CN106355587A - Method for calculating thickness of bituminous mixture mortar on basis of contact distance distribution - Google Patents

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

Asphalt mortar thickness computational methods based on contact range distribution

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:

$f\left(t\right)=1-e^{-{(t/\mathrm{\λ})}^k},t>0---\left(1\right)$

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 thickness_ave, As shown in Equation 2.

$t_{ave}=\frac{{\mathrm{\σ}}_{j=1}^{12}\frac{{\mathrm{\σ}}_{i=1}^{n_j}{t}_{ji}}{n_j}}{12}---\left(2\right)$

In formula:

n_jThe summation of all boundary pixel point of gathering materials on j-th section；

t_jiThe 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 t_ave, 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:

$f\left(t\right)=1-e^{-{(t/\mathrm{\λ})}^k},t>0---\left(1\right)$

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 thickness_ave, As shown in Equation 2.

$t_{ave}=\frac{{\mathrm{\σ}}_{j=1}^{12}\frac{{\mathrm{\σ}}_{i=1}^{n_j}{t}_{ji}}{n_j}}{12}---\left(2\right)$

In formula:

n_jThe summation of all boundary pixel point of gathering materials on j-th section；

t_jiThe 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:

$f\left(t\right)=1-e^{-{(t/\mathrm{\λ})}^k},t>0---\left(1\right)$

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 thickness_ave, such as Shown in formula 2；

$t_{ave}=\frac{{\σ}_{j=1}^{12}\frac{{\σ}_{i=1}^{n_j}{t}_{ji}}{n_j}}{12}---\left(2\right)$

In formula:

n_jThe summation of all boundary pixel point of gathering materials on j-th section；

t_jiThe mortar thickness at i-th boundary pixel point on j-th section, unit: mm.