A rapid field measurement method for the determination of Joint Roughness Coefficient of large rock joint surfaces

2017 ◽  
Vol 22 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Rui Yong ◽  
Xi Fu ◽  
Man Huang ◽  
Qifeng Liang ◽  
Shi-Gui Du
Keyword(s):  
Field Measurement ◽  
Measurement Method ◽  
Rock Joint ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Joint Surfaces

scholarly journals A New Method for Determination of Joint Roughness Coefficient of Rock Joints

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Shigui Du ◽  
Huicai Gao ◽  
Yunjin Hu ◽  
Man Huang ◽  
Hua Zhao
Keyword(s):  
Scale Effect ◽  
Fractal Model ◽  
Rock Joints ◽  
Effective Length ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Exposed Rock ◽  
Study Results

The joint roughness coefficient (JRC) of rock joints has the characteristic of scale effect. JRC measured on small-size exposed rock joints should be evaluated by JRC scale effect in order to obtain the JRC of actual-scale rock joints, since field rock joints are hardly fully exposed or well saved. Based on the validity analysis of JRC scale effect, concepts of rate of JRC scale effect and effective length of JRC scale effect were proposed. Then, a graphic method for determination of the effective length of JRC scale effect was established. Study results show that the JRC of actual-scale rock joints can be obtained through a fractal model of JRC scale effect according to the statistically measured results of the JRC of small-size partial exposed rock joints and by the selection of fractal dimension of JRC scale effect and the determination of effective length of JRC scale effect.

Determination of two-dimensional joint roughness coefficient using support vector regression and factor analysis

2017 ◽  
Vol 231 ◽  
pp. 238-251 ◽  
Author(s):  
Liangqing Wang ◽  
Changshuo Wang ◽  
Sara Khoshnevisan ◽  
Yunfeng Ge ◽  
Zihao Sun
Keyword(s):  
Factor Analysis ◽  
Support Vector Regression ◽  
Support Vector ◽  
Roughness Coefficient ◽  
Two Dimensional ◽  
Joint Roughness Coefficient ◽  
Joint Roughness

scholarly journals Suggested New Statistical Parameter for Estimating Joint Roughness Coefficient considering the Shear Direction

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhiqiang Zhang ◽  
Jiuyang Huan ◽  
Ning Li ◽  
Mingming He
Keyword(s):  
Statistical Parameter ◽  
Joint Surface ◽  
Accurate Estimation ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Statistical Parameters ◽  
Joint Roughness ◽  
Directional Property ◽  
Joint Surfaces ◽  
Area Difference

The 10 standard roughness joint profiles provided a visual comparison to get the joint roughness coefficient (JRC) of rock joint surface, but the accuracy of this method is influenced by human factors. Therefore, many researchers try to evaluate the roughness morphology of joint surface through the statistical parameter method. However, JRC obtained from most of the existing statistical parameters did not reflect the directional property of joint surface. Considering the 10 standard profiles as models of different roughness joints, we proposed a new idea for the accurate estimation of JRC. Based on the concept of area difference, the average of positive area difference (Sa) and sum of positive area difference (Ss) were first proposed to reflect the roughness of joint surfaces on the basis of directional property, and their fitting relationship with JRC was also investigated. The result showed that the Sa and Ss calculated by shearing from right to left (FRTL) and JRC backcalculated from right to left (FRTL) came to a satisfying power law. The correlation between JRC and Sa was better than that of Ss. The deviation between the predicted value calculated by Sa and the true value was smaller than that obtained from the existing statistical parameters. Therefore, Sa was recommended as a new statistical parameter to predict the JRC value of joint profile. As the sampling interval increased from 0.5 to 4 mm, the correlation between Sa and JRC gradually decreased, and the accuracy of the prediction results also declined. Compared with the single JRC values for joint profiles mentioned in the literature, the forward and reverse JRC were obtained. Based on the laboratory direct shear test of the natural joint surface, the JRC values of two joint surfaces in four shear directions were backcalculated by the JRC-JCS model. Based on 3D scanning and point cloud data processing technology, JRC of joint surface in different directions were obtained by Sa method, and they are very close to those obtained by JRC-JCS model. It is confirmed that Sa could accurately estimate the joint roughness coefficient and reflect its anisotropy.

Scale effect on the anisotropy characteristics of rock joint roughness

2021 ◽  
pp. qjegh2020-066
Author(s):  
Shi-Gui Du ◽  
Kai-Qian Du ◽  
Rui Yong ◽  
Jun Ye ◽  
Zhan-You Luo
Keyword(s):  
Scale Effect ◽  
Rock Joint ◽  
Rock Joints ◽  
Roughness Coefficient ◽  
Scale Dependency ◽  
Joint Roughness Coefficient ◽  
Local Anisotropy ◽  
Joint Roughness ◽  
Threshold Length

Accurate assessment of anisotropy and scale effect of rock joint roughness is essential for evaluating the mechanical behaviour of rock joints. However, in previous studies, how to quantify roughness anisotropy of rock joints remains largely unsolved, and the research about scale effect on roughness anisotropy is not conclusive. A statistical analysis on joint roughness coefficient of different sized profiles was implemented to investigate the scale-dependency of joint roughness. The scale effect on the roughness anisotropy were investigated based on class ratio transform approach. The roughness anisotropy was characterized by local anisotropy and global anisotropy. The global anisotropy tends to be almost constant when the sample size exceeds the stationarity threshold length of 70 cm. The result shows that the global anisotropy is scale-dependent. However, the scale effect on local anisotropy is less apparent. The case study indicates that the class ratio transform approach implies its superiority in roughness anisotropy investigation.

scholarly journals Correlation between the Joint Roughness Coefficient and Rock Joint Statistical Parameters at Different Sampling Intervals

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Man Huang ◽  
Cia-chu Xia ◽  
Peng Sha ◽  
Cheng-rong Ma ◽  
Shi-gui Du
Keyword(s):  
General Formula ◽  
Rock Joint ◽  
Statistical Parameter ◽  
Rock Joints ◽  
Estimation Accuracy ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
The Relationship ◽  
Fitting Parameters

Joint roughness coefficient (JRC) is a major factor that affects the mechanical properties of rock joints. Statistical methods that are used to calculate the JRC increasingly depend on a sampling interval (Δx). The variation rules of fitting parameters a, b, and b/a at different Δx values were analyzed on the basis of the relationship between the JRC and statistical parameter Z2. The relationship between the fitting parameters a and b was deduced in accordance with the ten standard profiles proposed by Barton. Empirical formulas for the JRC, Z2, and Δx were also established. The estimation accuracy of the JRC was the highest in the analysis of Δx values within 0.1–5.0 mm. JRC tests were conducted through inverse value comparative analysis. Results showed that the outcome calculated using the general formula and the JRC inverse values demonstrate improved agreement and verify the rationality of the general formula. The proposed formula can perform rapid and simple JRC calculation within the Δx range of 0.1–5.0 mm using Z2, thereby indicating favorable application prospects.

scholarly journals Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

2021 ◽  
Vol 54 (4) ◽  
pp. 1897-1917
Author(s):  
Kristofer Marsch ◽  
Tomas M. Fernandez-Steeger
Keyword(s):  
Statistical Methods ◽  
Comparative Evaluation ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Low Resolution ◽  
Large Dataset ◽  
Joint Roughness ◽  
Arbitrary Profile ◽  
Natural Rock

AbstractAfter the publication of the type-profiles for the estimation of the joint roughness coefficient (JRC) a discussion evolved about how to adequately use these traces. Based on the chart numerous researchers assembled mathematical correlations with various parameters seeking objectivity in the determination of JRC. Within these works differences concerning the database and the mathematical implementations exist. Consequently, each correlation, although predominantly the same parameters are used, leads to different JRC values. In theory, for any arbitrary profile, irrespective of the particular calculation approach, the same JRC should result. This is a requisite because of the referencing of all correlations to the 10 type-profiles. However, it is shown in this study that in most cases equal or even satisfactorily similar results are not obtained. The discrepancies are vast when non-standard profiles are evaluated, in this case, more than 40,000 traces from six different rock surfaces that cover a broad range of roughness categories. The simple intuitive parameter Z2 served as an agent for the statistical methods because of its broad use and consequently good comparability. On the part of the fractal approaches, three definitions were used. However, JRC inferred from fractal correlations are very much dependent on the particular calculation routine. In fact, the theory of fractals is overly complex for the sparse and low-resolution type-profiles. In summary, fractal approaches do not produce safer or more reliable estimates of roughness compared to simple statistical means and using Z2 perfectly suffices to determine the class of JRC.

scholarly journals Neutrosophic Functions of the Joint Roughness Coefficient and the Shear Strength: A Case Study from the Pyroclastic Rock Mass in Shaoxing City, China

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Ye ◽  
Rui Yong ◽  
Qi-Feng Liang ◽  
Man Huang ◽  
Shi-Gui Du
Keyword(s):  
Shear Strength ◽  
Rock Mass ◽  
Rock Joint ◽  
Sampling Bias ◽  
Rock Joints ◽  
Joint Surface ◽  
Pyroclastic Rock ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Joint Roughness

Many studies have been carried out to investigate the scale effect on the shear behavior of rock joints. However, existing methods are difficult to determinate the joint roughness coefficient (JRC) and the shear strength of rock joints with incomplete and indeterminate information; the nature of scale dependency of rock joints is still unknown and remains an ongoing debate. Thus, this paper establishes two neutrosophic functions of the JRC values and the shear strength based on neutrosophic theory to express and handle the incomplete and indeterminate problems in the analyses of the JRC and the shear strength. An example, including four rock joint samples derived from the pyroclastic rock mass in Shaoxing city, China, is provided to show the effectiveness and rationality of the developed method. The experimental results demonstrate that the proposed neutrosophic functions can express and deal with the incomplete and indeterminate problems of the test data caused by geometry complexity of the rock joint surface and sampling bias. They provide a new approach for estimating the JRC values of the different-sized test profiles and the peak shear strength of rock joints.

Sign in / Sign up

Export Citation Format

Share Document