Abstract
The geological strength index (GSI) plays an important role in the quality evaluation and stability analysis of rock mass. Traditional methods for quantitatively estimating GSI are often subjective, time-consuming, and dangerous. This paper proposed a method for rapid and quantitative GSI estimate using 3D point clouds, which can be generated through non-contact measurement methods such as photogrammetry and Light Detection and Ranging (LiDAR). The overall methodology is as follows: (1) point clouds were acquired using a terrestrial laser scanner; (2) discontinuities were identified through artificial neural networks (ANN) and density-based spatial clustering of applications with noise (DBSCAN); (3) geometric information was extracted for the detected discontinuities; (4) GSI was estimated according to the detection and characterization of discontinuities. The proposed method was used for the Yujiashan road cut to calculate the GSI and the GSI partitioning was performed simultaneously. Three sets of discontinuity were detected in the Yujiashan road cut, and Structure Rating (SR) and Surface Conditions Point Clouds Rating (SCPC) were calculated to be 13.2 and 25, respectively. Correspondingly, the GSI was estimated to be 32, which was consistent with the results of the in-situ evaluation (rating 25–40). Furthermore, the Yujiashan road cut was divided into 17 segments, and the effect of sampling size on the GSI calculation was discussed. The application results show that the GSI of the rock mass can be obtained objectively and efficiently through 3D point clouds, which can be used as a potential alternative to the traditional method for GSI estimation.
Article Highlights
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A semi-automatic method was developed to determine the rockmass GSI using laser scanning.
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Discontinuities were detected from the 3D point cloud using a machine-learning algorithm.
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Four geometric parameters of detected discontinuities were extracted for GSI calculation.
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The scale effect of GSI calculation was investigated using the proposed partitioning algorithm.
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GSI estimation obtained from the proposed method matched the traditional manual surveys.
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Availability of Data and Material
The data and materials that support the findings of this study are available from the first and corresponding author, Yunfeng Ge, upon reasonable request.
Code Availability
The codes that support the rock discontinuity detection are available at GitHub (https://Github.com/DisDetANN/DIsDetANNcode).
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Acknowledgements
Thanks to master students Geng Liu and Weixiang Chen for their hard work in the data collection process. We thank editors and anonymous reviewers for their useful comments.
Funding
This article was funded by National Natural Science Foundation of China (Grant no. 42077264).
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YG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing-original draft. QC: Data curation, Software, Investigation, Validation, Visualization, Writing-original draft. HT: Conceptualization, Funding acquisition, Project administration, Supervision. BC: Data curation, Software. WH: Writing-original draft.
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Ge, Y., Chen, Q., Tang, H. et al. A Semi-automatic Approach to Quantifying the Geological Strength Index Using Terrestrial Laser Scanning. Rock Mech Rock Eng 56, 6559–6579 (2023). https://doi.org/10.1007/s00603-023-03412-1
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DOI: https://doi.org/10.1007/s00603-023-03412-1