Emulsified asphalt polymer phase flow path evaluation method based on local gradient interface concentration

Abstract

The invention relates to a local gradient interface concentration-based emulsified asphalt polymer phase flow path evaluation method, which comprises the following steps of: storing emulsified asphalt tomography data as a pixel set, converting the pixel set into an effective discrete region by using a segmentation algorithm, and establishing a steady-state diffusion equation set of a polymer in an asphalt medium; executing ghosting node conditions at the boundaries of adjacent parallel pixels, and iteratively calculating the volume concentration difference of the adjacent pixels; further calculating the polymer phase transmission behavior change caused by the irregular structure in the heterogeneous asphalt medium, and establishing the convergence of the system by analyzing the change rate of the microstructure quantity; and visualizing the polymer interface concentration density and distribution in the emulsified asphalt chromatography imaging data, and evaluating the tortuosity of the emulsified asphalt polymer phase flow path. The method can effectively analyze the flow path and the diffusion behavior of the polymer phase in the emulsified asphalt, and can provide an effective technical means for analyzing the coupling behavior and the modification effect of the matrix phase and the polymer phase of the emulsified asphalt.

Description

Technical Field

[0001] This invention relates to the field of road raw material quality testing technology, specifically to a method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration. Background Technology

[0002] The multi-physical state transitions of polymer / asphalt composites, exhibiting corresponding viscoelastic characteristics, are one of the most attractive topics in research and the pavement industry. Chemically, these multi-physical state transitions are a fundamental behavior and fixed property of amorphous composites such as polymer / asphalt composites. Under different temperature and stress conditions, polymer / asphalt composites will exhibit distinctly different physical phases, such as a viscous phase, a rubbery phase, and a glassy phase, which directly determines their mechanical properties. In hot summer conditions, rising temperatures often soften asphalt and lead to permanent deformation, ultimately resulting in rutting damage. Conversely, in cold winter conditions, low temperatures cause asphalt to harden, leading to shrinkage cracking.

[0003] Besides the individual properties of asphalt and polymers, the morphological compatibility of composite components also dominates the viscoelastic transition and performance failure probability of polymer / asphalt composites. However, most studies focus only on the application performance of polymer / asphalt composites, neglecting their morphological compatibility. This topic deserves more research attention because the morphological compatibility of asphalt / polymer composites directly determines the dispersion effect of macroscopic phase separation and the expected performance of the blended composite system.

[0004] Several common methods exist for assessing the morphological compatibility of asphalt and polymers in composite systems, such as direct observation, microscopy, thermodynamic theory, and Fourier transform infrared spectroscopy. Furthermore, morphological theory can effectively evaluate the morphological compatibility between different phases in composite materials. Therefore, combining tomographic imaging technology may be a potential method for effectively analyzing the polymer flow path in emulsified asphalt matrices. Flow path evaluation models can generate quantitative evaluation parameters, quantitatively describe the interfacial compatibility of composite materials, and provide valuable information for interfacial interactions in asphalt / polymer composites and further prediction of multi-physics transition behavior. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the flow path of the polymer phase in emulsified asphalt based on the local gradient interface concentration. This method can effectively analyze the flow path and diffusion behavior of the polymer phase in emulsified asphalt, thereby providing an effective technical means for analyzing the coupling behavior and modification effect of the matrix phase and polymer phase in emulsified asphalt.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration, comprising the following steps:

[0007] Step S1: Store the emulsified asphalt tomographic imaging data as a pixel matrix set Q, and use a segmentation algorithm to convert it into an effective discrete region Φ. Y ;

[0008] Step S2: Utilize the effective discrete region Φ of the pixel matrix set obtained in step S1 Y Establish the steady-state diffusion equation set F of polymers in asphalt media;

[0009] Step S3: Using the steady-state diffusion equation set F obtained in step S2, perform the ghosting node condition at the boundary of adjacent parallel pixels and iteratively calculate the volume concentration difference ε between adjacent pixels.

[0010] Step S4: Calculate the changes in polymer phase transport behavior caused by the irregular structure in the heterogeneous asphalt medium;

[0011] Step S5: Utilizing the changes in polymer phase transport behavior obtained in Step S4, analyze the rate of change of microstructure quantities. Establish the convergence of the system;

[0012] Step S6: Visualize the polymer interface concentration density and distribution in the emulsified asphalt tomographic imaging data, and evaluate the tortuosity of the polymer phase flow path in the emulsified asphalt.

[0013] Further, in step S1, the method for acquiring the emulsified asphalt tomographic imaging data is as follows:

[0014] Image scanning slices are generated using an image scanning device according to the user's desired z-direction. i , where i is the slice number, then the image is scanned across the slice Slide. i Stacking;

[0015] The image scan slice Slide i By N x row and N y Composed of columns of pixels, N x N is the number of pixel units in the vertical direction of the image. y Slide represents the number of pixel units in the horizontal direction of the image. i Represented as a matrix of pixel units, i.e., Slide i =N x ×N y ;

[0016] The pixel matrix set Q={Slide i} n, i = 1, 2, 3, ..., n; n is the number of slices;

[0017] The pixel unit stores an identifier or pattern data;

[0018] The pixel matrix set Q also contains normal f, distance d, or other necessary information;

[0019] The normals f and z of the pixel matrix set Q are either the same or opposite;

[0020] The distance d of the pixel matrix set Q depends on the image scan slice Slide i The interval;

[0021] The segmentation algorithm uses the minimum value g between peaks in the histogram of emulsified asphalt tomography as the threshold Y. Based on the comparison between the gray level h of each pixel and the threshold Y, the pixel matrix set Q is classified: if h ≥ Y, it is divided into an effective discrete region Φ. Y If h ≤ Y, then the region is divided into an invalid discrete region Φ. N .

[0022] Furthermore, the specific implementation process of step S2 is as follows:

[0023] Step S21: For the effective discrete region Φ of the pixel matrix set Y Set the middle position of adjacent pixel lattices as the pixel boundary O=(0, 0, 0);

[0024] Step S22: Use vector X to represent a virtual cuboid region at boundary O, representing the medium state of the emulsified asphalt matrix under ideal conditions, that is:

[0025] X = (0, L x ) × (0, L y )× (0, L z )

[0026] Among them, L x L represents the virtual length of the virtual cuboid region along the x-direction. y L represents the virtual width of the virtual cuboid region along the y-direction. z This represents the virtual height of the virtual cuboid region along the z-direction;

[0027] Step S23: Set Ω as the porous dielectric conductor region inside the virtual cuboid region X, representing the region where the polymer phase effectively flows or diffuses in the emulsified asphalt matrix, and Ω ⊂ X;

[0028] Step S24: Set T, I, and B as two-dimensional subsets of the virtual cuboid region X, corresponding to the top, interface, and bottom of the virtual cuboid region X, respectively, such that ∂Ω = T∪I∪B, and ∂Ω| z=Lz = B, ∂Ω| z=0 =T,∂Ω| 0<z<Lz =I;

[0029] Step S25: The material concentration in each cell is described based on the material concentration of adjacent cells on the X-face of the virtual cuboid region. That is, the value at a point on the interface is equal to its average value conducted around the periphery. Therefore, the following linear second-order difference equations are used to model the flow rate C through the porous medium Ω, and the steady-state diffusion equations F of the polymer in the asphalt medium are established:

[0030]

[0031] Here, w is the outward pointing unit perpendicular to Ω.

[0032] Furthermore, the specific implementation process of step S3 is as follows:

[0033] Step S31: Set the density of the two parallel pixels as follows: and and between two parallel pixels point to The direction is divided into nodes a~f of a virtual cuboid region X;

[0034] Step S32: Set the boundaries of two parallel pixels, that is, the concentrations at points T and B of the virtual cuboid region X are C and C, respectively. min and C max ;

[0035] Step S33: Set the value of node e to the average value of its neighboring conductors, i.e.: e = (d + f + ... ) / 3;

[0036] Step S34: In order to achieve the desired results at node e and At the interface between them, the ghosting node condition is executed, making the following condition true: C max =(e+ ) / 2;

[0037] Step S35: Set e=(d+f+ ) / 3 and C max =(e+ If we combine and rearrange the two equations, then e = (d + f + 2C) / 2 max This eliminates parallel pixel density by 4 / 4. The impact;

[0038] Step S36: Concentration C at point B max The opposite boundary, i.e., the concentration C at T. min Set the value of node b to the average of its neighboring neighbors on the conductor plane, i.e.: b = (a + c + ... ) / 3;

[0039] Step S37: In order to achieve the desired results at node b and At the interface between them, the ghosting node condition is executed, making the following condition true: C min =(b+ ) / 2;

[0040] Step S38: Set b = (a + c + ... ) / 3 and C min =(b+ If we combine and rearrange the two equations, then b = (a + c + 2C) / 2 min This eliminates parallel pixel density by 4 / 4. The impact;

[0041] Step S39: Calculate the volume concentration difference ε=C between adjacent pixels. max - C min That is, ε=(4b+4e-dfac) / 4.

[0042] Further, in step S4, based on the volume concentration difference ε between adjacent pixels, the change in polymer transport and diffusion behavior caused by the irregular structure in the heterogeneous asphalt medium is calculated, and the altered effect of this transport and diffusion behavior is defined as the distortion factor τ.

[0043] The distortion factor τ is obtained by comparing the steady-state diffusion flow of the polymer in the emulsified asphalt matrix, and its calculation formula is as follows:

[0044]

[0045] Where d is the slice distance; ε represents the volume concentration difference between adjacent pixels; K is the medium property coefficient, representing the change in the physical properties of the medium caused by the concentration difference, and the viscosity correlation coefficient is used here;

[0046] The viscosity correlation coefficient K was calculated by comparing the viscosities of matrix-modified emulsified asphalt media and polymer-modified emulsified asphalt media:

[0047]

[0048] in, Indicates the viscosity of polymer-modified emulsified asphalt media; Indicates the viscosity of the matrix emulsified asphalt medium;

[0049] Effective diffusion rate The calculation formula is:

[0050]

[0051] Where D is the theoretical diffusivity of the conductor phase in the asphalt medium; ε represents the volume concentration difference between adjacent pixels; and τ represents the distortion factor.

[0052] The distortion factor τ, when τ=1, indicates a direct flow path; secondly, since no geometric structure in reality can spontaneously increase the flow rate, τ>1 is true for all transmission systems.

[0053] Furthermore, the specific implementation process of step S5 is as follows:

[0054] Step S51: Based on the effective discrete region Φ of the pixel matrix set Y The longest dimension and the degree of anisotropy set the convergence check and allow for an iteration interval j;

[0055] Step S52: Perform a convergence check on the current approximation of the distortion factor τ of the emulsified asphalt imaging system on the two surfaces where the ghosting node condition is applied, in order to assess stability;

[0056] Step S53: Iteratively calculate the rate of change of the distortion factor τ To establish the convergence of the system, the iteration termination condition is as follows:

[0057] if If the result is ≥1%, the system has not reached convergence; continue iterating.

[0058] if <1%, and the current approximation of the distortion factor τ This indicates that there is no volume density difference between adjacent pixels, so the iteration stops.

[0059] if <1%, and the current approximation of the distortion factor τ This indicates that the system has not reached convergence, so proceed to step S52.

[0060] Furthermore, the specific implementation process of step S6 is as follows:

[0061] Step S61: Based on the distortion factor τ and the rate of change of the emulsified asphalt imaging system The concentration density and distribution of polymer interfaces in emulsified asphalt tomographic imaging data were calculated and visualized.

[0062] Step S62: Determine the evaluation thresholds ΔH and ΔL for polymer transport and diffusion behavior in emulsified bitumen tomographic imaging data according to user requirements;

[0063] Step S63: Calculate the average effective diffusion rate of polymer transport and diffusion behavior in the emulsified bitumen tomographic imaging data, and accordingly... Change to:

[0064]

[0065] Step S64: When When ΔH ≥ ΔH, the polymer flow path is "unobstructed"; when ΔH > ΔH When ≥ ΔL, the polymer flow path is "normal"; when When ≤ ΔL, the polymer flow path is "rugged".

[0066] The present invention also provides a computer device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which implement the above-described method when executed by the processor.

[0067] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the above-described method.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] (1) Data of emulsified asphalt tomography is stored as a set of pixel matrices and converted into effective discrete regions using a segmentation algorithm. This can effectively simplify the target region of image processing, so as to accelerate the feature extraction process based on the effective discrete regions and avoid interference from invalid image data in large-scale emulsified asphalt tomography data.

[0070] (2) The ghosting node condition is executed at the boundary of adjacent parallel pixels. The concept of finite difference is effectively adopted. The material concentration in each unit is described only based on the material concentration of its adjacent units. That is, the value at a point on the interface must be equal to the average value of its transmission in the periphery, thus avoiding the influence of unknown concentration of parallel pixels.

[0071] (3) Visualize the polymer interface concentration density and distribution in the tomographic imaging data of emulsified asphalt, calculate the total concentration map through each voxel and the individual contributions from the directions through the plane and in the plane, and visualize the concentration map to intuitively show the local flocculation mechanism caused by the compatibility difference of modified emulsified asphalt.

[0072] (4) The changes in polymer phase transport behavior caused by the irregular structure in heterogeneous asphalt media, the calculated tortuosity factor, effective diffusion rate and average effective diffusion rate can provide quantitative indicators for evaluating the tortuosity of the flow path of polymer phase in emulsified asphalt matrix, breaking through the subjectivity of the existing technical solution that relies on visual judgment to determine the compatibility of emulsified asphalt polymer phase. Attached Figure Description

[0073] Figure 1 This is a flowchart of the method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration provided in the embodiments of the present invention;

[0074] Figure 2 This is a flowchart illustrating the process of establishing the steady-state diffusion equation set F of the polymer in asphalt medium in an embodiment of the present invention;

[0075] Figure 3 This is a flowchart of an embodiment of the present invention, which executes the ghosting node condition at the boundary of adjacent parallel pixels and iteratively calculates the interface concentration difference ε between adjacent pixels.

[0076] Figure 4 This is a flowchart illustrating the process of establishing the convergence of a system by analyzing the rate of change ∆τ of microstructural quantities in an embodiment of the present invention.

[0077] Figure 5 This is a flowchart illustrating the concentration and distribution of polymer interfaces in emulsified asphalt tomographic imaging data and evaluating the tortuosity of the flow path in an embodiment of the present invention.

[0078] Figure 6 These are scanning micrographs, interface concentration density, and interface concentration distribution of waterborne acrylic modified emulsified asphalt and waterborne liquid rubber modified emulsified asphalt in the embodiments of the present invention; wherein, (1) matrix unmodified emulsified asphalt; (2) waterborne acrylic modified emulsified asphalt; and (3) waterborne liquid rubber modified emulsified asphalt. Detailed Implementation

[0079] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0080] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0081] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0082] like Figure 1 As shown, this invention proposes a method for evaluating the flow path of the polymer phase in emulsified asphalt based on local gradient interface concentration, comprising the following steps:

[0083] (1) The emulsified asphalt tomographic imaging data is stored as a pixel matrix set Q, and a segmentation algorithm is used to convert it into an effective discrete region Φ. Y .

[0084] 1) Methods for acquiring emulsified asphalt tomographic imaging data include:

[0085] Image scanning slices are generated using an image scanning device according to the user's desired z-direction. i , where i is the slice number, then the image is scanned across the slice Slide. i Stacked. The image scanning device can be a field electron scanning microscope, a fluorescence excitation microscope, an X-ray tomography scanner, or other image scanning devices.

[0086] The image scan slice Slide i By N x row and N y Composed of columns of pixels, N x N is the number of pixel units in the vertical direction of the image. y Slide represents the number of pixel units in the horizontal direction of the image. i It can be represented as a matrix of pixel units, i.e., Slide i =N x ×N y The pixel matrix set Q={Slide i} n The pixel units i = 1, 2, 3, ..., n, where n is the number of slices; each pixel unit stores an identifier or pattern data; furthermore, the pixel matrix set Q also contains normal f, distance d, or other necessary information; the normal f and z of the pixel matrix set Q are the same or opposite; the distance d of the pixel matrix set Q depends on the image scan slice Slide. i The interval.

[0087] In this embodiment, an image scanning device is used to generate image scanning slices along the thickness direction z of the asphalt specimen. i 'i' represents the slide number, and then the slides are stacked for scanning electron microscopy. i Stored as a set of pixel matrices Q; image scan slice Slide i The interval is set to 1 mm.

[0088] Among them, the storage of emulsified asphalt tomography data can rely on data management software such as MATLAB and Python.

[0089] 2) Using the minimum value g between peaks in the histogram of emulsified asphalt tomography as the threshold Y, the pixel matrix set Q is classified into effective and ineffective discrete regions based on the comparison relationship between the gray level h of each pixel and the threshold Y: that is, if h ≥ Y, it is classified as an effective discrete region Φ. Y If h ≤ Y, then the region is divided into an invalid discrete region Φ. N .

[0090] Among them, the histogram reading of emulsified asphalt tomography and the determination of the minimum value g between each peak can rely on data management and image processing software such as MATLAB and ImageJ.

[0091] (2) Utilizing the effective discrete region Φ of the obtained pixel matrix set Y The steady-state diffusion equations F for the polymer in asphalt media were established. The flowchart for this step is shown below. Figure 2 As shown.

[0092] 1) The effective discrete region Φ of the acquired pixel matrix set Y Set the middle position of the adjacent pixel lattice as the pixel boundary O=(0, 0, 0).

[0093] 2) Vector X is used to represent a virtual cuboid region at boundary O, representing the medium state of the emulsified asphalt matrix under ideal conditions, i.e.:

[0094] X = (0, L x ) × (0, L y )× (0, L z )

[0095] Among them, L x L represents the virtual length of the virtual cuboid region along the x-direction. y L represents the virtual width of the virtual cuboid region along the y-direction. z This represents the virtual height of the virtual cuboid region along the z-direction.

[0096] 3) Set Ω as the porous dielectric conductor region inside the virtual cuboid region X, representing the region where the polymer phase effectively flows or diffuses in the emulsified asphalt matrix, and have Ω ⊂ X.

[0097] 4) Set T, I, and B as two-dimensional subsets of the virtual cuboid region X (corresponding to the top, interface, and bottom of the virtual cuboid region X, respectively), such that ∂Ω = T∪I∪B, and ∂Ω| z=Lz = B, ∂Ω| z=0 =T,∂Ω| 0<z<Lz =I.

[0098] 5) The concentration of the material in each cell is described by the concentration of the material in adjacent cells on the X-face of the virtual cuboid region. That is, the value at a point on the interface is equal to the average value of its conduction on the periphery. Therefore, the following linear second-order difference equations are used to model the flow rate C through the porous medium Ω, and the steady-state diffusion equations F of the polymer in the asphalt medium are established:

[0099]

[0100] Here, w is the outward pointing unit perpendicular to Ω.

[0101] As mentioned above, the steady-state diffusion equation set F of polymers in asphalt media can be established using data management software such as MATLAB.

[0102] (3) Using the obtained steady-state diffusion equations F, the ghosting node condition is applied at the boundary of adjacent parallel pixels, and the interface concentration difference ε between adjacent pixels is calculated iteratively. The flowchart of this step is as follows: Figure 3 As shown.

[0103] 1) Set the density of the two parallel pixels as follows: and and between two parallel pixels point to The direction is divided into nodes a~f of the virtual cuboid region X.

[0104] Among them, the node segmentation of the virtual cuboid X between parallel pixels can rely on data management software such as MATLAB.

[0105] 2) Set the boundaries of two parallel pixels, that is, the concentrations at points T and B of the virtual cuboid region X are C and C, respectively. min and C max .

[0106] 3) Set the value of node e to the average value of its neighboring conductors, i.e.: e = (d + f + ... ) / 3.

[0107] 4) In order to achieve the desired results at node e and For the ghosting node condition to be executed at the interface between them, the following condition must also be met: C max =(e+ ) / 2.

[0108] 5) Change e=(d+f+ ) / 3 and C max =(e+ If we combine and rearrange the two equations, then e = (d + f + 2C) / 2 max This eliminates the parallel pixel density by 4 / 4. The impact.

[0109] Step S36: Concentration C at point B max The opposite boundary, i.e., the concentration C at T. min Set the value of node b to the average of its neighboring neighbors on the conductor plane, i.e.: b = (a + c + ... ) / 3.

[0110] Step S37: In order to achieve the desired results at node b and At the interface between them, the ghosting node condition is executed, making the following condition true: C min =(b+ ) / 2.

[0111] Step S38: Set b = (a + c + ... ) / 3 and C min =(b+ If we combine and rearrange the two equations, then b = (a + c + 2C) / 2 min This eliminates parallel pixel density by 4 / 4. The impact.

[0112] Step S39: Calculate the volume concentration difference ε=C between adjacent pixels. max - C min That is, ε = (4b + 4e - dfac) / 4.

[0113] As mentioned above, the iterative calculation of the volume concentration difference ε between adjacent pixels can be achieved using data management software such as MATLAB and EXCEL.

[0114] (4) Calculate the changes in polymer phase transport behavior caused by the irregular structure in heterogeneous asphalt media.

[0115] 1) Based on the volume concentration difference ε between adjacent pixels, calculate the change in polymer transport and diffusion behavior caused by the irregular structure in the heterogeneous asphalt medium, and define the altered effect of this transport and diffusion behavior as the distortion factor τ.

[0116] The distortion factor τ is obtained by comparing the steady-state diffusion flow of the polymer in the emulsified asphalt matrix, and its calculation formula is as follows:

[0117]

[0118] Where d is the slice distance; ε represents the volume concentration difference between adjacent pixels; and K is the medium property coefficient, representing the change in the physical properties of the medium caused by the concentration difference. Here, the viscosity correlation coefficient is used.

[0119] 2) The viscosity correlation coefficient K is calculated by comparing the viscosity of the matrix-modified emulsified asphalt medium and the polymer-modified emulsified asphalt medium:

[0120]

[0121] in, Indicates the viscosity of polymer-modified emulsified asphalt media; This indicates the viscosity of the matrix emulsified asphalt medium.

[0122] 3) Effective diffusion rate The calculation formula is:

[0123]

[0124] Where D is the theoretical diffusivity of the conductor phase in the asphalt medium; ε represents the volume concentration difference between adjacent pixels; and τ represents the distortion factor.

[0125] The distortion factor τ, when τ=1, indicates a direct flow path; secondly, since no geometric structure in reality can spontaneously increase the flow rate, τ>1 is true for all transmission systems.

[0126] As mentioned above, the calculation of the distortion factor τ can be achieved using data management software such as MATLAB and EXCEL.

[0127] (5) Utilize the obtained changes in polymer phase transport behavior to analyze the rate of change of microstructure quantities. Establish the system's convergence. The flowchart for this step is as follows: Figure 4 As shown.

[0128] 1) Based on the effective discrete region Φ of the pixel matrix set Y The longest dimension and degree of anisotropy are used to set an appropriate first-order convergence check allowing the iteration interval j.

[0129] The iteration interval j is typically required to be less than or equal to the effective discrete region Φ. Y The maximum accuracy can be achieved by using one-thousandth of the longest dimension, but finite convergence tolerances should be used to avoid the unnecessary pursuit of high precision values.

[0130] In this embodiment, the allowed iteration interval for a single convergence check is set to j=5.

[0131] 2) Perform a convergence check on the current approximation of the torsion factor τ of the emulsified asphalt imaging system on two surfaces with the ghost node condition applied to assess stability.

[0132] 3) Iteratively calculate the rate of change of the distortion factor τ To establish the convergence of the system, the iteration termination condition is as follows:

[0133] if If the result is ≥1%, the system has not reached convergence; continue iterating.

[0134] if <1%, and the current approximation of the distortion factor τ This indicates that there is no volume density difference between adjacent pixels, so the iteration stops.

[0135] if <1%, and the current approximation of the distortion factor τ This indicates that the system has not reached convergence, so proceed to step 2 above.

[0136] (6) After determining that the system has reached the convergence threshold, visualize the polymer interface concentration density and distribution in the emulsified asphalt tomographic imaging data, and evaluate the tortuosity of the polymer phase flow path in the emulsified asphalt. The flowchart for this step is shown below. Figure 5 As shown.

[0137] 1) Based on the distortion factor τ and rate of change of the emulsified asphalt imaging system The concentration and distribution of polymer interfaces in emulsified bitumen tomographic imaging data were calculated and visualized.

[0138] Among them, the visualization of polymer interface concentration density and distribution in emulsified bitumen tomographic imaging data can rely on data management and image processing software such as MATLAB and ImageJ.

[0139] 2) Determine the evaluation thresholds ΔH and ΔL for polymer transport and diffusion behavior in emulsified bitumen tomography data based on user requirements, where ΔH > ΔL > 0.

[0140] 3) Calculate the average effective diffusion rate of polymer transport and diffusion behavior in emulsified bitumen tomographic imaging data, and accordingly, the effective diffusion rate can be calculated. Change to:

[0141]

[0142] 4) When When ΔH ≥ ΔH, the polymer flow path is "unobstructed"; when ΔH > ΔH When ≥ ΔL, the polymer flow path is "normal"; when When ≤ ΔL, the polymer flow path is "rugged".

[0143] Among them, scanning micrographs, interfacial concentration density, and interfacial concentration distribution of waterborne acrylic-modified emulsified asphalt and waterborne liquid rubber-modified emulsified asphalt are shown in the following figures. Figure 6 As shown. From Figure 6The visualization results show that the interfacial concentration density of the matrix emulsified asphalt samples is relatively uniform, with consistent interfacial concentration across different flow directions. This is reflected in the visualization image as a monotonous red color without complex textures. The interfacial concentration density of samples blended with different water-based polymers exhibits more complex texture characteristics, which can assist in observing localized polymer flocculation within the emulsified asphalt matrix. The reference indices for calculating the interfacial concentration of different emulsified asphalt samples are summarized in Table 1. It can be seen that the effective diffusion coefficient of the matrix emulsified asphalt (0.280 m...) is... 2 s -1 For reference, the effective diffusion coefficient of waterborne acrylate polymers in emulsified asphalt is 0.263 m. 2 s -1 The effective diffusion coefficient of liquid nitrile rubber (0.305 m) decreased, while the effective diffusion coefficient of liquid nitrile rubber (0.305 m) decreased. 2 s -1 The effective diffusion coefficient of the matrix emulsified asphalt is higher than that of the emulsified asphalt, indicating that it is more miscible. Therefore, if the effective diffusion coefficient of the matrix emulsified asphalt (0.280 m) is taken as the effective diffusion coefficient of the matrix emulsified asphalt, it is more likely to be miscible. 2 s -1 If ΔL is used as the evaluation index, then the flow path of the aqueous acrylate polymer in the emulsified asphalt matrix is ​​more "rugged," requiring necessary measures such as optimizing interfacial compatibility and adjusting process parameters during its preparation and modification. Based on the above key information, the evaluation model can be constructed using the scheme provided in this embodiment.

[0144] Table 1 Calculation parameters for the interfacial flow path of emulsified asphalt

[0145] sample Effective diffusion rate Average effective diffusion rate Number of iterations Calculation duration matrix emulsified asphalt 0.280 0.276 2002 16s Waterborne acrylate modified emulsified asphalt 0.263 0.261 2156 17s Waterborne nitrile rubber modified emulsified asphalt 0.305 0.304 1848 15s

[0146] In summary, this invention presents a method for evaluating the flow path of polymer phases in emulsified asphalt based on local gradient interface concentration. This method simplifies the target region for image processing by storing emulsified asphalt tomographic data as a pixel matrix set and converting it into an effective discrete region using a segmentation algorithm. This allows for faster feature extraction based on the effective discrete region and avoids interference from invalid image data in large-scale emulsified asphalt tomographic data. Furthermore, by applying ghosting node conditions at the boundaries of adjacent parallel pixels, the method effectively utilizes the concept of finite difference, describing the material concentration in each unit based only on the material concentration of its adjacent units, i.e., at a single point on the interface. The value must be equal to its average value of peripheral conduction to avoid the influence of unknown concentration of parallel pixels; the polymer interface concentration density and distribution in the tomographic imaging data of emulsified asphalt are visualized, the total concentration map through each voxel and the individual contributions from the directions through the plane and in the plane are calculated, and the visualized concentration map intuitively shows the local flocculation mechanism caused by the compatibility difference of modified emulsified asphalt; the changes in polymer phase transport behavior caused by the irregular structure in the heterogeneous asphalt medium, the calculated tortuosity factor, effective diffusion rate and average effective diffusion rate can provide quantitative indicators for evaluating the tortuosity of the flow path of polymer phase in emulsified asphalt matrix, and break through the subjectivity of the existing technical solution that relies on visual judgment to judge the compatibility of emulsified asphalt polymer phase.

[0147] This embodiment also provides a computer device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which implement the above-described method when executed by the processor.

[0148] This embodiment also provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method.

[0149] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for evaluating the flow path of polymer phase in emulsified asphalt based on local gradient interface concentration, characterized in that, Includes the following steps: Step S1: Store the emulsified asphalt tomographic imaging data as a pixel matrix set Q, and use a segmentation algorithm to convert it into an effective discrete region Φ. Y ; Step S2: Utilize the effective discrete region Φ of the pixel matrix set obtained in step S1 Y Establish the steady-state diffusion equation set F of polymers in asphalt media; Step S3: Using the steady-state diffusion equation set F obtained in step S2, perform the ghosting node condition at the boundary of adjacent parallel pixels and iteratively calculate the volume concentration difference ε between adjacent pixels. Step S4: Calculate the changes in polymer phase transport behavior caused by the irregular structure in the heterogeneous asphalt medium; Step S5: Utilizing the changes in polymer phase transport behavior obtained in Step S4, analyze the rate of change of microstructure quantities. Establish the convergence of the system; Step S6: Visualize the polymer interface concentration density and distribution in the emulsified asphalt tomographic imaging data, and evaluate the tortuosity of the polymer phase flow path in the emulsified asphalt.

2. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 1, characterized in that, In step S1, the method for acquiring the emulsified asphalt tomographic imaging data is as follows: Image scanning slices are generated using an image scanning device according to the user's desired z-direction. i , where i is the slice number, then the image is scanned across the slice Slide. i Stacking; The image scan slice Slide i By N x row and N y Composed of columns of pixels, N x N represents the number of pixel units in the vertical direction of the image. y Slide represents the number of pixel units in the horizontal direction of the image. i Represented as a matrix of pixel units, i.e., Slide i =N x ×N y ; The pixel matrix set Q={Slide i } n , i = 1, 2, 3, ..., n; n is the number of slices; The pixel unit stores an identifier or pattern data; The pixel matrix set Q also contains normal f, distance d, or other necessary information; The normals f and z of the pixel matrix set Q are either the same or opposite; The distance d of the pixel matrix set Q depends on the image scan slice Slide i The interval; The segmentation algorithm uses the minimum value g between peaks in the histogram of emulsified asphalt tomography as the threshold Y. Based on the comparison between the gray level h of each pixel and the threshold Y, the pixel matrix set Q is classified: if h ≥ Y, it is divided into an effective discrete region Φ. Y If h ≤ Y, then the region is divided into an invalid discrete region Φ. N .

3. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 1, characterized in that, The specific implementation process of step S2 is as follows: Step S21: For the effective discrete region Φ of the pixel matrix set Y Set the middle position of adjacent pixel lattices as the pixel boundary O=(0, 0, 0); Step S22: Use vector X to represent a virtual cuboid region at boundary O, representing the medium state of the emulsified asphalt matrix under ideal conditions, that is: X = (0, L x ) × (0, L y )× (0, L z ) Among them, L x L represents the virtual length of the virtual cuboid region along the x-direction. y L represents the virtual width of the virtual cuboid region along the y-direction. z This represents the virtual height of the virtual cuboid region along the z-direction; Step S23: Set Ω as the porous dielectric conductor region inside the virtual cuboid region X, representing the region where the polymer phase effectively flows or diffuses in the emulsified asphalt matrix, and Ω ⊂ X; Step S24: Set T, I, and B as two-dimensional subsets of the virtual cuboid region X, corresponding to the top, interface, and bottom of the virtual cuboid region X, respectively, such that ∂Ω = T∪I∪B, and ∂Ω| z=Lz = B, ∂Ω| z=0 =T,∂Ω| 0<z<Lz =I; Step S25: The material concentration in each cell is described based on the material concentration of adjacent cells on the X-face of the virtual cuboid region. That is, the value at a point on the interface is equal to its average value conducted around the periphery. Therefore, the following linear second-order difference equations are used to model the flow rate C through the porous medium Ω, and the steady-state diffusion equations F of the polymer in the asphalt medium are established: Here, w is the outward pointing unit perpendicular to Ω.

4. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 3, characterized in that, The specific implementation process of step S3 is as follows: Step S31: Set the density of the two parallel pixels as follows: and and between two parallel pixels point to The direction is divided into nodes a~f of a virtual cuboid region X; Step S32: Set the boundaries of two parallel pixels, that is, the concentrations at points T and B of the virtual cuboid region X are C and C, respectively. min and C max ; Step S33: Set the value of node e to the average value of its neighboring conductors, i.e.: e = (d + f + ... ) / 3; Step S34: In order to achieve the desired results at node e and At the interface between them, the ghosting node condition is executed, making the following condition true: C max =(e+ ) / 2; Step S35: Set e=(d+f+ ) / 3 and C max =(e+ If we combine and rearrange the two equations, then e = (d + f + 2C) / 2 max This eliminates parallel pixel density by 4 / 4. The impact; Step S36: Concentration C at point B max The opposite boundary, i.e., the concentration C at T. min Set the value of node b to the average of its neighboring neighbors on the conductor plane, i.e.: b = (a + c + ... ) / 3; Step S37: In order to achieve the desired results at node b and At the interface between them, the ghosting node condition is executed, making the following condition true: C min =(b+ ) / 2; Step S38: Set b = (a + c + ... ) / 3 and C min =(b+ If we combine and rearrange the two equations, then b = (a + c + 2C) / 2 min This eliminates parallel pixel density by 4 / 4. The impact; Step S39: Calculate the volume concentration difference ε=C between adjacent pixels. max - C min That is, ε=(4b+4e-dfac) / 4.

5. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 1, characterized in that, In step S4, based on the volume concentration difference ε between adjacent pixels, the change in polymer transport and diffusion behavior caused by the irregular structure in the heterogeneous asphalt medium is calculated, and the altered effect of this transport and diffusion behavior is defined as the distortion factor τ. The distortion factor τ is obtained by comparing the steady-state diffusion flow of the polymer in the emulsified asphalt matrix, and its calculation formula is as follows: Where d is the slice distance; ε represents the volume concentration difference between adjacent pixels; K is the medium property coefficient, representing the change in the physical properties of the medium caused by the concentration difference, and the viscosity correlation coefficient is used here; The viscosity correlation coefficient K was calculated by comparing the viscosities of matrix-modified emulsified asphalt media and polymer-modified emulsified asphalt media: in, Indicates the viscosity of polymer-modified emulsified asphalt media; Indicates the viscosity of the matrix emulsified asphalt medium; Effective diffusion rate The calculation formula is: Where D is the theoretical diffusivity of the conductor phase in the asphalt medium; ε represents the volume concentration difference between adjacent pixels; and τ represents the distortion factor. The distortion factor τ, when τ=1, indicates a direct flow path; secondly, since no geometric structure in reality can spontaneously increase the flow rate, τ>1 is true for all transmission systems.

6. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 5, characterized in that, The specific implementation process of step S5 is as follows: Step S51, based on the effective discrete region Φ of the pixel matrix set Y The longest dimension and the degree of anisotropy set the convergence check and allow for an iteration interval j; Step S52: Perform a convergence check on the current approximation of the distortion factor τ of the emulsified asphalt imaging system on the two surfaces where the ghosting node condition is applied, in order to assess stability; Step S53: Iteratively calculate the rate of change of the distortion factor τ To establish the convergence of the system, the iteration termination condition is as follows: if If the result is ≥1%, the system has not reached convergence; continue iterating. if <1%, and the current approximation of the distortion factor τ This indicates that there is no volume density difference between adjacent pixels, so the iteration stops. if <1%, and the current approximation of the distortion factor τ This indicates that the system has not reached convergence, so proceed to step S52.

7. The method for evaluating the flow path of emulsified asphalt polymer phase based on local gradient interface concentration according to claim 6, characterized in that, The specific implementation process of step S6 is as follows: Step S61: Based on the distortion factor τ and the rate of change of the emulsified asphalt imaging system The concentration density and distribution of polymer interfaces in emulsified asphalt tomographic imaging data were calculated and visualized. Step S62: Determine the evaluation thresholds ΔH and ΔL for polymer transport and diffusion behavior in emulsified bitumen tomographic imaging data according to user requirements; Step S63: Calculate the average effective diffusion rate of polymer transport and diffusion behavior in the emulsified bitumen tomographic imaging data, and accordingly... Change to: Step S64: When When ΔH ≥ ΔH, the polymer flow path is "unobstructed"; when ΔH > ΔH When ≥ ΔL, the polymer flow path is "normal"; when When ≤ ΔL, the polymer flow path is "rugged".

8. A computer device, characterized in that, include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-7.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by a processor, the method described in any one of claims 1-7 is implemented.

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