scholarly journals A New Statistical Parameter for Determining Joint Roughness Coefficient (JRC) considering the Shear Direction and Contribution of Different Protrusions

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiu-yang Huan ◽  
Zhi-qiang Zhang ◽  
Ming-ming He ◽  
Ning Li
Keyword(s):  
Functional Relationship ◽  
Statistical Parameter ◽  
Sampling Interval ◽  
Processing Technology ◽  
Joint Surface ◽  
Roughness Coefficient ◽  
Shear Direction ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Coordinate Data

The mechanical properties of joints are important factors affecting the safety and stability of rock mass. The joint roughness coefficient (JRC) is a parameter for describing the roughness morphology of the joint surface, and its accurate quantification is very important to predict the shear strength. In the current statistical parameter methods for the estimation of joint roughness, the size of different protrusions on the joint surface was completely ignored, which did not correspond to the real failure mechanism of rock joint during the shear process. In this study, a new statistical parameter WPA was proposed for the estimation of JRC considering the shear direction and the contributions of different protrusions. First, the 10 standard roughness joint profiles were digitized based on image processing technology, and the obtained coordinate data were proved to be reliable by the calculation results of existing parameters. Secondly, the WPA value of 10 standard roughness joint profiles was calculated at a 0.5 mm sampling interval in two directions. The functional relationship between WPA and JRC indicated that they should be established in the same shear direction to maintain a high correlation. The JRC values of 10 standard roughness joint profiles in direction 2 were obtained based on the functional relationship established between WPA and JRC in direction 1, and the roughness of these 10 joint profiles was confirmed to be influenced by direction. Next, the effect of sampling interval on WPA was investigated. As the sampling interval increases, the WPA values gradually decreased and the correlation between them and JRC gradually declined. In practical application, a smaller sampling interval was recommended for more accurate prediction. Finally, the geometric coordinate data of 21 joint profiles given in the literature and 4 natural joint surfaces were obtained by graphics processing technology and 3D scanning technology, respectively. The JRC values of them were separately estimated by WPA in different directions. The results showed that the new statistical parameter WPA proposed in this paper can well describe the joint roughness considering the shear direction and the contribution of different protrusions.

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.

scholarly journals A New Method to Estimate the Joint Roughness Coefficient by Back Calculation of Shear Strength

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jiu-yang Huan ◽  
Ming-ming He ◽  
Zhi-qiang Zhang ◽  
Ning Li
Keyword(s):  
Shear Strength ◽  
Joint Model ◽  
Rock Joints ◽  
New Method ◽  
Roughness Coefficient ◽  
Shear Direction ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Back Calculation ◽  
Smooth Joint Model

The joint roughness coefficient (JRC) is an important factor affecting the shear properties of rock joints, and its accurate estimation is a challenging task in rock engineering. Existing JRC evaluation approaches such as the empirical comparison method and the statistical parameter method have some unresolved defects. In this study, a new method is proposed for JRC estimation to overcome the deficiencies of existing approaches based on back calculation of shear strength. First, the 10 standard roughness joints are established in numerical rock samples generated by the bonded particle method (BPM). Secondly, the microscopic parameters of the intact rock and joints are calibrated, and a series of direct shear tests of joint samples are carried out under different normal stresses. Finally, the empirical relationships between shear strength and JRC are proposed under high correlation conditions. The results show that the modified smooth joint model (MSJM) is proved to better simulate the mechanical properties of rough joints than the smooth joint model (SJM). When the shear strength of target joint is substituted in the corresponding relationship, the JRC of joint along the shear direction can be conveniently obtained. In addition, the JRC values of 10 standard roughness joint profiles under shear direction of from right to left (FRTL) are obtained. By estimating the JRC of 9 target joints in the literature, it can be seen that the new method proposed in this paper can well reflect the directionality of roughness and it is convenient to apply.

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 A New Characterization Method for Rock Joint Roughness Considering the Mechanical Contribution of Each Asperity Order

2021 ◽  
Vol 11 (15) ◽  
pp. 6734
Author(s):  
Zhouhao Yuan ◽  
Yicheng Ye ◽  
Binyu Luo ◽  
Yang Liu
Keyword(s):  
Rock Joint ◽  
Statistical Parameter ◽  
Ensemble Empirical Mode Decomposition ◽  
Joint Surface ◽  
Roughness Coefficient ◽  
Joint Roughness ◽  
Red Sandstone ◽  
Direct Shear Tests ◽  
Waviness And Unevenness ◽  
The Relationship

The morphology of the joint surface is multi-scale, and it can be divided into first-order asperity (waviness) and second-order asperity (unevenness). At present, the joint roughness characterization formula considers only the morphology contribution of waviness and unevenness components and does not fully consider their mechanical contribution. At same time, the relationship between the mechanical contribution and the morphology contribution is still unclear. Thus, the characterization formula considering the mechanical contribution of waviness and unevenness needs to be further studied. In this study, the standard joint roughness coefficient (JRC) profiles were first decomposed into waviness and unevenness. Then, three types of joint specimens with different asperity orders (flat, the standard JRC profile, and the profile containing only waviness) were prepared by the 3D engraving technique. Finally, direct shear tests were carried out on 39 sets of red sandstone joint specimens under three normal stresses. The mechanical contributions of waviness and unevenness were studied, the relationship between the mechanical contribution and the morphology contribution of waviness and unevenness was analyzed, and the characterization formula considering the mechanical contribution of waviness and unevenness was established. The results showed that the following: (1) the method combining the ensemble empirical mode decomposition (EEMD) and the critical decomposition level could be used to separate the waviness and unevenness from the joint surface; (2) the mechanical contribution of the waviness and unevenness decreased with the increase in normal stress; (3) the relationship between the mechanical contribution ratio and the statistical parameter ratio of the waviness and unevenness can be describe by power function; and (4) the roughness characterization formula considering the mechanical contribution and morphology contribution was established. This study will enhance the accurate evaluation of the roughness coefficient and shear strength of the joint specimen.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document