Abstract
Obtaining accurate discontinuity information on a tunnel is essential for tunnel stability assessment, and usually requires geological surveys on the tunnel surface. However, traditional manual measurement methods are time-consuming, labor-intensive, and provide limited data, particularly when dealing with complex tunnel rock masses. To address this problem, this paper proposes a method to quickly obtain the point cloud model of the tunnel surface and semi-automatically identify discontinuity using 3D laser scanner. The method is centered on an improved Regional Growth (RG) algorithm, with key principles and processing flow encompassing: (1) Voxel filtering; (2) Normal calculation for point clouds; (3) Improved RG algorithm; (4) Calculation of discontinuity orientation. An analysis of parametric sensitivity which proved its good robustness was carried out to assess the performance of the method. To ascertain the effectiveness of the method in semi-automatically identifying tunnel discontinuities, three sets of test data (standard cube, rock slope in Colorado, and Xulong hydroelectric station tunnel) were chosen. By comparing the analysis results of the proposed method with those of alternative methods (DSE and CloudCompare), the validation of its efficacy in tunnel discontinuity detection was achieved.
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Enquiries about data availability should be directed to the authors.
Abbreviations
- \(p_{0}\) :
-
Seed point
- \(Q\) :
-
Discontinuous feature point set
- \(Q_{a}\) :
-
The plane normal vectors before adding neighbor to \(Q\)
- \(Q_{{\text{b}}}\) :
-
The plane normal vectors after adding neighbor to \(Q\)
- \(\lambda\) :
-
The threshold for the mean squared error of both \(Q_{a}\) and \(Q_{{\text{b}}}\)
- \(d\) :
-
The threshold for the vertical distance from the neighbor to the plane
- \(S\) :
-
The number of points in the plane \(Q_{{\text{b}}}\)
- \(\mathop f\limits^{ \to }\) :
-
The normal vector of the plane \(Q_{{\text{b}}}\)
- \(p_{s}\) :
-
The \(s\)th point in the plane \(Q_{{\text{b}}}\)
- \(m\) :
-
The center of mass of the plane \(Q_{{\text{b}}}\)
- \(N_{\min }\) :
-
The minimum value of the number of points in a feature point set
- \(N_{\max }\) :
-
The maximum value of the number of points in a feature point set
- \(N_{{}}\) :
-
The normals of the plane
- \(N_{x}\) :
-
The normal vector of the plane in the x-direction
- \(N_{y}\) :
-
The normal vector of the plane in the y-direction
- \(N_{z}\) :
-
The normal vector of the plane in the z-direction
- \(\alpha\) :
-
The dip direction of the discontinuity
- \(\beta\) :
-
The dip of the discontinuity.
- \(n\) :
-
The number of neighbors
- \(\theta\) :
-
The maximum growth angle
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Funding
The authors would like to thank the Nationa Natural Science Foundation of China (Grant No. 52009038), the Joint Funds of the National Nature Science Foundation of China (No. U22A20232) and Foundation of Hubei Key Laboratory of Blasting Engineering (No. BL2021-21).
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CN: Conceptualization, Supervision, Methodology, Writing—Review and Editing. AX: Validation, Writing—Original Draft, Visualization, Methodology. H-lX: Resources, Supervision. LL: Resources, Validation.
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Chen, N., **ao, A., Li, L. et al. Semi-automatic Identification of Tunnel Discontinuity Based on 3D Laser Scanning. Geotech Geol Eng 42, 2577–2599 (2024). https://doi.org/10.1007/s10706-023-02692-2
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DOI: https://doi.org/10.1007/s10706-023-02692-2