Recognition of structural planes and stability analysis of highly steep rock slopes on 3D point cloud data
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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:Objective The distribution of structural planes plays a significant role in determining the engineering and mechanical properties of rock masses. Accurately obtaining information on structural planes is crucial for analyzing the characteristics and stability of rock masses.
Methods 3D 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 used to semiautomatically recognize and classify the point cloud data, obtaining key parameters and clustering information of the structural planes of the rock slopes, such as occurrence, 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 constructed. Furthermore, a 3D block discrete element model of the highly steep slope was developed via the "Rhino-Griddle-3DEC" integrated modeling method, which is based on the point cloud data. The model was used to investigate the stability of the slope and potential failure areas.
Results Under gravity conditions, the safety factor of the entire slope was approximately 1.5. The dangerous rock mass protruding from the slope crest exhibited significant vertical displacement and a low safety factor, indicating a potential instability zone.
Conclusion Therefore, the structural plane parameters identified by this method can effectively 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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图 2 DSE识别结构面步骤
KNN. k nearest neighbor search（k最近邻搜索算法）；KDE. kernel density estimation（核密度估计法，用于估计随机变量的概率密度函数）；DBSCAN. density-based spatial clustering of applications with noise（基于密度的聚类分析算法）；PCA. principal component analysis（主成分分析法，一种数据降维算法）
Figure 2. Procedures of structural plane recognition via DSE
图 3 最近邻点搜索算法算法示意图
Pi. 岩体三维点云数据中的任意原始点；{P}. 原始点附近具有相同特征的若干点形成的点云集；K. 邻点搜索个数
Figure 3. Diagram of the nearest neighbor searching algorithm
图 4 点云集、优势结构面、聚类关系示意图
Figure 4. Relations among the point cloud set, principal structural plane and cluster
图 6 结构面直径的概率分布统计
Figure 6. Probability distribution statistics of the structural plane diameter
表 1 结构面产状
Table 1. Structural planes orientation
结构面组号 倾向/(°) 倾角/(°) 点云数量百分比/% J1 184.7 79.8 51.32 J2 126.6 85.1 34.48 J3 305.0 41.0 0.87 J4 41.9 62.5 3.26 
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表 2 结构面法向间距
Table 2. Normal spacing of structural planes
优势结构面组号 计算方式 最小值/m 最大值/m 平均值/m 标准差 J1 完全连续 0.01 2.13 0.37 0.53 不完全连续 0.05 5.30 0.82 1.01 J2 完全连续 0.01 0.78 0.23 0.19 不完全连续 0.02 3.43 0.81 0.62
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表 3 岩体DFN模型参数
Table 3. Parameters for DFN model
优势结构面组号 属性 分布 参数 J1 产状 F分布 倾向：184.7°，倾角：79.8°，K=100 位置 随机分布 在空间中均匀分布 尺寸 负指数分布 指数：1.08，尺寸范围：0.5～21 m 密度 P10 0.21 J2 产状 F分布 倾向：126.6°，倾角：85.1°，K=100 位置 随机分布 在空间中均匀分布 尺寸 负指数分布 指数：1.04，尺寸范围：0.5～13 m 密度 P10 0.11
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表 4 岩块和结构面物理力学参数
Table 4. Physical and mechanical parameters of rock blocks and structural planes
参数名称 密度/
(kg·m−3)杨氏模量/
GPa泊松比 体积模量/
GPa剪切模量/
GPa黏聚力/
MPa内摩擦角/
(°)抗拉强度/
MPa剪切刚度/
(GPa·m−1)法向刚度/
(GPa·m−1)岩块 2600 14.1 0.26 9.79 5.595 9 35 4 — — 真实裂隙 — — — — — 0 30 0 3.0 9 虚拟裂隙 — — — — — 9 35 4 559.5 1398
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