研究目的
To derive space-resolved normal joint spacing and in situ block size distribution data from terrestrial LIDAR point clouds in a rugged Alpine relief for improved rock mass characterisation.
研究成果
The automated point cloud analysis provides space-resolved data on joint spacing and block sizes that agree well with traditional scanline methods. It reveals significant spatial heterogeneities in rock mass properties, which are beneficial for geotechnical applications like rockfall analysis and quarry planning. Future work should include 3D visualizations for steeper slopes and integration with kinematic analyses.
研究不足
The approach may not detect joints that are not exposed at the rock surface, such as those only visible as traces. Large distances between scanner and slope can reduce accuracy and detail. Non-persistent joints lead to underestimation of real block sizes in calculations. The method requires adjustment of parameters for different point cloud characteristics and may not handle very steep slopes well without 3D visualization.
1:Experimental Design and Method Selection:
The study uses terrestrial laser scanning (TLS) and scanline surveys to characterize joint sets in a rock slope. A new automated analysis method for point cloud data is developed and implemented in software tools.
2:Sample Selection and Data Sources:
The case study is a 250-m-high, 500-m-wide NW-facing slope in Kühtai, Austria, composed of granodioritic gneisses with non-persistent joints. Data include TLS point clouds from seven scan positions and scanline measurements from 12 locations.
3:List of Experimental Equipment and Materials:
Riegl VZ 4000 terrestrial long-range scanner, RiScan Pro software, LIS Pro 3D software, SAGA GIS system, and standard field equipment for scanline surveys.
4:Experimental Procedures and Operational Workflow:
TLS data acquisition with specific angular resolutions, point cloud registration and geo-referencing, data pre-processing including homogenization and normal vector derivation, joint set clustering using kernel density estimation, joint plane identification via region-growing, normal spacing calculation, and creation of raster maps for fracture density and block sizes. Scanline data are collected and analyzed similarly for comparison.
5:Data Analysis Methods:
Statistical comparison of TLS and scanline data, kernel density estimation for joint orientations, Monte Carlo simulation for in situ block size distribution, and spatial analysis using GIS tools.
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