• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.73-80
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• 2001

The shear strength of jointed rock is influenced by effective normal stress, joint wall compressive strength, joint roughness and so on. Since joint roughness makes considerable influences on shear strength of jointed rock, many studies tried to get quantitative joint roughness parameter. Until now, Joint Roughness Coefficient, JRC proposed by Barton has been prevalently used as a rock joint roughness parameter In spite of its disadvantages. In this study, a quantification of rock joint roughness is performed using surface roughness parameter, Rs. Proposed method is applied to rock core specimens, field joint surfaces, and JRC profiles. The scale of fluctuation is introduced to extend the suggested method to the large scale field joint surface roughness. Based on the quantification of joint surface roughness, joint shear tests are performed with the portable shear box. The relationship between joint surface roughness and joint shear strength is investigated and finally, a rock joint shear strength equation is derived from these results. The equation has considerable credibility and originality in that it is obtained from laboratory tests and expressed with quantified parameter.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.561-577
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• 2021

The ten standard roughness profiles suggested by Barton and Choubey (1977) were extended to make three-dimensional (3D) joint models whose profiles were identical at any cross section. Replicas of joint models were produced using plaster of Paris, and direct shear tests were performed to verify the joint roughness coefficients (JRC) of the standard roughness profiles. Joint shear strengths measured by direct shear tests were compared with those predicted by the shear failure criterion suggested by Barton (1973) based on JRC, joint compressive strength (JCS), and joint basic friction angle (𝜙_b). Shear strengths measured from joints of the first and fourth standard roughness profiles were close to predicted values; however, shear strengths measured from the other joint models were lower than predicted, the differences increasing as the roughness of joints increased. Back calculated values for JRC, JCS, and from the results of the direct shear tests show measured shear strengths were lower than predicted shear strengths because of the JRC values. New JRC were back calculated from the measured shear strength and named JRC_m. Values of JRC_m were lower than the JRC for the standard roughness profiles but show a strong linear relationship to JRC. Corrected JRC_m values for the standard roughness profiles are provided and revised relationships between JRC_m and JRC, and new shear strength criterion are suggested.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.52-63
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• 2009

This is a study on large-scale rock joint roughness measurements using LIDAR (light detection and ranging) and the Split-FX point cloud processing software. The large-scale rock Joint Roughness Coefficient (JRC) is calculated using the maximum amplitude of joint asperities over the profile length on large-scale Joint surfaces of rock. As the profile length increases, JRC decreases due to scale-effects of rock specimens and is non-stationary. Also JRC shows anisotropy depending on the profile direction. The profile direction is measured relative to either dip or strike of the large-scale joint.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1205-1210
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• 2005

The behavior of rock mass and solute(e.g. groundwater, radioactivity) flow in fractured rock can be directly influenced by joint roughness. The characteristics of joint roughness is also a main factor for the rock classification(e.g. RMR, Q system) which is usually used in tunnel design. Nevertheless, most of JRC estimation has been carried out only by the examination with the naked eye. This JRC estimation has a lack of objectivity because each investigator judges JRC by his subjective opinion. Therefore, it will be desirable that the assessment of JRC is performed by a numerical analysis which can give a quantitative value corresponding to the characteristics of a roughness curve. Meanwhile, roughness curves for joint surfaces which are observed in drill cores have been obtained only along linear profiles. Although roughness curves are measured in the same joint surface, they can frequently show diverse aspects in a standpoint of roughness characteristics. If roughness curves can be measured along the elliptical circumferences of joint surfaces from core scanning images or Televiewer images, they will certainly be more comprehensive than those measured along linear profiles for roughness characteristics of joint surfaces. This study is focus on dealing with (1) extracting automatically roughness curves from core scan image or Televiewer image, (2) improving the accuracy of quantitative assessment of JRC using fractal dimension concept.

• The Journal of Engineering Geology
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• v.20 no.3
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• pp.319-327
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• 2010

Surface roughness profiles were measured from 19 joint samples using a laser scanner, and Joint Roughness Coefficient (JRC) values were calculated from 30 sections in each sample. Although JRC values varied with the location of the section, the average JRC values from any three sections provides an adequate representation of the average JRC value for the entire surface well. Direct shear tests were performed on nine joints reproduced using molds of real joints in samples of gypsum. The peak friction angles (${\phi}_p$) showed a linear relationship with the average JRC values, yielding the following relationship: ${\phi}_p=41.037+1.046JRC$. However, the shear strengths measured by direct shear tests differed from those calculated using Barton's criterion. The relationship between calculated from direct shear tests and JRC measured from joint surfaces is defined as $JRC_R=f{\cdot}JRC$, and the correction coefficient f is was calculated as $f=3.15JRC^{-0.5}$, as calculated by regression. A modified shear-strength criterion, is proposed using the correction coefficient, ${\tau}={\sigma}_n{\cdot}tan(3.15JRC^{0.5}{\bullet}{\log}_{10}\frac{JCS}{{\sigma}_n}+{\phi}_b)$. This criterion may be effective in calculating the shear strength of moderately weathered rock joints and highly weathered rock joints with low strength and ductile behavior.

• The Journal of Engineering Geology
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• v.15 no.3
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• pp.269-280
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• 2005

We assigned points on surface of standard roughness profile by 0.1mm along the length and measured coordinates of points. Then, the lengths of profile were measured with different scales using modified divider method. The fractal dimensions and intercepts of slopes were determined by plotting the length vs scale in log-log scale. The fractal dimensions as well as intercepts of slopes show well correlation with joint roughness coefficients(JRC). However, multiplication of the kactal dimension by intercept show better correlation with IRC and we derived a new equation to estimate JRC from fractal dimension and intercept. The crossover length in which we can determine the correct fractal dimension was between 0.3-3.2mm. We measured joint roughness of 26 natural joints and calculated JRC using the equation suggested by Tse and Cruden(1979) and new equation derived by us. IRC values calculated by both equations are almost the same, indicating new equation is effective in measuring IRC.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.461-468
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• 2004

In order to characterize hydraulic property dependant on join roughness in rock mass, this study computed permeability coefficients on each range of joint roughness coefficient (JRC) suggested by Barton(1976). For a quantitative analysis of roughness components spectral analysis using the fast fourier transform was performed to select effective frequencies on each PC range. The results of spectral analyses show that low ranges of the JRC are mainly composed of low frequency domain, while high ranges of the JRC have dominant components at high frequency domain. The inverse Fourier transform made it possible to generate joint models of each JRC range using the effective frequencies of roughness spectrum. The homogenization analysis was applied to calculate permeability coefficient at homogeneous microscale, and then, computes a homogenized permeability coefficient (C-permeability coefficient) at macro scale. Therefore, it is possible to analyze accurate characteristics of permeability reflected with local effect of facture geometry. According to the calculation results, permeability coefficients were distributed between $10^{-3}m/sec\;and\;10^{-4}/sec$. In cases of sheared joint models permeability coefficients were plotted between $10^{-4}m/sec\;and\;10^{-5}/sec$, showing irregular distribution of permeability coefficients on each IRC range. The differences of permeability coefficients for the same aperture models or for the sheared joint models indicate that changes of roughness pattern influence on permeability coefficients. Therefore, the effect of joint roughness should be considered to characterize hydraulic properties in rock joints.

• Geomechanics and Engineering
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• v.28 no.2
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• pp.181-195
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• 2022

Roughness and joint inclination angle are the important factors that affect the strength and deformation characteristics of jointed rock mass. In this paper, 3D printer has been employed to make molds firstly, and casting the jointed specimens with different joint roughness coefficient (JRC), and different joint inclination angle (α). Conventional triaxial compression tests were carried out on the jointed specimens, and the influence of JRC on the strength and deformation parameters was analyzed. At the same time, acoustic emission (AE) testing system has been adopted to reveal the AE characteristic of the jointed specimens in the process of triaxial compression. Finally, the morphological of the joint surface was observed by digital three-dimensional video microscopy system, and the relationship between the peak strength and JRC under different confining pressures has been discussed. The results indicate that the existence of joint results in a significant reduction in the strength of the joint specimen, JRC also has great influence on the morphology, quantity and spatial distribution characteristics of cracks. With the increase of JRC, the triaxial compressive strength increase, and the specimen will change from brittle failure to ductile failure.

• Economic and Environmental Geology
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• v.27 no.1
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• pp.81-91
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• 1994

There has been considerable research dealing with the influence of surface roughness along surfaces of rock discontinuities in relation to the peak shear strength of rock masses. Concepts accepted recently for measuring such strength include estimation of a roughness coefficient such as developed by Barton's studies. The method for estimation the Joint Roughness Coefficient (JRC) value of a measured roughness profile is subjective. The aim of this research is to estimate the JRC value of the roughness of a surface profile in a rock mass system using an objective method. The study of roughness of surfaces has included measurement of fractal geometric characteristics. Once the irregularity of the surface has been described by the fractal dimension, the spatial variation of the surface irregularities can be described using variogram and drift analysis. An empirical relationships between the roughness profiles of selected JRC ranges and their fractal dimension with variogram and drift were derived. The application of analyses of fractal dimension, variogram and drift was novel for the analysis of roughness profiles. Also, an empirical equation was applied to experimental data.

• The Journal of Engineering Geology
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• v.21 no.1
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• pp.1-13
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• 2011

Joint roughness is one of the most important parameters in analysis of rock slope stability. Especially in probabilistic analysis, the random properties of joint roughness influence the probability of slope failure. Therefore, a large dataset on joint roughness is required for the probabilistic analysis but the traditional direct measurement of roughness in the field has some limitations. Terrestrial LiDAR has advantagess over traditional direct measurement in terms of cost and time. JRC (Joint Roughness Coefficient) was calculated from statistical parameters which are known from quantitative methods of converting the roughness of the material surface into JRC. The mean, standard deviation and distribution function of JRC were obtained, and we found that LiDAR is useful in obtaining large dataset for random variables.