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 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 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 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 Neutrosophic Function for Assessing the Scale Effect of the Rock Joint Surface Roughness

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Yong ◽  
Shaonan Tan ◽  
Jun Ye ◽  
Shigui Du
Keyword(s):  
Surface Roughness ◽  
Scale Effect ◽  
Rock Joint ◽  
Small Samples ◽  
Joint Surface ◽  
Roughness Coefficient ◽  
Joint Roughness ◽  
Large Joint ◽  
Negative Effect ◽  
Jrc Value

A new investigation method is proposed for recording large-sized joint profiles and making statistical analyses of the joint roughness coefficient (JRC) values of the 10–300 cm sized profiles. The mechanical hand profilograph is used for joint roughness measurement due to its advantage of easy operation and high accuracy in recording joint traces. Based on the proposed method, it provides sufficient samples from various positions on the large joint profile, which allows the statistical evaluation of JRC values. A neutrosophic number (NN) is employed for revealing determinate and/or indeterminate information as it consists of determinate and indeterminate parts. Due to the uncertainty of JRC in the real world, NN is chosen to represent the JRC value, which is not only random but also a fuzzy indefinite parameter. The neutrosophic function is used to analyze and express the scale effect of joint surface roughness, and its derivative is used to describe the changing trend of the scale effect. The results show that the JRC value of the joint profile is related to the scale and has a negative effect on the surface roughness of the rock joint. The indeterminate information about the scale effect on joint roughness is described by the neutrosophic functions, and the derivative indicated that the JRC values of small samples are more sensitive than those of large-sized examples. When the length of the sample exceeds the stationarity limit of 80 cm, the roughness appears to be almost scale independent.

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 Scale Dependence of Waviness and Unevenness of Natural Rock Joints through Fractal Analysis

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yingchun Li ◽  
Shengyue Sun ◽  
Hongwei Yang
Keyword(s):  
Surface Roughness ◽  
Rock Joint ◽  
Fractal Dimensions ◽  
Window Size ◽  
Scale Dependence ◽  
Rock Joints ◽  
Joint Surface ◽  
Triangular Prism ◽  
Natural Rock ◽  
Waviness And Unevenness

The scale dependence of surface roughness is critical in characterising the hydromechanical properties of field-scale rock joints but is still not well understood, particularly when different orders of roughness are considered. We experimentally reveal the scale dependence of two-order roughness, i.e., waviness and unevenness through fractal parameters using the triangular prism surface area method (TPM). The surfaces of three natural joints of granite with the same dimension of 1000 mm×1000 mm are digitised using a 3D laser scanner at three different measurement resolutions. Waviness and unevenness are quantitatively separated by considering the area variation of joint surface as grid size changes. The corresponding fractal dimensions of waviness and unevenness in sampling window sizes ranging from 100 mm×100 mm to 1000 mm×1000 mm at an interval of 100 mm×100 mm are determined. We find that both the fractal dimensions of waviness and unevenness vary as the window size increases. No obvious stationarity threshold has been found for the three rock joint samples, indicating the surface roughness of natural rock joints should be quantified at the scale of the rock mass in the field.

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.

Evolution Process of Natural Rock Joint Roughness during Direct Shear Tests

2017 ◽  
Vol 17 (5) ◽  
Author(s):  
Yunfeng Ge ◽  
Huiming Tang ◽  
M. A. M. Ez Eldin ◽  
Liangqing Wang ◽  
Qiong Wu ◽  
...  
Keyword(s):  
Rock Joint ◽  
Evolution Process ◽  
Direct Shear ◽  
Shear Tests ◽  
Joint Roughness ◽  
Direct Shear Tests ◽  
Natural Rock

scholarly journals Neutrosophic Function with NNs for Analyzing and Expressing Anisotropy Characteristic and Scale Effect of Joint Surface Roughness

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Yong ◽  
Leiyu Gu ◽  
Jun Ye ◽  
Shi-Gui Du ◽  
Man Huang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Scale Effect ◽  
Large Scale ◽  
Rock Joint ◽  
Rock Joints ◽  
Joint Surface ◽  
Roughness Coefficient ◽  
Statistical Assessment ◽  
Natural Rock

The shear behavior of rock mass significantly depends upon the surface roughness of rock joints which is generally characterized by the anisotropy characteristic and the scale effect. The large-scale natural rock joint surfaces, at Qingshi Town, southeast of Changshan County, Zhejiang Province, China, were used as a case study to analyze the roughness characteristics. A statistical assessment of joint roughness coefficient (JRC) indicated the roughness anisotropy of different sized rock joints. The lower limit (JRCmean-σ) was regarded as the determinate information, and the difference between lower and upper limits represented indeterminate information. The neutrosophic number (NN) was calculated to express the various JRC values. The parametric equations for JRC anisotropic ellipse were presented based on the JRC statistical assessment of joint profiles of various orientations. The JRC values of different sized joint samples were then quantitatively described by the neutrosophic function. Finally, a neutrosophic parameter ψ for evaluating the scale effect on the surface roughness anisotropy was introduced using the ratio of maximum directional roughness to minimum directional roughness. The case study indicates that the proposed method has the superiority in moving forward from subjective assessment to quantitative and objective analysis on anisotropy characteristic and scale effect of joint surface roughness.

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