CN106355587B - Asphalt mortar thickness calculation method based on contact range distribution - Google Patents
Asphalt mortar thickness calculation method based on contact range distribution Download PDFInfo
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- CN106355587B CN106355587B CN201610805145.8A CN201610805145A CN106355587B CN 106355587 B CN106355587 B CN 106355587B CN 201610805145 A CN201610805145 A CN 201610805145A CN 106355587 B CN106355587 B CN 106355587B
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- 239000004570 mortar (masonry) Substances 0.000 title claims abstract description 86
- 239000010426 asphalt Substances 0.000 title claims abstract description 16
- 238000004364 calculation method Methods 0.000 title claims abstract description 13
- 239000000463 material Substances 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 14
- 239000000203 mixture Substances 0.000 claims description 11
- 230000001186 cumulative effect Effects 0.000 claims description 8
- 238000003709 image segmentation Methods 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000005260 corrosion Methods 0.000 claims description 3
- 230000007797 corrosion Effects 0.000 claims description 3
- 230000007423 decrease 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
- 230000008569 process Effects 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000009466 transformation Effects 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
- 238000011156 evaluation Methods 0.000 abstract description 4
- 150000001875 compounds Chemical class 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012545 processing Methods 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
-
- G06T5/70—
-
- G06T5/92—
-
- 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
Abstract
The invention discloses a kind of asphalt mortar thickness calculation method based on contact range distribution, define on gather materials a certain boundary pixel point and the adjacent boundary that gathers materials away from the mortar thickness that the distance between nearest pixel is at the point.While it is calculated as a result, proposing the index of an evaluation asphalt mortar distribution: average mortar thickness (Tave).The cross-sectional image for each compound test specimen that scanning obtains mainly is converted to grayscale image using MATLAB software by this method, then centered on the boundary pixel point that each gathers materials, annular is carried out outward extends out scanning, until scanning to other boundary pixel points of gathering materials, mortar thickness will be known as by the distance between two pixels at this time, and finally count the distribution situation of mortar thickness and calculate average mortar thickness index to evaluate asphalt mortar distribution characteristics.
Description
Technical field
The invention belongs to field of road maintenance, and in particular to a kind of asphalt mortar based on contact range distribution is thick
Spend calculation method.
Background technique
The internal structure that mixture is studied with Digital image technology is method more popular in recent years, with science and technology
Progress, technology and the algorithm for obtaining image are quite mature, and unique problem is to determine evaluation mixture internal structure
Contacting between index and searching microstructure and macro property.
Mortar thickness between gathering materials and gathering materials largely affects the transmitting of stress-strain in asphalt.
Since the rigidity of mortar at high temperature is lower, the permanent deformation of bituminous concrete also occurs mainly in the position of mortar.Excessive
Mortar thickness can cause the biggish permanent deformation in road surface, and too small mortar thickness can reduce the compatible deformation energy of mixture
Power.The research of mortar thickness and few was carried out from microcosmic angle in the past, and only research is also only laterally or longitudinally etc. limited
The thickness of direction calculating mortar, therefore obtained result precision is low, variability is big.Therefore a kind of more fully mortar is found
THICKNESS CALCULATION method, and propose that a rational index of evaluation mortar thickness is necessary.
Summary of the invention
The purpose of the invention is to overcome the shortcomings of existing mortar thickness calculation method, propose a kind of based on contact distance
The asphalt mortar thickness calculation method of distribution and the parameter of rational evaluation mortar thickness, it is anti-for bituminous concrete
The research of track performance.
A kind of the technical solution adopted by the present invention are as follows: asphalt mortar thickness calculating side based on contact range distribution
Method, comprising the following steps:
1) image is obtained and is handled
The filter of green is applied to dilute more blue channel existing for aggregate fractions to high-precision sectioning image first
Pixel.Before image segmentation, the form of grayscale image is converted images into order to subsequent analysis using MATLAB related algorithm.
The uniform picture noise of brightness disproportionation is eliminated respectively using top cap transformation and median filtering algorithm.In segmented image, threshold application
Method distinguishes coarse aggregate from asphalt mastic and gap (particle is greater than 1.18mm).In image segmentation process, by scan image point
It is segmented into the big rectangle regions such as 16 and carries out threshold value selection respectively, the optimal threshold in each region is calculated adaptively to be calculated using OTSU
Method.After segmentation, to whole binary picture application watershed transform and corrosion expansion algorithm, particle outline is checked
And amendment.
2) mortar thickness calculation method
It defines on gather materials a certain boundary pixel point and the adjacent boundary that gathers materials in the present invention away between nearest pixel
Distance be the point at mortar thickness.Circular are as follows: first click through the boundary pixel that each gathers materials on section
Row number, same volume of gathering materials are jack per line, difference volume of gathering materials is contrary sign, then chooses the boundary pixel point gathered materials, and are with the point
The heart carries out annular outward and extends out scanning, until detecting the boundary pixel point of a contrary sign, claims the distance between this two pixel
For mortar thickness, this mortar thickness can be used to determine two gather materials between exposure level.
3) index calculates
It is analyzed resulting mortar thickness is calculated on each section, program calculates all boundary pixels to gather materials of gained
The maximum value of the mortar thickness of point is T (unit: mm).Count pixel of the mortar thickness in every interval 0.1mm between 0-T
Percentage shared by quantity and cumulative percent.The distribution of mortar thickness can be divided into three phases: thickness is compared in minizone, pixel
Point quantity increases and rapid growth with mortar thickness, then tends towards stability, and when thickness continues to increase, pixel quantity is fast therewith
Speed decline.Two-parameter Weibull distribution model can be used to be fitted in the relationship of cumulative percent and mortar thickness, and model parameter includes
Scale parameter λ and form parameter k, distribution function are as shown in formula 1:
In formula:
K --- form parameter；
λ --- scale parameter；
T --- mortar thickness；
F (t) --- cumulative percent.
Scale parameter λ value is bigger, illustrates that the variation range of mortar thickness is bigger, and form parameter k value is bigger, illustrates mortar thickness
Degree is more evenly distributed in some thickness section.
Excessive mortar thickness easily causes to be permanently deformed, and too small mortar thickness will affect the compatible deformation of mixture
Ability, therefore the present invention defines this index of average mortar thickness T by calculating resulting mortar thickness distribution curveave,
As shown in formula 2.
In formula:
Nj--- the summation of all boundary pixel points of gathering materials on j-th of section；
Tji--- the mortar thickness at i-th of boundary pixel point on j-th of section, unit: mm.
Beneficial effects of the present invention: the asphalt mortar thickness proposed by the present invention based on contact range distribution calculates
Method is simple and easy, calculates the minimum mortar on mixture section at each boundary point by image processing techniques and program
Thickness, and define average this index of mortar thickness to evaluate the reasonability of mixture mortar distribution, and then from microcosmic angle
The mechanism that track phenomenon generates is disclosed, following will also become judges the whether reasonable important means of asphalt mixture design.
Detailed description of the invention
Fig. 1 is mortar thickness calculation method schematic diagram；
Fig. 2 is mortar thickness distribution map of these three mixtures of AC-13, SMA-13, SUP-13 in every section 0.1mm.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and detailed description.
A kind of asphalt mortar thickness calculation method based on contact range distribution, comprising the following steps:
1) image is obtained and is handled
The filter of green is applied to dilute more blue channel existing for aggregate fractions to high-precision sectioning image first
Pixel.Before image segmentation, the form of grayscale image is converted images into order to subsequent analysis using MATLAB related algorithm.
The uniform picture noise of brightness disproportionation is eliminated respectively using top cap transformation and median filtering algorithm.In segmented image, threshold application
Method distinguishes coarse aggregate from asphalt mastic and gap (particle is greater than 1.18mm).In image segmentation process, by scan image point
It is segmented into the big rectangle regions such as 16 and carries out threshold value selection respectively, the optimal threshold in each region is calculated adaptively to be calculated using OTSU
Method.After segmentation, to whole binary picture application watershed transform and corrosion expansion algorithm, particle outline is checked
And amendment.
2) mortar thickness calculation method
It defines on gather materials a certain boundary pixel point and the adjacent boundary that gathers materials in the present invention away between nearest pixel
Distance be the point at mortar thickness.Circular is as shown in Figure 1, A, B, C respectively represent three gathers materials, the boundary of A
The boundary pixel point number that the boundary pixel point number that pixel number is 1, B is 2, C is 3, some side that selection A first gathers materials
Boundary pixel O1, annular is carried out outward centered on the point and extends out scanning, until detecting the boundary pixel point O that B gathers materials2,
Claim O1O2The distance between be mortar thickness Tave, this mortar thickness can be used to determine two gather materials between exposure level.
3) index calculates
It is analyzed resulting mortar thickness is calculated on each section, program calculates all boundary pixels to gather materials of gained
The maximum value of the mortar thickness of point is 4mm.Count pixel quantity of the mortar thickness in every interval 0.1mm between 0-4mm
Shared percentage and cumulative percent.As shown in Fig. 2, the distribution of mortar thickness can be divided into three phases: thickness is compared with minizone
Interior, pixel quantity increases and rapid growth with mortar thickness, then tends towards stability, when thickness continues to increase, pixel number
Measure rapid decrease therewith.Two-parameter Weibull distribution model can be used to be fitted in the relationship of cumulative percent and mortar thickness, model
Parameter includes scale parameter λ and form parameter k, and distribution function is as shown in formula 1:
In formula:
K --- form parameter；
λ --- scale parameter；
T --- mortar thickness；
F(t) --- cumulative percent.
Scale parameter λ value is bigger, illustrates that the variation range of mortar thickness is bigger, and form parameter k value is bigger, illustrates mortar thickness
Degree is more evenly distributed in some thickness section.Specific fitting result is as shown in table 1.
The mortar thickness weber models fitting result of 1: three kind of mixture of table
Excessive mortar thickness easily causes to be permanently deformed, and too small mortar thickness will affect the compatible deformation of mixture
Ability, therefore the present invention defines this index of average mortar thickness T by calculating resulting mortar thickness distribution curveave,
As shown in formula 2.
In formula:
Nj--- the summation of all boundary pixel points of gathering materials on j-th of section；
Tji--- the mortar thickness at i-th of boundary pixel point on j-th of section, unit: mm.
It should be pointed out that for those skilled in the art, without departing from the principle of the present invention,
Several improvements and modifications can also be made, these modifications and embellishments should also be considered as the scope of protection of the present invention.In the present embodiment not
The available prior art of specific each component part is realized.
Claims (1)
1. a kind of asphalt mortar thickness calculation method based on contact range distribution, it is characterised in that: including following step
It is rapid:
1) image is obtained and is handled
The filter of green is applied to dilute more blue channel pixel existing for aggregate fractions to high-precision sectioning image first
Point；Before image segmentation, the form of grayscale image is converted images into order to subsequent analysis using MATLAB related algorithm；Using
Top cap transformation and median filtering algorithm eliminate the uniform picture noise of brightness disproportionation respectively；In segmented image, threshold application method from
Coarse aggregate is distinguished in asphalt mastic and gap；In image segmentation process, scan image is divided into the big rectangle regions such as 16
Threshold value selection is carried out respectively, and the optimal threshold in each region, which calculates, uses OTSU adaptive algorithm；After segmentation, to whole two
System image application watershed transform and corrosion expansion algorithm, check particle outline and are corrected；
2) mortar thickness calculation method
It is the point that definition, which is gathered materials on a certain boundary pixel point and the adjacent boundary that gathers materials away from the distance between nearest pixel,
The mortar thickness at place；Circular are as follows: each boundary pixel point gathered materials on section is numbered first, same collection
It is jack per line that material, which is compiled, and difference gathers materials volume for contrary sign, then chooses the boundary pixel point gathered materials, and carries out annular outward centered on the point
Scanning is extended out, until detecting the boundary pixel point of a contrary sign, the distance between this two pixel is referred to as mortar thickness, this sand
Slurry thickness can be used to determine two gather materials between exposure level；
3) index calculates
It is analyzed resulting mortar thickness is calculated on each section, program calculates all boundary pixel points gathered materials of gained
The maximum value of mortar thickness is T；Mortar thickness is counted between 0-T hundred shared by the pixel quantity in every interval 0.1mm
Divide rate and cumulative percent；The distribution of mortar thickness can be divided into three phases: thickness is compared in minizone, and pixel quantity is with mortar
Thickness increases and rapid growth, then tends towards stability, when thickness continues to increase, pixel quantity rapid decrease therewith；It is accumulative
Two-parameter Weibull distribution model can be used to be fitted in the relationship of percentage and mortar thickness, model parameter include scale parameter λ and
Form parameter k, shown in distribution function formula 1:
In formula:
K --- form parameter；
λ --- scale parameter；
T --- mortar thickness；
F (t) --- cumulative percent；
Scale parameter λ value is bigger, illustrates that the variation range of mortar thickness is bigger, and form parameter k value is bigger, illustrates mortar thickness more
Add and is evenly distributed in some thickness section；
Excessive mortar thickness easily causes to be permanently deformed, and too small mortar thickness will affect the compatible deformation energy of mixture
Power, therefore by calculating resulting mortar thickness distribution curve, define this index of average mortar thickness Tave, such as 2 institute of formula
Show；
In formula:
Nj--- the summation of all boundary pixel points of gathering materials on j-th of section；
Tji--- the mortar thickness at i-th of boundary pixel point on j-th of section, unit: mm.
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| CN110473225B (en) * | 2019-08-22 | 2023-06-06 | 哈尔滨工业大学 | Non-uniform illuminance asphalt mixture particle identification method |
| 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 |
| 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354241A (en) * | 2008-07-11 | 2009-01-28 | 长安大学 | Method and system for evaluating aggregate digital image |
| 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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| US20160253816A1 (en) * | 2014-02-27 | 2016-09-01 | Andrew Niemczyk | Method of Carrying Out Land Based Projects Using Aerial Imagery Programs |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354241A (en) * | 2008-07-11 | 2009-01-28 | 长安大学 | Method and system for evaluating aggregate digital image |
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| Validity of asphalt binder film thickness concept in hot-mix asphalt;Mostafa A. Elseifi等;《Transportation Research Record: Journal of the Transportation Research Board》;20081231;第2057卷(第1期);第37–45页 |
| 沥青混合料集料接触特性切片图像评价方法;魏鸿等;《土木建筑与环境工程》;20100630;第32卷(第3期);第69-74页 |
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