A method for identifying the stone content distribution of a soil-stone mixture sample

Abstract

The application discloses a kind of soil and stone mixture sample stone content distribution identification method, including soil and stone mixture sample sectioning, the section digital image of soil and stone mixture sample after sectioning is obtained and saved;Using the average stone content calculation formula obtains the average stone content of soil and stone mixture sample after sectioning;The axial and radial partition quantity of section digital image and block stone, soil boundary gray are set, and it and saved section digital image, the average stone content is read into soil and stone mixture stone content distribution identification software, obtains the RGB value of each pixel point in the section digital image and the number of axial and radial pixel points;The section digital image is processed, and the gray value of each pixel point in the section digital image after partition processing is calculated, and the stone content identification formula is used to preliminarily identify the stone content of each area of section digital image, and the stone content of the digital image of preliminary identification is corrected using the stone content correction formula.

Description

Technical Field

[0001] This invention relates to the field of civil engineering technology, and in particular to a method for identifying the distribution of stone content in soil-rock mixture samples. Background Technology

[0002] In geotechnical and geological engineering, such as slope (landslide) treatment, foundation pit engineering, and roadbed and bridge foundation projects, a mixture of soil and rock, known as soil-rock mixture, is widely used. Obtaining virgin soil-rock mixture data by sampling the mixture in situ and then conducting geotechnical tests on cylindrical specimens, including direct shear, single shear, ring shear, lateral shear compression, and triaxial tests, and comparing and correcting the data with finite element numerical calculations, is an effective way to gain a deeper understanding of the properties of soil-rock mixtures and to develop and improve soil mechanics theory and numerical calculations.

[0003] In undisturbed soil-rock mixture samples at engineering sites, the stone content is generally not uniformly distributed along the axial and radial directions. However, when modeling soil-rock mixture samples using finite element numerical calculation software, the distribution of stone content along the axial and radial directions in the sample is not known. Therefore, it is generally treated as a uniform distribution of stone content, which causes a difference between the finite element numerical calculation model and the actual soil-rock mixture sample. The comparison and correction of simulation data and experimental data based on this will have a certain degree of error, which is not conducive to the soil mechanics theoretical research of soil-rock mixtures. Summary of the Invention

[0004] The purpose of this invention is to address the technical deficiencies in the existing technology by providing a method for identifying the distribution of stone content in soil-rock mixture samples.

[0005] The technical solution adopted to achieve the purpose of this invention is:

[0006] A method for identifying the stone content distribution of a soil-rock mixture sample includes the following steps:

[0007] Step 1: The soil-rock mixture sample is cut along the axial direction and the sample is divided into two equal parts. Digital images of the cross-section of the cut soil-rock mixture sample are acquired, pre-processed and saved.

[0008] Step 2: Calculate the average stone content α of the soil-rock mixture sample. t The average stone content α t The ratio of the volume of the boulders to the volume of the soil-rock mixture sample is given.

[0009] Step 3: Set the number of axial partitions n1 and radial partitions n2 of the profile digital image and the gray level h of the boundary between the rock and soil, and obtain the R, G, B values ​​of each pixel in the read profile digital image and the number of axial pixels N1 and the number of radial pixels N2 in the profile digital image.

[0010] Step 4: Based on the number of axial and radial partitions in the profile digital image, divide the profile digital image into n1×n2 regions, denoted as A(x1, x2), where x1 = 1…n1, x2 = 1…n2, and each region contains N pixels. (x1，x2) = (N1×N2) / (n1×n2);

[0011] Step 5: Calculate the gray value Gray of each pixel in the partitioned digital image of the cross-section. Add the gray values ​​Gray of all pixels in region A(x1, x2) of the digital image of the sample cross-section to obtain the sum of the gray values ​​of the pixels in that region. And the stone content identification formula was used to determine the stone content α' of each region in the profile digital image. (x1,x2) Conduct preliminary identification;

[0012] Step 6: Sum the stone content of each region in the preliminarily identified profile digital image from Step 5 to obtain the stone content and α' of each region in the profile digital image. sum The stone content correction formula was used to adjust the stone content α' of each region in the initially identified digital image. (x1,x2) After correction, the stone content α of each zone of the final soil-rock mixture sample was obtained. (x1,x2) .

[0013] In the above technical solution, the acquisition of the profile digital image of the soil-rock mixture sample in step 1 is obtained by taking pictures of the profile of the soil-rock mixture sample with a digital camera.

[0014] In the above technical solution, the preprocessing in step 1 is to remove the background image outside the soil-rock mixture sample profile in the acquired profile digital image and save it as a jpg format.

[0015] In the above technical solution, step 2 includes the following steps:

[0016] S2.1: Measure the volume V and density ρ of the soil-rock mixture sample, and sieve the cut soil-rock mixture sample to separate the rocks from the soil.

[0017] S2.2: Weigh the separated stones to obtain the mass m of the separated stones;

[0018] S2.3: Calculate the average stone content, and the formula for calculating the average stone content is as follows:

[0019] α t = 2·m / (ρ·V)×100

[0020] In the formula, α tρ represents the average stone content of the soil-rock mixture sample, m represents the mass of the separated stones, ρ represents the density of the stones, and V represents the volume of the soil-rock mixture sample.

[0021] In the above technical solution, in step 5, the weighted average method is used to calculate the gray value Gray of each pixel in the profile digital image after partitioning, by combining the gray h of the boundary between the rock and soil and the R, G, and B values ​​of each pixel in the profile digital image.

[0022] In the above technical solution, the formula for calculating the grayscale value of each pixel in step 5 is as follows:

[0023]

[0024] In the formula, Gray represents the gray value of each pixel in the profile digital image, R represents the R value of the pixel in the profile digital image, G represents the G value of the pixel in the profile digital image, B represents the B value of the pixel in the digital image, and h represents the gray value of the boundary between the rock and the soil.

[0025] In the above technical solution, the expression for the formula for identifying the stone content in step 5 is as follows:

[0026]

[0027] In the formula, The initial identification of the lithology in each region of the digital profile image. This represents the sum of the gray values ​​of pixels in each region of the cross-sectional digital image. The number of pixels in each region of a cross-sectional digital image.

[0028] In the above technical solution, step 6 uses a stone content correction formula to correct the stone content of the initially identified digital image, resulting in the final stone content of the soil-rock mixture sample, including:

[0029] S6.1: Determine the stone content correction coefficient based on the average stone content of the soil-rock mixture sample and the number of axial and radial partitions in the profile digital image;

[0030] S6.2: Correct the stone content of each area of ​​the preliminarily identified profile digital image based on the stone content correction coefficient to obtain the final stone content of the soil-rock mixture sample.

[0031] In the above technical solution, the expression for the stone content correction coefficient is as follows:

[0032] γ=(α t ·n1·n2) / α' sum

[0033] In the formula, γ represents the stone content correction coefficient, and αt α' represents the average stone content of the soil-rock mixture sample, n1 represents the number of axial partitions in the digital profile image, n2 represents the number of radial partitions in the digital profile image, and α' sum The lithology of each zone in the digital cross-section image is represented by the lithology.

[0034] In the above technical solution, the expression for the stone content of each zone of the corrected soil-rock mixture sample is as follows:

[0035] α (x1,x2) =γ·α' (x1,x2)

[0036] In the formula, α (x1,x2) γ represents the stone content of each zone of the corrected soil-rock mixture sample, α' represents the stone content correction factor, and γ' represents the stone content correction factor. (x1,x2) The stone content of the digital image representing the initial identification.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. The method for identifying the stone content distribution of soil-rock mixture samples proposed in this invention, through steps such as parameter and graphic input, cross-sectional digital image partitioning, preliminary identification of stone content distribution, and correction of stone content distribution, can achieve accurate identification of the stone content distribution of soil-rock mixture samples. It can effectively solve the problem of inaccurate establishment of finite element numerical calculation models caused by unclear stone content distribution of soil-rock mixture samples.

[0039] 2. The method for identifying the distribution of stone content in soil-rock mixture samples of the present invention is also applicable to identifying the distribution of ice content in soil-rock mixture samples. Attached Figure Description

[0040] Figure 1 The diagram shown is a flowchart for identifying the stone content distribution of soil-rock mixture samples according to the present invention.

[0041] Figure 2 The image shows a schematic diagram of the software interface for identifying the stone content distribution of soil-rock mixtures according to the present invention.

[0042] Figure 3 The diagram shown is a cross-sectional digital image partition coordinate diagram of the present invention.

[0043] Figure 4 The diagram shown is a partitioned schematic of the cross-sectional digital image of Example 1.

[0044] Figure 5 The diagram shown is a partitioning diagram of the cross-sectional digital image after grayscale value calculation in Example 1.

[0045] Figure 6The diagram shown is a preliminary identification result of the stone content in each region of the cross-sectional digital image of Example 1.

[0046] Figure 7 The diagram shown is a schematic representation of the corrected stone content results for each region of the cross-sectional digital image of Example 1. Detailed Implementation

[0047] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0048] Example 1

[0049] See Figure 1 A method for identifying the stone content distribution of soil-rock mixture samples, comprising the following steps:

[0050] Step 1: Cut the soil-rock mixture sample along the axial direction, acquire the profile digital image of the cut soil-rock mixture sample, remove the background image outside the soil-rock mixture sample profile in the acquired profile digital image and save it.

[0051] Furthermore, in step 1, the acquisition of the profile digital image of the soil-rock mixture sample is achieved by taking a picture of the profile of the soil-rock mixture sample directly in front of a digital camera.

[0052] Furthermore, the profile digital image saved in step 1 is in JPG format.

[0053] Step 2: Obtain the average stone content of the soil-rock mixture sample using the formula for calculating the average stone content.

[0054] Furthermore, step 2 includes the following steps:

[0055] S2.1: Measure the volume V and density ρ of the soil-rock mixture sample in step 1, sieve the cut sample, and separate the rock from the soil.

[0056] S2.2: Weigh the separated stones to obtain the mass m of the separated stones;

[0057] S2.3: Calculate the average stone content, and the formula for calculating the average stone content is as follows:

[0058] α t = 2·m / (ρ·V)×100

[0059] In the formula, α t ρ represents the average stone content of the soil-rock mixture sample, m represents the mass of the separated stones, ρ represents the density of the stones, and V represents the volume of the soil-rock mixture sample.

[0060] Step 3, set the number of axial partitions n1 and radial partitions n2 of the profile digital image, and the gray level h of the boundary between the rock and soil (see...). Figure 2 In this embodiment, the number of axial and radial partitions of the profile digital image is preferably 6 and 3, and the gray level of the boundary between the rock and soil is preferably 150. The saved profile digital image, the average stone content of the cut soil-rock mixture sample, the number of axial and radial partitions of the profile digital image and the gray level of the boundary between the rock and soil are read into the soil-rock mixture stone content distribution recognition software to obtain the R, G, B values ​​of each pixel in the read profile digital image and the number of axial pixels N1 and the number of radial pixels N2 in the profile digital image.

[0061] Step 4: The cross-sectional digital image is partitioned according to the number of axial and radial partitions. The partitioning of the cross-sectional digital image can be found in [reference needed]. Figure 4 The expression for the zone number of the cross-sectional digital image after partitioning is as follows: x1 = 1...n1, x2 = 1...n2, where x1 represents the axial coordinate of a pixel in the profile digital image, and x2 represents the radial coordinate of a pixel in the profile digital image. See details... Figure 3 , Figure 4 .

[0062] Furthermore, the expression for the number of zones in the profile digital image after partitioning in step 4 is as follows:

[0063] M = n1 × n2

[0064] In the formula, M represents the number of zones in the digital profile image after partitioning, n1 represents the number of axial partitions in the digital profile image, and n2 represents the number of radial partitions in the digital profile image.

[0065] Furthermore, the expression for the number of pixels in each region of the cross-sectional digital image is as follows:

[0066] N (x1，x2) = (N1×N2) / (n1×n2)

[0067] In the formula, N (x1，x2) x1 represents the number of pixels in each region of the profile digital image, x2 represents the radial coordinate of the pixels in the profile digital image, N1 represents the number of axial pixels in the profile digital image, N2 represents the number of radial pixels in the profile digital image, n1 represents the number of axial partitions in the profile digital image, and n2 represents the number of radial partitions in the profile digital image.

[0068] Step 5: Using a weighted average method, combining the grayscale values ​​of the boundary between the rock and soil, and the R, G, and B values ​​of each pixel in the profile digital image, the grayscale value of each pixel in the partitioned profile digital image is calculated. Based on the grayscale values ​​of each pixel in the profile digital image, the sum of the grayscale values ​​of pixels in each region of the profile digital image is determined, and the stone content identification formula is used to preliminarily identify the stone content of each region of the profile digital image. The partitioning of the profile digital image after grayscale value calculation is described in [reference needed]. Figure 5 The preliminary identification results of the stone content in each region of the profile digital image in this embodiment are shown below. Figure 6 .

[0069] Furthermore, the formula for calculating the grayscale value of each pixel in the cross-sectional digital image after partitioning in step 5 is as follows:

[0070]

[0071] In the formula, Gray represents the gray value of each pixel in the profile digital image, R represents the R value of the pixel in the profile digital image, G represents the G value of the pixel in the profile digital image, B represents the B value of the pixel in the digital image, and h represents the gray value of the boundary between the rock and the soil.

[0072] Furthermore, the expression for the stone content identification formula is as follows:

[0073]

[0074] In the formula, The stone content of each zone in the digital profile image. This represents the sum of the gray values ​​of pixels in each region of the cross-sectional digital image. The number of pixels in each region of a cross-sectional digital image.

[0075] Step 6: Sum the stone content of each region in the preliminarily identified digital profile image from Step 5 to obtain the sum of the stone content of each region in the digital profile image. Then, use the stone content correction formula to correct the stone content of the preliminarily identified digital image to obtain the final stone content of the soil-rock mixture sample. The corrected stone content results for each region of the digital profile image in this embodiment are shown in [reference needed]. Figure 7 .

[0076] Furthermore, in step 6, the stone content correction formula is used to correct the stone content of the initially identified digital image, resulting in the final stone content of the soil-rock mixture sample, including:

[0077] S6.1: Determine the stone content correction coefficient based on the average stone content of the cut soil-rock mixture sample and the number of axial and radial partitions in the digital profile image;

[0078] S6.2: Correct the stone content of each area of ​​the preliminarily identified profile digital image based on the stone content correction coefficient to obtain the final stone content of the soil-rock mixture sample.

[0079] The expression for the stone content correction coefficient is as follows:

[0080] γ=(α t ·n1·n2) / α' sum

[0081] In the formula, γ represents the stone content correction coefficient, and α t α' represents the average stone content of the cut soil-rock mixture sample, n1 represents the number of axial partitions in the digital profile image, n2 represents the number of radial partitions in the digital profile image, and α' sum The lithology of each zone in the digital cross-section image is represented by the lithology.

[0082] The expression for the corrected stone content of the soil-rock mixture sample is as follows:

[0083] α (x1,x2) =γ·α' (x1,x2)

[0084] In the formula, α (x1,x2) γ represents the corrected stone content of the soil-rock mixture sample, α' represents the stone content correction coefficient, and α' represents the corrected stone content. (x1,x2) The lithology represents the lithology of each region in the digital cross-sectional image. In this embodiment, the initial lithology and α' sum The percentage is 630.3%, the stone content correction factor γ is 0.93, and the corrected results for the stone content distribution in each area are as follows: Figure 7 As shown.

[0085] The present invention has been described above by way of example. It should be noted that any simple modifications, alterations or other equivalent substitutions that can be made by those skilled in the art without creative effort without departing from the core of the present invention fall within the protection scope of the present invention.

Claims

1. A method for identifying the distribution of stone content in soil-rock mixture samples, characterized in that, Includes the following steps: Step 1: The soil-rock mixture sample is cut along the axial direction and the sample is divided into two equal parts. Digital images of the cross-section of the cut soil-rock mixture sample are acquired, pre-processed and saved. Step 2: Calculate the average stone content α of the soil-rock mixture sample. t The average stone content α t The ratio of the volume of the boulders to the volume of the soil-rock mixture sample is given. Step 3: Set the number of axial partitions n1 and radial partitions n2 of the profile digital image and the gray level h of the boundary between the rock and soil, and obtain the R, G, B values ​​of each pixel in the read profile digital image and the number of axial pixels N1 and the number of radial pixels N2 in the profile digital image. Step 4: Based on the number of axial and radial partitions in the profile digital image, divide the profile digital image into n1×n2 regions, denoted as A(x1, x2), where x1 = 1…n1, x2 = 1…n2, and each region contains N pixels. (x1，x2) = (N1×N2) / (n1×n2); Step 5: Calculate the gray value Gray of each pixel in the partitioned digital image of the cross-section. Add the gray values ​​Gray of all pixels in region A(x1, x2) of the digital image of the sample cross-section to obtain the sum of the gray values ​​of the pixels in that region. The formula for identifying the stone content α′ in each region of the profile digital image was used. (x1,x2) Conduct preliminary identification; Step 6: Sum the stone content of each region in the preliminarily identified profile digital image from Step 5 to obtain the stone content and α′ of each region in the profile digital image. sum The stone content correction formula was used to adjust the stone content α′ of each region in the initially identified digital image. (x1,x2) After correction, the stone content α of each zone of the final soil-rock mixture sample was obtained. (x1,x2) .

2. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 1, characterized in that, In step 1, the digital image of the cross-section of the soil-rock mixture sample is obtained by taking a picture of the cross-section of the soil-rock mixture sample with a digital camera.

3. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 1, characterized in that, In step 1, the preprocessing involves removing the background image outside the soil-rock mixture sample profile from the acquired digital profile image and saving it as a jpg file.

4. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 1, characterized in that, Step 2 includes the following steps: S2.1: Measure the volume V and density ρ of the soil-rock mixture sample, and sieve the cut soil-rock mixture sample to separate the rocks from the soil. S2.2: Weigh the separated stones to obtain the mass m of the separated stones; S2.3: Calculate the average stone content, and the formula for calculating the average stone content is as follows: a t =2 m / (ρ V)×100 In the formula, α t ρ represents the average stone content of the soil-rock mixture sample, m represents the mass of the separated stones, ρ represents the density of the stones, and V represents the volume of the soil-rock mixture sample.

5. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 1, characterized in that, In step 5, a weighted average method is used to calculate the gray value Gray of each pixel in the partitioned digital profile image by combining the gray h of the boundary between the rock and soil and the R, G, and B values ​​of each pixel in the profile digital image.

6. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 5, characterized in that, In step 5, the formula for calculating the grayscale value of each pixel is as follows: In the formula, Gray represents the gray value of each pixel in the profile digital image, R represents the R value of the pixel in the profile digital image, G represents the G value of the pixel in the profile digital image, B represents the B value of the pixel in the digital image, and h represents the gray value of the boundary between the rock and the soil.

7. The method for identifying the stone content distribution of soil-rock mixture samples as described in claim 1, characterized in that, The formula for identifying the stone content in step 5 is expressed as follows: In the formula, The initial identification of the lithology in each region of the digital profile image. This represents the sum of the gray values ​​of pixels in each region of the cross-sectional digital image. The number of pixels in each region of a cross-sectional digital image.

8. The method for identifying the distribution of stone content in soil-rock mixture samples as described in claim 1, characterized in that, In step 6, the stone content correction formula is used to correct the stone content of the initially identified digital image, resulting in the final stone content of the soil-rock mixture sample, including: S6.1: Determine the stone content correction coefficient based on the average stone content of the soil-rock mixture sample and the number of axial and radial partitions in the profile digital image; S6.2: Correct the stone content of each area of ​​the preliminarily identified profile digital image based on the stone content correction coefficient to obtain the final stone content of the soil-rock mixture sample.

9. The method for identifying the stone content distribution of soil-rock mixture samples as described in claim 8, characterized in that, The expression for the stone content correction coefficient is as follows: γ=(α t ·n1·n2) / α′ sum In the formula, γ represents the stone content correction coefficient, and α t α′ represents the average stone content of the soil-rock mixture sample, n1 represents the number of axial partitions in the digital profile image, n2 represents the number of radial partitions in the digital profile image, and α′ sum The lithology of each zone in the digital cross-section image is represented by the lithology.

10. The method for identifying the stone content distribution of soil-rock mixture samples as described in claim 1, characterized in that, The expression for the stone content of each zone of the corrected soil-rock mixture sample is as follows: a (x1,x2) =γ·α′ (x1,x2) In the formula, α (x1,x2) γ represents the stone content of each zone of the corrected soil-rock mixture sample, α′ represents the stone content correction coefficient, and α′ represents the stone content correction coefficient. (x1,x2) The stone content of the digital image representing the initial identification.

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