Abstract
The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass. The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation, which is very important for analysis of slope stability. The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane, but large amount of point cloud data, uneven density distribution, and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology. A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed. Based on the distribution states of the original point cloud in different neighborhoods in space, the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform (RRHT). The normal vector difference and the final eigenvalue are proposed for characteristic distinction, and the identification of rock mass structural surfaces is completed through regional growth, which strengthens the difference expression of point clouds. In addition, nearest Voxel downsampling is also introduced in the RRHT calculation, which further reduces the number of sources of neighborhood noises, thereby improving the accuracy and stability of the calculation. The advantages of the method have been verified by laboratory models. The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries. The method works well in the identification of joints, fissures, and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area, China. It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes, which is beneficial in engineering practice.
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Acknowledgments
We are very grateful to the reviewers for their constructive revisions. This work is supported by the National Natural Science Foundation of China (51909136); the Open Research Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(China Three Gorges University), Ministry of Education, Grant No. 2022KDZ21, and Fund of National Major Water Conservancy Project Construction (0001212022CC60001)
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XU Zhi-hua: data curation, methodology. GUO Ge: investigation, writing-original draft. SUN Qian- Cheng: methodology, writing-original draft, writing- review and editing. WANG Quan: data curation, Investigation. ZHANG Guo-dong: methodology, funding acquisition. YE Run-qing, writing-review and editing.
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Xu, Zh., Guo, G., Sun, Qc. et al. Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform. J. Mt. Sci. 20, 3376–3391 (2023). https://doi.org/10.1007/s11629-023-7914-z
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DOI: https://doi.org/10.1007/s11629-023-7914-z