CN106355587A - Method for calculating thickness of bituminous mixture mortar on basis of contact distance distribution - Google Patents
Method for calculating thickness of bituminous mixture mortar on basis of contact distance distribution Download PDFInfo
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- CN106355587A CN106355587A CN201610805145.8A CN201610805145A CN106355587A CN 106355587 A CN106355587 A CN 106355587A CN 201610805145 A CN201610805145 A CN 201610805145A CN 106355587 A CN106355587 A CN 106355587A
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- 239000004570 mortar (masonry) Substances 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 19
- 239000000203 mixture Substances 0.000 title abstract description 6
- 239000000463 material Substances 0.000 claims description 28
- 239000010426 asphalt Substances 0.000 claims description 12
- 150000001875 compounds Chemical class 0.000 claims description 10
- 238000000205 computational method Methods 0.000 claims description 10
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000005260 corrosion Methods 0.000 claims description 3
- 230000007797 corrosion Effects 0.000 claims description 3
- 238000007323 disproportionation reaction Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 239000013521 mastic Substances 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims 1
- 239000004576 sand Substances 0.000 claims 1
- 239000002002 slurry Substances 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000008187 granular material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
- G06T5/92—Dynamic range modification of images or parts thereof based on global image properties
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20152—Watershed segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20168—Radial search
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
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- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a method for calculating thickness of bituminous mixture mortar on the basis of contact distance distribution. The method comprises the following steps: defining the distance from a pixel point of a certain boundary of aggregate to a pixel point which is the closest to the boundary of adjacent aggregate as a mortar thickness of the point; and meanwhile, proposing an index for evaluating bituminous mixture mortar distribution by using a result obtained by calculating, wherein the index is average mortar thickness (Tave). By the method, scanned section images of various mixture test pieces are converted into grey-scale maps by MATLAB software mainly, then annular external expansion scanning is carried out outwards by using the pixel point of the boundary of each aggregate as a center until pixel points of boundaries of other aggregates are scanned, at the moment, the distance between each two pixel points is called as the mortar thickness, finally, the distribution condition of the mortar thicknesses is counted, and the average mortar thickness index is calculated to evaluate the distribution characteristics of the bituminous mixture mortar.
Description
Technical field
The invention belongs to field of road maintenance is and in particular to a kind of asphalt mortar based on contact range distribution is thick
Degree computational methods.
Background technology
The internal structure studying compound with Digital image technology is method more popular in recent years, with scientific and technological
Progressive, the technology and the algorithm that obtain image are quite ripe, and unique problem is to determine evaluates compound internal structure
Contacting between index and searching microstructure and macro property.
Mortar thickness between gathering materials and gathering materials largely affects the transmission of stress-strain in asphalt.
Because mortar rigidity at high temperature is relatively low, the permanent deformation of bituminous concrete also occurs mainly in the position of mortar.Excessive
Mortar thickness can cause the larger permanent deformation in road surface, and too small mortar thickness can reduce the compatible deformation energy of compound
Power.In the past carried out the research of mortar thickness few from microcosmic angle, only research is also simply laterally or longitudinally etc. limited
The thickness of direction calculating mortar, therefore obtained result precision is low, and variability is big.Therefore find one kind more fully mortar
THICKNESS CALCULATION method, and it is necessary to propose an evaluation rational index of mortar thickness.
Content of the invention
The invention aims to overcoming the shortcomings of existing mortar thickness computational methods, proposing a kind of being based on and contacting distance
The asphalt mortar thickness computational methods of distribution and the parameter of rational evaluation mortar thickness, resist for bituminous concrete
The research of rut performance.
The technical solution used in the present invention is: a kind of asphalt mortar thickness calculating side based on contact range distribution
Method, comprises the following steps:
1) Image Acquisition and process
The filter that first high-precision sectioning image is applied with green is to dilute the more blue channel that aggregate fractions exist
Pixel.Before image segmentation, convert images into the form of gray-scale maps in order to follow-up analysis using matlab related algorithm.
Application top cap conversion and median filtering algorithm eliminate the even picture noise of brightness disproportionation respectively.Segmentation figure as when, threshold application
Method distinguishes coarse aggregate (granule is more than 1.18mm) from asphalt mastic and space.In image segmentation process, scanogram is divided
It is segmented into 16 big rectangle regions of grade and carries out threshold value selection respectively, the optimal threshold in each region calculates to be calculated using otsu self adaptation
Method.After segmentation terminates, to overall binary picture application watershed transform and corrosion expansion algorithm, particle outline is checked
And correction.
2) mortar thickness computational methods
A certain boundary pixel point of gathering materials defined in the present invention with the adjacent border that gathers materials between the pixel of its nearest neighbours
Distance be mortar thickness at this point.Circular is: clicks through the boundary pixel that on section, each gathers materials first
Line number, same volume of gathering materials is jack per line, and difference gathers materials volume for contrary sign, then chooses the boundary pixel point gathered materials, in this point being
The heart outwards carries out annular and extends out scanning, until the boundary pixel point of a contrary sign is detected, claims the distance between this two pixel
For mortar thickness, this mortar thickness can be used to judge two gather materials between exposure level.
3) index calculates
The mortar thickness calculating gained on each section is analyzed, program calculates all boundary pixels gathering materials of gained
The maximum of the mortar thickness of point is t (unit: mm).Count pixel in every 0.1mm is spaced for the mortar thickness between 0-t
Percentage rate shared by quantity and accumulative pass through percentage rate.The distribution of mortar thickness can be divided into three phases: in thickness relatively minizone,
Pixel quantity with mortar thickness increase and rapid growth, subsequently tend towards stability, when thickness continue increase when, pixel quantity with
Rapid decrease.Cumulative percent can come matching, model parameter using two-parameter Weibull distribution model with the relation of mortar thickness
Including scale parameter λ and form parameter k, its distribution function as shown in Equation 1:
In formula:
K form parameter;
λ scale parameter;
T mortar thickness;
F (t) Cumulative logit model.
Scale parameter λ value is bigger, illustrates that the excursion of mortar thickness is bigger, form parameter k value is bigger, illustrates that mortar is thick
It is interval interior that degree is more evenly distributed in certain thickness.
Excessive mortar thickness easily causes permanent deformation, and too small mortar thickness can affect the compatible deformation of compound
Ability, the therefore present invention are passed through to calculate the mortar thickness distribution curve of gained, define this index t of average mortar thicknessave,
As shown in Equation 2.
In formula:
njThe summation of all boundary pixel point of gathering materials on j-th section;
tjiThe mortar thickness at i-th boundary pixel point on j-th section, unit: mm.
Beneficial effects of the present invention: the asphalt mortar thickness calculating based on contact range distribution proposed by the present invention
Method is simple, calculates the minimum mortar at each boundary point on compound section by image processing techniquess and program
Thickness, and define this index of average mortar thickness to evaluate the reasonability of compound mortar distribution, and then the angle from microcosmic
Disclose the mechanism that rut phenomenon produces, future also will become and judge the whether rational important means of asphalt mixture design.
Brief description
Fig. 1 is mortar thickness computational methods schematic diagram;
Fig. 2 is mortar thickness scattergram in every 0.1mm interval for these three compounds of ac-13, sma-13, sup-13.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and detailed description.
A kind of asphalt mortar thickness computational methods based on contact range distribution, comprise the following steps:
1) Image Acquisition and process
The filter that first high-precision sectioning image is applied with green is to dilute the more blue channel that aggregate fractions exist
Pixel.Before image segmentation, convert images into the form of gray-scale maps in order to follow-up analysis using matlab related algorithm.
Application top cap conversion and median filtering algorithm eliminate the even picture noise of brightness disproportionation respectively.Segmentation figure as when, threshold application
Method distinguishes coarse aggregate (granule is more than 1.18mm) from asphalt mastic and space.In image segmentation process, scanogram is divided
It is segmented into 16 big rectangle regions of grade and carries out threshold value selection respectively, the optimal threshold in each region calculates to be calculated using otsu self adaptation
Method.After segmentation terminates, to overall binary picture application watershed transform and corrosion expansion algorithm, particle outline is checked
And correction.
2) mortar thickness computational methods
A certain boundary pixel point of gathering materials defined in the present invention with the adjacent border that gathers materials between the pixel of its nearest neighbours
Distance be mortar thickness at this point.Circular as shown in figure 1, a, b, c represent three respectively gathers materials, the border of a
It is that to number be that to number be 3 for the boundary pixel point of 2, c for the boundary pixel point of 1, b that pixel is numbered, and chooses certain side that a gathers materials first
Boundary pixel o1, outwards carry out annular centered on this point and extend out scanning, until boundary pixel point o that b gathers materials is detected2,
Claim o1o2The distance between be mortar thickness tave, this mortar thickness can be used to judge two gather materials between exposure level.
3) index calculates
The mortar thickness calculating gained on each section is analyzed, program calculates all boundary pixels gathering materials of gained
The maximum of the mortar thickness of point is 4mm.Count pixel quantity in every 0.1mm is spaced for the mortar thickness between 0-4mm
Shared percentage rate and accumulative pass through percentage rate.As shown in Fig. 2 the distribution of mortar thickness can be divided into three phases: thickness is less
In interval, pixel quantity increases and rapid growth with mortar thickness, subsequently tends towards stability, when thickness continues to increase, pixel
Point quantity rapid decrease therewith.Cumulative percent can carry out matching using two-parameter Weibull distribution model with the relation of mortar thickness,
Model parameter includes scale parameter λ and form parameter k, its distribution function as shown in Equation 1:
In formula:
K form parameter;
λ scale parameter;
T mortar thickness;
F (t) Cumulative logit model.
Scale parameter λ value is bigger, illustrates that the excursion of mortar thickness is bigger, form parameter k value is bigger, illustrates that mortar is thick
It is interval interior that degree is more evenly distributed in certain thickness.Concrete fitting result is as shown in table 1.
The mortar thickness weber models fitting result of 1: three kind of compound of table
Excessive mortar thickness easily causes permanent deformation, and too small mortar thickness can affect the compatible deformation of compound
Ability, the therefore present invention are passed through to calculate the mortar thickness distribution curve of gained, define this index t of average mortar thicknessave,
As shown in Equation 2.
In formula:
njThe summation of all boundary pixel point of gathering materials on j-th section;
tjiThe mortar thickness at i-th boundary pixel point on j-th section, unit: mm.
It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention,
Some improvements and modifications can also be made, these improvements and modifications also should be regarded as protection scope of the present invention.In the present embodiment not
The all available prior art of clearly each ingredient is realized.
Claims (1)
1. a kind of asphalt mortar thickness computational methods based on contact range distribution it is characterised in that: include following walking
Rapid:
1) Image Acquisition and process
The filter that first high-precision sectioning image is applied with green is to dilute the more blue channel pixel that aggregate fractions exist
Point;Before image segmentation, convert images into the form of gray-scale maps in order to follow-up analysis using matlab related algorithm;Application
Top cap conversion and median filtering algorithm eliminate the even picture noise of brightness disproportionation respectively;Segmentation figure as when, threshold application method from
Coarse aggregate is distinguished in asphalt mastic and space;In image segmentation process, scanogram is divided into 16 big rectangle regions of grade
Carry out threshold value selection respectively, the optimal threshold in each region calculates and adopts otsu adaptive algorithm;After segmentation terminates, to overall two
Enter imaged application watershed transform and corrosion expansion algorithm, particle outline is checked and revises;
2) mortar thickness computational methods
A certain boundary pixel point of gathering materials defined in the present invention with the adjacent border that gathers materials between the pixel of its nearest neighbours away from
Mortar thickness away from for this point;Circular is: is compiled the boundary pixel point that on section, each gathers materials first
Number, same gather materials volume be jack per line, difference gather materials volume be contrary sign, then choose the boundary pixel point gathered materials, centered on this point to
Carry out outward annular and extend out scanning, until the boundary pixel point of a contrary sign is detected, the distance between this two pixel is called sand
Slurry thickness, this mortar thickness can be used to judge two gather materials between exposure level;
3) index calculates
The mortar thickness calculating gained on each section is analyzed, program calculates all boundary pixel point gathered materials of gained
The maximum of mortar thickness is t;Count hundred between 0-t shared by pixel quantity in every 0.1mm is spaced for the mortar thickness
Divide rate and add up to pass through percentage rate;The distribution of mortar thickness can be divided into three phases: thickness compared with minizone, pixel quantity with
Mortar thickness increases and rapid growth, subsequently tends towards stability, when thickness continues to increase, pixel quantity rapid decrease therewith;
Cumulative percent can carry out matching using two-parameter Weibull distribution model with the relation of mortar thickness, and model parameter includes scale parameter
λ and form parameter k, shown in its distribution function formula 1:
In formula:
K form parameter;
λ scale parameter;
T mortar thickness;
F (t) Cumulative logit model;
Scale parameter λ value is bigger, illustrates that the excursion of mortar thickness is bigger, form parameter k value is bigger, illustrates mortar thickness more
Plus it is interval interior to be evenly distributed in certain thickness;
Excessive mortar thickness easily causes permanent deformation, and too small mortar thickness can affect the compatible deformation energy of compound
Power, the therefore present invention are passed through to calculate the mortar thickness distribution curve of gained, define this index t of average mortar thicknessave, such as
Shown in formula 2;
In formula:
njThe summation of all boundary pixel point of gathering materials on j-th section;
tjiThe mortar thickness at i-th boundary pixel point on j-th section, unit: mm.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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CN110473225A (en) * | 2019-08-22 | 2019-11-19 | 哈尔滨工业大学 | A kind of Nonuniform illumination asphalt particle recognition method |
CN113658117A (en) * | 2021-08-02 | 2021-11-16 | 浙江大学 | Method for identifying and dividing aggregate boundaries in asphalt mixture based on deep learning |
CN114235599A (en) * | 2021-12-22 | 2022-03-25 | 江苏镇淮建设集团有限公司 | Asphalt mortar low-temperature fracture performance testing method based on semicircular bending testing mode |
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CN103091480A (en) * | 2013-01-07 | 2013-05-08 | 河北工业大学 | Entropy weight-based underground road bituminous pavement service performance evaluation method |
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Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110473225A (en) * | 2019-08-22 | 2019-11-19 | 哈尔滨工业大学 | A kind of Nonuniform illumination asphalt particle recognition method |
CN110473225B (en) * | 2019-08-22 | 2023-06-06 | 哈尔滨工业大学 | Non-uniform illuminance asphalt mixture particle identification method |
CN113658117A (en) * | 2021-08-02 | 2021-11-16 | 浙江大学 | Method for identifying and dividing aggregate boundaries in asphalt mixture based on deep learning |
CN113658117B (en) * | 2021-08-02 | 2023-09-15 | 安徽省交通控股集团有限公司 | Method for identifying and dividing aggregate boundary in asphalt mixture based on deep learning |
CN114235599A (en) * | 2021-12-22 | 2022-03-25 | 江苏镇淮建设集团有限公司 | Asphalt mortar low-temperature fracture performance testing method based on semicircular bending testing mode |
CN114235599B (en) * | 2021-12-22 | 2022-11-08 | 江苏镇淮建设集团有限公司 | Asphalt mortar low-temperature fracture performance testing method based on semicircular bending testing mode |
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