Influence of Roughness Digitisation Error on Predictions of Discontinuity Shear Strength

A key component of the new stochastic approach for discontinuity shear strength (referred to as StADSS) is characterising the roughness of natural rock discontinuities at full scale to mitigate well-known scale effects on shear strength predictions. An investigation was conducted using the software Blender (v4.0) to determine the influence of camera orientation and position on the estimation of the standard deviation of gradients, a parameter used to quantify roughness and to make predictions of discontinuity shear strength. The existing literature has investigated the various distortions of images due to camera position and orientation; however, a comprehensive understanding of their unique influence on error in shear strength prediction is still missing. The investigation revealed that a ground sampling distance of less than 1.36 mm/pixel allows the standard deviation of gradients to be quantified within approximately ±10% relative error to the control data set. Based on investigations into camera orientation relative to the planar control data set, error on the roughness parameter due to perspective distortion was quantified. Recommendations were made to reduce perspective distortions and improve seed trace digitisation errors, including capturing images perpendicular to the seed trace with no rotation and getting as close as possible to the trace during capture to improve ground sampling distance. Lastly, the influence of these different trace digitisation errors on predictions of shear strength obtained using StADSS was investigated. Digitisation errors were found to have a disproportionate influence on shear strength prediction error, especially for rough discontinuities at low normal stress. The investigation highlighted the importance of accurately digitising the standard deviation of gradients to predict discontinuity shear strength.