ISSN 1003-8035 CN 11-2852/P
    程雨柯,李亚虎,夏金梧,等. 无人机技术在超高陡边坡危岩体半自动识别中的应用[J]. 中国地质灾害与防治学报,2024,35(1): 143-154. DOI: 10.16031/j.cnki.issn.1003-8035.202310028
    引用本文: 程雨柯,李亚虎,夏金梧,等. 无人机技术在超高陡边坡危岩体半自动识别中的应用[J]. 中国地质灾害与防治学报,2024,35(1): 143-154. DOI: 10.16031/j.cnki.issn.1003-8035.202310028
    CHENG Yuke,LI Yahu,XIA Jinwu,et al. Application UAV technology semi-automatic identification dangerous rock masses on ultra-high steep slopes[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 143-154. DOI: 10.16031/j.cnki.issn.1003-8035.202310028
    Citation: CHENG Yuke,LI Yahu,XIA Jinwu,et al. Application UAV technology semi-automatic identification dangerous rock masses on ultra-high steep slopes[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 143-154. DOI: 10.16031/j.cnki.issn.1003-8035.202310028

    无人机技术在超高陡边坡危岩体半自动识别中的应用

    Application UAV technology semi-automatic identification dangerous rock masses on ultra-high steep slopes

    • 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 hazardous rock masses are often restricted by the complex and steep terrain. To improve the efficiency and automation of hazardous 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 hazardous 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 hazardous 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 hazardous 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 hazardous rock masses, enhance survey efficiency, and mitigate the inaccuracies associated with manual data collection. This approach holds significant practical value for assessing hazardous rock masses on ultra-high steep slopes.

       

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