Abstract
Roughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in this study, where the 3D laser scanning technique and self‐compiled Python code, as well as the statistical parameter methods, were applied. Then, the shear strength of jointed rock was evaluated via Barton′s model, and therefore, a comprehensive comparison between the calculating results and experimental results was executed. Ultimately, the influencing factors of roughness profile extraction on the accuracy of JRC value, such as the measuring point interval, profile number, and measuring direction, were investigated. The results show that (1) equipped with the 3D laser scanning technique, the roughness profiles can be accurately extracted via the self‐compiled Python code, (2) an excellent consistency of shear strength could be observed between the calculating value and experimental results, verifying the validity and accuracy of the proposed method, and (3) a smaller measuring point interval can produce a more accurate digital profile and more accurate JRC value. To a certain extent, the more the sample numbers of profiles, the smaller the value of JRC.
Highlights
Rock joints widely exist in rock engineering [1,2,3,4]. e mechanical properties of rock joints are considered as the controlling factors of rock mass engineering stability [5,6,7,8]
The 3D scanning technique was applied to obtain the 3D coordinate data, the Python code was used to extract the roughness profile, and the value of joint roughness coefficient (JRC) was calculated via the statistical parameter methods, where Z2, structure function (SF, representing the changes in surface texture), and Rp were selected. en, the arithmetic average of JRC of all regions was solved to represent the roughness of the joint surface, denoted as JRCp, which avoids the subjective estimation
3.3. e Influencing Factors of Roughness Profile Extraction. e previously described strategy of roughness determination in this study indicates that the real challenge to obtain an accurate value of the JRC of joint emerges as extracting appropriate roughness profiles, which is quite susceptible by subjective factors
Summary
Rock joints widely exist in rock engineering [1,2,3,4]. e mechanical properties of rock joints are considered as the controlling factors of rock mass engineering stability [5,6,7,8]. Experimental evidences [27] showed that the difference of the JRC is up to 4 when the error of fluctuation measurement is 1 mm In this case, some other optional methods, such as statistical parameter, straight edge, elongation, and geometric fractal methods, have been developed to quantitatively determine the value of JRC [16, 17, 28,29,30]. The 3D scanning technique was applied to obtain the 3D coordinate data, the Python code was used to extract the roughness profile, and the value of JRC was calculated via the statistical parameter methods, where Z2 (the first derivative root mean square of roughness profiles), structure function (SF, representing the changes in surface texture), and Rp (the length ratio of the trace line to the straight line) were selected. The influencing factors of roughness profile extraction on the accuracy of JRCp, such as the measuring point interval, profile number, and measuring direction, were investigated
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