A new method for automated estimation of joint roughness coefficient for 2D surface profiles using power spectral density

In this study, a new method for the objective and accurate estimation of the joint roughness coefficient (JRC) of surface profiles, which are extracted from terrestrial laser scanner point clouds, is proposed. The requirements of objectivity, accuracy, reliability, and suitability for automatic analysis have been the basic criteria in assessing the performance of the procedure for JRC estimation. The procedure to estimate the JRC value of a sample profile is based on a similarity measure, between the third-order polynomial function fits to the power spectral density in the spatial frequency domain of the sample surface profile and Barton's reference profiles. The procedure is tested on the one hundred and two digitized surface profiles found in the literature. Normal probability density distribution of estimation errors of the results show that the JRC estimation by the proposed method is more accurate and precise compared to the results from the three versions of the well-known and commonly used Z2 method.