A Semi-automatic Approach to Quantifying the Geological Strength Index Using Terrestrial Laser Scanning

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.

Automated summary

This paper proposes a method for rapid and quantitative estimation of geological strength index (GSI) using 3D point clouds generated through non-contact measurement methods. The method includes acquiring point clouds using a terrestrial laser scanner, identifying discontinuities through artificial neural networks (ANN) and density-based spatial clustering of applications with noise (DBSCAN), extracting geometric information for the detected discontinuities, and estimating GSI according to the detection and characterization of discontinuities. The application results show that 3D point clouds can provide an objective and efficient way to obtain GSI values of rock mass and can be used as a potential alternative to the traditional GSI estimation method.