Three-dimensional (3D) images of rock faces contain key information for rock mass characterization. In an underground field trial, 3D laser imaging is used to measure surface roughness. A controlled validation study is performed comparing surface roughness measured manually and from the 3D data. At the same location as the manual measurements, joint orientation is first measured from the 3D point cloud data using principal component analysis. This process calculates the best fitting plane and its associated normal vector to the imaged joint surface. The normal vector being known, a 2D surface profile can be generated along the strike and dip. The profile length of the maximum asperity amplitude measured from the 3D data is used to estimate the Joint Roughness Coefficient (JRC). In addition, the 3D point cloud data contains a wealth of information typically not available. The surface roughness measurements can be made across the entire image to generate a surface roughness map. Joint characteristics such as surface roughness anisotropy can be evaluated efficiently.

Additional Metadata
Keywords 3D imaging, Anisotropy, Joint sets, Laser, Mapping, Surface roughness
Persistent URL dx.doi.org/10.1016/j.ijrmms.2012.08.001
Journal International Journal of Rock Mechanics and Mining Sciences
Citation
Mah, J. (Jason), Samson, C, McKinnon, S.D. (Stephen D.), & Thibodeau, D. (Denis). (2013). 3D laser imaging for surface roughness analysis. International Journal of Rock Mechanics and Mining Sciences, 58, 111–117. doi:10.1016/j.ijrmms.2012.08.001