Joint Roughness Coefficient Values Research Articles

Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model

In order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, the estimation of JRC from a three-dimensional (3D) digital surface model of rock discontinuities was presented. Secondly, an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes. Meanwhile, the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths, and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests. Then, by combining the theory of point cloud coordinate computation error with the equation of JRC calculation, a composite error model controlled by the shooting parameters was proposed. This newly proposed model was validated via the 3D model dataset, demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters. Furthermore, the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement. Subsequently, our proposed error model was integrated into the shooting parameter selection algorithm, thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes. Moreover, the optimal combination of three shooting parameters was offered. JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data. Finally, the application scope and limitations of the newly proposed approach were further addressed.

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

Determining the joint roughness accurately will better serve the peak shear strength estimation models of rock joints used for stability assessment of rock masses. Considering the defects of the existing quantitative characterization parameters for two-dimensional (2D) joint roughness, especially the lack of explicit geometric and physical meaning, we proposed two new 2D roughness parameters, θ2D and h2D. The former, θ2D, represents the average inclination angle of all potential contact asperities over the entire joint profile, while the latter, h2D, characterizes their average undulation height. Both parameters are closely related to the shear strength of rock joints. Then, the roughness parameters θ2D and h2D of 102 rock joint profiles digitized at 0.5 mm sampling interval were calculated, and a new nonlinear regression equation for the determination of the 2D joint roughness coefficient (JRC) was established by combining the calculated results of the two roughness parameters. It was verified that the proposed equation could give accurate JRC estimation values of the 10 standard profiles of rock joints. Through the comparative analysis of the experimental data collected from earlier studies for the peak shear strength of 73 rock joint samples and corresponding estimated values, the equation was further verified to be applicable and accurate for estimating the JRC values of rock joints. Furthermore, we discussed the effects of shear direction and sampling interval on roughness and further provided another equation that could be applied to estimate the JRC values of joint profiles at the sampling interval of 1.0 mm.

Experimental study of uniaxial compressive mechanical properties of rough jointed rock masses based on 3D printing

Abstract Roughness and inclination are important factors affecting the strength and deformation properties of jointed rock masses. Serrated joint specimens with varying joint roughness coefficient (JRC) and inclination angle were manufactured by 3D printing technique and cement mortar material. Then, uniaxial compression tests were performed for serrated joint specimens. The results show that when inclination angle equals 0° or 90°, the stress–strain curves of serrated joint specimens with various JRC values are basically the same and display a similar variation trend as that of the complete specimen, hence JRC presents a very little impact. When inclination angle varies from 30° to 60°, the stress–strain curves display a significant difference for various JRC values. Both the compressive strength and peak strain increase with the JRC value. With the increase in JRC value, the stress–strain curve exhibits a stress drop point, and with the further increase in JRC value, the stress drop point obviously delays or disappears directly. Variation in uniaxial compressive strength and deformation modulus with inclination angle is approximately U-shape for serrated joint specimens and displays typical anisotropic characteristics. Due to the variation in inclination angles and JRC values, failure modes of serrated joint specimens under uniaxial compression varies from splitting tensile or shear slip failure to compound tensile and shear failure. Rough serrated joint has a strengthening effect on the resistance ability to vertical load, and large roughness can effectively slow down the shear slip failure of jointed rock masses.

Fractal analysis to determine JRC on sandstones and its correlation to SRF, Ende-Lianunu Regency, East Nusa Tenggara Province, Indonesia

The study area is located in Ende-Lianunu Regency, East Nusa Tenggara Province, Indonesia, with mountainous and hilly morphology, and is part of the Kiro Formation which is composed of tuffaceous sandstones. Discontinuities in the sandstone are in the form of bedding planes and joints, which affect the mechanical properties of the rock mass, reduce its strength, and affect slope stability. The discontinuity condition that affects mechanical behavior is surface roughness. This research aims to define the fractal dimension value of surface roughness using a box-counting method, joint roughness coefficient (JRC), and simulate the strength reduction factor (SRF) value on slopes that have a certain JRC. The fractal dimension of fine-grained samples = 1.0010 and coarse-grained = 1.0056. The average JRC value is 6.25 (Range 4-6). Simulation at JRC 0-20, gives different SRF values. On slopes with JRC = 0, the critical SRF = 1.35. If JRC = 20, the critical SRF = 1.47. It can be inferred, that the fractal dimension of the roughness of the sliding plane correlates with JRC and SRF. As the fractal dimension value increases, so do the JRC and SRF values, resulting in more stable slope conditions.

Experimental study on unloading induced shear performances of 3D saw-tooth rock fractures

A fractal model governing saw-tooth fractures was first introduced to replicate sandstone samples containing an inclined 3D penetrating rough fracture surface with various joint roughness coefficients (JRC). In conventional triaxial compression, the peak strength for fractured samples increased with both confining pressure and JRC. During the unloading confining pressure process, the normal stress of fractures declined but the shear stress increased, resulting in shear sliding of fractures. The shear displacement of fractures exponentially increased, and the positive normal displacement decreased gradually to negative values under coupling effects of shear contraction caused by normal stress and shear dilation due to climbing effects of fractures. Transition from quasi-static to dynamic sliding of the fractures was identified. The sliding resistance duration increased with confining pressure but decreased with JRC. After pre-peak unloading, the fracture surfaces presented a more significant surface wear response and JRC values decreased by 1.70%–59.20% due to more remarkable asperity degradation compared with those after conventional triaxial compression. The theoretical model for shear strength of fractures was established through improving the Ladanyi & Archambault model by introducing the relations between normal stress and surface wear ratios of fractures, which agreed well with the experimental results.

Evaluation of surface roughness of rock-like joints using close range photogrammetry method

The surface roughness of the joints affects their hydraulic and mechanical behavior. There are various methods for assessing the surface roughness of discontinuities. With the development of photography technology and the release of powerful software, a photogrammetric analyzer has been introduced as a non-contact surface evaluation method. In this research, a three-dimensional model of the fracture surface was constructed using the close-range photogrammetric procedure and the joint roughness coefficient (JRC) is derived from the surface profiles. Also, the surface profiles were surveyed using the Profilometers (Barton Comb) and the JRC values were obtained using the Z2 method. Calculations were performed in two sampling steps of 0.42 and 1.27 mm. Ultimately, the results of the two methods were compared. A Sony Cybershot HX1 digital camera was used to capture the images. To process the images and build the 3D model, they were loaded in the “Agisoft metashape” software. A point cloud data was obtained with very high accuracy with a distance of 0.13 mm between points in the 3D model. The results show that the JRC values obtained from the photogrammetry method, for the upper surface of the joint, recorded 8% and 11% difference from the joint surface for sampling intervals of 1.27 and 0.42 mm, respectively. While for the bottom surface of the joint, these differences were 6.1% and 10% for sampling intervals of 1.27 and 0.42 mm, respectively.

Reconstruction of rough rock joints: 2D profiles and 3D surfaces

The 3D reconstruction of rock joint surfaces is crucial for quantifying rock joints. Through the uniform discretization of 2D and 3D rock joints, it was determined that both the climbing angles along the shear direction and the projection lengths of the consecutive ascending/descending segments follow normal distributions . This statistical distribution of climbing angles and projection lengths provides an important reference for rock joint reconstruction, although hypothesis testing indicated that some joint profiles/surfaces do not follow normal distributions. A normal distribution-based method and a bubbling method were used to reconstruct the 2D joint profiles. The bubbling method demonstrated good applicability for the generation of 3D rock joint surfaces, especially because it included surfaces with different roughness in two orthogonal directions. Therefore, ten typical 3D rock joint surfaces were reconstructed using the bubbling method, where both the size of asperities (waviness or unevenness) and the joint roughness coefficient values were quantified. To reconstruct a joint surface with a certain roughness coefficient, scaling the elevation of a generated surface was demonstrated as an effective supplementary approach. • Rock joints were statistically analyzed via normal distributions owing to a uniform discretization operation. • The normal distribution-based method and the bubbling method were both suitable for reconstructing 2D joint profiles. • The bubbling method could generate 3D joint surfaces with different roughness in two orthogonal directions. • Rock joints with different joint roughness coefficients were reconstructed by scaling their elevation coordinates.

Shearing Performance of Natural Joints with Different Roughnesses under Direct Shearing Tests

Roughness is one of the major characteristics of natural fracture in hydrocarbon and geothermal reservoirs, which has a significant influence on the activation of fractures during hydraulic stimulation. The main purpose of this study is to investigate the effect of different roughnesses and high normal stresses on shear behaviors of rock joints. To do this, replicas of natural rock joint surfaces were constructed using the 3D optical scanning and 3D rigid engraving technique. Direct shear tests were performed on rock joint replicas with three joint roughness coefficient (JRC) indices (i.e., 1.42, 4.98, and 7.96) at two high normal load conditions (i.e., 15 and 30 MPa). The results show that the shear strength dependence on surface roughness is similar at normal stresses of 15 and 30 MPa. In addition, JRC-JCS shear strength criterion can reasonably describe the peak shear strengths that obtained from experimental curves, and the peak shear stresses have a positive correlation with JRC values at both normal stresses. Moreover, at normal stresses of 15 and 30 MPa, the shearing mechanism of joints with different JRC values is asperity shearing off. Results also show that the friction coefficient of joint surface increases as joint roughness increases, and higher normal loads may lead to a decrease of apparent cohesion, which weakens the residual strength during the slip. The results of this study improve insight into the natural fracture activation behavior during hydraulic fracturing.

Data-driven estimation of joint roughness coefficient

Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass. Therefore, the joint roughness coefficient (JRC) estimation is of paramount importance in geomechanics engineering applications. Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values. Therefore, alternative data-driven methods are proposed to assess the JRC values. In this study, Gaussian process (GP), K-star, random forest (RF), and extreme gradient boosting (XGBoost) models are employed, and their performance and accuracy are compared with those of benchmark regression formula (i.e. Z2, Rp, and SDi) for the JRC estimation. To analyze the models’ performance, 112 rock joint profile datasets having eight common statistical parameters (Rave, Rmax, SDh, iave, SDi, Z2, Rp, and SF) and one output variable (JRC) are utilized, of which 89 and 23 datasets are used for training and validation of models, respectively. The interpretability of the developed XGBoost model is presented in terms of feature importance ranking, partial dependence plots (PDPs), feature interaction, and local interpretable model-agnostic explanations (LIME) techniques. Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations, indicating the generalization ability of the data-driven models in better estimation accuracy.

Quantification of joint roughness using various empirical relations between JRC and roughness parameters

The mechanical and hydraulic properties of rock joints are significantly controlled by the joint surface roughness at low normal stresses. For this reason, it is necessary to accurately quantify joint roughness. The Joint Roughness Coefficient (JRC) has been widely used in engineering practice to quantify joint roughness and to estimate discontinuity shear strength over the years. However, several researchers have pointed out drawbacks of the JRC. This study presents a review of various attempts that have been made over the years to improve JRC and reduce its subjectivity. In addition, various empirical equations that are available in the literature between JRC and roughness parameters at 0.5 mm sampling interval have been assessed and compared using some widely accepted roughness parameters including the root mean square of the slope of the profile, Z 2 , structure function, SF, the maximum apparent dip angle in the shear direction/an empirical roughness parameter + 1, θ*Max/[C+1] 2D , and the standard deviation of the inclination angle of the profile, SD θ P . Using a one-meter square joint surface digitized at 0.5 mm sampling interval, over 1715 joint profiles in the X direction and 1,515 joint profiles in the Y direction have been used to calculate JRC values through the aforementioned roughness parameters and empirical equations at joint lengths of 1000 mm, 500 mm, 250 mm and 125 mm, and the roughness anisotropy has been studied. New empirical relations have also been suggested in this study between JRC and four roughness parameters based on Barton’s ten standard profiles digitized at 0.5 mm. The suggested empirical relations were successfully used to quantify the roughness of the studied rock joint surface.

Intelligently Predict the Rock Joint Shear Strength Using the Support Vector Regression and Firefly Algorithm

AbstractTo propose an effective and reasonable excavation plan for rock joints to control the overall stability of the surrounding rock mass and predict and prevent engineering disasters, this study is aimed at predicting the rock joint shear strength using the combined algorithm by the support vector regression (SVR) and firefly algorithm (FA). The dataset of rock joint shear strength collected was employed as the output of the prediction, using the joint roughness coefficient (JRC), uniaxial compressive strength (σc), normal stress (σn), and basic friction angle (φb) as the input for the machine learning. Based on the database of rock joint shear strength, the training subset and test subset for machine learning processes are developed to realize the prediction and evaluation processes. The results showed that the FA algorithm can adjust the hyperparameters effectively and accurately, obtaining the optimized SVR model to complete the prediction of rock joint shear strength. For the testing results, the developed model was able to obtain values of 0.9825 and 0.2334 for the coefficient of determination and root-mean-square error, showing the good applicability of the SVR-FA model to establish the nonlinear relationship between the input variables and the rock joint shear strength. Results of the importance scores showed that σn is the most important factor that affects the rock joint shear strength while σc has the least significant effect. As a factor influencing the shear stiffness from the perspective of physical appearance, the change of the JRC value has a significant impact on the rock joint shear strength.

Dynamic response mechanism of a rock-filling interfacial coupling body to blasting in it

The interfacial coupling structure between the backfill and ore rock body in the filling mining method will be continuously disturbed by the influence of mining and blasting. In the filling-rock interfacial coupling, it is easy to produce the behaviors of debonding and fissure expansion, which will bring potential safety hazards to underground production. Because the field experiment is time-consuming and laborious, and it is difficult to observe the impact effect and the rock crack propagation process when the explosive is detonated, the simulation method was adopted for research. In the simulation, reasonable simplification is particularly important. According to the actual situation of blasting hole layout, the three rows of blasting holes that were detonated at one time were simplified into a single-row blasting hole model and an edge blasting hole model. According to the research results in related literatures, the coupling surfaces of the filling bodies and the ore rocks were simplified into three kinds (a flat interface, wavy interface and serrated interface). The three different shapes of the interfaces correspond to the different roughnesses of the interfaces, respectively. By considering that the hole arrangement method used in stope blasting is vertical hole arrangement, the holes are parallel, and at the same time, in order to improve the calculation of the software efficiency, simplified the three-dimensional model of the stope into a two-dimensional plane model. After a series of simplifications, a physical model for the filling-rock interface coupling body was proposed, and the corresponding geometric analysis model was established by using the ANSYS/LS-DYNA software And different material parameters were assigned to the different parts of the model, and the blasting effect was analyzed by software calculation. The mechanical influence of the interface coupling body structure was obtained, and the response law of different interface roughness, the curing age of the filling body and the blasting method on the blasting shock was obtained, and the mechanism of the blasting dynamics was discussed. The research results can reveal mechanical behaviors such as debonding and crack propagation at the coupling of filling-rock interface, and clarify the influence of different factors on the law of blasting shock response and the mechanism of blasting dynamics, which has certain guiding significance for downhole safety production. The results show as follows. (1) The blasting impact has three mechanical effects in the interface coupling body: tension, pressure and shear. When the stress wave passes through the interface, the peak acceleration of the monitoring point at the interface will increase due to different degrees of refraction. After passing through the coupling interface, the stress wave energy decays rapidly. (2) The impact of different interface roughness on blasting action is different. The joint roughness coefficient (JRC) represents the roughness of the interface coupling body. With the increase of the JRC value, the interface stress tends to rise first and then decline, but the overall damage decreases. (3) As the curing time of the backfill increases, the fracture range at the coupling interface shrinks, and the interface damage gradually changes from tensile damage to shear damage. (4) The damage of different detonation modes to the interface coupling body is different, and the damage of simultaneous detonation to the coupling interface is weaker than that of hole-by-hole detonation.

Characterization of joint roughness using close-range UAV-SfM photogrammetry

The Structure from Motion (SfM) photogrammetric technique has emerge as an efficient alternative for remote 3D rock mass characterization, compared to laser scanner (LiDAR) or stereoscopic photogrammetry, due to its economy and ease of use. In a similar way, the recent development of the drone-based technology has turn UAVs (“Unmanned Aerial Vehicles”) in a more accessible device for field applications in geotechnical engineering; allowing the acquisition of high quality images from a safe distance and without the need to stablish direct contact with the rock mass. However, the close distance applicability of UAV-SfM photogrammetry has not yet been investigated in detail to characterize joint roughness at close range (<10 m). In this work we employ the SfM technique for the generation of 3D models of the joint surfaces from aerial images taken at a relatively short distance from the slope (10, 7.5, 5, and 2.5 m). Roughness profiles are extracted from the 3D data, and their Z2 statistical parameter is used to estimate the Joint roughness coefficient (JRC). Finally, the JRC value of those profiles-obtained with the UAV-SfM approach-have been compared with those obtained with traditional measurements based on manual methods. The proposed methodology is applied to a real case in an ancient open-cast mine in Northern Spain. The results obtained at different distances are compared to analyze the potential of UAV-SfM photogrammetry to develop accurate close-distance models. Results show that it is not necessary to get too close to the slope in order to get the best results, as this may cause overestimation of the JRC value.

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

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.

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.

Direct Shear Test and Shear Strength Model of Clay-Filled Joints

Abstract To better study the shear characteristics of infilled joints with soil having different moisture contents, the influence of the moisture content on the shear characteristics of the infilled joint was explored in this paper, and a revised shear strength model of infilled joint surface is proposed. The results show that the shear dilatation modes of joints can be divided into four types: pure shear dilatation, pure shear compression, shear dilation-shear compression and shear compression-shear dilation. As the joint roughness coefficient (JRC) value increases, the normal displacement of the joint surface gradually increases during the shearing process and the normal stress has an inhibitory effect on dilatation. The infill material will weaken the peak shear stress of the joint surface. When the JRC value of joint surface is small, the weakening effect of soil with moisture content of 30% on peak shear stress is obvious. The revised joint roughness coefficient-joint wall compressive strength (JRC-JCS) model of infilled joint is proposed and the maximum shear stress value calculated by the model has a good linear relationship with the test value.

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.

Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic

Roughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in this study, where the 3D laser scanning technique and self‐compiled Python code, as well as the statistical parameter methods, were applied. Then, the shear strength of jointed rock was evaluated via Barton′s model, and therefore, a comprehensive comparison between the calculating results and experimental results was executed. Ultimately, the influencing factors of roughness profile extraction on the accuracy of JRC value, such as the measuring point interval, profile number, and measuring direction, were investigated. The results show that (1) equipped with the 3D laser scanning technique, the roughness profiles can be accurately extracted via the self‐compiled Python code, (2) an excellent consistency of shear strength could be observed between the calculating value and experimental results, verifying the validity and accuracy of the proposed method, and (3) a smaller measuring point interval can produce a more accurate digital profile and more accurate JRC value. To a certain extent, the more the sample numbers of profiles, the smaller the value of JRC.

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.

Characterization of joint roughness using spectral frequencies and photogrammetric techniques

In this study we propose a novel methodology to characterize the roughness of rock joints using photogrammetry techniques and frequency analyses. The process starts with the generation of a 3D model (a dense point cloud) using the Structure from Motion (SfM) technology. Joint profiles are then extracted from this model and their frequency spectra are obtained using the Fast Fourier Transform. Finally, several approaches to parametrize the amplitude-frequency relationships are proposed, so that the roughness of the joint profiles are characterized with the fitting parameters. The methodology can differentiate between waviness and roughness, so that it could be used in future analysis of the shear behaviour of joints affected by this distinction. The proposed methodology is applied to two samples of granite representative of an unweathered fresh-cut surface with natural roughness. The results from the parametrization of the frequency spectra are used to carry out a classification analysis to study if their best-fitting parameters can estimate the Joint Roughness Coefficient (JRC) adequately. Results show that JRC values estimated with the spectral information tend to be quite similar (with errors less or equal to ±2 in about 80% of cases) to those estimated using the Ζ 2 statistical parameter, therefore validating the use of frequency spectra to characterize the roughness of rock joints.