Automatic characterization of rock mass discontinuities using 3D point clouds

Abstract

Direct contact measurements for rock mass discontinuities are generally difficult to perform, time consuming, biased, and often dangerous. This paper presents an automatic characterization method for rock mass discontinuities that uses 3D point clouds, which can be obtained through non-contact measuring techniques such as photogrammetry and Light Detection and Ranging (LiDAR). In this method, five discontinuity parameters, namely, the orientation, trace, spacing, roughness, and aperture, are extracted automatically. The overall methodology is as follows: (1) orientation is determined by using an improved K-means clustering method; (2) trace segments are detected by using the Normal Tensor Voting Theory, and four post-processing techniques are employed to compute the trace length; (3) spacing is calculated by plotting virtual normal scan lines on the projected traces; (4) roughness is evaluated by the correlation between the Joint Roughness Coefficient (JRC) and the root mean square of the discontinuity surface profile; and (5) aperture is obtained by computing the average minimum width based on sub-pixel edge detection. The proposed method was applied to a drill-and-blast rock tunnel, where the extracted discontinuity parameters were used to calculate the Rock Mass Rating (RMR) value and Geological Strength Index (GSI) of the rock mass. The application results showed that photogrammetry was more objective and efficient for acquiring rock mass discontinuity information, and that it could be used as a potential alternative to the traditional discontinuity mapping method.