CN107560991A - A kind of more characteristic parameters evaluation method of asphalt mixture gap distribution character - Google Patents
A kind of more characteristic parameters evaluation method of asphalt mixture gap distribution character Download PDFInfo
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Abstract
The invention belongs to road engineering technical field, discloses a kind of more characteristic parameters evaluation method of asphalt mixture gap distribution character, including：Obtain the cementitious matter space three-phase medium picture that gathers materials of asphalt；To the three-phase medium picture binaryzation, and extract space distributed intelligence；Using multi-fractal box-covering method, multifractal spectra parameter is calculated according to the space distributed intelligence；The multiple features evaluating of bianry image is calculated according to the multifractal spectra parameter：Spectrum width Δ α, discrepancy in elevation Δ f and peak value f (α)max.Evaluation method provided by the invention, based on multi-fractal Theory, extract the more characteristic parameters of bianry image void distribution character, the inhomogeneities of space distribution can not only be reflected on the whole, the entire area size and localized voids distribution situation in space can also be reflected simultaneously, so as to reflect the distribution character of asphalt void more fully hereinafter.
Description
Technical Field
The invention relates to the technical field of road engineering, in particular to a multi-characteristic parameter evaluation method for the void distribution characteristics of an asphalt mixture.
Background
The asphalt mixture is a composite material consisting of aggregate, cementing material and a gap three-phase medium, wherein the gap plays an important role in the pavement performance and the mechanical property of the asphalt mixture. Particularly for porous asphalt pavements, the distribution of the voids directly influences the drainage, noise reduction and other properties of the pavements, so that the comprehensive and fine description of the distribution characteristics of the voids in the asphalt mixture is an important theoretical basis for researching the properties of the mixture.
The existing indoor asphalt mixture design method comprises related parameters such as asphalt mixture void ratio (VV), coarse aggregate framework void ratio (VCA) and asphalt mixture mineral aggregate void ratio (VMA), which describe the overall characteristics of the asphalt mixture macroscopically, but do not specifically consider the distribution condition of internal voids, so that the distribution condition of the internal voids of the mixture cannot be reflected from a microscopic or microscopic view. For the asphalt mixture microscopic image, slice images with different gray values are mainly obtained through a CT scanning technology, a plurality of slice images are further subjected to three-dimensional reconstruction to obtain a relatively real gap distribution structure, but when the gap distribution characteristics are analyzed, evaluation indexes of the slice images are mainly based on two-dimensional slice images, such as parameters of gap area percentage, gap contour fractal dimension and the like, the index parameters only singly describe the gap distribution characteristics and lack more detailed description of gaps, for example, only the area ratio of the gaps is considered, and the specific distribution condition of the gap size or the distribution is ignored.
Disclosure of Invention
The invention provides a multi-characteristic parameter evaluation method for the void distribution characteristics of an asphalt mixture, which solves the technical problem that the evaluation conclusion is unreliable one-sidedly because the evaluation method in the prior art can only carry out overall evaluation macroscopically and cannot realize more detailed and specific microscopic evaluation.
In order to solve the technical problem, the invention provides a multi-characteristic parameter evaluation method for the void distribution characteristics of an asphalt mixture, which comprises the following steps:
aggregate-cementing material-gap three-phase medium picture for obtaining asphalt mixture
Carrying out binarization on the three-phase medium picture, and extracting gap distribution information;
calculating to obtain a multi-fractal spectrum parameter of the gap distribution according to the gap distribution information by adopting a multi-fractal box counting method;
calculating multi-feature parameters of the binary image according to the multi-fractal spectrum parameters: spectral width Δ α, height difference Δ f, and peak value f (α) max 。
Further, the acquiring of the aggregate-cement-void three-phase medium picture of the asphalt mixture comprises:
and obtaining the grey-scale picture of the asphalt mixture containing the gap-cementing material-aggregate three-phase medium by a CT scanning method.
Further, the binarizing the three-phase medium picture and extracting the gap distribution information includes:
processing the asphalt mixture gray image, and selecting a gray threshold value of a gap to convert the asphalt mixture gray image into a binary black-and-white image;
wherein the voids are converted to white pixels and the aggregate and binder are converted to black pixels.
Further, the calculating the multi-fractal spectrum parameters according to the gap distribution information by using the multi-fractal box counting method includes:
equally dividing the binary black-and-white image into N grids with the pixel size of epsilon and counting the number V of white pixels in each grid under given epsilon i (epsilon), wherein i (i is more than or equal to 1 and less than or equal to N) is a grid ordinal number;
the number V of white pixels in the ith square i (epsilon) by the number of white pixels in all the squares, to obtain the ratio of pixels in each square, i.e. a probability measure P i (ε) the formula:
orthogonalizing probability measures of each square lattice and constructing a distribution function u i (q, ε), the calculation formula is:
in the formula, q is an order, a q value is preliminarily given, and a distribution function under the value is obtained through calculation; changing the size of epsilon, and repeating the steps to obtain distribution functions under different epsilon;
the above formula is combined, and the multi-fractal spectrum parameters a (q) and f (q) are calculated, wherein the calculation formula is as follows:
the picture size of the asphalt mixture gray-scale picture is L pixels; epsilon is taken in an exponential manner, with 0< epsilon < L.
Further, the multi-feature parameter spectrum width delta alpha, the height difference delta f and the peak value f (alpha) of the binary image are calculated according to the multi-fractal spectrum parameter max The method comprises the following steps:
calculating a multi-fractal spectral width Δ α = α max -α min In which α is max 、α min A maximum value and a minimum value of α (q), respectively;
expanding the upper bound or the lower bound of the value range of q by 1, if the variation rate of the multi-fractal spectrum width is less than 0.2%, taking the upper bound or the lower bound as the optimal value boundary, and if not, continuously expanding, and repeating the steps until the conditions are met;
the height difference Δ f = f (α) at this time was calculated min )-f(α max ) And is between alpha min ～α max Peak value of f (alpha) max 。
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the multi-feature parameter evaluation method for the void distribution characteristics of the asphalt mixture provided by the embodiment of the application is based on a multi-fractal theory, extracts the multi-feature parameters of the void distribution characteristics in the binary image, can reflect the nonuniformity of the void distribution as a whole, and can reflect the whole area size and the local void distribution condition of the voids, and the multi-feature parameter evaluation method can reflect the void distribution characteristics in the asphalt mixture more comprehensively.
Drawings
FIG. 1 is a flow chart for acquiring void distribution multi-characteristic parameters of an asphalt mixture according to an embodiment of the present invention;
FIG. 2 is a CT gray scale view of a three-phase medium of asphalt cement, aggregate and void provided by an embodiment of the invention;
FIG. 3 is a binarized view of a space in a CT gray-scale image according to an embodiment of the present invention;
FIG. 4 is a multi-fractal spectrum f (α) diagram of three asphalt mixtures according to an embodiment of the present invention.
Detailed Description
The embodiment of the application provides a multi-characteristic parameter evaluation method for the void distribution characteristics of the asphalt mixture, and solves the technical problem that evaluation conclusions are unreliable on one side because the evaluation method in the prior art can only carry out overall evaluation on a macro scale and cannot realize more detailed and specific micro evaluation.
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings of the specification and the specific embodiments, and it should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and the examples of the present application may be combined with each other without conflict.
A multi-characteristic parameter evaluation method for the void distribution characteristics of an asphalt mixture comprises the following steps:
aggregate-cementing material-gap three-phase medium picture for obtaining asphalt mixture
Carrying out binarization on the three-phase medium picture, and extracting gap distribution information;
calculating to obtain a multi-fractal spectrum parameter of the gap distribution according to the gap distribution information by adopting a multi-fractal box counting method;
calculating the multi-fractal multi-feature parameters of the binary image according to the multi-fractal spectrum parameters: spectral width Δ α, height difference Δ f, and peak value f (α) max 。
Specifically, the acquiring of the aggregate-cement-void three-phase medium picture of the asphalt mixture comprises:
and obtaining the grey-scale picture of the asphalt mixture containing the gap-cementing material-aggregate three-phase medium by a CT scanning method.
Referring to fig. 2, a CT slice image of the AM, ATB and SMA marshall mixture test piece is obtained by a CT scanning method, the three test pieces are designed to have a diameter of 10cm, and square areas with the same area of 6cm × 6cm are respectively selected from the three CT images to eliminate the influence of irregular shape and edges of the mixture, as shown in fig. 2, wherein the three CT images contain three-phase medium of cementing material, aggregate and gap, and the size of the image is set to be 1024 × 1024 pixels.
Further, the binarizing the three-phase medium picture and extracting the gap distribution information includes:
processing the asphalt mixture gray image, and selecting a gray threshold value of a gap to convert the asphalt mixture gray image into a binary black-and-white image;
wherein the voids are converted to white pixels and the aggregate and binder are converted to black pixels.
Referring to fig. 3, a photo shop software is used to select a gray threshold of the gap to perform black-and-white binarization processing on the picture, and then the inversion processing is performed to convert the gap area into white pixels and the aggregate and binder area into black pixels.
Further, referring to fig. 1, the picture is imported into MATLAB software, where the white pixel is "1" and the black pixel is "0" to form a two-dimensional array of picture pixels, and a multi-fractal box counting method is adopted to calculate the multi-fractal spectrum parameter of the binary picture according to the gap distribution information, where the specific calculation method is as follows:
equally dividing the binary black-and-white image into N grids with the pixel size of epsilon and counting the number V of white pixels in each grid under given epsilon i (epsilon), wherein i (i is more than or equal to 1 and less than or equal to N) is a grid ordinal number;
the number V of white pixels in the ith square i (epsilon) by the number of white pixels in all the squares, to obtain the ratio of pixels in each square, i.e. a probability measure P i (ε), the calculation formula is:
orthogonalizing each square lattice probability measure and constructing a distribution function u i (q, ε), the calculation formula is:
in the formula, q is an order, a q value is preliminarily given, and a distribution function under the value is obtained through calculation; changing the size of epsilon, and repeating the steps to obtain distribution functions under different epsilon;
the above formula is combined, and the multi-fractal spectrum parameters a (q) and f (q) are calculated, wherein the calculation formula is as follows:
the image size of the asphalt mixture gray image is L × L pixels; epsilon is taken in an exponential manner, with 0< epsilon < L.
In this embodiment, L takes the value of 1024, and the corresponding value range of epsilon is: 2 n And n is a natural number of 1 to 10.
Further, the multi-feature parameter spectrum width delta alpha, the height difference delta f and the peak value f (alpha) of the binary image are calculated according to the multi-fractal spectrum parameter max The method comprises the following steps:
calculating a multi-fractal spectral width Δ α = α max -α min In which α is max 、α min A maximum value and a minimum value of α (q), respectively;
expanding the upper bound or the lower bound of the value range of q by 1, if the variation rate of the multi-fractal spectrum width is less than 0.2%, taking the upper bound or the lower bound as the optimal value boundary, and if not, continuously expanding, and repeating the steps until the conditions are met;
the height difference Δ f = f (α) at this time was calculated min )-f(α max ) And is between alpha min ～α max Peak value of f (alpha) max 。
The multi-fractal spectrum calculation parameters and results of the three asphalt mixtures are shown in table 1.
TABLE 1 Multi-fractal Spectrum calculation parameters and results
Referring to FIG. 4, the calculated parameters Δ α, Δ f, f (α) max And the space distribution characteristic binary diagram shows that:
the spectral width Δ α =0.360992 of AM is the largest in the binary plots of void distribution for the three mixes, indicating the least uniform overall void distribution, while the peak AM value f (α) max =1.646452 max, indicating white pixel area is largest, i.e. emptyThe whole area of the gap is maximum;
on the other hand, the height difference Δ f =0.529572 of the multi-fractal spectrum of the ATB mixture is the largest, which indicates that white pixels are most concentrated, and a binary image shows that a part of areas have more white pixels and other areas have less white pixels;
the evaluation result accords with the void distribution characteristics in the binary image, and the evaluation method is reasonable and applicable.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the multi-feature parameter evaluation method for the void distribution characteristics of the asphalt mixture provided by the embodiment of the application is based on a multi-fractal theory, extracts the multi-feature parameters of the void distribution characteristics in the binary image, can reflect the nonuniformity of the void distribution as a whole, and can reflect the whole area size and the local void distribution condition of the voids, and the multi-feature parameter evaluation method can reflect the void distribution characteristics in the asphalt mixture more comprehensively.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (5)
1. A multi-characteristic parameter evaluation method for the void distribution characteristics of an asphalt mixture is characterized by comprising the following steps:
aggregate-cementing material-gap three-phase medium picture for obtaining asphalt mixture
Carrying out binarization on the three-phase medium picture, and extracting gap distribution information;
calculating to obtain a multi-fractal spectrum parameter of the gap distribution according to the gap distribution information by adopting a multi-fractal box counting method;
according to said pluralityAnd (3) calculating multi-feature parameters of the binary image by using the fractal spectrum parameters: spectral width Δ α, height difference Δ f, and peak value f (α) max 。
2. The method for evaluating the multi-characteristic parameters of the void distribution characteristics of the asphalt mixture according to claim 1, wherein the step of obtaining the aggregate-cement-void three-phase medium picture of the asphalt mixture comprises the following steps:
and obtaining a grey image of the asphalt mixture containing the gap-cementing material-aggregate three-phase medium by a CT scanning method.
3. The method for evaluating the multi-feature parameter of the void distribution characteristic of the bituminous mixture according to claim 2, wherein the binarizing the three-phase medium picture and extracting void distribution information comprises:
processing the asphalt mixture gray image, and selecting a gray threshold value of a gap to convert the asphalt mixture gray image into a binary black-and-white image;
wherein the voids are converted to white pixels and the aggregate and binder are converted to black pixels.
4. The multi-feature parameter evaluation method for the void distribution characteristics of the bituminous mixture according to claim 3, wherein the calculating the multi-fractal spectrum parameters of void distribution according to the void distribution information by using the multi-fractal box counting method comprises:
equally dividing the binary black-and-white image into N grids with the pixel size of epsilon and counting the number V of white pixels in each grid under given epsilon i (epsilon), wherein i (i is more than or equal to 1 and less than or equal to N) is a grid ordinal number;
the number V of white pixels in the ith square i (epsilon) by the number of white pixels in all the squares, to obtain the ratio of pixels in each square, i.e. a probability measure P i (ε), the calculation formula is:
orthogonalizing each square lattice probability measure and constructing a distribution function u i (q, ε), the calculation formula is:
in the formula, q is an order, a q value is preliminarily given, and a distribution function under the value is obtained through calculation; changing the size of the epsilon, and repeating the steps to obtain distribution functions under different epsilon;
the above formula is combined, and the multi-fractal spectrum parameters a (q) and f (q) are calculated, wherein the calculation formula is as follows:
the picture size of the asphalt mixture gray-scale picture is L pixels; epsilon is taken in an exponential manner, with 0< epsilon < L.
5. The method for evaluating the multi-feature parameters of the void distribution characteristics of the bituminous mixture according to claim 4, wherein the multi-feature parameter spectrum width Δ α, the height difference Δ f and the peak value f (α) of the binary image are calculated according to the multi-fractal spectrum parameters max The method comprises the following steps:
calculating a multi-fractal spectral width Δ α = α max -α min In which α is max 、α min A maximum value and a minimum value of α (q), respectively;
expanding the upper bound or the lower bound of the value range of q by 1, if the variation rate of the multi-fractal spectrum width is less than 0.2%, taking the upper bound or the lower bound as the optimal value boundary, and if not, continuously expanding, and repeating the steps until the conditions are met;
the height difference Δ f = f (α) at this time was calculated min )-f(α max ) And between alpha min ～α max Peak value of f (alpha) max 。
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| CN110443793A (en) * | 2019-08-07 | 2019-11-12 | 南京林业大学 | A kind of asphalt mixture gap distributing homogeneity evaluation method |
| CN110473225A (en) * | 2019-08-22 | 2019-11-19 | 哈尔滨工业大学 | A kind of Nonuniform illumination asphalt particle recognition method |
| CN110596358A (en) * | 2019-10-22 | 2019-12-20 | 赵文政 | Method and device for detecting self-healing performance of mixture and storage medium |
| CN112082910A (en) * | 2020-09-08 | 2020-12-15 | 长沙理工大学 | Characterization method of surface metal dispersity of supported metal catalyst based on multi-fractal |
| CN113433306A (en) * | 2021-07-20 | 2021-09-24 | 重庆交通大学 | Construction evaluation analysis method for loose asphalt pavement |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443793A (en) * | 2019-08-07 | 2019-11-12 | 南京林业大学 | A kind of asphalt mixture gap distributing homogeneity evaluation method |
| CN110443793B (en) * | 2019-08-07 | 2023-04-25 | 南京林业大学 | Asphalt mixture void distribution uniformity evaluation method |
| CN110473225A (en) * | 2019-08-22 | 2019-11-19 | 哈尔滨工业大学 | A kind of Nonuniform illumination asphalt particle recognition method |
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| CN110596358A (en) * | 2019-10-22 | 2019-12-20 | 赵文政 | Method and device for detecting self-healing performance of mixture and storage medium |
| CN112082910A (en) * | 2020-09-08 | 2020-12-15 | 长沙理工大学 | Characterization method of surface metal dispersity of supported metal catalyst based on multi-fractal |
| CN113433306A (en) * | 2021-07-20 | 2021-09-24 | 重庆交通大学 | Construction evaluation analysis method for loose asphalt pavement |
| CN113433306B (en) * | 2021-07-20 | 2022-01-28 | 重庆交通大学 | Construction evaluation analysis method for loose asphalt pavement |
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