Coarse aggregate three-dimensional morphological characteristic characterization method

Abstract

The invention discloses a method for characterizing three-dimensional morphological characteristics of coarse aggregate, and belongs to the field of road building materials. According to the method, the problem that the three-dimensional morphological characteristics of the coarse aggregate cannot be comprehensively represented by the existing method is solved. The method comprises the following steps: firstly, acquiring a three-dimensional point cloud image of the surface of the coarse aggregate, enveloping and fitting the three-dimensional point cloud image of the coarse aggregate by using an ellipsoid, and then determining the shape of the coarse aggregate according to the lengths of three half axes of the ellipsoid; then, deleting a part with relatively large corner angle change in the coarse aggregate three-dimensional point cloud image, and representing the corner angle of the aggregate by using the calculated volume change rate; and smoothing the three-dimensional point cloud image of the whole coarse aggregate surface, and representing the texture roughness of the aggregate by using the calculated surface area change rate. According to the method, the shape, the corner angle and the texture of the coarse aggregate can be evaluated by quantifying the three-dimensional indexes. The method can be applied to characterization of the three-dimensional morphological characteristics of the coarse aggregate.

Description

Technical Field

[0001] The invention belongs to the field of road construction materials, and in particular relates to a method for characterizing three-dimensional morphological characteristics of coarse aggregate. Background Art

[0002] Asphalt pavement has the advantages of being smooth, low noise, and wear-resistant. Currently, most highways are built with asphalt concrete. Aggregates account for more than 90% of the total mass of asphalt mixtures, of which coarse aggregates account for 50% to 80% of the total mass of asphalt mixtures. Asphalt mixtures composed of coarse aggregates, fine aggregates, mineral powders, fillers and other materials have a very complex structure. Asphalt mixtures contain substances in three forms: gas, liquid and solid. The morphological characteristics of coarse aggregates will also affect the grading and performance of asphalt mixtures, and ultimately change the mechanical properties and performance of asphalt mixtures. In particular, it plays an important role in the load transfer path and the interlocking structure of the mixture, and determines the mechanical properties and service life of the pavement.

[0003] At present, researchers have reached some similar conclusions about the impact of coarse aggregate on the performance of asphalt mixture. When the content of needle-shaped coarse aggregate increases, the oil-stone ratio and porosity of the asphalt mixture with the best performance will increase, and the high temperature stability, water stability and fatigue resistance of the asphalt mixture will deteriorate. The increase in the angularity of coarse aggregate will improve the ability of the asphalt mixture to resist permanent deformation. The complex surface texture of coarse aggregate will improve the high temperature stability, water stability and low temperature crack resistance of the asphalt mixture to a certain extent. It can be seen that accurately evaluating the morphological characteristics of coarse aggregate can help people to better grasp the quality of coarse aggregate and design asphalt mixtures with better road performance.

[0004] Engineers will use the content of needle-like coarse aggregate, major and minor axes, etc. to evaluate the morphological characteristics of coarse aggregate. However, these indicators are often unable to describe the morphological characteristics of coarse aggregate well, and the information contained is not sufficient. Therefore, the existing methods still cannot fully characterize the three-dimensional morphological characteristics of coarse aggregate. It is very necessary to propose a method to comprehensively characterize the three-dimensional morphological characteristics of coarse aggregate. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the existing methods cannot fully characterize the three-dimensional morphological characteristics of coarse aggregate, and propose a method for characterizing the three-dimensional morphological characteristics of coarse aggregate.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a method for characterizing the three-dimensional morphological characteristics of coarse aggregate, the method specifically comprising the following steps:

[0007] Step 1: obtaining a three-dimensional point cloud original image of the surface of a coarse aggregate particle, and using the obtained original image as a first coarse aggregate image;

[0008] Step 2: Fit the minimum enveloping ellipsoid of the obtained three-dimensional point cloud of the coarse aggregate particle surface, record the length of the major semi-axis of the minimum enveloping ellipsoid as a, the length of the middle semi-axis as b, and the length of the minor semi-axis as c; then calculate the ratio k1 of c to b, and the ratio k2 of b to a;

[0009] Step 3, removing points whose curvature change is greater than a threshold value in the acquired three-dimensional point cloud of the coarse aggregate particle surface, and saving the remaining point cloud as a second coarse aggregate image;

[0010] Step 4, calculating the volume change rate between the first coarse aggregate image and the second coarse aggregate image;

[0011] Step 5: Smoothing the original three-dimensional point cloud image of the surface of the coarse aggregate particles, and using the smoothed image as the third coarse aggregate image;

[0012] Step 6, calculating the surface area change rate between the first coarse aggregate image and the third coarse aggregate image;

[0013] Step 7: Use the ratio k1 and the ratio k2 to determine the shape of the coarse aggregate particles, and use the shape, volume change rate and surface area change rate to characterize the three-dimensional morphological characteristics of the coarse aggregate.

[0014] Furthermore, the original image of the three-dimensional point cloud in step one is obtained by a three-dimensional laser scanner, and the obtained original image is an image in HSP format.

[0015] Furthermore, the method of fitting the minimum enveloping ellipsoid of the acquired three-dimensional point cloud of the coarse aggregate particle surface adopts the nearest neighbor method.

[0016] Furthermore, in the step 2, the minimum enveloping ellipsoid of the obtained three-dimensional point cloud of the coarse aggregate particle surface is fitted, and the specific process is:

[0017] The equation of the ellipsoid is written as:

[0018]

[0019] Where a represents the length of the major semi-axis of the ellipsoid, in mm;

[0020] b represents the length of the median axis of the ellipsoid, in mm;

[0021] c represents the length of the minor semi-axis of the ellipsoid, in mm;

[0022] x represents the x-axis coordinate of the point on the ellipsoid;

[0023] y represents the y-axis coordinate of the point on the ellipsoid;

[0024] z represents the z-axis coordinate of the point on the ellipsoid;

[0025] Construct the loss function f based on the ellipsoid equation:

[0026]

[0027] Among them, (x i ,y i ,z i ) represents the coordinates of the i-th three-dimensional point cloud of the surface of the coarse aggregate particles, i = 1, 2, ..., n, and n represents the number of three-dimensional point clouds on the surface of the coarse aggregate particles;

[0028] Minimize the value of the loss function f:

[0029]

[0030] And simplify formula (3) to formula (4):

[0031]

[0032] in,

[0033] By solving formula (4), we can calculate and value.

[0034] Furthermore, the curvature method is used in step three.

[0035] Furthermore, the specific process of step three is:

[0036] Step 3: Select any cross section of the coarse aggregate particle, and then obtain the point cloud belonging to the cross section from the three-dimensional point cloud of the surface of the coarse aggregate particle. The two adjacent points in the obtained point cloud are respectively recorded as M and M', and the arc length MM' between point M and point M' is s, then the curvature k of point M is:

[0037]

[0038] Among them, Δα is the angle between the tangent line through point M and the tangent line through point M';

[0039] Similarly, the curvature of each point belonging to the cross section is calculated separately, and the points with curvature greater than the threshold are removed;

[0040] Step 32: Use the method of step 31 to process the point cloud of each cross section of the coarse aggregate particles separately, and save the remaining point cloud as the second coarse aggregate image.

[0041] Furthermore, in the step five, the three-dimensional point cloud original image of the surface of the coarse aggregate particles is smoothed by using a bilateral filtering smoothing algorithm.

[0042] Furthermore, the specific process of the bilateral filtering smoothing algorithm is as follows:

[0043] Take any point u=(x,y) in the three-dimensional point cloud of the surface of coarse aggregate particles as an example

[0044]

[0045] Among them, N(u) is the set of points in the neighborhood of point u, p∈N(u), and point p satisfies ||up||<2σ c , ||up|| is the distance between point u and point p, W c (·) is a Gaussian filter, ||·|| represents the calculation of the 2-norm, σ c is the standard deviation of the Gaussian filter, e is the base of the natural logarithm, W s (·) is the similarity weight function, σ s is the standard deviation of the similarity weight function, |·| represents the calculated absolute value, I(u) is the image at point u, I(p) is the image at point p, It is the image at point u after bilateral filtering smoothing.

[0046] Furthermore, in step seven, the shape of the coarse aggregate particles is determined using the ratio k1 and the ratio k2, specifically:

[0047] like and Then the shape of coarse aggregate particles is plate-like;

[0048] like and Then the shape of the coarse aggregate particles is columnar;

[0049] like and Then the shape of the coarse aggregate particles is cake-like;

[0050] like and The shape of the coarse aggregate particles is cubic.

[0051] The beneficial effects of the present invention are:

[0052] The present invention first obtains a three-dimensional point cloud image of the coarse aggregate surface, uses an ellipsoid to fit the envelope of the three-dimensional point cloud image of the coarse aggregate, and then determines the shape of the coarse aggregate according to the lengths of the three semi-axes of the ellipsoid; then, deletes the portion of the three-dimensional point cloud image of the coarse aggregate with a large angular change, and uses the calculated volume change rate to characterize the angularity of the aggregate; the three-dimensional point cloud image of the entire coarse aggregate surface is smoothed, and the calculated surface area change rate is used to characterize the texture roughness of the aggregate. The method of the present invention can evaluate the shape, angularity and texture of the coarse aggregate by quantifying three-dimensional indicators, solves the problem that the existing method cannot comprehensively characterize the three-dimensional morphological characteristics of the coarse aggregate, provides reference value for the selection of coarse aggregate, and has obvious practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a method for characterizing three-dimensional morphological characteristics of coarse aggregate according to the present invention;

[0054] Figure 2 It is a schematic diagram of the minimum ellipsoid envelope of the present invention;

[0055] Figure 3a It is a three-dimensional image of the aggregate before the surface of the coarse aggregate is smoothed;

[0056] Figure 3b It is a three-dimensional image of the aggregate after the surface of the coarse aggregate is smoothed. DETAILED DESCRIPTION

[0057] Specific implementation method 1: Combination Figure 1 The present embodiment describes a method for characterizing the three-dimensional morphological characteristics of coarse aggregate, wherein the method of the present invention is performed on each coarse aggregate particle, and the method specifically comprises the following steps:

[0058] Step 1: obtaining a three-dimensional point cloud original image of the surface of a coarse aggregate particle, and using the obtained original image as a first coarse aggregate image;

[0059] Step 2: Fit the minimum enveloping ellipsoid of the obtained three-dimensional point cloud of the coarse aggregate particle surface, such as Figure 2 As shown, the length of the major semi-axis of the minimum envelope ellipsoid is recorded as a, the length of the middle semi-axis is recorded as b, and the length of the minor semi-axis is recorded as c; then calculate the ratio of c to b k1, and the ratio of b to a k2;

[0060] It should be noted that a is the length of the longest semi-axis of the three-dimensional minimum enveloping ellipsoid, and c is the length of the shortest semi-axis of the three-dimensional minimum enveloping ellipsoid;

[0061] Step 3, removing points whose curvature change is greater than a threshold value in the acquired three-dimensional point cloud of the coarse aggregate particle surface, and saving the remaining point cloud as a second coarse aggregate image;

[0062] Step 4, calculating the volume change rate between the first coarse aggregate image and the second coarse aggregate image; that is, subtracting the volume of the coarse aggregate in the second coarse aggregate image from the volume of the coarse aggregate in the first coarse aggregate image, and then calculating the ratio of the subtraction result to the volume of the coarse aggregate in the first coarse aggregate image;

[0063] Step 5: Smoothing the original three-dimensional point cloud image of the surface of the coarse aggregate particles, and using the smoothed image as the third coarse aggregate image;

[0064] Step 6, calculating the surface area change rate between the first coarse aggregate image and the third coarse aggregate image; that is, subtracting the coarse aggregate surface area in the third coarse aggregate image from the coarse aggregate surface area in the first coarse aggregate image, and then calculating the ratio of the subtraction result to the coarse aggregate surface area in the first coarse aggregate image;

[0065] Step 7: Use the ratio k1 and the ratio k2 to determine the shape of the coarse aggregate particles, and use the shape, volume change rate and surface area change rate to characterize the three-dimensional morphological characteristics of the coarse aggregate.

[0066] The present invention uses laser scanning and reconstructs a three-dimensional model of coarse aggregate in a computer to explore the three-dimensional morphological characteristics of the coarse aggregate. Parameter indicators of the shape, edges and corners, and texture of the coarse aggregate are established based on the three-dimensional model of the coarse aggregate. Compared with the indirect detection method, the method of the present invention can be collected conveniently and quickly, does not limit the number of collected samples, is less affected by the collectors, can more objectively reflect the true texture condition of the coarse aggregate surface, has a simple processing flow, and can easily make the detection technology continuous. It can help researchers to obtain the shape information of the coarse aggregate texture contour more quickly, correctly and objectively, evaluate the morphological characteristics of the coarse aggregate, and provide a basis for the construction and maintenance of asphalt pavements.

[0067] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that the original image of the three-dimensional point cloud in step 1 is obtained by a three-dimensional laser scanner, and the obtained original image is an image in HSP format.

[0068] The other steps and parameters are the same as those in the first embodiment.

[0069] Specific implementation method three: This implementation method is different from specific implementation method one or two in that the minimum enveloping ellipsoid of the acquired three-dimensional point cloud on the surface of the coarse aggregate particles is fitted using the nearest neighbor method.

[0070] The other steps and parameters are the same as those in the first or second embodiment.

[0071] Specific implementation method 4: This implementation method is different from any one of specific implementation methods 1 to 3 in that, in step 2, the minimum enveloping ellipsoid of the acquired three-dimensional point cloud of the coarse aggregate particle surface is fitted, and the specific process is as follows:

[0072] The equation of the ellipsoid is written as:

[0073]

[0074] Where a represents the length of the major semi-axis of the ellipsoid, in mm;

[0075] b represents the length of the median axis of the ellipsoid, in mm;

[0076] c represents the length of the minor semi-axis of the ellipsoid, in mm;

[0077] x represents the x-axis coordinate of the point on the ellipsoid;

[0078] y represents the y-axis coordinate of the point on the ellipsoid;

[0079] z represents the z-axis coordinate of the point on the ellipsoid;

[0080] Construct the loss function f based on the ellipsoid equation:

[0081]

[0082] Among them, (x i ,y i ,z i ) represents the coordinates of the i-th three-dimensional point cloud of the surface of the coarse aggregate particles, i = 1, 2, ..., n, and n represents the number of three-dimensional point clouds on the surface of the coarse aggregate particles;

[0083] Minimize the value of the loss function f:

[0084]

[0085] And simplify formula (3) to formula (4):

[0086]

[0087] in,

[0088] By solving formula (4), we can calculate and value.

[0089] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0090] The loss function result of formula (3) can be regarded as the gap between the calculated model and the actual model. The smaller the calculation result of the loss function, the higher the authenticity and effectiveness of the model, and the more accurate the model. The larger the calculation result of the loss function, the greater the contrast between the model and the actual model.

[0091] Specific implementation method five: This implementation method is different from any one of specific implementation methods one to four in that the curvature method is used in step three.

[0092] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0093] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that the specific process of step 3 is as follows:

[0094] Step 3: Select any cross section of the coarse aggregate particle, and then obtain the point cloud belonging to the cross section from the three-dimensional point cloud of the surface of the coarse aggregate particle. The two adjacent points in the obtained point cloud are respectively recorded as M and M', and the arc length MM' between point M and point M' is s, then the curvature k of point M is:

[0095]

[0096] Among them, Δα is the angle between the tangent line through point M and the tangent line through point M';

[0097] Similarly, the curvature of each point belonging to the cross section is calculated respectively, and the points whose curvature is greater than a threshold (the threshold value can be set according to actual conditions) are removed;

[0098] Step 32: Use the method of step 31 to process the point cloud of each cross section of the coarse aggregate particles separately, and save the remaining point cloud as the second coarse aggregate image.

[0099] The other steps and parameters are the same as those in Specific Implementations 1 to 5.

[0100] The present invention selects the point cloud based on the size of the principal curvature to ensure the accuracy of the selection.

[0101] Specific implementation method seven: This implementation method is different from any one of specific implementation methods one to six in that, in step five, the three-dimensional point cloud original image of the surface of the coarse aggregate particles is smoothed using a bilateral filtering smoothing algorithm.

[0102] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0103] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the specific process of the bilateral filtering smoothing algorithm is as follows:

[0104] Take any point u=(x,y) in the three-dimensional point cloud of the surface of coarse aggregate particles as an example

[0105]

[0106] Among them, N(u) is the set of points in the neighborhood of point u, p∈N(u), and point p satisfies ||up||<2σ c , ||up|| is the distance between point u and point p, W c (·) is a standard Gaussian filter, ||·|| represents the calculation of the 2-norm, σ c is the standard deviation of the Gaussian filter, e is the base of the natural logarithm, W s (·) is the similarity weight function, σ s is the standard deviation of the similarity weight function, |·| represents the calculated absolute value, I(u) is the image at point u, I(p) is the image at point p, It is the image at point u after bilateral filtering smoothing.

[0107] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0108] The smoothing algorithm that can be used in this embodiment is not limited to the bilateral filtering smoothing algorithm. Figure 3a As shown in the figure, the three-dimensional image of the aggregate after smoothing is as follows: Figure 3b shown.

[0109] Specific embodiment 9: This embodiment is different from any one of specific embodiments 1 to 8 in that, in step 7, the shape of the coarse aggregate particles is determined using the ratio k1 and the ratio k2, specifically:

[0110] like and Then the shape of coarse aggregate particles is plate-like;

[0111] like and Then the shape of the coarse aggregate particles is columnar;

[0112] like and Then the shape of the coarse aggregate particles is cake-like;

[0113] like and The shape of the coarse aggregate particles is cubic.

[0114] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0115] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for characterizing the three-dimensional morphological characteristics of coarse aggregate, characterized in that: The method specifically comprises the following steps: Step 1: obtaining a three-dimensional point cloud original image of the surface of a coarse aggregate particle, and using the obtained original image as a first coarse aggregate image; Step 2: Fit the minimum enveloping ellipsoid of the obtained three-dimensional point cloud of the coarse aggregate particle surface, record the length of the major semi-axis of the minimum enveloping ellipsoid as a, the length of the middle semi-axis as b, and the length of the minor semi-axis as c; then calculate the ratio k1 of c to b, and the ratio k2 of b to a; Step 3, removing points whose curvature change is greater than a threshold value in the acquired three-dimensional point cloud of the coarse aggregate particle surface, and saving the remaining point cloud as a second coarse aggregate image; Step 4, calculating the volume change rate between the first coarse aggregate image and the second coarse aggregate image; Step 5: Smoothing the original three-dimensional point cloud image of the surface of the coarse aggregate particles, and using the smoothed image as the third coarse aggregate image; Step 6, calculating the surface area change rate between the first coarse aggregate image and the third coarse aggregate image; Step 7: Use the ratio k1 and the ratio k2 to determine the shape of the coarse aggregate particles, and use the shape, volume change rate and surface area change rate to characterize the three-dimensional morphological characteristics of the coarse aggregate.

2. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: The original image of the three-dimensional point cloud in step 1 is obtained by a three-dimensional laser scanner, and the obtained original image is an image in the HSP format.

3. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: The method of fitting the minimum envelope ellipsoid of the acquired three-dimensional point cloud on the surface of the coarse aggregate particles adopts the nearest neighbor method.

4. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1 or 3, characterized in that: In the second step, the minimum enveloping ellipsoid of the obtained three-dimensional point cloud of the coarse aggregate particle surface is fitted, and the specific process is: The equation of the ellipsoid is written as: Where a represents the length of the major semi-axis of the ellipsoid, in mm; b represents the length of the median axis of the ellipsoid, in mm; c represents the length of the minor semi-axis of the ellipsoid, in mm; x represents the x-axis coordinate of the point on the ellipsoid; y represents the y-axis coordinate of the point on the ellipsoid; z represents the z-axis coordinate of the point on the ellipsoid; Construct the loss function f based on the ellipsoid equation: Among them, (x i ,y i ,z i ) represents the coordinates of the i-th three-dimensional point cloud of the surface of the coarse aggregate particles, i = 1, 2, ..., n, and n represents the number of three-dimensional point clouds on the surface of the coarse aggregate particles; Minimize the value of the loss function f: And simplify formula (3) to formula (4): in, By solving formula (4), we can calculate and value.

5. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: The curvature method is used in step three.

6. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1 or 5, characterized in that: The specific process of step three is: Step 3: Select any cross section of the coarse aggregate particle, and then obtain the point cloud belonging to the cross section from the three-dimensional point cloud of the surface of the coarse aggregate particle. The two adjacent points in the obtained point cloud are respectively recorded as M and M', and the arc length MM' between point M and point M' is s, then the curvature k of point M is: Among them, Δα is the angle between the tangent line through point M and the tangent line through point M'; Similarly, the curvature of each point belonging to the cross section is calculated separately, and the points with curvature greater than the threshold are removed; Step 32: Use the method of step 31 to process the point cloud of each cross section of the coarse aggregate particles separately, and save the remaining point cloud as the second coarse aggregate image.

7. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: In the step 5, the three-dimensional point cloud original image of the surface of the coarse aggregate particles is smoothed by using a bilateral filtering smoothing algorithm.

8. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1 or 7, characterized in that: The specific process of the bilateral filtering smoothing algorithm is as follows: Take any point u=(x,y) in the three-dimensional point cloud of the surface of coarse aggregate particles as an example Among them, N(u) is the set of points in the neighborhood of point u, p∈N(u), and point p satisfies ||up||<2σ c , ||up|| is the distance between point u and point p, W c (·) is a Gaussian filter, ||·|| represents the calculation of the 2-norm, σ c is the standard deviation of the Gaussian filter, e is the base of the natural logarithm, W s (·) is the similarity weight function, σ s is the standard deviation of the similarity weight function, |·| represents the calculated absolute value, I(u) is the image at point u, I(p) is the image at point p, It is the image at point u after bilateral filtering smoothing.

9. A method for characterizing three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: In step 7, the shape of the coarse aggregate particles is determined using the ratio k1 and the ratio k2, specifically: like and Then the shape of coarse aggregate particles is plate-like; like and Then the shape of the coarse aggregate particles is columnar; like and Then the shape of the coarse aggregate particles is cake-like; like and The shape of the coarse aggregate particles is cubic.

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