A method for determining the roughness of stone interface based on stone profile

Abstract

The present invention relates to a method for determining the roughness of a block stone interface based on a block stone contour. The method comprises: acquiring a digital image of a block stone, obtaining block stone contour data points by image recognition, resetting the center of gravity of the block stone point data to the coordinate origin (0, 0), and smoothing the block stone contour point data set P by using Savitzky‑Golay convolution. s (x, y), the original contour point data set P1 (x, y) and the smoothed point data set P s (x, y) is converted into polar coordinate data format, and linear interpolation, difference operation, Fourier series expansion fitting and other means are performed to obtain a digital interface model f(x, y) that can reflect the real roughness of the block stone surface, and finally the roughness value JRC of the block stone interface is obtained. This method can efficiently and repeatably digitize irregular block stone contours, has the characteristics of repeatability, efficiency, convenience, and high scientific credibility, and can be widely used in geotechnical engineering.

Description

Technical Field

[0001] The invention relates to the technical field of determination and application of interface roughness in geotechnical engineering, and in particular to a method for determining the roughness of a block stone interface based on the block stone profile. Background Art

[0002] Geotechnical engineering covers a wide range of areas, including above-ground and underground projects such as mining, water conservancy and hydropower, and civil engineering. In some projects, soil-rock mixtures are widely present, such as tailings ponds, slopes, earth-rock dams, and gravel foundations in construction projects. In these mentioned projects, the mechanical properties of soil-rock mixtures are often the key factors controlling their stability and safety. The mechanical properties of soil-rock mixtures are not only related to the content and morphology of matrix soil and block stones, but also to the mechanical properties between soil and rock. The mechanical properties between soil and rock mainly depend on the friction and bonding force between the soil and rock interface. The friction properties depend on the roughness of the block stone interface. Therefore, the roughness of the soil-rock interface, that is, the roughness of the block stone, has an important influence on the mechanical properties of the soil-rock mixture, and is an important factor that cannot be ignored in the stability and safety assessment of slopes, tailings ponds, etc.

[0003] The acquisition of roughness of blocks in soil-rock mixtures is very different from the acquisition of roughness of rock structure surfaces. For example, the acquisition of rock structure surface roughness in the patent "Method for Determining the Size Effect of Roughness Coefficient of Engineering Rock Structure Surface" ([Chinese Invention, Chinese Invention Authorization] CN201710457541.0) is based on the outcrop of the structure surface, directly measuring the degree of undulation of the exposed part of the structure surface, thereby obtaining the roughness of the structure surface. However, the shape of blocks is usually not nearly parallel, but basically elliptical or other irregular shapes. Its contour contains two information, shape and undulation, so the roughness of the block surface cannot be directly obtained.

[0004] At present, there are few research results on the roughness of blocks in soil-rock mixtures, and in most soil-rock mixture models, the block outline is smoothed or simplified to consider factors such as meshing and computational efficiency, which will affect the mechanical properties of the entire specimen. How to obtain the roughness of blocks and evaluate the mechanical properties of the soil-rock interface is an inevitable key issue in the study of soil-rock mixtures and the basis for building a refined model of soil-rock mixtures. In response to the above problems, it is necessary to explore a method for obtaining the roughness of blocks that is repeatable, convenient, and efficient. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for determining the roughness of the stone interface based on the stone contour, which has the advantages of efficiently and conveniently obtaining the JRC value of the stone interface roughness, and solves the problem of obtaining the quantitative value JRC of the stone interface roughness in the current research on the mechanical properties of soil-rock mixtures.

[0006] To achieve the above object, the present invention provides a method for determining the roughness of a block stone interface based on a block stone profile, comprising the following steps:

[0007] 1) Take digital images of the stone blocks and convert them into grayscale to obtain the grayscale value f G (x,y);

[0008] Grayscale calculation uses the formula:

[0009] f G (x,y)=0.299f1(x,y)+0.587f2(x,y)+0.114f3(x,y)

[0010] In the formula, f1(x, y), f2(x, y), and f3(x, y) respectively represent the color values ​​of R, G, and B at the (x, y) coordinates in the image. G (x,y) is the grayscale value at the (x,y) coordinate in the image after grayscale conversion.

[0011] 2) Use edge detection algorithm to identify the outline of the digital image of the block stone and obtain the block stone outline point data set P0 (x, y).

[0012] First, a Gauss smoothing filter is used to convolve f G (x, y) is subjected to noise reduction processing, and the discretized two-dimensional Gauss kernel function used is in the form of:

[0013]

[0014] Where σ is the standard deviation and K(x,y) is the grayscale value after noise reduction at the (x,y) coordinate in the image.

[0015] Then use the Sobel operator G x , G y Calculate the edge gradient G[x,y] and direction θ of each pixel to enhance the image, where:

[0016]

[0017] Finally, the maximum value suppression calculation is performed on the pixel points with blurred edges detected in the digital image of the block stone, and the grayscale threshold f is determined. thr Thus, a more accurate edge point data set P0(x,y) is obtained.

[0018] 3) Obtain the center of gravity of the stone (x0, y0), translate the stone contour point data set P0 (x, y) so that the center of gravity is at the coordinate origin (0, 0), and the point data set is now P1 (x, y);

[0019] Assume that P0(x,y) contains n points (x i,y i )(i=1,2,3,…,n), then the centroid of the point data set P0(x,y) is:

[0020]

[0021] Subtract x0 and y0 from the x and y values ​​of the n points in P0(x,y) to obtain P1(x,y). At this time, the center of gravity of the stone falls on the coordinate origin (0,0).

[0022] 4) Use Savitzky-Golay convolution to smooth the stone contour point data set P1(x,y) and obtain the smoothed point data set P s (x,y);

[0023] 5) The original contour point data set P1(x,y) and the smoothed point data set P s (x, y) is converted into polar coordinate data format to obtain the polar coordinate form of the original contour point data set P1(ρ, θ) and the polar coordinate contour point data set P s (ρ,θ);

[0024] The conversion between rectangular coordinates (x, y) and polar coordinates (ρ, θ) is performed using the following formula:

[0025]

[0026] 6) For P1(ρ,θ) and P s (ρ, θ) is linearly interpolated to make the data points denser, and P1(ρ′, θ′) and P s (ρ′,θ′), which makes the subsequent contour perimeter calculation more accurate;

[0027] 7) Based on P s (ρ′,θ′) calculate the contour perimeter L corresponding to each data point of the smoothed contour s (ρ′,l);

[0028] Assume P s (ρ′,θ′) contains n points (ρ′ i ,θ′ i )(i=1,2,3,…,n), then L s (ρ′,l) where l is:

[0029]

[0030] 8) P1(ρ′,θ′ , ) and P sEach item ρ′ in (ρ′,θ′) is subjected to difference operation to obtain a data set P(ρ″,θ) that can reflect the true roughness of the stone surface. ρ″ in P(ρ″,θ) is used as the ordinate, L s In (ρ′,l), l is used as the horizontal coordinate, and the point set B(ρ″,l) is obtained;

[0031] 9) Use Fourier series expansion to fit the point set B(ρ″,l), digitize the block stone interface, and obtain the digital interface model f(x,y);

[0032] The Fourier series expansion used is as follows:

[0033]

[0034] Where y(x) is the obtained interface point data set, L is the total length of the interface, a0, a n 、b n are Fourier coefficients.

[0035] 10) According to the digital interface f(x,y), the roughness value JRC of the block stone interface is calculated by the following formula:

[0036]

[0037] in:

[0038]

[0039] Where α is a constant, h is the difference between the maximum and minimum y values ​​of the interface, and L is the entire length of the interface.

[0040]

[0041] In the formula, Dx is the point interval in the interface extension direction (x-axis), y is the y-axis coordinate value of the sampling point, and M is the number of points along the interface contour. This formula takes into account the relationship between the interface roughness and the shear direction. The dilatancy angle of the slope in the same direction as the shear direction is positive, and vice versa.

[0042] Compared with the prior art, the method for determining the roughness of the block stone interface based on the block stone profile of the present invention has the following beneficial effects after adopting the above technical solution:

[0043] The method for determining the roughness of the stone interface based on the stone contour is to obtain the stone contour point data by capturing the digital image of the stone, using image recognition, and resetting the center of gravity of the stone point data to the coordinate origin (0,0). Then, the Savitzky-Golay convolution is used to smooth the stone contour point data set, difference operation, Fourier series expansion fitting, etc. to obtain the digital interface model f(x,y) that can reflect the real roughness of the stone surface, and finally obtain the roughness value JRC of the stone interface. The advantages of this method are as follows:

[0044] (1) This method can efficiently and repeatably digitize irregular stone contours;

[0045] (2) This method innovatively applies the traditional JRC index calculation method of structural surface to the calculation of block rock profile roughness, providing a basis for the study of the mechanical properties of soil-rock interface;

[0046] (3) This method uses methods with clear mathematical meanings, such as image recognition, data interpolation, and Fourier technology expansion. It is repeatable, efficient, convenient, and has high scientific credibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the grayscale image of the captured stone block image;

[0048] Figure 2 The original outline and smoothed outline of the digital image of the stone block;

[0049] Figure 3 To obtain the real roughness dataset of the stone surface;

[0050] Figure 4 Fitting interface profile using Fourier series expansion method;

[0051] Figure 5 This is an illustration of some parameters for obtaining the interface JRC value. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe a method for determining the roughness of a block stone interface based on the block stone profile. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1-5 In this embodiment, a method for determining the roughness of a block stone interface based on a block stone profile is obtained by the following steps:

[0054] S1. Collect representative stones at the construction site and use a camera to capture stone images in the laboratory. Use the grayscale calculation formula to grayscale the digital image of the stone to obtain the image grayscale value f G (x,y), the grayscale image is as follows Figure 1 As shown;

[0055] The grayscale calculation used is as follows:

[0056] f G (x,y)=0.299f1(x,y)+0.587f2(x,y)+0.114f3(x,y)

[0057] In the formula, f G (x, y) (k = 1, 2, 3) represents the color values ​​of R, G, and B at the (x, y) coordinates in the image, respectively. G (x,y) is the grayscale value at the (x,y) coordinate in the image after grayscale conversion.

[0058] S2. Use edge detection algorithm to identify the outline of the digital image of the block stone and obtain the block stone outline point data set P0(x,y). In this example, P0(x,y) contains 82 points (x i ,y i )(i=1,2,3,…,82), and find the centroid (12.135,-1.619) of the data set P0(x,y), translate the block stone contour point data set P0(x,y) so that the centroid is at the coordinate origin (0,0), and the point data set is now P1(x,y), as shown in Figure 2 The original stone outlines shown in;

[0059] S3, use Savitzky-Golay convolution to smooth the stone contour point dataset P1(x,y), and get Figure 2 The smoothed stone outline shown in;

[0060] S4, the original contour point data set P1 (x, y) and the smoothed point data set P s (x, y) is converted into polar coordinate data format to obtain the polar coordinate form of the original contour point data set P1(ρ, θ) and the polar coordinate contour point data set P s (ρ,θ), the result is as follows Figure 3 As shown, the x-axis is the polar angle θ and the ordinate is the polar diameter ρ.

[0061] S5. In order to make the subsequent contour perimeter more accurate, P1(ρ,θ) and P s (ρ, θ) to make the data points denser, that is, P1(ρ′, θ′) and P s(ρ′,θ′), here the data points are increased to 360, and the following formula is used to obtain the s (ρ′,θ′) The contour perimeter L corresponding to each data point of the smoothed contour s (ρ′,l);

[0062]

[0063] S6, P1(ρ′,θ′ , ) and P s Each item ρ′ in (ρ′,θ′) is subjected to difference operation to obtain a data set P(ρ″,θ) that can reflect the real roughness of the stone surface, as follows: Figure 3 The bottom line is the data set P(ρ″,θ). Let ρ″ in P(ρ″,θ) be the vertical coordinate, L s In (ρ′,l), l is used as the horizontal coordinate, and the point set B(ρ″,l) is obtained, such as Figure 4 The point dataset shown in .

[0064] S7. Use the Fourier series expansion to fit the point set B(ρ″,l), digitize the block stone interface, and obtain the digital interface model f(x,y), such as Figure 4 The Fourier fitting results are shown in .

[0065] S8. According to the digital interface f(x, y), the roughness value JRC of the block stone interface is calculated by the following formula:

[0066]

[0067] in:

[0068]

[0069] In the formula, α is a constant; h is the difference between the maximum and minimum y values ​​of the interface, such as Figure 5 As shown, in the example, h = 37.57 mm; L is the entire length of the interface, and in the example, L = 383.1 mm.

[0070]

[0071] Wherein, Dx is the point interval in the interface extension direction (x-axis), and in the example Dx = 2 mm; y is the y-axis coordinate value of the sampling point, and M is the number of points along the interface contour.

[0072] In this example, Z′2=0.0355 is obtained, and finally the roughness value of the block stone interface JRC=13.0 is obtained.

[0073] It should be noted that, in the present invention, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining the roughness of a block stone interface based on the block stone profile, characterized in that The steps include: 1) Take the stone image and convert it into grayscale to obtain the image grayscale value f G (x, y); 2) Using edge detection algorithm to identify the outline of the digital image of the block stone, and obtain the block stone outline point data set P0 (x, y); 3) Obtain the center of gravity of the stone (x0, y0), translate the stone contour point data set P0 (x, y) so that the center of gravity is at the coordinate origin (0, 0), and the point data set is P1 (x, y); 4) Use Savitzky-Golay convolution to smooth the stone contour point data set P1(x, y) and obtain the smoothed point data set P s (x, y); 5) The original contour point data set P1 (x, y) and the smoothed point data set P s (x, y) is converted into polar coordinate data format to obtain the polar coordinate form of the original contour point data set P1 (ρ, θ) and the polar coordinate contour point data set P s (ρ, θ); 6) For P1(ρ, θ) and P s (ρ, θ) is linearly interpolated to obtain P1(ρ′, θ′) and P s (ρ′, θ′), which makes the subsequent contour perimeter calculation more accurate; 7) Based on P s (ρ′, θ′) calculate the contour perimeter L corresponding to each data point of the smoothed contour s (ρ′, l); 8) P1(ρ′, θ′,) and P s Each item ρ′ in (ρ′, θ′) is subjected to difference operation to obtain a data set P(ρ″, θ) that can reflect the true roughness of the stone surface. ρ″ in P(ρ″, θ) is used as the ordinate, L s In (ρ′, l), l is used as the horizontal coordinate, and the point set B(ρ″, l) is obtained; 9) Using Fourier series expansion to fit the point set B(ρ″, l), digitize the block stone interface, and obtain the digital interface model f(x, y); 10) According to the digital interface f(x, y), the roughness value JRC of the block stone interface is calculated by formulas (1), (2) and (3): in: In the formula, α is a constant, h is the difference between the maximum and minimum y values ​​of the interface, and L is the entire length of the interface. Where Dx is the point interval in the interface extension direction (x-axis), y is the y-axis coordinate value of the sampling point, and M is the number of points along the interface contour. This formula takes into account the relationship between the interface roughness and the shear direction. The dilatancy angle of the slope in the same direction as the shear direction is positive, and vice versa.

2. The method for determining the roughness of a block stone interface based on a block stone profile according to claim 1 is characterized in that: The grayscale calculation in step 1) adopts the formula: f G (x,y)=0.299f1(x,y)+0.587f2(x,y)+0.114f3(x,y) Where f1(x, y), f2(x, y), and f3(x, y) represent the color values ​​of R, G, and B at the (x, y) coordinates in the image, respectively.

3. The method for determining the roughness of a block stone interface based on a block stone profile according to claim 1 is characterized in that: In step 2), firstly, a Gauss smoothing filter is used to convolve f G (x, y) is subjected to noise reduction processing, and then the Sobel operator G is used x , G y The edge gradient G[x, y] and direction θ of each pixel are calculated to enhance the image. Finally, the maximum value suppression calculation is performed on the pixel points with blurred edges in the digital image of the block stone, and the grayscale threshold f is determined. thr Thus, a more accurate edge point data set P0(x, y) is obtained.

4. The method for determining the roughness of a stone block interface based on a stone block profile according to claim 1 is characterized in that: In step 7), assume that P s (ρ′, θ′) contains n points (ρ′ i ,θ′ i )(i=1,2,3,...,n), then L s (ρ′, l) where l is:

5. The method for determining the roughness of a stone block interface based on a stone block profile according to claim 1 is characterized in that: In step 9), the Fourier series expansion used is as follows: Where y(x) is the obtained interface point data set, L is the total length of the interface, a0, a n 、b n are Fourier coefficients.

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