Discontinuities dominantly influence the mechanical behavior of rock masses. Thus, it is of crucial importance in rock engineering to have a profound knowledge about the discontinuity network. Traditional measuring techniques provide only a rough knowledge about a discontinuity network and are prone to human bias. To increase the reliability of discontinuity models, digital mapping techniques using data from remote sensing, like Close-Range Terrestrial Digital Photogrammetry, were developed. This paper focuses on the plane identification within 3-D point clouds using MATLAB® (The Mathworks Inc.) and DIPS® (Rocscience Inc.). The 3-D point cloud is generated with the program ShapeMetriX3D (3GSM GmbH). To verify the plane identification with MATLAB® the results are compared with mapping results from ShapeMetriX3D. This research is part of an approach to automate the rock mass characterization by combining information attained by digital image processing and the analysis of the digital surface model.


There hardly exists a rock mass without three or more different discontinuity sets. Discontinuities, like bedding planes, joints or cracks, represent the weak zones, which separate singular polyhedrons of competent rock blocks and control failure mechanisms. Thus, discontinuities have a huge influence on the mechanical properties and the behavior of rock masses. Hence it is of crucial importance in rock engineering to have a profound knowledge about the discontinuity network within a rock mass.

Geotechnical engineers traditionally acquire this data by geological and geotechnical surveys, which combine visual and manual technologies to characterize rock masses. The most common tool is a geological compass to directly measure the dip and dip direction of discontinuities. This method can be extended by scan lines and mapping windows which reduce the inherent bias of a single measurement. Ulusay & Hudson (2007) suggest ten different discontinuity properties (orientation, spacing, persistence, roughness, wall strength, aperture, filling, seepage, number of sets and block size) to describe a rock face. But the traditional geotechnical data acquisition is limited in accessibility, geological or geotechnical knowledge, time and scale. The results are often subjective rather than objective and therefore not reproducible (Gaich et al. 2006, Kemeny & Post 2003).

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