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Bulletin of Engineering Geology and the Environment

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01-01-2024 | Original Paper

New 2D joint roughness profiles based on pattern recognition technique

Authors: Mahdi Moosavi, Ali Mohamad Pakdaman

Published in: Bulletin of Engineering Geology and the Environment | Issue 1/2024

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Abstract

Surface roughness is a major factor controlling the shear strength of discontinuities. Various methods have so far been presented for determining surface roughness based on 2D profiles. These methods use statistical, fractal, geostatistical, directional, and spectral features of the surface profiles most of which were established through the relationship between the features and Barton standard profiles. To overcome some of the shortcomings of the standard JRC profiles, this paper presents a new method based on the investigation of 2D profiles within “pattern recognition” framework for rock surface recognition. In this way, more than 9000 profiles were gathered from 84 natural rock samples for the calculation of the features, and the joint roughness coefficient was back-calculated from test results. A representative feature vector and profile of a surface were defined, and two methods of principal component analysis (PCA) and liner discriminant analysis (LDA) were applied in order to prepare inputs for the rock surface classification. Then, minimum mean distance (MMD) and K-nearest neighbors (KNN) were used for the classification. The results show that the latter provides lower classification error (44.4% when a level difference between the predicted and back-calculated classes is accepted) due to reducing the effect of dissimilar samples. Comparison of the results with those of single-variable equations that are based on statistical (\(\delta\) and \({Z}_{2}\)) and directional parameters \(\left(\frac{{\theta }_{\mathrm{max}}^{*}}{1+C}\right)\) with 53.57%, 52.38%, and 48.8% errors, respectively, proves that the proposed procedure is an efficient tool for the estimation of JRC. Furthermore, based on the results obtained from the pattern recognition analysis, 10 natural and artificial reference profiles attained based on a fully quantified approach were proposed as a replacement for the current JRC profiles.

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Title: New 2D joint roughness profiles based on pattern recognition technique
Authors: Mahdi Moosavi
Ali Mohamad Pakdaman
Publication date: 01-01-2024
Publisher: Springer Berlin Heidelberg
Published in: Bulletin of Engineering Geology and the Environment / Issue 1/2024
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-023-03472-1

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