A tunnel surrounding rock geological strength index rapid identification method based on image recognition

Abstract

The application discloses a tunnel surrounding rock geological strength index rapid identification method based on image recognition, which comprises the following steps: S1, rock mass geological strength index value quantification facing image recognition; S2, rock mass image segmentation and size calibration; S3, obtaining rock mass structure grade through image recognition; S4, obtaining joint roughness coefficient through image recognition; and S5, completing identification of the tunnel surrounding rock geological strength index. The application can realize rapid acquisition of the construction site surrounding rock geological strength index through quantification of rock mass geological strength index value and based on the image recognition technology, and has wide popularization value and remarkable economic and social benefits.

Description

Technical Field

[0001] The present invention relates to the field of geotechnical engineering technology, and in particular to a method for quickly identifying geological strength indicators of tunnel surrounding rocks based on image recognition. Background Art

[0002] Currently, tunnel surrounding rock stability analysis requires a thorough understanding of the rock's physical and mechanical parameters, such as elastic modulus, Poisson's ratio, cohesion, and internal friction angle. These rock parameters are difficult to obtain directly, typically requiring core sampling for indoor mechanical testing. The mechanical parameters of the rock are then reduced and converted into the rock's mechanical parameters. A commonly used method currently involves reducing core parameters based on the generalized Hoek-Brown criterion. However, the Geological Strength Index (GSI) within the Hoek-Brown criterion is currently primarily obtained through table lookup. The parameter values ​​in the table are all based on qualitative analysis, heavily reliant on personnel experience, and subject to significant subjectivity. This, coupled with the inefficiency of manual analysis, makes it difficult to accurately and timely analyze the surrounding rock's condition during actual construction. This results in insufficient accuracy and untimely analysis. Summary of the Invention

[0003] The present invention provides a method for quickly identifying the geological strength index of tunnel surrounding rock based on image recognition. The method can obtain the structural grade and joint roughness coefficient of the rock mass through image recognition, and then obtain the geological strength index (GSI) using a quantitative calculation method. The result is then compared with the values ​​in the GSI parameter chart to obtain the corresponding geological strength index of the tunnel surrounding rock. This solves the problem that the parameters of the geological strength index in the existing technology need to be obtained through manual qualitative analysis and table lookup, which is inefficient, untimely and lacks accuracy.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A method for quickly identifying the geological strength index of tunnel surrounding rock based on image recognition is designed, which includes the following steps:

[0006] S1. Quantification of rock mass geological strength index values ​​for image recognition;

[0007] First, the GSI parameter chart is normalized to obtain the normalization formula I for GSI value;

[0008] The rock mass structure grade SR is selected as the parameter to characterize the rock mass structure, and the joint roughness coefficient JRC is selected as the parameter to characterize the joint roughness. According to the corresponding quantitative calculation method, the quantitative calculation formula II of the relationship between GSI and SR and JRC is obtained. At the same time, the chart is converted into a surface formula to meet the needs of the image recognition algorithm.

[0009] S2, rock mass image segmentation and size calibration;

[0010] Take photos of the rock mass to be tested, segment the image using a laser grid, and calibrate the image's true size to obtain a segmented standard image, which provides a basis for subsequent image recognition algorithms;

[0011] S3. Obtaining rock mass structure grade through image recognition;

[0012] By performing image recognition on the standard graph obtained in step S2, the joint volume density is obtained, and the joint volume density J is obtained. V Calculate the rock mass structure grade SR;

[0013] S4. Obtaining the joint roughness coefficient through image recognition;

[0014] By performing image recognition on the standard graph obtained in step S2, the fractal dimension of the joint is obtained, and based on the Barton standard section line, the fractal dimension is used as a parameter for calculating the joint roughness coefficient JRC, and the joint roughness coefficient JRC is calculated by the fractal dimension;

[0015] S5. Complete the identification of the geological strength index of the tunnel surrounding rock;

[0016] The rock mass structure grade SR obtained in step S3 and the joint roughness coefficient JRC obtained in step S4 are substituted into the quantitative calculation formula II of the relationship between GSI, SR and JRC in step (1). The obtained GSI value is compared with the value in the GSI parameter chart to obtain the corresponding tunnel surrounding rock geological strength index, thus completing the identification of the tunnel surrounding rock geological strength index based on image recognition.

[0017] Furthermore, in step S1, the specific method of performing parameter normalization processing on the GSI value chart includes:

[0018] A coordinate system is established with joint roughness as the horizontal coordinate and rock mass structural characteristics as the vertical coordinate. The rock mass structural characteristics are represented by coefficient s, which ranges from 0 to 1. A larger value indicates more developed joints. Joint roughness is represented by j, which ranges from 0 to 1. A larger value indicates rougher joints. After parameter normalization, the relationship between GSI and coefficients s and j is expressed in the coordinate system. The normalization formula is expressed as:

[0019] GSI=-44.54s+24.77j-3.76s 2 +26.87j 2 +45.53-Formula I.

[0020] Furthermore, in step S1, the quantitative calculation method of GSI is:

[0021] The rock mass structure grade SR is selected as the parameter to characterize the rock mass structure characteristics, and the roughness coefficient JRC is selected as the parameter to characterize the joint roughness;

[0022] The rock mass structure grade (SR) ranges from 0 to 100, with larger values ​​indicating more complete rock mass and smaller values ​​indicating more developed joints. The roughness coefficient (JRC) ranges from 0 to 20, with larger values ​​indicating rougher joints and smaller values ​​indicating smoother joints.

[0023] The relationship between GSI, SR and JRC is expressed in the coordinate system, and the calculation formula is expressed as:

[0024] GSI=2.6SR+0.25JRC-0.0094SR 2 +0.0027JRC 2 -2.7762-Formula II.

[0025] Furthermore, in step S2, the method of dividing the image by a laser grid and calibrating the true size of the image is as follows: when taking the photo, a 1m×1m laser grid is emitted to the rock mass to be measured, so as to obtain the actual size of the rock mass in the image; at the same time, the laser grid is used to divide the image into several 1m×1m standard images for subsequent image recognition.

[0026] Furthermore, in step S3, the method for obtaining the rock mass structure grade through image recognition includes:

[0027] S3.1 Calculate the joint volume density using two perpendicular lines

[0028] Calculate the joint volume density J through two perpendicular measurement lines V The calculation method is:

[0029]

[0030] Where Nx and Ny are the number of joints along the horizontal and vertical directions of the survey line; Lx and Ly are the lengths of the survey line along the horizontal and vertical directions;

[0031] S3.2 Calculation of joint volume density from 2D images

[0032] First, pre-process the standard rock mass image obtained in step S2. Then, lay out survey lines in the horizontal and vertical directions. n survey lines are laid out in both directions at equal intervals. The horizontal lines are: lx1, lx2, ... lxn. The vertical lines are: ly1, ly2, ... lyn. Calculate the number of joints that intersect each survey line and take the average value:

[0033]

[0034] After calculating Nx and Ny, the joint volume density J of the joint can be calculated by formula (3): V , in the calculation, the lengths of the survey lines Lx and Ly in the horizontal and vertical directions are both 1m;

[0035] S3.3 Improved calculation method for rock mass structure grade

[0036] According to different orders of magnitude of joint volume density J V The relationship between the rock mass structure grade SR and its range is divided according to the joint volume density J calculated in step S3.2. V The rock mass structure grade SR is calculated based on the different grades of rock mass:

[0037]

[0038]

[0039] Furthermore, the method for preprocessing the standard rock mass image in step S3.2 is as follows: first, the segmented 1m×1m rock mass image is converted into a binary image, and the grayscale value of any pixel in the image is 0 or 255, representing black and white respectively; then the image is filtered to filter out the shadow part in the image to reduce interference with the image recognition calculation.

[0040] Furthermore, the specific method of obtaining the joint roughness coefficient by image recognition in step S4 includes:

[0041] S4.1 Obtaining joint fractal dimension through image recognition

[0042] Perform image recognition on the standard rock mass image obtained in step S2, extract the geometric shapes of all cracks in the image, and calculate the fractal dimension D by the box counting method;

[0043] S4.2 Calculation of Joint Roughness Coefficient (JRC) by Fractal Dimension of Joints

[0044] The fractal dimension D is used to calculate the formula for the joint roughness coefficient JRC:

[0045] JRC=-178.9985(D-1)-2.1039 —(8)

[0046] Substituting the fractal dimension D obtained in step S4.1 into formula (8) can calculate the joint roughness coefficient JRC.

[0047] The beneficial effects of the present invention are:

[0048] The present invention proposes a method for quickly identifying the geological strength indicators of tunnel surrounding rocks based on image recognition. The method can quickly identify the geological strength indicators of surrounding rocks. By taking pictures of the surrounding rocks and using image recognition technology, the geological strength indicators of surrounding rocks can be quickly acquired, thereby avoiding the problems of low efficiency, strong subjectivity, delay and low accuracy in obtaining relevant parameters through table lookup by inspection personnel, reducing dependence on personnel experience and the subjectivity of qualitative analysis, and greatly improving inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of GSI chart parameter normalization;

[0050] Figure 2 It is a schematic diagram for quantifying GSI chart parameters;

[0051] Figure 3 Schematic diagram of image segmentation method;

[0052] Figure 4 Schematic diagram of image processing method;

[0053] Figure 5 This is a schematic diagram of survey line layout;

[0054] Figure 6 Schematic diagram of joint fractal dimension extraction. DETAILED DESCRIPTION

[0055] The following examples illustrate specific embodiments of the present invention. However, the following examples are intended only to illustrate the present invention in detail and are not intended to limit the scope of the present invention in any way. Unless otherwise specified, all device components involved in the following examples are conventional device components; and all methods and algorithms involved are existing methods and algorithms unless otherwise specified.

[0056] Example 1: A method for quickly identifying the geological strength index of tunnel surrounding rock based on image recognition, comprising the following steps:

[0057] S1. Quantification of rock mass geological strength index values ​​for image recognition;

[0058] GSI value chart ( Figure 1 The number 1) in the figure is obtained by qualitatively analyzing the rock mass structure and joint roughness, determining their position on the chart and thus obtaining the corresponding GSI value. Image recognition technology can determine the number, spacing, and geometry of joints, but it cannot perform complex qualitative analysis. To enable the parameters obtained by image recognition to be directly used in GSI calculations, the GSI value chart was quantified.

[0059] First, the GSI parameter chart is normalized to obtain the GSI value normalization formula I. The specific method for normalizing the GSI value chart includes:

[0060] S1.1. Establish a coordinate system with joint roughness as the horizontal axis and rock mass structural characteristics as the vertical axis. The rock mass structural characteristics are represented by the coefficient s, which ranges from 0 to 1. A larger value indicates more developed joints. Joint roughness is represented by j, which ranges from 0 to 1. A larger value indicates rougher joints. After parameter normalization, the relationship between GSI and coefficients s and j is expressed in the coordinate system. See Figure 1 The label 2 in the normalization formula is expressed as:

[0061] GSI=-44.54s+24.77j-3.76s 2 +26.87j 2 +45.53-Formula I.

[0062] The rock mass structure grade SR is selected as the parameter to characterize the rock mass structure, and the joint roughness coefficient JRC is selected as the parameter to characterize the joint roughness. According to the corresponding quantitative calculation method, the quantitative calculation formula II of the relationship between GSI and SR and JRC is obtained. At the same time, the chart is converted into a surface formula to meet the needs of the image recognition algorithm. The GSI quantitative calculation method is:

[0063] S1.2. In order to make the GSI calculation method suitable for image recognition, it is necessary to select parameters that can be easily obtained through image recognition as calculation parameters. Here, the rock mass structure grade SR is selected as the parameter to characterize the rock mass structure characteristics, and the roughness coefficient JRC is selected as the parameter to characterize the joint roughness.

[0064] The rock mass structure grade (SR) ranges from 0 to 100, with larger values ​​indicating more complete rock mass and smaller values ​​indicating more developed joints. The roughness coefficient (JRC) ranges from 0 to 20, with larger values ​​indicating rougher joints and smaller values ​​indicating smoother joints.

[0065] The relationship between GSI, SR and JRC is expressed in the coordinate system, see Figure 2 As shown in the number 3; the calculation formula is expressed as:

[0066] GSI=2.6SR+0.25JRC-0.0094SR 2 +0.0027JRC 2 -2.7762-Formula II.

[0067] S2, rock mass image segmentation and size calibration;

[0068] Photographs of the rock mass to be tested are taken, and the images are segmented using a laser grid and calibrated to their true size to obtain a standard segmented image, providing a basis for subsequent image recognition algorithms.

[0069] Traditional image recognition requires calibration by placing a reference object to obtain the relationship between pixel size and real size. This method emits a 1m×1m laser grid to the rock mass to be measured when taking a photo, so as to obtain the actual size of the rock mass in the image, such as Figure 3 At the same time, the laser grid is used to segment the image into several 1m×1m standard images for subsequent image recognition.

[0070] S3. Obtaining rock mass structure grade through image recognition;

[0071] By performing image recognition on the standard graph obtained in step S2, the joint volume density is obtained, and the joint volume density J is obtained. V Calculate the rock mass structure grade SR.

[0072] The rock mass structural grade SR is usually determined by the volume density of the joints J V Calculate the volume density of the joints J by traditional methods. V , three perpendicular lines are required for measurement. Since the photographs are two-dimensional, it is not possible to obtain three perpendicular lines. Therefore, this method is improved to calculate the joint volume density using two perpendicular lines.

[0073] The specific method for obtaining rock mass structure grade through image recognition is as follows:

[0074] S3.1 Calculate the joint volume density using two perpendicular lines

[0075] Calculate the joint volume density J through two perpendicular measurement lines V The calculation method is:

[0076]

[0077] Where Nx and Ny are the number of joints along the horizontal and vertical survey lines; Lx and Ly are the lengths of the survey lines along the horizontal and vertical directions.

[0078] S3.2 Calculation of joint volume density from 2D images

[0079] First, the rock mass standard image obtained in step S2 is preprocessed. First, the segmented 1m×1m rock mass image is converted into a binary image. The grayscale value of any pixel in the image is 0 or 255, representing black and white respectively. Then, the image is filtered to filter out the shadow part in the image to reduce the interference of image recognition calculation. The whole process is as follows: Figure 4 shown.

[0080] After completing the image preprocessing, the survey lines are laid out in the horizontal and vertical directions, such as Figure 5 As shown. In this embodiment, 11 survey lines with a spacing of 0.1m are arranged in two directions. The horizontal directions are: lx1, lx2, ... lx11. The vertical directions are: ly1, ly2, ... ly11. The number of joints intersecting with each survey line is measured. In this embodiment, Figure 5 There are 8 joints intersecting the lx1 line, so Nx1 = 8. This method is used to calculate the number of joints on each line, and then the average is taken:

[0081]

[0082] After calculating Nx and Ny, the volume density J of the joint can be calculated by formula (3): V In the calculation, the lengths of the horizontal and vertical lines, Lx and Ly, are both 1 m.

[0083] After calculating Nx and Ny, the joint volume density J of the joint can be calculated by formula (3): V , in the calculation, the lengths of the survey lines Lx and Ly in the horizontal and vertical directions are both 1m;

[0084] S3.3 Improved calculation method for rock mass structure grade

[0085] Currently, SR is calculated through the volume density JV of the joints, mainly based on the formula:

[0086] SR=-17.5lnJ V +79.8—(6)

[0087] However, when J V At different orders of magnitude, the relationship with SR is very different and difficult to express with a single formula.

[0088] Therefore, in this method, the calculation method of rock mass structure grade is improved, and the joint volume density J of different orders of magnitude is used to calculate the rock mass structure grade. V The relationship between the rock mass structure grade SR and its range is divided according to the joint volume density J calculated in step S3.2. V The rock mass structure grade SR is calculated based on the different grades of rock mass:

[0089]

[0090]

[0091] S4. Obtaining the joint roughness coefficient through image recognition;

[0092] The joint fractal dimension is obtained by performing image recognition on the standard graph obtained in step S2. The fractal dimension is used as a parameter for calculating the joint roughness coefficient JRC based on the Barton standard profile line. The joint roughness coefficient JRC is calculated using the fractal dimension.

[0093] The JRC value of a joint ranges from 0 to 20 and is usually obtained based on the Barton standard profile, which is highly subjective. In the present invention, the fractal dimension D is used for calculation, and the specific method is:

[0094] S4.1 Obtaining joint fractal dimension through image recognition

[0095] Perform image recognition on the rock mass standard image obtained in step S2, extract the geometric shapes of all cracks in the image, and calculate the fractal dimension D by the box counting method, as shown in Figure 6 As shown;

[0096] S4.2 Calculation of Joint Roughness Coefficient (JRC) by Fractal Dimension of Joints

[0097] The fractal dimension D is used to calculate the formula for the joint roughness coefficient JRC:

[0098] JRC=-178.9985(D-1)-2.1039-(8).

[0099] Substituting the fractal dimension D obtained in step S4.1 into formula (8) can calculate the joint roughness coefficient JRC.

[0100] S5. Complete the identification of the geological strength index of the tunnel surrounding rock;

[0101] The rock mass structure grade SR obtained in step S3 and the joint roughness coefficient JRC obtained in step S4 are substituted into the quantitative calculation formula II of the relationship between GSI, SR and JRC in step (1). The obtained GSI value is compared with the value in the GSI parameter chart to obtain the corresponding tunnel surrounding rock geological strength index, thus completing the identification of the tunnel surrounding rock geological strength index based on image recognition.

[0102] The image recognition used in the present invention can be implemented using existing algorithms. Those skilled in the art can understand the functions and effects to be achieved, and will not be described in detail here.

[0103] The box counting method in step S4.1 is an existing calculation method. The specific process is briefly described as follows:

[0104] (1) Prepare a square grid to cover the entire fractal object. The size of the grid is usually variable so that the details of the object at different scales can be studied.

[0105] (2) Place the grid on the fractal object to be measured. Make sure the object is completely covered by the grid. The size of the grid can be adjusted according to the scale of the fractal.

[0106] (3) Record the number of fractal objects contained in each grid (or box). This number is usually called the box number.

[0107] (4) Adjust the size of the grid to make it smaller or larger, and then repeat steps (2) and (3). In this way, the number of boxes of the fractal object at different scales can be obtained.

[0108] (5) Plot the number of boxes (N) against the box size (ε) in a log-log plot. The size ε is the size of the grid, which is usually a function of the grid side length.

[0109] (6) Fractal dimension (D) is usually calculated by linear regression to calculate the slope. Specifically, the fractal dimension can be estimated by the relationship between the number of boxes and the box size:

[0110]

[0111] Where N(ε) is the number of boxes of size ε.

[0112] Those skilled in the art can understand the above calculation method and use it to calculate the fractal dimension D in the present invention.

[0113] The present invention has been described in detail above with reference to the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be modified to form multiple specific embodiments, which are all within the common variation range of the present invention and will not be described in detail here.

Claims

1. A method for quickly identifying the geological strength index of tunnel surrounding rock based on image recognition, characterized in that: The following steps are involved: S1. Quantification of rock mass geological strength index values ​​for image recognition; First, the GSI parameter chart is normalized to obtain the normalization formula I for GSI value; The rock mass structure grade SR is selected as the parameter to characterize the rock mass structure, and the joint roughness coefficient JRC is selected as the parameter to characterize the joint roughness. According to the corresponding quantitative calculation method, the quantitative calculation formula II of the relationship between GSI and SR and JRC is obtained; The quantitative calculation method of GSI is: The rock mass structure grade SR is selected as the parameter to characterize the rock mass structure characteristics, and the roughness coefficient JRC is selected as the parameter to characterize the joint roughness; The rock mass structure grade (SR) ranges from 0 to 100, with larger values ​​indicating more complete rock mass and smaller values ​​indicating more developed joints. The roughness coefficient (JRC) ranges from 0 to 20, with larger values ​​indicating rougher joints and smaller values ​​indicating smoother joints. The relationship between GSI, SR and JRC is expressed in the coordinate system, and the calculation formula is expressed as: GSI=2.6SR+0.25JRC-0.0094SR 2 +0.0027JRC 2 -2.7762-Formula II; S2, rock mass image segmentation and size calibration; Take photos of the rock mass to be tested, segment the image using a laser grid, and calibrate the image's true size to obtain a segmented standard image, which provides a basis for subsequent image recognition algorithms; S3. Obtaining rock mass structure grade through image recognition; By performing image recognition on the standard graph obtained in step S2, the joint volume density is obtained, and the joint volume density J is obtained. V Calculate the rock mass structure grade SR; S4. Obtaining the joint roughness coefficient through image recognition; By performing image recognition on the standard graph obtained in step S2, the fractal dimension of the joint is obtained, and based on the Barton standard section line, the fractal dimension is used as a parameter for calculating the joint roughness coefficient JRC, and the joint roughness coefficient JRC is calculated by the fractal dimension; S5. Complete the identification of the geological strength index of the tunnel surrounding rock; The rock mass structure grade SR obtained in step S3 and the joint roughness coefficient JRC obtained in step S4 are substituted into the quantitative calculation formula II of the relationship between GSI, SR and JRC in step (1). The obtained GSI value is compared with the value in the GSI parameter chart to obtain the corresponding tunnel surrounding rock geological strength index, thus completing the identification of the tunnel surrounding rock geological strength index based on image recognition.

2. The method for rapid identification of tunnel surrounding rock geological strength indicators based on image recognition according to claim 1 is characterized in that: In step S1, the specific method of performing parameter normalization processing on the GSI value chart includes: A coordinate system is established with joint roughness as the horizontal coordinate and rock mass structural characteristics as the vertical coordinate. The rock mass structural characteristics are represented by coefficient s, which ranges from 0 to 1. A larger value indicates more developed joints. Joint roughness is represented by j, which ranges from 0 to 1. A larger value indicates rougher joints. After parameter normalization, the relationship between GSI and coefficients s and j is expressed in the coordinate system. The normalization formula is expressed as: GSI=-44.54s+24.77j-3.76s 2 +26.87j 2 +45.53-Formula I.

3. The method for rapid identification of tunnel surrounding rock geological strength indicators based on image recognition according to claim 1 is characterized in that: In step S2, the method of segmenting the image by laser grid and calibrating the true size of the image is as follows: when taking the photo, a 1m×1m laser grid is emitted to the rock mass to be measured, so as to obtain the actual size of the rock mass in the image; at the same time, the laser grid is used to segment the image into several 1m×1m standard images for subsequent image recognition.

4. The method for rapid identification of tunnel surrounding rock geological strength indicators based on image recognition according to claim 3 is characterized in that: In step S3, the method for obtaining the rock mass structure grade through image recognition includes: S3.1 Calculate the joint volume density using two perpendicular lines Calculate the joint volume density J through two perpendicular measurement lines V The calculation method is: Where Nx and Ny are the number of joints along the horizontal and vertical directions of the survey line; Lx and Ly are the lengths of the survey line along the horizontal and vertical directions; S3.2 Calculation of joint volume density from 2D images First, pre-process the standard rock mass image obtained in step S2. Then, lay out survey lines in the horizontal and vertical directions. n survey lines are laid out in the two directions at equal intervals. The horizontal directions are: lx1, lx2, ... lxn, and the vertical directions are: ly1, ly2, ... lyn. Calculate the number of joints intersecting with each survey line and then take the average value: After calculating Nx and Ny, the joint volume density J of the joint can be calculated by formula (3): V , in the calculation, the lengths of the survey lines Lx and Ly in the horizontal and vertical directions are both 1m; S3.3 Improved calculation method for rock mass structure grade According to different orders of magnitude of joint volume density J V The relationship between the rock mass structure grade SR and its range is divided according to the joint volume density J calculated in step S3.

2. V The rock mass structure grade SR is calculated based on the different grades of rock mass:

5. The method for rapid identification of tunnel surrounding rock geological strength indicators based on image recognition according to claim 4 is characterized in that: The method for preprocessing the standard rock mass image in step S3.2 is as follows: first, the segmented 1m×1m rock mass image is converted into a binary image, where the grayscale value of any pixel in the image is 0 or 255, representing black and white, respectively; then, the image is filtered to filter out the shadow part in the image to reduce interference with the image recognition calculation.

6. The method for quickly identifying the geological strength index of tunnel surrounding rock based on image recognition according to claim 4 is characterized in that: The specific method for obtaining the joint roughness coefficient by image recognition in step S4 includes: S4.1 Obtaining joint fractal dimension through image recognition Perform image recognition on the standard rock mass image obtained in step S2, extract the geometric shapes of all cracks in the image, and calculate the fractal dimension D by the box counting method; S4.2 Calculation of Joint Roughness Coefficient (JRC) by Fractal Dimension of Joints The fractal dimension D is used to calculate the formula for the joint roughness coefficient JRC: JRC=-178.9985(D-1)-2.1039—(8); Substituting the fractal dimension D obtained in step S4.1 into formula (8) can calculate the joint roughness coefficient JRC.

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