Preparation method of rock standard component for natural joint test

Abstract

The invention belongs to the technical field of rock mechanics and geological engineering, and aims to solve the problems of insufficient precision, low preparation efficiency and high cost of natural joint morphology simulation at present. The preparation method of the rock standard component for the natural joint test comprises the following steps: collecting a to-be-tested rock block, and cutting to obtain a rock test block for later use; constructing point cloud data based on a wavelet basis function and a random phase; generating specification grid points from points in the point cloud data, and calculating a height value corresponding to each grid point to obtain a grid file; checking whether the simulated natural rough joint surface has statistical characteristics of natural joint fluctuation morphology or not based on normal distribution; and importing the grid file into a rock carving machine to generate a carving path, and carrying out rough carving and fine carving to obtain the rock standard part for the test. According to the invention, the multi-scale roughness characteristic and anisotropy of the natural joint can be efficiently and accurately simulated, and a high-quality standardized test piece is provided for a rock mechanical test.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of rock mechanics and geological engineering, and particularly relates to a preparation method of a rock standard piece for a natural joint test. BACKGROUND

[0002] Rock mass is a complex geological structure composed of rock blocks and joint networks. The shear slip of joint surfaces is one of the main reasons for engineering geological disasters. Studies have shown that the topography of joint surfaces has a significant impact on the strength, mechanical properties, and hydraulic characteristics of rock mass. Therefore, in-depth research on the topography of joint surfaces and its influence mechanism on the mechanical behavior of rock mass is of great significance in the field of rock mass engineering.

[0003] The topography of natural joint surfaces is formed by the combined action of tectonic stress, weathering, and erosion during geological evolution, and usually exhibits complex characteristics such as irregular shape, multi-scale undulation, and anisotropy. In order to study the influence of joint surface topography on the mechanical properties of rock mass, obtaining rock test pieces with natural joint topography and conducting indoor tests is an important means. However, the existing joint surface simulation methods have many shortcomings in accuracy and practicality, which cannot meet the needs of scientific research and engineering practice.

[0004] Currently, there are many difficulties in the preparation method of rock test pieces with natural joints, which limits the accuracy and reliability of the test. The main defects are as follows: (1) Many studies use regular zigzag joint test pieces to simulate the shear behavior of natural joint surfaces. However, the topography of such test pieces is too regular, and cannot fully reflect the complexity and randomness of natural joint surfaces, leading to large deviations in interpreting the real rock mass mechanical behavior.

[0005] (2) The artificial splitting method can directly obtain natural joint surfaces, but the generated joint surfaces lack repeatability, and the roughness is difficult to accurately control, limiting its application in systematic research.

[0006] (3) The method of obtaining point cloud data of natural joint surfaces through three-dimensional laser scanning or unmanned aerial vehicle photogrammetry technology, and reconstructing the joint surface using reverse modeling technology is high in cost, complex in operation, and has high requirements for experimental environment, which is difficult to be widely promoted. SUMMARY

[0007] The present application provides a preparation method of a rock standard piece for a natural joint test to solve at least one of the above technical problems in the prior art.

[0008] This invention employs the following technical solution: a method for preparing a standard rock specimen for natural joint testing, comprising the following steps: S1: collecting a rock block to be tested, cutting it to obtain a rock specimen for later use, wherein the rock specimen has a straight joint surface; S2: constructing point cloud data based on wavelet basis functions and random phase, wherein the point cloud data constitutes a natural rough joint surface with natural joint undulations; S3: dividing the points in the point cloud data to generate a standard grid, calculating the height value corresponding to each grid point using interpolation, thereby obtaining a grid file with a simulated natural rough joint surface. S4: Based on the normal distribution, test whether the simulated natural rough joint surface has the statistical characteristics of natural joint undulation. If it does not have the statistical characteristics of natural joint undulation, return and repeat steps S2-S3. If it has the statistical characteristics of natural joint undulation, execute steps S5 and S6 in sequence. S5: Calculate the roughness coefficient of the simulated natural rough joint surface. S6: Import the mesh file into the rock carving machine to generate the carving path. Based on the carving path, perform rough carving and fine carving on the rock specimen to obtain a test rock standard with natural joint morphology.

[0009] Preferably, the rock block to be tested is obtained by two cutting processes, wherein the first cutting process is to cut the rock block to be tested into an original test block, and the second cutting process is to cut the original test block into two identical rock test blocks along its own height center line.

[0010] Preferably, the step of constructing point cloud data based on the wavelet basis function and random phase includes: S21: Define wavelet basis functions for reconstructing the multi-scale characteristics of joint surfaces; S22: Generate multi-scale waveform surface functions based on the wavelet basis functions; S23: Generate a random phase field for the waveform surface function at each scale; S24: Combine the multi-scale waveform surface function with the random phase field to generate point cloud data with natural joint undulation morphology.

[0011] Preferably, the step of obtaining the mesh file with the simulated natural rough joint surface includes: S31: Read the point cloud data with natural joint undulations and perform noise reduction and smoothing processing; S32: Define the resolution of the interpolation grid and generate regular grid points. The number of grid points is determined based on the range of the point cloud data and the resolution of the interpolation grid. S33: For each grid point, the height value corresponding to the grid point is calculated using cubic spline interpolation; S34: Generate grid data based on the grid points and their corresponding height values, smooth the grid data, and trim or extend the boundaries of the grid data to obtain a grid file with simulated natural rough joint surfaces. Export the grid file as an .STL file and save it.

[0012] Preferably, the step of calculating the height value corresponding to the grid point using cubic spline interpolation includes: S331: Find the 16 nearest neighboring points around each of the grid points; S332: Represent the height value corresponding to the grid point as a bicubic polynomial; S333: Based on the point cloud data of the 16 nearest neighboring points around the grid point, solve the coefficients of the bicubic polynomial using the least squares method; S334: Substitute the grid points into the bicubic polynomial to obtain the height value corresponding to the grid points.

[0013] Preferably, the step of verifying whether the simulated natural rough joint surface possesses the statistical characteristics of natural joint undulation morphology based on a normal distribution includes: S41: Divide the grid data of the generated natural rough joint surface in the grid file into new point cloud data at equal intervals; S42: Randomly select from the new point cloud data N Points, and extract the data. N A profile line at any point among the points, and the profile line is parallel to the X-axis or Y-axis; S43: Calculate all N The ascent angle of the two profile lines corresponding to each point in the data. and the projected length of continuous rising or falling segments ; S44: The ascent angle of the profile line extracted from the simulated natural rough joint surface based on a normal distribution fitting. and the projected length of continuous rising or falling segments ; S45: Verify the ascent angle of the extracted profile line based on the chi-square test. and the projected length of continuous rising or falling segments Does it follow a normal distribution?

[0014] Preferably, the step of calculating the roughness coefficient of the simulated natural rough joint surface includes: Set the sampling interval, extract 100-200 joint profile lines along the shear direction, and calculate the root mean square of the slope of each profile line; The roughness coefficient of each profile line is calculated based on the root mean square of the slope. The roughness coefficient of the simulated natural rough joint surface is obtained by averaging the roughness coefficients of all the cross-sectional lines.

[0015] Preferably, when carving the rock sample, if the rock sample has obvious edges and corners, the edges and corners of the rock sample are carved first, and then the middle part of the rock sample is carved; at the same time, a large-diameter relief carving tool is selected to rough carve the rock sample, and then a small-diameter relief carving tool is selected to fine carve the rock sample.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention proposes a simple, efficient, high-precision, and batch-reproducible method for preparing standard rock specimens with natural joints for experimental use. This method generates multi-scale waveform surfaces using wavelet basis functions, combines this with a random phase field to simulate the complex morphological features of natural joint surfaces, uses interpolation to convert point cloud data into high-precision mesh data, and ensures the consistency between simulated joint surfaces and natural joints through geometric similarity checks and roughness quantification. Finally, high-precision engraving technology is used to batch-produce standard rock specimens with natural joint morphology.

[0017] Based on wavelet basis functions and random phase field theory, this invention can accurately simulate the multi-scale roughness characteristics and anisotropy of natural joint surfaces, ensuring geometric consistency and mechanical similarity between the simulated joint surfaces and natural joints. Through digital modeling and high-precision engraving technology, standard rock specimens with natural joint characteristics can be mass-produced rapidly, significantly reducing preparation costs and time. Geometric similarity verification and roughness quantification ensure high consistency and repeatability of the prepared standard rock specimens, providing reliable standardized specimens for rock mechanics testing. This invention is applicable to multiple fields such as rock mechanics testing, geological engineering simulation, and teaching research, providing scientific basis and technical support for solving complex rock mass engineering problems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the generation of naturally rough joint surfaces in this invention.

[0021] Figure 3 This is a schematic diagram of the extraction of cross-sectional lines in this invention.

[0022] Figure 4 This diagram illustrates the calculation of the ascent angle and the projected length of the continuous ascending or descending segment of the profile line in this invention.

[0023] Figure 5 This is a flowchart of the geometric similarity test method for simulated natural rough joint surfaces in this invention.

[0024] Figure 6 This is a statistical analysis result of the geometric similarity test of the simulated natural rough joint surface in this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should fall within the scope of the technical content disclosed in the present invention. It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0027] This invention provides an embodiment: like Figures 1 to 6 As shown, a method for preparing a standard rock specimen for testing with natural joints includes the following steps: S1: Collect the rock block to be tested, cut it to obtain a rock test block for later use. The rock test block has a flat joint surface.

[0028] A rock sample of a certain size was collected at the engineering site. The sample underwent two cutting processes to obtain rock test blocks. The first cutting process involved cutting the rock sample into an original test block. The second cutting process involved cutting the original test block along its center line into two identical rock test blocks. The original test block was 100mm long and wide, and 100±mm high. A rectangular test block of mm, This represents the maximum elevation difference of a naturally rough joint surface.

[0029] S2: Point cloud data is constructed based on wavelet basis functions and random phases. The point cloud data constitutes a natural rough joint surface with natural joint undulation morphology.

[0030] The steps for constructing point cloud data based on wavelet basis functions and random phase include: S21: Define wavelet basis functions for reconstructing the multi-scale features of the joint surface; select appropriate wavelet basis functions (such as Daubechies wavelet basis functions, Haar wavelet basis functions, etc.). In this embodiment, the wavelet basis functions are... The expression for is shown in equation (1): (1) In the formula, The scaling parameter is used to control the wavelength of the wavelet. The larger the value, the longer the corresponding wavelength and the gentler the fluctuation. These are translation parameters used to control the position of the wavelet; and The coordinates are in two-dimensional space, representing the position of the joint surface in the horizontal direction; The mother wavelet function can be of different types, such as Daubechies and Haar, to generate local fluctuations.

[0031] S22: Generate multi-scale waveform surfaces based on wavelet basis functions; the expression for the waveform surface at each scale is shown in equation (2): (2) In the formula, For the first A wave-shaped surface; For the first The amplitude of each waveform surface is used to control the height of the fluctuations; For the first The scale parameters of the waveform surface are used to control the wavelength of the undulations; For the first The translation parameters of the waveform surface are used to control the position of the undulations.

[0032] S23: Generate a random phase field for the waveform surface at each scale; the random phase field can be generated based on a uniform distribution or a Gaussian distribution, as shown in equations (3) and (4): (3) (4) In the formula, For the first The random phase field corresponding to each waveform surface; Indicates uniform distribution in interval; The mean is The variance is The Gaussian distribution.

[0033] S24: By combining multi-scale waveform surfaces with random phase fields, point cloud data with natural joint undulations is generated. The expression for the point cloud data is shown in equation (5): (5) In the formula, For the first Point cloud data corresponding to each waveform surface; The number of waveform surfaces; Used to introduce random phases and enhance the natural randomness of joint surfaces.

[0034] S25: Generate point cloud data Coordinates can be saved in a common format such as .txt or .xyz.

[0035] S3: Divide the points in the point cloud data to generate standard grid points, and calculate the height value corresponding to each grid point through interpolation to obtain a grid file with simulated natural rough joint surfaces.

[0036] The steps to obtain a mesh file with simulated natural rough joint surfaces include: S31: Read point cloud data with natural joint undulations, preprocess the point cloud data, use statistical filtering or radius filtering to remove noise, and use Gaussian filtering or moving least squares (MLS) to smooth the point cloud data. S32: Define the resolution of the interpolation grid. Generate regular grid points The number of grid points is determined based on the range of the point cloud data and the resolution of the interpolation grid; S33: For each grid point The height values ​​corresponding to grid points are calculated using cubic spline interpolation. Cubic interpolation is based on bicubic polynomial fitting, which can provide higher accuracy interpolation results.

[0037] The steps for calculating the height value corresponding to the grid point using cubic spline interpolation include: S331: Find the 16 nearest neighbors around each grid point; S332: The height value corresponding to the grid point is represented as a bicubic polynomial, as shown in equation (6): (6) In the formula, For polynomial coefficients, and These are the coordinates of the grid point in the two-dimensional plane. S333: Solving the coefficients of a bicubic polynomial using the least squares method based on point cloud data of the 16 nearest neighbors of each grid point. ; S334: Grid points Substituting into the bicubic polynomial, we obtain the height values ​​corresponding to the grid points. .

[0038] S34: Generate grid data based on grid points and their corresponding height values, smooth the grid data, and trim or extend the boundaries of the grid data to obtain a grid file with simulated natural rough joint surfaces. Export the grid file as an .STL file and save it.

[0039] S4: Based on the normal distribution test, check whether the simulated natural rough joint surface has the statistical characteristics of natural joint undulation morphology. If it does not have the statistical characteristics of natural joint undulation morphology, return and repeat steps S2-S3. If it has the statistical characteristics of natural joint undulation morphology, execute steps S5 and S6 in sequence.

[0040] The steps for testing whether a simulated natural rough joint surface possesses the statistical characteristics of natural joint undulations based on a normal distribution include: S41: Divide the mesh data with natural rough joint surfaces generated in the mesh file into new point cloud data at equal intervals; S42: Randomly select 100 points in the new point cloud data and extract the profile line passing through any one of the 100 points, and the profile line is parallel to the X-axis or Y-axis. S43: Calculate the ascent angle of the two profile lines corresponding to each of the 100 points. and the projected length of continuous rising or falling segments ; S44: Climbing angle of the profile line extracted from the natural rough joint surface based on normal distribution fitting simulation. and the projected length of continuous rising or falling segments As shown in equation (7): (7) In the formula, A Represents the angle of ascent Projected length of continuous rising or falling segments ; Standard deviation; for A The probability density function, It is the natural logarithm.

[0041] S45: Verify the ascent angle of the extracted profile line based on the chi-square test. and the projected length of continuous rising or falling segments Whether it follows a normal distribution. In this embodiment, the chi-square test is existing technology and will not be described in detail here.

[0042] S46: If extracting the climbing angle of the profile line and the projected length of continuous rising or falling segments If the joint surface follows a normal distribution, or approximately follows a normal distribution, it indicates that the simulated joint surface has good geometric consistency with the natural rough joint surface. Figure 6 middle, This is the average of the ascent angles; The variance of the ascent angle; It is the average of the projected lengths of the continuous rising or falling segments; This is the variance of the projected length of a continuous rising or falling segment.

[0043] S5: Calculate the roughness coefficient of the simulated natural rough joint surface. In this application, the roughness coefficient of the simulated natural rough joint surface is a theoretical calculation before carving. It is not clearly related to the subsequent rough and fine carving of the rock specimen based on the carving path. The operator only needs to understand the roughness coefficient before carving, but the roughness coefficient does not participate in the subsequent actual carving.

[0044] The steps for calculating the roughness coefficient of a simulated naturally rough joint surface include: Set the sampling interval, extract 100-200 joint profile lines along the shear direction, and calculate the root mean square of the slope for each profile line. As shown in equation (8): (8) In the formula, For the first section line discrete points coordinate; For the first section line discrete points coordinate; For the first section line discrete points Coordinates, i.e., the height of the undulation at discrete points; This represents the number of samples for the discrete points of the profile line.

[0045] The roughness coefficient of each profile line is calculated based on the root mean square of the slope. The roughness coefficient of the simulated natural rough joint surface is obtained by averaging the roughness coefficients of all the cross-sections.

[0046] S6: Import the mesh file into the rock carving machine to generate the carving path. Based on the carving path, perform rough carving and fine carving on the rock specimen to obtain a standard rock specimen for testing containing natural joint morphology. When carving the rock specimen, if the rock specimen has obvious edges, carve the edges first, and then carve the middle part of the rock specimen; at the same time, select a large-diameter relief carving tool to rough carve the rock specimen, and then select a small-diameter relief carving tool to fine carve the rock specimen.

[0047] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for preparing a standard rock specimen for testing with natural joints, characterized in that, Includes the following steps: S1: Collect the rock block to be tested, cut it to obtain a rock test block for later use. The rock test block has a straight joint surface. S2: Construct point cloud data based on wavelet basis functions and random phase, wherein the point cloud data constitutes a natural rough joint surface with natural joint undulation morphology; S3: Divide the points in the point cloud data to generate standard grid points, and calculate the height value corresponding to each grid point by interpolation to obtain a grid file with simulated natural rough joint surface; S4: Based on the normal distribution, test whether the simulated natural rough joint surface has the statistical characteristics of natural joint undulation morphology. If it does not have the statistical characteristics of natural joint undulation morphology, return and repeat steps S2-S3. If it has the statistical characteristics of natural joint undulation morphology, execute steps S5 and S6 in sequence. S5: Calculate the roughness coefficient of the simulated natural rough joint surface; S6: Import the mesh file into the rock carving machine to generate a carving path, and perform rough carving and fine carving on the rock test block based on the carving path to obtain a test rock standard with natural joint morphology.

2. The method for preparing a standard rock specimen for testing containing natural joints according to claim 1, characterized in that: The rock block to be tested is cut into a rock test block after two cutting processes. The first cutting process is to cut the rock block to be tested into an original test block, and the second cutting process is to cut the original test block into two identical rock test blocks along its own height center line.

3. The method for preparing a standard rock specimen for testing containing natural joints according to claim 1, characterized in that: The steps for constructing point cloud data based on the wavelet basis function and random phase include: S21: Define wavelet basis functions for reconstructing the multi-scale characteristics of joint surfaces; S22: Generate multi-scale waveform surface functions based on the wavelet basis functions; S23: Generate a random phase field for the waveform surface function at each scale; S24: Combine the multi-scale waveform surface function with the random phase field to generate point cloud data with natural joint undulation morphology.

4. The method for preparing a standard rock specimen for testing containing natural joints according to claim 3, characterized in that: The steps to obtain the simulated mesh file with naturally rough jointed surfaces include: S31: Read the point cloud data with natural joint undulations and perform noise reduction and smoothing processing; S32: Define the resolution of the interpolation grid and generate regular grid points. The number of grid points is determined based on the range of the point cloud data and the resolution of the interpolation grid. S33: For each grid point, the height value corresponding to the grid point is calculated using cubic spline interpolation; S34: Generate grid data based on the grid points and their corresponding height values, smooth the grid data, and trim or extend the boundaries of the grid data to obtain a grid file with simulated natural rough joint surfaces. Export the grid file as an .STL file and save it.

5. The method for preparing a standard rock specimen for testing containing natural joints according to claim 4, characterized in that: The steps for calculating the height values ​​corresponding to grid points using cubic spline interpolation include: S331: Find the 16 nearest neighboring points around each of the grid points; S332: Represent the height value corresponding to the grid point as a bicubic polynomial; S333: Based on the point cloud data of the 16 nearest neighboring points around the grid point, solve the coefficients of the bicubic polynomial using the least squares method; S334: Substitute the grid points into the bicubic polynomial to obtain the height value corresponding to the grid points.

6. The method for preparing a standard rock specimen for testing containing natural joints according to claim 5, characterized in that: The steps for testing whether the simulated natural rough joint surface possesses the statistical characteristics of natural joint undulation morphology based on the normal distribution include: S41: Divide the grid data of the generated natural rough joint surface in the grid file into new point cloud data at equal intervals; S42: Randomly select from the new point cloud data N Points, and extract the data. N A profile line at any point among the points, and the profile line is parallel to the X-axis or Y-axis; S43: Calculate all N The ascent angle of the two profile lines corresponding to each point in the data. and the projected length of continuous rising or falling segments ; S44: The ascent angle of the profile line extracted from the simulated natural rough joint surface based on a normal distribution fitting. θ i and the projected length of continuous rising or falling segments ; S45: Verify the ascent angle of the extracted profile line based on the chi-square test. and the projected length of continuous rising or falling segments Does it follow a normal distribution? 7. A method for preparing a standard rock specimen for testing containing natural joints according to claim 6, characterized in that: The steps for calculating the roughness coefficient of the simulated natural rough joint surface include: Set the sampling interval, extract 100-200 joint profile lines along the shear direction, and calculate the root mean square of the slope of each profile line; The roughness coefficient of each profile line is calculated based on the root mean square of the slope. The roughness coefficient of the simulated natural rough joint surface is obtained by averaging the roughness coefficients of all the cross-sectional lines.

8. The method for preparing a standard rock specimen for testing containing natural joints according to claim 1, characterized in that: When carving the rock specimen, if the rock specimen has sharp edges, carve the sharp edges first, and then carve the middle part of the rock specimen; at the same time, select a large-diameter relief carving tool to rough carve the rock specimen, and then select a small-diameter relief carving tool to fine carve the rock specimen.

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