Relationship between Joint Roughness Coefficient and Statistical Roughness Parameters and Its Sensitivity to Sampling Interval

Yong Luo (), Yakun Wang, Heng Guo, Xiaobo Liu, Yihui Luo and Yanan Liu
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Yong Luo: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Yakun Wang: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Heng Guo: State Key Laboratory of the Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400037, China
Xiaobo Liu: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Yihui Luo: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Yanan Liu: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China

Sustainability, 2022, vol. 14, issue 20, 1-23

Abstract: Accurate determination of the surface roughness is of significant importance in estimating the mechanical and hydraulic behaviors of rock joints. The correlation between joint roughness coefficient ( JRC ) and various statistical roughness parameters calculated from digitized Barton’s roughness profiles was explored with Pearson’s correlation coefficient method. The results show the strongest correlation between the standard deviation of the roughness angle and JRC following an excellent linear relationship. In addition, the correlation in the JRC with textural parameters is better than its correlation with amplitude parameters. Twenty-nine rock joint surfaces from fine sandstone, coarse sandstone and granite joint samples with a wide range of surface morphology were digitized using a high-resolution 3D scanner instrument. Further, the statistical roughness parameter values were calculated for each joint profile at eight different sampling intervals for sensitivity analysis of these statistical roughness parameters with regard to the sampling interval. The result indicated that textural parameters generally have a certain degree of dependency on sampling interval, following a power-law relationship. Specifically, when the sampling interval increases, the structure function value increases whereas it decreases for other textural parameters. In contrast, the dependence of the amplitude parameters on the sampling interval is not significant.

Keywords: rock joint; joint roughness coefficient; roughness parameter; sampling interval (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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