Scholarlink

<p>The characteristics of rock mass discontinuities, including their orientation, spacing, and volumetric joint count, are pivotal in assessing rock quality. Accurate identification of these discontinuities is essential to compute these parameters. Traditional approaches for identifying discontinuities often rely on clustering algorithms, which necessitate manual analysis of cluster numbers on the basis of stereographic projections. This manual process introduces the possibility of errors and inconsistencies, thus constraining the level of automation and repeatability of such methods. To improve the automatic determination of the number of rock mass discontinuity clusters, this paper introduces a novel method known as density analysis of stereographic projection (DASP). DASP facilitates the automatic calculation of the number of discontinuity clusters. In addition, the supervoxel clustering of three-dimensional point clouds (3D-SPVC) method is utilised to automatically classify and identify rock mass discontinuities. The methodology encompasses the following steps: (1) computation of the local dip direction and dip angle by establishing a spatial index; (2) automatic determination of the number of discontinuity sets and the primary dip direction and dip angle through DASP analysis; (3) identification of rock discontinuities via the 3D-SPVC algorithm; and (4) assessment of rock mass quality by calculating the discontinuity spacing, volumetric joint count, and structural rating. The proposed method automatically identifies rock discontinuities, thereby eliminating the need for manual intervention, and was validated through comparative analyses across multiple datasets. This innovative and promising technique has significant potential in rock engineering applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automatic Recognition of Rock Discontinuities and Rock Quality Evaluation in 3D Point Clouds Based on DASP and 3D-SPVC

  • 期刊论文

  • 发表日期：

    2025年7月3日

查看全文
  • Zirui Zhang,
  • Jiateng Guo,
  • Tianhong Yang,
  • Wancheng Zhu,
  • Juanli Zhang

摘要

The characteristics of rock mass discontinuities, including their orientation, spacing, and volumetric joint count, are pivotal in assessing rock quality. Accurate identification of these discontinuities is essential to compute these parameters. Traditional approaches for identifying discontinuities often rely on clustering algorithms, which necessitate manual analysis of cluster numbers on the basis of stereographic projections. This manual process introduces the possibility of errors and inconsistencies, thus constraining the level of automation and repeatability of such methods. To improve the automatic determination of the number of rock mass discontinuity clusters, this paper introduces a novel method known as density analysis of stereographic projection (DASP). DASP facilitates the automatic calculation of the number of discontinuity clusters. In addition, the supervoxel clustering of three-dimensional point clouds (3D-SPVC) method is utilised to automatically classify and identify rock mass discontinuities. The methodology encompasses the following steps: (1) computation of the local dip direction and dip angle by establishing a spatial index; (2) automatic determination of the number of discontinuity sets and the primary dip direction and dip angle through DASP analysis; (3) identification of rock discontinuities via the 3D-SPVC algorithm; and (4) assessment of rock mass quality by calculating the discontinuity spacing, volumetric joint count, and structural rating. The proposed method automatically identifies rock discontinuities, thereby eliminating the need for manual intervention, and was validated through comparative analyses across multiple datasets. This innovative and promising technique has significant potential in rock engineering applications.