟, 张文广, 等. 基于点云数据的高陡岩质边坡结构面识别与稳定性分析[J]. 地质科技通报, 2025,44(4):330-339.

奚望, 陈宜伟, 张文广, et al. 基于点云数据的高陡岩质边坡结构面识别与稳定性分析[J]. Bulletin of Geological Science and Technology, 2025, 44(4): 330-339.
奚望, 陈宜伟, 张文广, 等. 基于点云数据的高陡岩质边坡结构面识别与稳定性分析[J]. 地质科技通报, 2025,44(4):330-339. DOI： 10.19509/j.cnki.dzkq.tb20230667.

奚望, 陈宜伟, 张文广, et al. 基于点云数据的高陡岩质边坡结构面识别与稳定性分析[J]. Bulletin of Geological Science and Technology, 2025, 44(4): 330-339. DOI： 10.19509/j.cnki.dzkq.tb20230667.

摘要

结构面分布对岩体的工程与力学性质具有重要影响，准确获取结构面信息对于分析岩体特性及其稳定性具有重要意义。通过三维激光扫描技术获取某高陡岩质边坡三维点云数据，通过对点云数据进行滤波前处理，采用开源程序Discontinuity Set Extractor (DSE)对点云数据进行半自动化识别与分类，获取边坡岩体结构面的产状、迹长、间距等关键参数信息及点云聚类信息。通过对点云聚类信息进行拟合分析得到其概率分布模型并建立岩体的离散裂隙网络（DFN）模型，进一步基于点云数据采用“Rhino-Griddle-3DEC”联合建模方法建立了高陡岩质边坡的三维块体离散元模型，通过离散元模拟分析了该边坡的稳定性与潜在失稳区域。结果表明：在重力作用下，边坡整体安全系数约为1.5，坡顶突出危岩体竖向位移较大且安全系数较小，为潜在失稳区域。因此，采用该方法识别获取的结构面参数信息能够较好地反映岩体工程力学性质，对高陡岩质边坡稳定性分析与评价具有重要指导意义。

Abstract

ObjectiveThe distribution of structural planes plays a significant role in determining the engineering and mechanical properties of rock masses. Accurately obtaining information of structural planes is crucial for analyzing the characteristics and stability of rock masses. Methods3D point cloud data of a highly steep rock slope were acquired via 3D laser scanning technology. After filtering and preprocessing of the point cloud data

the open-source program Discontinuity Set Extractor (DSE) was then used to semiautomatically recognize and classify the point cloud data

obtaining key parameters and clustering information of the structural planes of rock slopes

such as attitude

trace length

and spacing. By fitting the point cloud clustering information

a probability distribution model was created

and a discrete fracture network (DFN) model was established. Furthermore

a 3D block discrete element model of the steep slope was developed via the "Rhino-Griddle-3DEC" integrated modeling method

which is based on point cloud data. The model investigated the stability of the slope and potential failure areas. ResultsUnder gravity conditions

the safety factor of the entire slope is approximately 1.5

and the potentially unstable area is the dangerous rock mass located at the top of the slope. ConclusionTherefore

the structural plane parameters identified by this method can better reflect the engineering properties of the rock mass

providing important guidance for the analysis and evaluation of the stability of highly steep rock slopes.

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