A method for detecting the compaction degree of cement-improved copper tailing roadbed construction based on image processing

Abstract

The present application belongs to the technical field of subgrade engineering, and in particular to a cement-improved copper tailings subgrade construction compactness detection method based on image processing. The specific steps of the present application are as follows: S1, 3D scanning technology is used to obtain point cloud data of the subbase and the cement-improved copper tailings subbase layer after paving and rolling, and the volume of the cement-improved copper tailings subbase layer in the detection point range after paving and rolling is calculated; S2, SEM scanning technology is used to collect images of the surface of the cement-improved copper tailings subbase layer in the detection point range after paving, and the images are filtered and histogram equalization processed, and the area of the surface pores in the detection point range is calculated; S3, the material quality of the cement-improved copper tailings subbase layer in the detection point range is calculated; and S4, the compactness of the cement-improved copper tailings subbase is calculated. The present application can be used for non-destructive, rapid and scientific guidance of the cement-improved copper tailings subgrade construction compactness detection, and effectively controls the quality in the construction process.

Description

Technical Field

[0001] This invention belongs to the field of roadbed engineering technology, and in particular relates to a method for detecting the compaction degree of cement-modified copper tailings roadbed construction based on image processing. Background Technology

[0002] In the beneficiation process of copper ore, 95% of the material forms tailings, which are mainly backfilled into the mine in the form of paste, and cannot be completely disposed of. A large amount of iron tailings that are not comprehensively utilized are mainly disposed of by stockpiling. This not only forces enterprises to build tailings dams and bear high infrastructure costs, but also occupies valuable land resources.

[0003] Subgrade engineering is a large-scale project in road construction, requiring a huge amount of filling materials. With increasing environmental awareness, road construction is increasingly inclined to choose other materials to replace soil. Copper tailings, a byproduct of mineral processing, has uniform particles and stable properties, making it a viable alternative to soil. However, copper tailings lack cohesive components and cannot form good adhesion. Therefore, cement is generally used for solidification to improve its strength and stability, thus making it an excellent subgrade filler. In cement-modified copper tailings construction, compaction degree is the most critical indicator for controlling construction quality. However, current methods for testing compaction degree in cement-modified copper tailings subgrades generally employ destructive methods such as sand cone and water bag methods. These methods can damage the subgrade and have low testing efficiency, with the accuracy of the results heavily reliant on manual labor.

[0004] Therefore, there is an urgent need for an image processing-based method for detecting the compaction degree of cement-modified copper tailings subgrade construction to solve the above problems. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, this invention provides a method for detecting the compaction degree of cement-modified copper tailings subgrade construction based on image processing. This invention can be used for non-destructive, rapid, and scientific guidance of compaction degree detection in cement-modified copper tailings subgrade construction, ensuring effective quality control during the construction process.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing, the specific steps of which are as follows:

[0008] S1. Determine the known coordinates and point cloud coordinates. Based on the relationship between the known coordinates and collected coordinates of the survey station and control points, construct the rotation matrix and translation matrix for coordinate transformation, and perform coordinate transformation on the point cloud data.

[0009] S2. Set up detection points, and then, with the detection points as the center, within a radius r, perform surface reconstruction in three stages: after the lower layer, after the cement-modified copper tailings road base is paved, and after the cement-modified copper tailings road base is compacted. Calculate the volume of the cement-modified copper tailings road base within the detection point area after paving and compaction.

[0010] S3. Using SEM scanning equipment, with the detection point as the center, within a radius r, the finished surface of the cement-modified copper tailings road base is imaged. The image is then filtered and histogram equalized to extract the pore area in the SEM scan image and calculate the mass of the cement-modified copper tailings road base material within the detection point range.

[0011] S4. Calculate the compaction degree of cement-improved copper tailings roadbed based on the volume change rate and loose density during the compaction process.

[0012] Preferably, in step S3, the quality of the cement-modified copper tailings road base material within the detection point range is calculated according to the following formula:

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula: ρ h —Cement-modified copper tailings material synthesis density, g / cm³ 3 ;

[0017] α—Moisture content of cement-modified copper tailings roadbed material, %

[0018] β-Cement content of improved copper tailings roadbed material, %

[0019] ρ t — Apparent density of dry copper tailings, g / cm³ 3 ;

[0020] ρ s —Density of water, g / cm³ 3 ;

[0021] ρ n — Apparent density of cement, g / cm³ 3 ;

[0022] h p —Average thickness of cement-modified copper tailings roadbed within the testing area, in cm;

[0023] n—Number of point cloud coordinates within the detection point range;

[0024] Z xi —The elevation of the i-th point cloud coordinates within the detection range after paving, in cm;

[0025] Z ti —Elevation of the underlying layer at the i-th point cloud coordinates within the detection range, in cm;

[0026] m g —The mass of cement-modified copper tailings within the testing area, in grams;

[0027] V xt —Loose volume of cement-modified copper tailings within the monitoring point area, in cm³ 3 ;

[0028] S k —The pore area extracted from the SEM scan image within the detection point area, in cm² 2 .

[0029] Preferably, in step S4, the compaction degree of the cement-modified copper tailings road base is calculated according to the following formula:

[0030] ;

[0031] ×100%;

[0032] In the formula: ρ g — Density of cement-modified copper tailings roadbed after compaction, g / cm³ 3 ;

[0033] m g —The mass of cement-modified copper tailings within the testing area, in grams;

[0034] V nt —Volume of cement-modified copper tailings after compaction within the testing area, in cm³ 3 ;

[0035] K—Compaction degree of cement-improved copper tailings roadbed base, %

[0036] α—Moisture content of cement-modified copper tailings roadbed material, %

[0037] ρ z —Maximum dry density of cement-modified copper tailings material, g / cm³ 3 .

[0038] Preferably, the specific steps of step S1 are as follows:

[0039] a. Based on the working conditions of the copper tailings roadbed construction face, determine the layout scheme of 3D scanning survey stations and control points, use a total station to measure the coordinates and elevations of the survey stations and control points, and obtain the spatial coordinates of the survey stations and control points as known coordinates;

[0040] b. Using a 3D scanner, the surface of the underlying layer, the construction section after the cement-modified copper tailings road base is laid, and the construction section after the cement-modified copper tailings road base is compacted are scanned to obtain point cloud coordinates.

[0041] c. Based on the control points, the point cloud coordinates collected from different measuring stations are stitched together. Based on the relationship between the known coordinates of the measuring stations and control points and the point cloud coordinates, a rotation matrix and a translation matrix for coordinate transformation are constructed to perform coordinate transformation on the coordinate point cloud data collected by the 3D scanner.

[0042] Preferably, in step S1, the rotation matrix and translation matrix for coordinate transformation are constructed, the nonlinear minimum value of the objective function is constructed based on least squares, and the rotation matrix and translation matrix are calculated according to the following formulas:

[0043] ;

[0044] ;

[0045] = +B;

[0046] +B- ;

[0047] ;

[0048] In the formula: R—rotation matrix;

[0049] ε x — Rotation angle in the X-axis direction;

[0050] ε y — Rotation angle in the Y-axis direction;

[0051] ε z —Z-axis rotation angle;

[0052] B—Translation matrix;

[0053] X0—Translation value in the X-axis direction;

[0054] Y0 — Translation value in the Y-axis direction;

[0055] Z0—Translation value in the Z-axis direction;

[0056] X 新 Y 新 Z 新 —Transformed coordinates;

[0057] X 旧 Y 旧 Z旧 —Coordinates before transformation;

[0058] V—Residual;

[0059] G—Objective function.

[0060] Preferably, a BP neural network model is used to reconstruct the underlying surface using point cloud coordinate data in space. After the cement-modified copper tailings road base layer is paved, the curved surface is The surface after the cement-improved copper tailings road base compaction construction is as follows: The volume of the cement-modified copper tailings subgrade within the testing area after paving and compaction is calculated using the following formula:

[0061] - ) ;

[0062] - ) ;

[0063] In the formula: V xt —Loose volume of cement-modified copper tailings within the monitoring point area, in cm³ 3 ;

[0064] V nt —Volume of cement-modified copper tailings after compaction within the testing area, in cm³ 3 .

[0065] Preferably, the detection point range is centered on the detection point, with a radius r of 5~7cm.

[0066] Preferably, the copper tailings have a particle size ≤ 0.3 mm, of which the proportion of particles smaller than 0.075 mm is ≥ 60%.

[0067] Preferably, the cement grade is 42.5; the cement content for cement-improved copper tailings roadbed is 2% to 4%.

[0068] Preferably, the loose-laid thickness of the cement-modified copper tailings road base is 35~40cm.

[0069] The advantages of this invention are:

[0070] (1) Based on the principle of compaction calculation, this invention uses laser scanning technology to analyze the volume change of cement-modified copper tailings road base before and after compaction, accurately obtains the volume value before and after compaction within the detection point range, and uses image processing technology to analyze the spatial composition state of cement-modified copper tailings, accurately obtains the quality of cement-modified copper tailings material within the detection point range, thus proposing a scientific and accurate method for obtaining the compaction index of cement-modified copper tailings road base.

[0071] (2) This invention is a non-destructive testing method that will not damage the roadbed. It uses laser scanning technology, image processing technology and information processing methods to calculate the compaction degree of cement-modified copper tailings roadbed, avoiding dependence on manual labor, thereby greatly improving the efficiency of testing and evaluation. The technical content is simple to operate and easy to implement. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0073] Example 1

[0074] S1. Conduct compaction tests indoors using 42.5 grade cement. Determine the optimum moisture content and maximum dry density of the raw materials as shown in Table 1-3 below:

[0075] Table 1 Raw material test results

[0076]

[0077] Table 2. Percentage of different particle sizes in copper tailings

[0078]

[0079] Table 3 Compaction Test Results

[0080]

[0081] S2. Determine the layout of 3D scanning measurement stations and control points, and select 3 measurement stations and 4 control points.

[0082] The following methods were used to measure and process the data to obtain point cloud data for the underlying layer, the loosely laid cement-modified copper tailings road base, and the compacted cement-modified copper tailings road base. The loosely laid thickness of the cement-modified copper tailings road base was controlled at 38cm, and the on-site moisture content of the cement-modified copper tailings was controlled at 12%.

[0083] (1) Use a total station to determine the coordinates and elevations of the station and control points. The determined coordinates are ( , , )......( , , );

[0084] (2) Using a 3D scanner, three measurement stations were set up to collect spatial information of the road surface, thereby obtaining the road surface coordinate point cloud data (x10, y10, z10)......(x1 n ,y1 n ,z1 n ), (x20,y20,z20)......(x2 n ,y2 n z2 n ), (x30,y30,z30)......(x3 n y3 n z3 n );

[0085] (3) Based on the control points, the coordinates collected from different monitoring stations are stitched together to form point cloud data. The stitched point cloud coordinates are (x0, y0, z0)......(x m ,y m ,z m );

[0086] (4) Substitute the known coordinates of the surveying station and control point obtained by the total station and the surveying station and coordinates obtained by the 3D scanner into the following formula, construct the nonlinear minimum value of the objective function based on least squares, and use Maltlab software to calculate the rotation matrix and translation matrix.

[0087] ;

[0088] ;

[0089] = +B;

[0090] +B- ;

[0091] ;

[0092] (5) Collect coordinate point cloud data from the 3D scanner, obtain the rotation matrix R and translation matrix B according to the above formula, and perform coordinate transformation on the coordinates collected by the 3D scanner. The transformed coordinates are (X0, Y0, Z0)......(X m ,Y m Z m );

[0093] = +B.

[0094] S3. Set the coordinates of the detection point (Xb, Yb). The detection range is within a radius r of 6cm centered on detection point 1. Segment the data according to the detection range. Use Maltlab software to establish a BP neural network model for the data within the detection point range. Reconstruct the underlying surface using point cloud coordinate data in space. After the cement-modified copper tailings road base layer is paved, the curved surface is Cement-modified copper tailings road base compaction construction completed curved surface The volume of the cement-modified copper tailings subgrade within the testing area after paving and compaction is calculated using the following formula:

[0095] - ) ;

[0096] - ) ;

[0097] Within the calculated detection range, after the cement-modified copper tailings roadbed paving is completed... =4281.3cm 3 Volume after compaction =3597.4cm 3 .

[0098] Using a SEM scanning device, images were acquired on the finished surface of the cement-modified copper tailings road base layer within a radius r of 6 cm, centered at detection point 1. The images were then filtered and histogram equalized. The pore area extracted from the SEM scan images was determined to be 43.96 cm². 2 .

[0099] S4. Calculate the composite density of cement-modified copper tailings material as 2.567 g / cm³ according to the following formula. 3 The average thickness of the cement-modified copper tailings road base course within the test point area after compaction was calculated to be 31.05 cm; the mass of the cement-modified copper tailings road base course material within the test point area was 7486.2 g.

[0100] ;

[0101] ;

[0102] .

[0103] S5. The compaction degree of the cement-modified copper tailings road base is calculated to be 95.3% according to the following formula;

[0104] ;

[0105] .

[0106] Two more test points were selected, and compaction was tested using the method described above. The sand cone method was also used to test the compaction at the test points. The overall results are shown in Table 4 below:

[0107] Table 4 Comparison of Compaction Degrees

[0108]

[0109] Comparing the results in Table 4 above, it can be seen that the method of the present invention has high reliability.

[0110] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the compaction degree of cement-modified copper tailings subgrade construction based on image processing, characterized in that, The specific steps are as follows: S1. Determine the known coordinates and point cloud coordinates. Based on the relationship between the known coordinates and collected coordinates of the survey station and control points, construct the rotation matrix and translation matrix for coordinate transformation, and perform coordinate transformation on the point cloud data. S2. Set up detection points, and then, with the detection points as the center, within a radius r, perform surface reconstruction in three stages: after the lower layer, after the cement-modified copper tailings road base is paved, and after the cement-modified copper tailings road base is compacted. Calculate the volume of the cement-modified copper tailings road base within the detection point area after paving and compaction. S3. Using SEM scanning equipment, with the detection point as the center, within a radius r, the finished surface of the cement-modified copper tailings road base is imaged. The image is then filtered and histogram equalized to extract the pore area in the SEM scan image and calculate the mass of the cement-modified copper tailings road base material within the detection point range. S4. Calculate the compaction degree of cement-improved copper tailings roadbed based on the volume change rate and loose density during the compaction process.

2. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that, In step S3, the quality of the cement-modified copper tailings road base material within the detection point range is calculated according to the following formula: ； ； ； In the formula: ρ h —Cement-modified copper tailings material synthesis density, g / cm³ 3 ; α—Moisture content of cement-modified copper tailings roadbed material, % β-Cement content of improved copper tailings roadbed material, % ρ t — Apparent density of dry copper tailings, g / cm³ 3 ; ρ s —Density of water, g / cm³ 3 ; ρ n — Apparent density of cement, g / cm³ 3 ; h p —Average thickness of cement-modified copper tailings roadbed within the testing area, in cm; n—Number of point cloud coordinates within the detection point range; Z xi —The elevation of the i-th point cloud coordinates within the detection range after paving, in cm; Z ti —Elevation of the underlying layer at the i-th point cloud coordinates within the detection range, in cm; m g —The mass of cement-modified copper tailings within the testing area, in grams; V xt —Loose volume of cement-modified copper tailings within the monitoring point area, in cm³ 3 ; S k —The pore area extracted from the SEM scan image within the detection point area, in cm² 2 .

3. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that, In step S4, the compaction degree of the cement-modified copper tailings road base is calculated according to the following formula: ； ×100%； In the formula: ρ g — Density of cement-modified copper tailings roadbed after compaction, g / cm³ 3 ; m g —The mass of cement-modified copper tailings within the testing area, in grams; V nt —Volume of cement-modified copper tailings after compaction within the testing area, in cm³ 3 ; K—Compaction degree of cement-improved copper tailings roadbed base, % α—Moisture content of cement-modified copper tailings roadbed material, % ρ z —Maximum dry density of cement-modified copper tailings material, g / cm³ 3 .

4. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that, The specific steps of step S1 are as follows: a. Based on the working conditions of the copper tailings roadbed construction face, determine the layout scheme of 3D scanning survey stations and control points, use a total station to measure the coordinates and elevations of the survey stations and control points, and obtain the spatial coordinates of the survey stations and control points as known coordinates; b. Using a 3D scanner, the surface of the underlying layer, the construction section after the cement-modified copper tailings road base is laid, and the construction section after the cement-modified copper tailings road base is compacted are scanned to obtain point cloud coordinates. c. Based on the control points, the point cloud coordinates collected from different measuring stations are stitched together. Based on the relationship between the known coordinates of the measuring stations and control points and the point cloud coordinates, a rotation matrix and a translation matrix for coordinate transformation are constructed to perform coordinate transformation on the coordinate point cloud data collected by the 3D scanner.

5. A method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing, as described in claim 1 or 4, characterized in that... In step S1, the rotation and translation matrices for coordinate transformation are constructed. The nonlinear minimum of the objective function is constructed using least squares, and the rotation and translation matrices are calculated according to the following formulas: ； ； = +B； +B- ； ； In the formula: R—rotation matrix; ε x — Rotation angle in the X-axis direction; ε y — Rotation angle in the Y-axis direction; ε z —Z-axis rotation angle; B—Translation matrix; X0—Translation value in the X-axis direction; Y0 — Translation value in the Y-axis direction; Z0—Translation value in the Z-axis direction; X 新 Y 新 Z 新 —Transformed coordinates; X 旧 Y 旧 Z 旧 —Coordinates before transformation; V—Residual; G—Objective function.

6. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that, Using a BP neural network model and point cloud coordinate data in space, the underlying surface is reconstructed as follows: After the cement-modified copper tailings road base layer is paved, the curved surface is The surface after the cement-improved copper tailings road base compaction construction is as follows: The volume of the cement-modified copper tailings subgrade within the testing area after paving and compaction is calculated using the following formula: - ） ； - ） ； In the formula: V xt —Loose volume of cement-modified copper tailings within the monitoring point area, in cm³ 3 ; V nt —Volume of cement-modified copper tailings after compaction within the testing area, in cm³ 3 .

7. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that: The range of the detection point is defined as a radius r of 5-7 cm centered on the detection point.

8. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that: The copper tailings have a particle size ≤ 0.3 mm, of which the proportion of particles smaller than 0.075 mm is ≥ 60%.

9. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that: The cement grade is 42.5; the cement usage for improving the copper tailings roadbed is 2% to 4%.

10. The method for detecting the compaction degree of cement-modified copper tailings subgrade based on image processing according to claim 1, characterized in that: The loose-laid thickness of the cement-modified copper tailings roadbed is 35~40cm.

Images