Scale effect on the anisotropy characteristics of rock joint roughness

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.

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

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.

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

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.

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

After 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.

Study on the Accurate Measurement and Quantitative Evaluation Methods of Aggregate Surface Roughness

In this work, to quantitatively analyze the roughness of the surfaces of road aggregates, the contact measurement technique and contactless scanning technique were, respectively, used to capture the coordinate data of point clouds on the aggregate surface, which were then used to reconstruct the digital elevation models of aggregate particles. Then, the joint roughness coefficient (JRC) was used as an evaluation index, and the quantitative calculation methods of the two-dimensional (2D) contour line roughness and three-dimensional (3D) contour surface roughness of aggregate particles were, respectively, studied. Finally, the anisotropic characteristics and size effect of the roughness coefficients of aggregates with different lithologies were, respectively, investigated, based on which the practicability of the 3D roughness coefficient index was proven. The results demonstrate that the roughness of a road aggregate surface can be quantitatively described by the point cloud data. The 2D roughness of aggregate profile lines exhibits anisotropy, while the 3D roughness of the aggregate contour surface indicates the size effect. The subtle morphological changes of the surface textures of aggregates can be accurately described by the 3D joint roughness coefficient (JRC3D) calculated by the feature parameter method.