3D laser imaging for surface roughness analysis

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.

[1]  Stephen D. McKinnon,et al.  3D laser imaging for joint orientation analysis , 2011 .

[2]  Jerry P. Fairley,et al.  Estimating surface roughness of terrestrial laser scan data using orthogonal distance regression , 2011 .

[3]  Gianfranco Forlani,et al.  Advanced Geostructural Survey Methods Applied to Rock Mass Characterization , 2009 .

[4]  Alexandre Brouste,et al.  Characterization of Fault Roughness at Various Scales: Implications of Three-Dimensional High Resolution Topography Measurements , 2008, 0810.1109.

[5]  Nick Barton,et al.  Review of a new shear-strength criterion for rock joints , 1973 .

[6]  S. Slob,et al.  Identification and characterisation of rock mass discontinuity sets using 3D laser scanning , 2006 .

[7]  D. Crandall,et al.  Numerical simulations examining the relationship between wall-roughness and fluid flow in rock fractures , 2010 .

[8]  Giovanni Grasselli,et al.  A method to evaluate the three-dimensional roughness of fracture surfaces in brittle geomaterials. , 2009, The Review of scientific instruments.

[9]  François Blais.  Review of 20 years of range sensor development , 2004, J. Electronic Imaging.

[10]  Claire Samson,et al.  Triangulation-based 3D laser imaging for underground rock mass characterization: Impact of operational parameters , 2008 .

[11]  N. Barton,et al.  The shear strength of rock joints in theory and practice , 1977 .

[12]  Mariusz MŁynarczuk,et al.  Description and classification of rock surfaces by means of laser profilometry and mathematical morphology , 2010 .

[13]  O. Fernández,et al.  Obtaining a best fitting plane through 3D georeferenced data , 2005 .

[14]  E. Hoek,et al.  Rock slope engineering , 1974 .

[15]  Mostafa Sharifzadeh,et al.  Analysis of Aperture Evolution in a Rock Joint Using a Complex Network Approach , 2008, ArXiv.

[16]  Francois Blais,et al.  The Neptec Three-Dimensional Laser Camera System: From Space Mission STS-105 to Terrestrial Applications , 2004 .

[17]  Erik Eberhardt,et al.  The use of LiDAR to overcome rock slope hazard data collection challenges at Afternoon Creek, Washington , 2006 .

[18]  G. Grasselli,et al.  A new 2D discontinuity roughness parameter and its correlation with JRC , 2010 .

[19]  Mark S. Diederichs,et al.  Optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses , 2009 .

[20]  Francois Blais,et al.  Neptec 3D Laser Camera System: from space mission STS-105 to terrestrial applications , 2002 .

[21]  M. Menenti,et al.  Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning , 2011 .

[22]  Nick Barton,et al.  Engineering classification of rock masses for the design of tunnel support , 1974 .