Application UAV technology semi-automatic identification dangerous rock masses on ultra-high steep slopes
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摘要:
在新疆山区开展危岩体勘察时，由于工程区存在复杂且陡峭的山体，传统人工勘察危岩体的方案往往受限。为了有效地提高危岩体调查的效率与自动化程度，本研究提出了一种基于无人机的高陡边坡危岩体半自动勘察技术。将无人机贴近摄影测量技术与精确的仿地飞行路线规划相结合，获取超高边坡精确三维点云模型；应用CloudCompare软件点云剖分工具结合危岩体突出于边坡表面的形态特征对异型滑移式块体进行语义分割；并通过分析异型滑移式块体的三维特征，实现对危岩体的定性分析。将上述理论方法应用于玉龙喀什水利工程左岸超高边坡坝址，在试验区提取出了4块危岩体。所有危岩体稳定性系数（K）均低于0.9，平均体积均在2000 m3左右，最大高差在7～11 m。危岩体的空间位置分布和三维特征与现场人工勘测的基本一致。试验表明，结合危岩体特征的高精度边坡点云模型能有效识别危岩体，提高调查效率并解决人工数据模糊的问题，对高陡边坡的危岩体评估具有实际应用价值。
Abstract:In the mountainous regions of Xinjiang, traditional manual survey methods for dangerous rock masses are often restricted by the complex and steep terrain. To improve the efficiency and automation of dangerous rock masses surveys, this study proposes a semi-automatic technique using unmanned aerial vehicle (UAV) for high and steep slopes. This methodology integrates close-range photogrammetry with precise terrain-following flight path planning to generate accurate 3D point cloud models of ultra-high steep slopes. Considering the distinctive shapes of dangerous rock masses protruding from the slope surfaces, this research leveraged CloudCompare software's point cloud segmentation tool to perform semantic segmentation of these profiled blocks. Furthermore, a qualitative assessment of dangerous rock masses is achieved through an analysis of their three-dimensional features. This methodology was applied to the ultra-high slope dam site on the left bank of the Yulong Kashi Hydropower Project. In the test area, four dangerous rock masses were identified (all with stability coefficients lower than 0.9, average around 2000 m³ in volume, with height differences ranging from 7-11m), aligning closely with manual field surveys. The research shows that high-precision slope point cloud models, integrated with rock body characteristics, can effectively detect dangerous rock masses, enhance survey efficiency, and mitigate the inaccuracies associated with manual data collection. This approach holds significant practical value for assessing dangerous rock masses on ultra-high steep slopes.
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Key words:
- unmanned aerial vehicle /
- accurate point cloud /
- profiled block /
- dangerous rock masses
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表 1 单平面滑动岩体稳定性评价
Table 1. Stability evaluation of single-plane sliding rock mass
稳定性系数 稳定性分级 
稳定 
基本稳定 
欠稳定 
不稳定 
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表 2 水电工程危险岩体规模分级
Table 2. Scale classification of dangerous rock mass in hydropower projects
评价依据 小型 中型 大型 超大型 体积/m3 
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表 3 异形滑移式块体特征数据统计
Table 3. Statistical analysis of characteristic data for profiled blocks
块体编号 后壁倾角/（°） 后壁面积/m2 块体体积/m3 最大高差/m L1 50.35 728.15 1712.30 7.65 L2 45.58 835.53 2398.60 10.60 L3 40.42 842.94 2299.47 9.48 L4 26.23 706.89 3294.37 14.53 L5 46.61 886.78 1690.18 7.71
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表 4 异形滑移式块体物理力学参数
Table 4. Physical and mechanical parameters of profiled block
参数 块体重度/（kN·m−3） 结构面黏聚力/MPa 结构面内摩擦角/（°） 取值 25.8 0.100 35
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表 5 危险岩体稳定系数的计算与定性
Table 5. Calculation and qualitative characterization of stability coefficient of dangerous rock mass
块体编号 稳定性系数 稳定性分析 块体性质 L1 0.58 不稳定 是 L2 0.69 不稳定 是 L3 0.82 不稳定 是 L4 1.42 稳定 否 L5 0.66 不稳定 是
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表 6 危险岩体与岩体体积评价
Table 6. dangerous rock mass and rock massvolume evaluation
危岩体编号 危岩体体积/m3 危岩体体积评价 L1 1712.30 大型 L2 2398.60 大型 L3 2299.47 大型 L5 1690.18 大型
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