Fusion interface diffusion behavior multi-scale evaluation method based on new and old asphalt multiple regeneration process

Abstract

The invention discloses a fusion interface diffusion behavior multi-scale evaluation method based on a new and old asphalt multiple regeneration process, and relates to the technical field of asphalt regeneration, and the method comprises the steps: carrying out the feature screening of fusion interface diffusion behavior data, and constructing a fusion interface diffusion behavior evaluation substrate; fitting a fusion interface diffusion behavior deep feature factor layer based on the fusion interface diffusion behavior evaluation base, obtaining a deep feature evaluation factor group, calculating a multi-factor comprehensive weight and the credibility of each evaluation factor in the deep feature evaluation factor group, and constructing a credibility matrix based on the credibility; and performing random forest model regression analysis on the multi-factor comprehensive weight and the credibility matrix to obtain a final evaluation model, and performing fusion interface diffusion behavior multi-scale evaluation according to the final evaluation model. According to the method, the diffusion behaviors of the new asphalt and the aged asphalt of different degrees are quantitatively represented through a multi-scale test method, and a theoretical and test foundation is laid for the design and performance optimization of the subsequent repeatedly-regenerated asphalt mixture.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt regeneration, and more specifically to a multi-scale evaluation method for diffusion behavior of a fusion interface based on multiple regeneration processes of new and old asphalt. Background Art

[0002] Asphalt pavement multiple recycling refers to the pavement paving technology that recycles the regenerated pavement for a second or more times when the pavement has reached the end of its service life. In recent years, the regenerated asphalt pavements built in the early stage of my country have reached or are about to reach the end of their service life. Whether they can be recycled multiple times has become an important scientific and technical issue facing the current development of recycling technology.

[0003] Compared with single-stage regeneration, the difference between multiple regeneration of asphalt pavement is that the asphalt wrapped on the old aggregate is aged asphalt to varying degrees, that is, diffusion between new asphalt and aged asphalt to varying degrees will occur during the regeneration process. Under certain conditions, the diffusion behavior of the interface between new and old asphalt directly affects the performance and service life of the regenerated asphalt pavement. Moreover, as the degree of asphalt aging deepens, the diffusion problem of new and old asphalt caused by partial mixing will become more obvious. Therefore, it is necessary to study the more complex diffusion behavior of asphalt in the process of multiple regeneration, which will lay a theoretical and experimental foundation for the design of multiple recycled asphalt mixtures and their performance optimization.

[0004] At present, the research on multiple regeneration of asphalt pavement mainly focuses on the overall macro-performance evaluation of asphalt and its mixture, and analyzes the change law of the performance of recycled asphalt and its mixture with the increase of the number of regeneration. Asphalt can be restored to the performance level of the original asphalt after each aging and regeneration; as for asphalt mixture, with the increase of the number of regeneration, the low-temperature performance decreases rapidly, and the high-temperature performance shows a logarithmic growth. The macro-performance evaluation system proves the feasibility of multiple regeneration technology, but it is more inclined to the analysis of test results, and cannot explain the fundamental reason for the performance evolution, ignoring the particularity of multiple regeneration, that is, the aging times of asphalt wrapped on the old aggregate are not the same, so the diffusion of new asphalt and asphalt of different degrees of aging will occur during the regeneration process. Under certain conditions, the diffusion behavior of the interface between new and old asphalt directly affects the performance and service life of the recycled asphalt pavement, and as the degree of asphalt aging deepens, the diffusion problem of new and old asphalt caused by partial mixing will become more obvious. Therefore, it is necessary to study the more complex diffusion behavior of asphalt in the process of multiple regeneration, which will lay a theoretical and experimental foundation for the design of multiple recycled asphalt mixtures and their performance optimization.

[0005] Therefore, how to propose a multi-scale evaluation method for the diffusion behavior of the fusion interface based on the multiple regeneration process of new and old asphalt, and use the multi-scale experimental method to quantitatively characterize the diffusion behavior of the fusion interface between new asphalt and asphalt of different degrees of aging during the multiple regeneration process, so as to lay a theoretical and experimental foundation for the subsequent design of multiple recycled asphalt mixtures and their performance optimization is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0006] In view of this, the present invention provides a multi-scale evaluation method for the diffusion behavior of the fusion interface of new and old asphalt during multiple regeneration processes. The multi-scale test method is used to quantitatively characterize the diffusion behavior of the fusion interface of new asphalt and asphalt of different degrees of aging during multiple regeneration processes, laying a theoretical and experimental foundation for the subsequent design of multiple regenerated asphalt mixtures and their performance optimization. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt, including:

[0008] Perform feature screening on the diffusion behavior data of the fusion interface, and construct a fusion interface diffusion behavior evaluation base based on the screened features;

[0009] Based on the fusion interface diffusion behavior evaluation base, the deep characteristic factor layer of the fusion interface diffusion behavior is fitted to obtain the deep characteristic evaluation factor group;

[0010] Calculate the comprehensive weight of multiple factors according to the deep feature evaluation factor group, calculate the trust of each evaluation factor in the deep feature evaluation factor group, and construct a trust matrix based on the trust;

[0011] The multi-factor comprehensive weights and trust matrix were subjected to random forest model regression analysis to obtain the final evaluation model, and a multi-scale evaluation of the diffusion behavior of the fusion interface was performed based on the final evaluation model.

[0012] Optionally, the feature screening of the fusion interface diffusion behavior data includes: using the maximum information coefficient MIC to perform feature screening to remove irrelevant factors.

[0013] Optionally, the fusion interface diffusion behavior evaluation substrate includes substrate factors, and the substrate factors are macro-scale evaluation, meso-scale evaluation, micro-scale evaluation and molecular-scale evaluation.

[0014] Optionally, the deep characteristic factor layer includes beam body factors, and the beam body factors are diffusion depth and average diffusion rate.

[0015] Optionally, the macro-scale evaluation includes: evaluating the macro-performance of the fusion of new and old asphalt by measuring the change values ​​of the three major indicators of asphalt, evaluating the stability of the asphalt structure by turbidity titration, and dynamic shear rheology test to obtain viscosity, complex shear modulus, storage modulus, loss modulus and phase angle test results.

[0016] Optionally, the diffusion depth and average diffusion rate include:

[0017] The images were acquired by fluorescence microscopy, and the Image Pro Plus image processing software was used to quantitatively calculate the area and grayscale value of the gray shift region in the image to obtain the diffusion depth.

[0018] Tracer elements are added to the new asphalt, and the distribution map of the tracer elements is obtained by scanning with an energy spectrometer to dynamically track the diffusion depth and diffusion impact area of ​​the new asphalt during each fusion process with the old asphalt.

[0019] A scanning electron microscope combined with an energy dispersive spectrometer was used to analyze the composition of different intervals of the fusion of new and old asphalt;

[0020] With the help of the peak force tapping mode of atomic force microscopy, the changes in the two-dimensional and three-dimensional microscopic morphology of different sections during the fusion process of new and old asphalt were observed, and the roughness, Young's modulus, and adhesion micromechanical parameters were collected. The Hertz contact model and the DMT model were used for fitting, and the effective modulus of the interface between the new and old asphalt was calculated each time.

[0021] By setting a specific time interval, the average diffusion rate is obtained to evaluate the speed of diffusion under different conditions.

[0022] Optionally, the method of fitting the fused interface diffusion behavior deep characteristic factor layer based on the fused interface diffusion behavior evaluation base to obtain the deep characteristic evaluation factor group includes: dividing the base factors of the fused interface diffusion behavior evaluation base into multiple beam body factors according to the K-means clustering method to construct a fused interface diffusion behavior deep factor layer; and sequentially composing the multiple beam body factors included in the fused interface diffusion behavior deep factor layer into a deep characteristic evaluation factor group.

[0023] Optionally, the multi-factor comprehensive weights and trust matrix are subjected to random forest model regression analysis to obtain a final evaluation model including: a random forest is composed of M decision trees. During training, the M decision trees are trained in a cycle, and the training samples of each decision tree are obtained by Bootstrap sampling from the original training set. The features used when training each node of the decision tree are also randomly sampled from the new feature space N. Each decision tree is recursively split according to the judgment criteria. After the training of the M decision trees is completed, the mean of each leaf node is the evaluation result of the final regression.

[0024] Optionally, each decision tree is recursively split according to the judgment criteria, including: the extracted sample set constitutes the root node, and according to the judgment criteria, the sample set is split into a first sub-sample and a second sub-sample; the left subtree is recursively established with the first sub-sample, and the right subtree is established with the second sub-sample; and the condition for stopping the splitting is set. When the splitting cannot continue, the node is marked as a leaf node and a value is assigned at the same time.

[0025] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a multi-scale evaluation method for diffusion behavior of the fusion interface based on the multiple regeneration process of new and old asphalt, which has the following beneficial effects:

[0026] The present invention proposes a multi-scale evaluation method for the diffusion behavior of the fusion interface based on the multiple regeneration process of new and old asphalt, including: feature screening of the diffusion behavior data of the fusion interface, and constructing a diffusion behavior evaluation base of the fusion interface according to the features after screening; fitting the deep characteristic factor layer of the diffusion behavior of the fusion interface based on the diffusion behavior evaluation base of the fusion interface to obtain the deep characteristic evaluation factor group; calculating the multi-factor comprehensive weight according to the deep characteristic evaluation factor group, and calculating the trust of each evaluation factor in the deep characteristic evaluation factor group, and constructing a trust matrix based on the trust; performing random forest model regression analysis on the multi-factor comprehensive weight and the trust matrix to obtain the final evaluation model, and performing multi-scale evaluation of the diffusion behavior of the fusion interface according to the final evaluation model. The present invention has conducted preliminary performance evaluation and partial micro-scale experimental research on the multiple regeneration asphalt, with the theme of the more complex diffusion behavior of the new and old asphalt fusion interface in the multiple regeneration process, quantitatively characterizing the diffusion behavior of the new asphalt and the asphalt of different degrees of aging, and using a multi-scale experimental method to quantitatively characterize the diffusion behavior of the fusion interface of the new asphalt and the asphalt of different degrees of aging in the multiple regeneration process, laying a theoretical and experimental foundation for the subsequent design of multiple regeneration asphalt mixtures and their performance optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0028] Figure 1 A schematic flow chart of a multi-scale evaluation method for diffusion behavior of a fusion interface based on multiple regeneration processes of new and old asphalt provided by the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The embodiment of the present invention discloses a multi-scale evaluation method for diffusion behavior of the fusion interface based on the multiple regeneration process of new and old asphalt, such as Figure 1 As shown, including:

[0031] Perform feature screening on the diffusion behavior data of the fusion interface, and construct a fusion interface diffusion behavior evaluation base based on the screened features;

[0032] Based on the fusion interface diffusion behavior evaluation base, the deep characteristic factor layer of the fusion interface diffusion behavior is fitted to obtain the deep characteristic evaluation factor group;

[0033] Calculate the comprehensive weight of multiple factors according to the deep feature evaluation factor group, calculate the trust of each evaluation factor in the deep feature evaluation factor group, and construct a trust matrix based on the trust;

[0034] The multi-factor comprehensive weights and trust matrix were subjected to random forest model regression analysis to obtain the final evaluation model, and a multi-scale evaluation of the diffusion behavior of the fusion interface was performed based on the final evaluation model.

[0035] Furthermore, the feature screening of the fusion interface diffusion behavior data includes: using the maximum information coefficient MIC to perform feature screening and remove irrelevant factors.

[0036] In a specific embodiment, the feature screening using the maximum information coefficient MIC to remove irrelevant factors includes: calculating the correlation coefficient Ω=(Ω1, Ω2, ..., Ω n ), the maximum value of the correlation coefficient Ω max The corresponding feature is selected as the first feature, assuming that q1 = Y k ; Then calculate the MIC value M between feature q1 and other features = (m1, ..., m k-1 , m k+1 , ..., m n ), let P = 0.5*Ω(i≠k)+0.5*(1-M), and P maxThe corresponding feature is selected as the second feature f2; the first feature is removed and the above steps are repeated until a sufficient number of features are obtained or the maximum value of the current MIC is less than a certain threshold. Based on the macroscopic scale evaluation, the mesoscopic scale evaluation, the microscopic scale evaluation and the molecular scale evaluation, the feature vector of each fusion interface diffusion behavior is expressed as A i =(α1, α2, α 3， α 4， α5), the characteristic space of all fusion interface diffusion behaviors is expressed as A = (A1, A2, ..., A n ) T , where n is the number of diffusion behaviors at the fusion interface, and the corresponding diffusion behavior at the fusion interface is expressed as γ = (γ1, γ2, ..., γ n ) T ,First, all the features are variance normalized, and then the relationship between ,each factor and the evaluation results is analyzed.

[0037] Furthermore, the fusion interface diffusion behavior evaluation substrate includes substrate factors, and the substrate factors are macroscopic scale evaluation, mesoscopic scale evaluation, microscopic scale evaluation and molecular scale evaluation.

[0038] In a specific implementation method, micro-scale evaluation: fluorescence image analysis of old asphalt with tracer added is performed using a fluorescence microscope test instrument, and Image-ProPlus image analysis software is used to quantitatively analyze the fluorescence image through three optical parameters: mean optical density (MOD), mean gray value (MGV), and cumulative optical density (IOD) to evaluate the degree of fusion between new and old asphalt. Micro-scale evaluation: Fourier transform infrared spectrometer (FTIR), gel permeation chromatography (GPC), and atomic force microscopy (AFM) are used to study the fusion behavior of the interface between new and old asphalt from the perspective of microstructure, including parameters such as carboxyl index (I(S=O)), sulfoxide index (I(C=O)), macromolecular size (LMS), and roughness index. Molecular scale evaluation: Molecular dynamics (MD) simulation and dissipative particle dynamics (DPD) simulation techniques are used to explore the interaction behavior characteristics of the regeneration agent-old asphalt and regeneration agent-old asphalt-aggregate interfaces under warm mix conditions, and evaluate the fusion of new and old asphalt from a molecular level.

[0039] Furthermore, the deep characteristic factor layer includes beam body factors, and the beam body factors are diffusion depth and average diffusion rate.

[0040] Furthermore, the macro-scale evaluation includes: evaluating the macro-performance of the fusion of new and old asphalt by measuring the change values ​​of the three major indicators of asphalt, evaluating the stability of the asphalt structure by turbidity titration, and dynamic shear rheology test, to obtain the viscosity, complex shear modulus, storage modulus, loss modulus and phase angle test results.

[0041] Furthermore, the diffusion depth and average diffusion rate include:

[0042] The images were acquired by fluorescence microscopy, and the Image Pro Plus image processing software was used to quantitatively calculate the area and grayscale value of the gray shift region in the image to obtain the diffusion depth.

[0043] Tracer elements are added to the new asphalt, and the distribution map of the tracer elements is obtained by scanning with an energy spectrometer to dynamically track the diffusion depth and diffusion impact area of ​​the new asphalt during each fusion process with the old asphalt.

[0044] A scanning electron microscope combined with an energy dispersive spectrometer was used to analyze the composition of different intervals of the fusion of new and old asphalt;

[0045] With the help of the peak force tapping mode of atomic force microscopy, the changes in the two-dimensional and three-dimensional microscopic morphology of different sections during the fusion process of new and old asphalt were observed, and the roughness, Young's modulus, and adhesion micromechanical parameters were collected. The Hertz contact model and the DMT model were used for fitting, and the effective modulus of the interface between the new and old asphalt was calculated each time.

[0046] By setting a specific time interval, the average diffusion rate is obtained to evaluate the speed of diffusion under different conditions.

[0047] Furthermore, the method of fitting the fused interface diffusion behavior deep characteristic factor layer based on the fused interface diffusion behavior evaluation base to obtain the deep characteristic evaluation factor group includes: dividing the base factors of the fused interface diffusion behavior evaluation base into multiple beam body factors according to the K-means clustering method to construct a fused interface diffusion behavior deep factor layer; and sequentially composing the multiple beam body factors included in the fused interface diffusion behavior deep factor layer into a deep characteristic evaluation factor group.

[0048] Furthermore, it also includes: evaluating factor groups according to deep features, and obtaining comprehensive weights of multiple factors based on game theory, specifically including:

[0049] A total of K different subjective and objective weight determination methods are used to calculate the basic weight vector set of all evaluation factors in the deep feature evaluation factor group; based on the basic weight vector set, a linear combination of weights is obtained by introducing weight coefficients; based on the linear combination of weights and the basic weight vector set, a multi-objective game set model is constructed; based on the multi-objective game set model, the multi-factor comprehensive weight is calculated; based on the differential properties of the matrix, the optimal first-order derivative condition and matrix expression of the multi-objective game set model are obtained; the matrix expression is obtained to obtain the linear combination coefficient and normalize it to obtain the normalized linear combination coefficient; the basic weight vector set and the weight coefficient obtained after the linear combination coefficient is normalized are multiplied and summed to obtain the final multi-factor comprehensive weight.

[0050] Furthermore, it also includes: calculating the trust of each beam factor in the deep feature evaluation factor group, and constructing a fuzzy relationship matrix based on the trust, specifically including:

[0051] The diffusion behavior of the fusion interface is divided into four evaluation levels: normal, basically normal, subnormal and abnormal. The degree of trust of each beam factor in the deep feature evaluation factor group in the evaluation level is calculated. The size or property of each factor is divided according to the four normality evaluation levels to clarify the trust of each factor, which is the degree to which each factor trusts each target state. The qualitative factors are obtained by expert scoring method and empirical method based on confidence index. The quantitative factors are divided into four ranges according to the numerical value and the evaluation level, and the trapezoidal trust function is selected for calculation. The factors with larger values ​​are better, and the ascending semi-trapezoidal distribution function is used; the factors with smaller values ​​are better, and the descending semi-trapezoidal distribution function is used. The monitoring values ​​of each factor are substituted into the trapezoidal trust function to calculate the degree of trust of each beam factor in the deep feature evaluation factor group in the evaluation level. A fuzzy relationship matrix is ​​constructed based on the trust.

[0052] Furthermore, the random forest model regression analysis of the multi-factor comprehensive weights and the trust matrix is ​​performed to obtain the final evaluation model, including: the random forest is composed of M decision trees. During training, the M decision trees are trained in a loop, and the training samples of each decision tree are obtained by Bootstrap sampling from the original training set. The features used when training each node of the decision tree are also obtained by random sampling from the new feature space N. Each decision tree is recursively split according to the judgment criteria. After the training of the M decision trees is completed, the mean of each leaf node is the evaluation result of the final regression. Specifically, Bootstrap sampling is to extract n samples with replacement from a set of n samples to form a data set.

[0053] Furthermore, each decision tree is recursively split according to the judgment criteria, including: the extracted sample set constitutes a root node, and according to the judgment criteria, the sample set is split into a first sub-sample H1 and a second sub-sample H2; the first sub-sample H1 is used to recursively establish a left subtree, and the second sub-sample H2 is used to establish a right subtree; and the condition for stopping the splitting is set. When the splitting cannot continue, the node is marked as a leaf node and a value is assigned at the same time.

[0054] In a specific implementation, the implementation method of the decision criterion is as follows: Calculate the regression error of the root node, that is, the mean square error of the label value and the regression value of all samples, which is defined as: Based on the multi-factor comprehensive weight and trust matrix, the characteristic vector of each fusion interface diffusion behavior is expressed as B i=(β1, β2), the characteristic space of all fusion interface diffusion behaviors is expressed as B = (B1, B2, ..., B j ) T , where j is the number of diffusion behaviors of the fusion interface. A new feature space B is constructed by multi-factor comprehensive weights and trust matrix, and features B are randomly extracted from the new feature space B. t , sort the extracted training samples H according to the value of the feature from small to large; use the evaluation results of the diffusion behavior of each fusion interface as the threshold in turn, divide the samples into left and right parts, and then calculate the mean square error of the left and right subtrees; the error index of the split is defined as the regression error before the split minus the regression error of the left and right subtrees after the split: G = G(H)-G(H1)-G(H2); continue to split when this index is maximized; when the depth of the decision tree set by humans is reached or the calculated regression error is greater than the threshold set by humans, stop splitting, set the node as a leaf node, and the value of the leaf node is the mean of the label value of the sample set of this node; at this point, the training of a single decision tree is completed.

[0055] In a specific implementation, a multi-scale evaluation method for diffusion behavior of the fusion interface based on the multiple regeneration process of new and old asphalt is used. For artificially aged asphalt and naturally aged asphalt extracted from on-site milling old materials, an orthogonal experimental design is used to conduct dynamic shear viscosity, multi-stress creep recovery test (MSCR), frequency scanning, time scanning, temperature scanning and other tests at the macro level to evaluate the rheological properties of the repeatedly regenerated asphalt during the fusion process; at the micro level, a fluorescence microscope, an energy dispersive spectrometer (EDS), a scanning electron microscope (SEM), an atomic force microscope (AFM) and other technical means are used to observe and analyze the microscopic morphology, diffusion depth and average diffusion rate at the asphalt fusion diffusion interface. The specific steps include:

[0056] (1) Comparison and evaluation analysis of different rheological test indicators;

[0057] (2) Quantitative study of the interface fusion diffusion depth and average diffusion rate between new asphalt and asphalt of different aging degrees under different influencing factors;

[0058] (3) The intrinsic relationship between diffusion depth, average diffusion rate and macroscopic performance indicators, and the selection of macroscopic rheological tests and microscopic technical means with the best correlation.

[0059] Specifically, 1) Pre-preparation of multiple regenerated asphalt: In this embodiment, the multiple asphalt regenerations are one, two, and three regenerations, respectively, where the old asphalt in the first and second regenerations comes from artificial aging and natural aging of the road surface. During the test, the test results of the fusion of the aged asphalt from two different sources were compared and analyzed. The selection of new asphalt and aged asphalt materials is shown in Table 1.

[0060] Table 1 Raw material selection

[0061]

[0062] The control variables in the process of preparing recycled asphalt are shown in Table 2, where the second-aged asphalt is obtained by aging the first-aged asphalt after fusion, and the same is true for the thrice-aged asphalt.

[0063] Table 2 Summary of control variable factors

[0064]

[0065]

[0066] 2) Comparison and evaluation analysis of different rheological test indicators: The rheological parameters of asphalt materials can reflect their fusion-diffusion behavior, mainly for dynamic monitoring and analysis of the overall rheological properties of multiple fusions of new and old asphalt.

[0067] This part uses a double-layer specimen with new and old asphalt superimposed (old asphalt at the bottom). A Brookfield viscometer is used to measure the dynamic changes in viscosity during the fusion process of new and old asphalt at different test temperatures (same mixing temperature), and the viscosity results are recorded every 5 seconds until the results are stable. The frequency scanning, time scanning, and temperature scanning tests of the dynamic shear rheometer (DSR) are used. Under the stress (variable) control mode, the rheological parameters of the sample such as complex shear modulus, storage modulus, loss modulus, phase angle, etc. are recorded. The differences in the test results during each asphalt fusion process are analyzed.

[0068] 3) Quantitative study of diffusion depth and average diffusion rate: mainly to define the diffusion influence area of ​​the new and old asphalt fusion interface during multiple regeneration, and calculate the average diffusion rate based on the evolution of the diffusion area during different mixing times, so as to intuitively characterize the fusion-diffusion behavior.

[0069] A fluorescence microscope was used to observe the migration of the ash area at the interface between the new and old asphalt. Multiple regenerated asphalt samples were frozen at a specific stirring time, and then cut at low temperature. The cross-section at the interface between the new and old asphalt was taken as the observation object, and the image under the fluorescence microscope was obtained. Image Pro Plus image processing software was used to quantitatively calculate the area and grayscale value of the ash migration area to obtain the diffusion depth. Element tracing technology was used to select elements that were not in the asphalt as tracers. In this embodiment, cobalt, lead, and titanium are used as three tracer elements. The tracers are cobalt cyclohexane, lead isooctanoate, and nano-TiO2, respectively. They are added to the new asphalt, and the distribution map of the tracer elements is obtained by scanning with an energy dispersive spectrometer (EDS), dynamically tracking the diffusion depth of the new asphalt during each fusion process with the old asphalt and the diffusion-affected area. A scanning electron microscope (SEM) was used in conjunction with EDS to analyze the components of different intervals of the fusion of new and old asphalt. With the help of the peak force tapping mode (Peak Force QNM) of atomic force microscopy (AFM), the changes in the two-dimensional and three-dimensional micromorphologies of different sections during the fusion process of new and old asphalt were observed, and the micromechanical parameters such as roughness, Young's modulus, and adhesion were collected. The Hertz contact model and the Derjaguin-Muller-Toporov (DM T) model were used for fitting, and the effective modulus of the interface between the new and old asphalt was calculated for each fusion.

[0070] Specifically, the calculation of the effective modulus includes: The Hertz contact model is suitable for describing the mechanical behavior of two materials when they are in contact, especially in the case of elastic contact. For the interface between new and old asphalt, the formula of the Hertz model can be used: E eff =4 / 3*F / δ 3 *R, where: E eff is the effective modulus, F is the contact force, δ is the contact deformation, and R is the contact radius. The effective modulus is calculated using the Hertz model by experimentally measuring the contact force and contact deformation. The DMT model provides a more accurate description when considering the effects of contact force and surface energy, and is suitable for smaller contact areas. The effective modulus calculation formula of the DMT model is: DMT =3 / 2*(F+2πRγ) / δ 3 , where γ is the surface energy. The other variables are the same as above. By comparing the results of the Hertz model and the DMT model, we can have a more comprehensive understanding of the effective modulus of the interface between the new and old asphalt.

[0071] Using a variety of advanced experimental techniques, the internal microstructure of the fusion of new and old asphalt is studied from the perspectives of components, molecules, and nanometers. The average diffusion rate is obtained at specific time intervals to evaluate the speed of diffusion under different conditions.

[0072] Specifically, in the diffusion experiment design, appropriate experimental methods (such as thin film diffusion method, column diffusion method, etc.) are selected to measure the diffusion characteristics at the interface between new and old asphalt, and the concentration changes of the diffusing substances are recorded at different time intervals; the average diffusion rate is calculated. According to Fick's law, the average diffusion rate can be expressed as: D avg =ΔC / (Δt*A), where ΔC is the concentration change, Δt is the time interval, and A is the diffusion area. Through the above method, the effective modulus and microstructure of the fusion of new and old asphalt can be systematically studied, and the diffusion characteristics under different conditions can be evaluated. This will provide important theoretical basis and experimental support for the optimization and application of asphalt materials.

[0073] 4) The intrinsic relationship between diffusion depth, average diffusion rate and macro performance indicators: Through macro performance tests, test results such as viscosity, complex shear modulus, storage modulus, loss modulus, phase angle, etc. are obtained, and they are respectively associated with diffusion depth and average diffusion rate, and the correlation between these indicators is analyzed. The macro performance test with a greater correlation with the diffusion behavior characterization parameters (diffusion depth, average diffusion rate) is selected to characterize the diffusion behavior of the multiple regeneration interface. Specifically, the fusion interface diffusion behavior data is subjected to feature screening, and a fusion interface diffusion behavior evaluation base is constructed based on the screened features; the fusion interface diffusion behavior deep feature factor layer is fitted based on the fusion interface diffusion behavior evaluation base to obtain a deep feature evaluation factor group; the multi-factor comprehensive weight is calculated based on the deep feature evaluation factor group, and the trust of each evaluation factor in the deep feature evaluation factor group is calculated, and a trust matrix is ​​constructed based on the trust; the multi-factor comprehensive weight and the trust matrix are subjected to random forest model regression analysis to obtain the final evaluation model, and a multi-scale evaluation of the fusion interface diffusion behavior is performed based on the final evaluation model.

[0074] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0075] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt, characterized in that: include: Perform feature screening on the diffusion behavior data of the fusion interface, and construct a fusion interface diffusion behavior evaluation base based on the screened features; Based on the fusion interface diffusion behavior evaluation base, the deep characteristic factor layer of the fusion interface diffusion behavior is fitted to obtain the deep characteristic evaluation factor group; Calculate the comprehensive weight of multiple factors according to the deep feature evaluation factor group, calculate the trust of each evaluation factor in the deep feature evaluation factor group, and construct a trust matrix based on the trust; The multi-factor comprehensive weights and trust matrix were subjected to random forest model regression analysis to obtain the final evaluation model, and a multi-scale evaluation of the diffusion behavior of the fusion interface was performed based on the final evaluation model.

2. According to claim 1, a multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt is characterized in that: The feature screening of the fusion interface diffusion behavior data includes: using the maximum information coefficient MIC to perform feature screening and remove irrelevant factors.

3. According to claim 1, a multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt is characterized in that: The fusion interface diffusion behavior evaluation substrate includes substrate factors, and the substrate factors are macroscopic scale evaluation, mesoscopic scale evaluation, microscopic scale evaluation and molecular scale evaluation.

4. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 1 is characterized in that: The deep characteristic factor layer includes beam body factors, and the beam body factors are diffusion depth and average diffusion rate.

5. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 3 is characterized in that: The macro-scale evaluation includes: evaluating the macro-performance of the fusion of new and old asphalt by measuring the change values ​​of the three major indicators of asphalt, evaluating the stability of the asphalt structure by turbidity titration, and dynamic shear rheology test, and obtaining the viscosity, complex shear modulus, storage modulus, loss modulus and phase angle test results.

6. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 4 is characterized in that: The diffusion depth and average diffusion rate include: The images were acquired by fluorescence microscopy, and the Image Pro Plus image processing software was used to quantitatively calculate the area and grayscale value of the gray shift region in the image to obtain the diffusion depth. Tracer elements are added to the new asphalt, and the distribution map of the tracer elements is obtained by scanning with an energy spectrometer to dynamically track the diffusion depth and diffusion impact area of ​​the new asphalt during each fusion process with the old asphalt. A scanning electron microscope combined with an energy dispersive spectrometer was used to analyze the composition of different intervals of the fusion of new and old asphalt; With the help of the peak force tapping mode of atomic force microscopy, the changes in the two-dimensional and three-dimensional microscopic morphology of different sections during the fusion process of new and old asphalt were observed, and the roughness, Young's modulus, and adhesion micromechanical parameters were collected. The Hertz contact model and the DMT model were used for fitting, and the effective modulus of the interface between the new and old asphalt was calculated each time. By setting a specific time interval, the average diffusion rate is obtained to evaluate the speed of diffusion under different conditions.

7. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 1 is characterized in that: The method of fitting a fused interface diffusion behavior deep feature factor layer based on a fused interface diffusion behavior evaluation base to obtain a deep feature evaluation factor group includes: dividing the base factors of the fused interface diffusion behavior evaluation base into multiple beam body factors according to a K-means clustering method to construct a fused interface diffusion behavior deep factor layer; and sequentially composing a deep feature evaluation factor group with multiple beam body factors included in the fused interface diffusion behavior deep factor layer.

8. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 1 is characterized in that: The random forest model regression analysis of the multi-factor comprehensive weights and the trust matrix is ​​performed to obtain the final evaluation model, which includes: the random forest is composed of M decision trees. During training, the M decision trees are trained in a cycle, and the training samples of each decision tree are obtained by Bootstrap sampling from the original training set. The features used in training each node of the decision tree are also randomly sampled from the new feature space N. Each decision tree is recursively split according to the judgment criteria. After the training of the M decision trees is completed, the mean of each leaf node is the evaluation result of the final regression.

9. The multi-scale evaluation method for diffusion behavior of fusion interface based on multiple regeneration processes of new and old asphalt according to claim 8 is characterized in that: Each decision tree is recursively split according to the judgment criteria, including: the extracted sample set constitutes a root node, and according to the judgment criteria, the sample set is split into a first sub-sample and a second sub-sample; the left sub-tree is recursively established with the first sub-sample, and the right sub-tree is established with the second sub-sample; and the condition for stopping the splitting is set. When the splitting cannot continue, the node is marked as a leaf node and a value is assigned at the same time.

Images