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<p>In rock slope engineering, concealment and inaccessibility often hinder the reliable positioning of potentially unstable rock masses on high and steep slopes, posing significant safety and economic risks. This study presents a progressive and integrated system for the efficient and intelligent positioning of such rock masses, demonstrated on the slope of a flood discharge system at a hydropower station in southwestern China. The workflow encompasses high-precision three-dimensional (3D) slope modeling, efficient discontinuity mapping, and rapid identification of unstable zones. First, a multi-drone and multi-stage acquisition strategy was developed to capture high-resolution optical imagery and LiDAR point clouds, which were fused into a slope outcrop fusion model (SOFM). The SOFM aligns detailed outcrop textures with pixel-wise density point clouds, enabling a unified and high-fidelity representation of discontinuity geometry and surface features. Second, an enhanced discontinuity-surface-extractor algorithm coupled with an artificial intelligence-powered 3D trace detector automated the mapping and statistical analysis of 358 plane-type and 597 line-type discontinuities. After removing pseudo-linear features, 533 valid discontinuities were retained. Finally, a refined rock slope kinematic analysis (ROKA) method—based on the traditional Markland’s test but incorporating actual discontinuity geometries and local slope curvature—was applied using the SOFM. This approach allows for rapid and accurate positioning of potentially unstable rock masses. In contrast to the conventional kinematic analysis, which tends to overpredict unstable discontinuity intersections, ROKA delivers improved precision and reliability in failure zone localization and visualization, thereby providing a robust foundation for targeted mitigation strategies.</p>

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Efficient and Intelligent Positioning of Potentially Unstable Rock Masses in Rock Slope Engineering: Case Study from a Hydropower Station in Southwest China

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Yongqiang Liu,
  • Da Huang,
  • Jiewei Zhan,
  • Changle Pu,
  • Zhaowei Yao,
  • Seyedahmad Mehrishal,
  • Jae Joon Song,
  • Zhanglei Wu

摘要

In rock slope engineering, concealment and inaccessibility often hinder the reliable positioning of potentially unstable rock masses on high and steep slopes, posing significant safety and economic risks. This study presents a progressive and integrated system for the efficient and intelligent positioning of such rock masses, demonstrated on the slope of a flood discharge system at a hydropower station in southwestern China. The workflow encompasses high-precision three-dimensional (3D) slope modeling, efficient discontinuity mapping, and rapid identification of unstable zones. First, a multi-drone and multi-stage acquisition strategy was developed to capture high-resolution optical imagery and LiDAR point clouds, which were fused into a slope outcrop fusion model (SOFM). The SOFM aligns detailed outcrop textures with pixel-wise density point clouds, enabling a unified and high-fidelity representation of discontinuity geometry and surface features. Second, an enhanced discontinuity-surface-extractor algorithm coupled with an artificial intelligence-powered 3D trace detector automated the mapping and statistical analysis of 358 plane-type and 597 line-type discontinuities. After removing pseudo-linear features, 533 valid discontinuities were retained. Finally, a refined rock slope kinematic analysis (ROKA) method—based on the traditional Markland’s test but incorporating actual discontinuity geometries and local slope curvature—was applied using the SOFM. This approach allows for rapid and accurate positioning of potentially unstable rock masses. In contrast to the conventional kinematic analysis, which tends to overpredict unstable discontinuity intersections, ROKA delivers improved precision and reliability in failure zone localization and visualization, thereby providing a robust foundation for targeted mitigation strategies.