Bituminous mineral powder interface modulus distribution calculation method, system, device and storage medium

Abstract

The application discloses a kind of asphalt mineral powder interface modulus distribution calculation method, system, equipment and storage medium.Method includes: asphalt mineral powder interface equal thickness is divided into preset number of sublayer;Suppose asphalt film thickness is preset thickness, suppose the modulus of innermost sublayer is mineral powder modulus, according to the Poisson's ratio of material, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration and mineral powder layer radius combination distribution function, the predicted value of different frequency of mortar modulus is calculated;Mortar modulus predicted value and measured mortar modulus are compared to calculate relative error, when relative error is less than preset error, output asphalt film thickness and the modulus of innermost sublayer, to calculate the prediction accuracy of different distribution functions when mineral powder volume concentration changes.The prediction accuracy of different distribution functions is compared in the application, the most suitable distribution function is selected, and the modulus distribution of asphalt mineral powder interface is predicted by optimal distribution function.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micromechanics, in particular to an asphalt mineral powder interface modulus distribution calculation method, system, device and storage medium. BACKGROUND

[0002] Material digital design breaks through the limitations of traditional empirical design by constructing the digital mapping relationship between material microstructure information and macroscopic mechanical response, realizes the accurate prediction of material performance, and provides a new path for the development of low-carbon high-toughness asphalt pavement materials. In the digital design of asphalt mixture, the cross-scale model is the core theory, which describes the influence of microstructure parameters on macroscopic mechanical behavior through mathematical and physical equations, thereby determining the reliability of material digital design.

[0003] In the asphalt and aggregate mortar system, the mineral particles and the asphalt matrix form a mortar adsorption layer through physical and chemical action. The adsorption layer has a significant gradient shear modulus characteristic, and the modulus value gradually decreases along the radial direction from the mineral powder surface to the asphalt matrix. This gradient effect significantly affects the overall shear modulus of the mortar.

[0004] However, the traditional mortar model usually simplifies the adsorption layer as a homogeneous phase, which is difficult to accurately characterize the spatial gradient distribution of the shear modulus. SUMMARY

[0005] Therefore, it is necessary to provide an asphalt mineral powder interface modulus distribution calculation method, system, device and storage medium, which aims to solve the technical problem that the contact surface modulus distribution cannot be directly and accurately obtained due to the non-uniformity of the asphalt mineral powder interface mortar connecting surface.

[0006] In order to solve the above technical problems, in a first aspect, the present application provides an asphalt mineral powder interface modulus distribution calculation method, comprising:

[0007] The asphalt mineral powder interface is divided into a preset number of sublayers; wherein the innermost sublayer of the sublayer connects the mineral powder from inside to outside, and the outermost sublayer connects the asphalt;

[0008] Assuming that the asphalt film thickness is a preset thickness, assuming that the modulus of the innermost sublayer is the mineral powder modulus, and combining the distribution function according to the material Poisson's ratio, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration and mineral powder layer radius, the mortar modulus prediction value at different frequencies is calculated;

[0009] The mortar modulus prediction value is compared with the measured mortar modulus, and the relative error is calculated, and when the relative error is less than a preset error, the asphalt film thickness and the modulus of the innermost sublayer are output;

[0010] According to the asphalt film thickness and the modulus of the innermost sublayer, the prediction accuracy of different distribution functions is calculated when the volume concentration of the mineral powder changes, and the distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface.

[0011] In a possible implementation, in the step of calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the volume concentration of the mineral powder and the mineral powder layer radius, the asphalt film thickness is assumed to be a preset thickness, and the modulus of the innermost sublayer is assumed to be the mineral powder modulus.

[0012] The asphalt film thickness is assumed to be a preset thickness, and the radius of the outermost sublayer is calculated in combination with the volume concentration of the mineral powder, and the radius of each sublayer is calculated according to the number of sublayers, where the modulus of the outermost sublayer is the asphalt modulus.

[0013] The modulus of the innermost sublayer is assumed to be the mineral powder modulus, parameters of different distribution functions are solved according to the asphalt modulus and the mineral powder modulus, and the shear modulus of all sublayers is calculated based on the distribution functions and the sublayer radius; where the distribution functions include an exponential function, an S-L function, a Wang function and a linear function.

[0014] The predicted value of the mortar modulus at different frequencies is calculated according to the shear modulus and the Poisson's ratio of the material.

[0015] In a possible implementation, after the step of comparing the predicted value of the mortar modulus with the measured mortar modulus and calculating the relative error, the method further includes:

[0016] When the relative error at any frequency is greater than a preset threshold, the modulus of the innermost sublayer is controlled to decrease by a preset modulus.

[0017] The step of calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the volume concentration of the mineral powder and the mineral powder layer radius is performed.

[0018] In a possible implementation, after the step of comparing the predicted value of the mortar modulus with the measured mortar modulus and calculating the relative error, the method further includes:

[0019] When the relative error at any frequency is greater than a preset threshold, and the modulus of the innermost sublayer is less than the asphalt modulus, the asphalt film thickness is controlled to increase by a preset thickness.

[0020] The step of calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the volume concentration of the mineral powder and the mineral powder layer radius is performed.

[0021] In a possible implementation, after the step of comparing the mortar modulus prediction value with the measured mortar modulus and calculating the relative error, the method further comprises:

[0022] When the relative error at each frequency is greater than the preset threshold, the iteration is terminated and the parameters are outputted.

[0023] When the asphalt film thickness is greater than the preset maximum value and the relative error is greater than the preset threshold, the data is recorded as being unable to be optimized.

[0024] In a possible implementation, when the asphalt film thickness and the modulus of the innermost layer of the sublayer are used to calculate the prediction accuracy of different distribution functions when the volume concentration of the mineral powder changes, the step of selecting the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface comprises:

[0025] The mortar modulus prediction value of different distribution functions is calculated when the volume concentration of the mineral powder changes according to the asphalt film thickness and the modulus of the innermost layer of the sublayer.

[0026] The mortar modulus prediction value is compared with the measured mortar modulus, the distribution function with the highest prediction accuracy is selected, and the modulus distribution of the asphalt-mineral powder interface is predicted by the distribution function with the highest prediction accuracy.

[0027] The distribution function with the highest prediction accuracy is compared with the uniform model at different volume concentrations, and the volume concentration with the maximum difference in prediction accuracy between the distribution function and the uniform model is outputted as the working concentration.

[0028] In a possible implementation, the step of comparing the mortar modulus prediction value with the measured mortar modulus and selecting the distribution function with the highest prediction accuracy comprises:

[0029] The prediction accuracies of different distribution functions at the same volume concentration are compared, and the distribution function with the highest prediction accuracy at each concentration is outputted.

[0030] When the distribution function with the highest prediction accuracy at each volume concentration is the same function, the distribution function is selected as the distribution function with the highest prediction accuracy.

[0031] On the other hand, the present application further provides a system for calculating the modulus distribution of an asphalt-mineral powder interface with a non-uniform interface, comprising:

[0032] A model construction module is configured to divide an asphalt-mineral powder interface with the same thickness into a preset number of sublayers, wherein the sublayers are connected to the mineral powder from the inside to the outside, and the innermost layer of the sublayer is connected to the asphalt.

[0033] The data assumption module is configured to assume that the asphalt film thickness is a preset thickness, assume that the modulus of the innermost sublayer is a mineral powder modulus, and calculate a predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and a distribution function in combination with the mineral powder layer radius.

[0034] The data determination module is configured to compare the measured mortar modulus with the predicted value of the mortar modulus, calculate a relative error, and output the asphalt film thickness and the modulus of the innermost sublayer when the relative error is less than a preset error.

[0035] The modulus output module is configured to calculate the prediction accuracy of different distribution functions when the mineral powder volume concentration changes according to the asphalt film thickness and the modulus of the innermost sublayer, and select the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface.

[0036] In a second aspect, the present application further provides an electronic device, comprising a memory and a processor, wherein,

[0037] The memory is configured to store a program.

[0038] The processor is coupled with the memory and is configured to execute the program stored in the memory to implement the steps of the asphalt-mineral powder interface modulus distribution calculation method in any of the implementation manners.

[0039] In a third aspect, the present application further provides a computer readable storage medium for storing computer readable programs or instructions, which can implement the steps of the asphalt-mineral powder interface modulus distribution calculation method in any of the implementation manners when executed by a processor.

[0040] The asphalt-mineral powder interface modulus distribution calculation method provided by the application has the beneficial effects that: the asphalt-mineral powder interface phase is evenly divided into multiple equal parts, each part is equivalent to a uniform interface with the same material performance, the asphalt film thickness is assumed to be a preset thickness, the modulus of the innermost layer is assumed to be the modulus of the mineral powder, the modulus prediction value of the asphalt mortar at different frequencies is calculated by combining the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration and the mineral powder layer radius distribution function, the modulus of the innermost layer and the asphalt film thickness are inversely calculated by comparing the measured asphalt mortar modulus with the asphalt mortar modulus prediction value, and the prediction accuracy of different distribution functions is calculated when the mineral powder volume concentration changes, the most suitable distribution function is selected, and the modulus distribution of the asphalt-mineral powder interface is predicted through the optimal distribution function. Further, the modulus distribution of the asphalt-mineral powder interface calculated based on the distribution function is improved in accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0042] Figure 1 The flowchart of an embodiment of the asphalt-mineral powder interface modulus distribution calculation method provided by the application;

[0043] Figure 2 The modulus of the innermost layer of different distribution functions provided by the application;

[0044] Figure 3 An embodiment flowchart of S102 in the application; Figure 1

[0045] Figure 4 An embodiment flowchart of S103 in the application; Figure 1

[0046] Figure 5 An embodiment flowchart of S104 in the application; Figure 1

[0047] Figure 6 The model prediction results of different distribution functions at a concentration of 25% provided by the application;

[0048] Figure 7 ​​​The model prediction results of different distribution functions at 40% concentration provided by the application are shown in the following table:

[0049] Figure 8 The comparison of the prediction results of different models provided by the application is shown in the following table:

[0050] Figure 9 An embodiment structure schematic diagram of the asphalt mineral powder interface modulus distribution calculation system provided by the application is shown in the following figure:

[0051] Figure 10 An embodiment structure schematic diagram of the electronic device provided by the application is shown in the following figure. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the application will be clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0053] In the description of the embodiments of the application, unless otherwise specified, the meaning of "a plurality of" is two or more than two. The association relationship of the associated objects is described, which means that there can be three relationships, for example: A and / or B, which can represent the three cases of A alone, A and B together, and B alone.

[0054] The "first", "second" and the like described in the embodiments of the application are only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the technical features limited by "first" and "second" can explicitly or implicitly include at least one of the features.

[0055] In this document, the reference to "embodiments" means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive or alternative to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] Before the embodiments are described, the following terms are explained.

[0057] Modulus: also known as shear modulus (G), is a quantitative index of the material's ability to resist shear deformation under shear stress, defined as the ratio of shear stress to shear strain. For asphalt mixture, its value directly reflects the material's ability to resist shear flow under dynamic load, and is the core parameter for evaluating high temperature rutting resistance.

[0058] Poisson's ratio (μ): refers to the ratio of lateral strain to longitudinal strain of a material under uniaxial stress. For asphalt mixtures, this parameter reflects the lateral deformation characteristics of the material under load.

[0059] This invention provides a method, system, electronic device, and storage medium for calculating the interfacial modulus distribution of bitumen powder, which will be described below.

[0060] Figure 1 A schematic flowchart of an embodiment of the method for calculating the interfacial modulus distribution of asphalt mineral powder provided by the present invention is shown below. Figure 1 As shown, the calculation method for the interfacial modulus distribution of asphalt powder includes:

[0061] S101. Divide the asphalt-mineral powder interface into a predetermined number of sub-layers of equal thickness; wherein, from the inside out, the innermost sub-layer is connected to the mineral powder, and the outermost sub-layer is connected to the asphalt.

[0062] S102. Assuming the asphalt film thickness is the preset thickness and the modulus of the innermost sub-layer is the mineral powder modulus, the predicted values ​​of the mastic modulus at different frequencies are calculated based on the material's Poisson's ratio, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration, and mineral powder layer radius combined with the distribution function.

[0063] S103. Compare the predicted value of the asphalt mortar modulus with the measured value of the asphalt mortar modulus, and calculate the relative error. When the relative error is less than the preset error, output the thickness of the asphalt film and the modulus of the innermost sub-layer.

[0064] S104. Based on the thickness of the asphalt film and the modulus of the innermost sublayer, calculate the prediction accuracy of different distribution functions when the mineral powder volume concentration changes, and select the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface.

[0065] Understandably, based on the characteristics of the non-uniform interface, the asphalt-mineral powder interface is uniformly divided into multiple equal parts, each equivalent to a uniform interface with the same material properties. The core of this application is the multi-layer segmentation of the adsorption layer, with a preset number of twenty layers. The adsorbed asphalt layer of the asphalt-mineral powder interface is divided into twenty sub-layers of equal thickness, and the adsorbed asphalt layer corresponds to the N-layer spherical inclusion model. The mineral powder is located in the first layer, with a mineral powder modulus of G1 = 19000 MPa and a mineral powder Poisson's ratio of... The adsorbed bitumen layer comprises twenty sublayers, referred to from the inside out as the second to the twenty-first layers. The modulus of each layer gradually decreases radially, with the decreasing pattern depending on the modulus distribution function. The modulus of the outermost sublayer... Take the asphalt modulus The modulus of the innermost sublayer Unknown; asphalt is located on the twenty-second layer, asphalt modulus The value measured by the test, varies with frequency, Poisson's ratio of asphalt .

[0066] In some embodiments of the present application, N-layer composite balls are placed in an infinite equivalent medium (the N+1 phase material), which represents the overall paste, and the modulus is , which is the value to be predicted in this application, referred to as the predicted paste modulus, which is also measured by the test, denoted as the measured paste modulus . The composite ball composed of N-layer concentric spherical shells (1st phase to Nth phase), with the core being the 1st phase and the outermost layer being the Nth phase, is embedded in an infinite equivalent medium (N+1 phase), so that the mechanical behavior of the composite ball in the medium is consistent with the actual multi-layer structure.

[0067] The Poisson's ratio of each layer material is known , the asphalt modulus is , the mineral powder modulus is , the asphalt layer radius is , and the mineral powder layer radius is , the asphalt modulus at different frequencies is measured by the test, and the mineral powder volume concentration is set to 15%. The mineral powder volume concentration refers to the percentage of the volume of mineral powder in the total volume of solid particles (mineral aggregate + mineral powder) in the asphalt mixture; it is assumed that the asphalt film thickness is a preset thickness, which is the starting point for subsequent iterative solution, and the asphalt film thickness will gradually increase until the predicted paste modulus is close to the measured paste modulus . It is assumed that the modulus of the innermost sublayer of the adsorbed asphalt layer is the mineral powder modulus, which will gradually decrease in the optimization process. According to the above parameters, the predicted value of the paste shear modulus at different frequencies is calculated by combining the distribution function.

[0068] The measured paste modulus is compared with the predicted paste modulus , and the relative error is calculated. The error analysis is from high frequency to low frequency, and the relative error at each frequency is calculated in turn:

[0069] The preset error can be 5%, and if the relative error at all frequencies is less than the preset threshold of 5%, the asphalt film thickness and the modulus of the innermost sublayer under different distribution functions are output.

[0070] It can be understood that through the back calculation of different distribution functions, the asphalt film thickness and the modulus of the innermost sublayer are shown in Table 1 and Figure 2 :

[0071]

[0072] Table 1 Asphalt film thickness under different distribution functions

[0073] It should be noted that the asphalt film thickness and the modulus of the innermost sublayer are substituted into the model to predict the asphalt mineral powder interface modulus under other volume concentrations (25%, 40%), and the prediction accuracy under different distribution functions is calculated by comparing the predicted results with the measured modulus, and the distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt mineral powder interface.

[0074] The embodiment of the present application studies the mechanical model of the asphalt mineral powder interface with a non-uniform interface, divides the asphalt mineral powder interface into multiple equal parts, each part is equivalent to a uniform interface with the same material properties, assumes that the initial asphalt film thickness is a preset thickness, assumes that the modulus of the innermost sublayer is the mineral powder modulus, and the modulus of the asphalt mineral powder interface is calculated by combining the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and the mineral powder layer radius.

[0075] In some embodiments of the present application, as shown in Figure 3 , step S102 includes:

[0076] S301, assuming that the asphalt film thickness is a preset thickness, the radius of the outermost sublayer is calculated in combination with the mineral powder volume concentration, and the radius of each layer is calculated according to the number of sublayers, wherein the modulus of the outermost sublayer is the asphalt modulus;

[0077] S302, assuming that the modulus of the innermost sublayer is the mineral powder modulus, the parameters of different distribution functions are solved according to the asphalt modulus and the mineral powder modulus, and the shear modulus of all sublayers is calculated based on the distribution function and the sublayer radius; wherein the distribution function includes a power exponent function, an S-L function, a Wang function and a linear function;

[0078] S303, the asphalt mineral powder interface modulus is calculated according to the shear modulus and the Poisson's ratio of the material.

[0079] It can be understood that the preset thickness can be 0.5 microns, assuming that the asphalt film thickness is 0.5 microns, the asphalt film volume concentration, i.e. the asphalt adsorption ratio, is calculated using geometric relationships. By inputting the mineral powder volume concentration and the asphalt volume concentration (1- ), the mineral powder layer radius and the asphalt layer radius are calculated using the following formula

[0080] wherein, represents the volume concentration of the mineral powder, represents the volume fraction of the kth layer of material. The proportion of adsorbed asphalt calculated based on the asphalt film thickness is substituted into the above formula to calculate the radius of the adsorbed asphalt layer , and the radius of each layer in the adsorbed asphalt layer is calculated according to the preset number n = 20 of fixed split layers ~ ).

[0081] It should be noted that since the components that can be adsorbed by the filler in asphalt are limited, the adsorbed components usually account for 25%-60% of the volume of asphalt. The filler has a maximum adsorption potential, and when the filler reaches a saturated adsorption state, an asphalt film thickness is formed. Therefore, when the filler concentration is low, the system achieves an asphalt film thickness. The following four commonly used distribution functions of the modulus of the adsorbed layer with respect to the thickness are used for calculation, and the specific forms are shown in Table 2.

[0082]

[0083] Table 2 Different functions of the modulus distribution of the mineral powder-asphalt interface

[0084] wherein, represents the outermost boundary radius of the adsorbed asphalt layer; represents the innermost boundary radius of the adsorbed asphalt layer; represents the modulus at the surface of the mineral powder particle; represents the modulus closest to the asphalt; and the rest are function coefficients.

[0085] In some embodiments of the present application, for the modulus distribution in the adsorbed asphalt split layer, one of the above four functions is initially assumed, and the modulus gradually decreases from the innermost layer to the outermost layer of the adsorbed asphalt layer, that is, from the assumed innermost layer sublayer modulus tends to the asphalt modulus along the function. The outermost layer sublayer modulus of the adsorbed asphalt is equal to the asphalt modulus, the innermost layer sublayer modulus of the assumed is the modulus of the mineral powder, and the parameters of the distribution function of the modulus are solved. The shear modulus of each layer sublayer in the adsorbed asphalt layer is calculated based on the distribution function and the sublayer radius of each layer .

[0086] It should be noted that according to the Poisson's ratio of each layer of material of the split layer , the asphalt modulus , the mineral powder modulus , the asphalt layer radius , the mineral powder layer radius , the sublayer radius of each layer ~ , and the shear modulus of each layer sublayer The coefficients A, B and C are expressed by parameters of a recursive matrix as follows:

[0087] The coefficients A, B and C obtained by the above calculation are substituted into the equation as follows:

[0088] The modulus prediction value of the asphalt mortar at different frequencies is calculated by using a solving method of the implicit equation .

[0089] In the embodiments of the present application, the initial asphalt film thickness is assumed to be a preset thickness, the modulus of the innermost sublayer is assumed to be the mineral powder modulus, and the modulus prediction value of the asphalt mortar at different frequencies is calculated by combining the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration and the mineral powder layer radius with a distribution function.

[0090] In some embodiments of the present application, as shown in Figure 4 , the step S103 comprises:

[0091] S401, when the relative error at any frequency is greater than a preset threshold, the modulus of the innermost sublayer is controlled to decrease by a preset modulus;

[0092] S402, when the relative error at any frequency is greater than a preset threshold and the modulus of the innermost sublayer is less than the asphalt modulus, the asphalt film thickness is controlled to increase by a preset thickness;

[0093] S403, when the relative error at each frequency is greater than a preset threshold, the iteration is terminated and the parameters are outputted;

[0094] S404, when the asphalt film thickness is greater than a preset maximum value and the relative error is greater than a preset threshold, the data is recorded as being unable to be optimized.

[0095] In some embodiments of the present application, if the relative error at a certain frequency exceeds a preset threshold of 5%, the system performs iterative optimization by adjusting the modulus of the innermost sublayer of the adsorbed asphalt layer, reduces the modulus of the innermost sublayer of the adsorbed asphalt layer by a step of 0.05 times the mineral powder modulus, until the asphalt modulus is reached, then the parameters of the distribution function of the asphalt-mineral powder interface modulus are solved again, and the coefficients A, B and C are calculated again for prediction, and then the relative error is calculated. By reducing the modulus of the innermost sublayer, the lubricating effect of asphalt on mineral powder is simulated to be enhanced, thereby improving the accuracy of modulus prediction. The preset modulus can be 0.05 times the mineral powder modulus, or a smaller adjustment step (such as 0.02 times the mineral powder modulus) can be used.

[0096] In some embodiments of the present application, if the relative error is still greater than 5% after the modulus of the innermost sublayer at a certain frequency is adjusted to the modulus of asphalt, the accuracy requirement cannot be met, the thickness of the asphalt film is increased, the step size is 0.01 microns, then the sublayer radius of each layer of the asphalt adsorption layer and the shear modulus of all sublayers are recalculated, and the coefficients A, B and C are recalculated and predicted after the modulus of the innermost sublayer is adjusted.

[0097] In some embodiments of the present application, if the prediction error at all frequency points is less than the threshold value, the iteration is terminated and the optimized parameters are output.

[0098] In some embodiments of the present application, if the film thickness exceeds the preset maximum value of 1.5 microns and the accuracy requirement at a certain frequency still cannot be met, the relative error is still greater than 5%, and the situation that cannot be optimized is recorded.

[0099] In this embodiment, the asphalt film thickness and the modulus of the innermost sublayer are inversely calculated by error analysis of the predicted value of the asphalt mortar modulus and the measured asphalt mortar modulus of a specific volume concentration asphalt mortar. Assuming that the asphalt film thickness is the preset thickness, the asphalt adsorption ratio is calculated, the modulus of the innermost sublayer is assumed to be the modulus of the mineral powder, the parameters of each distribution function are calculated, the asphalt-mineral powder interface is divided into a preset number of sublayers, the sublayer radius of each layer is calculated, the shear modulus of all sublayers is calculated according to the sublayer radius and the distribution function, the coefficients A, B and C are calculated according to the shear modulus of all sublayers, the predicted value of the asphalt mortar modulus is calculated, and error analysis is performed on the measured asphalt mortar modulus. When the relative error exceeds the preset threshold value of 5%, the modulus of the innermost sublayer is reduced by 0.05 times the modulus of the mineral powder, the parameters of each distribution function and the subsequent steps are repeatedly calculated, and the next distribution function is continued when the relative error is less than the preset threshold value of 5%. When the modulus of the innermost sublayer is equal to the modulus of asphalt, the relative error still exceeds the preset threshold value of 5%, the test condition with the lowest temperature and the highest frequency is converted, the asphalt film thickness is increased by 0.01 microns, the calculation of the asphalt adsorption ratio and the subsequent steps are repeatedly performed, and the relative error is less than the preset threshold value of 5%.

[0100] In some embodiments of the present application, as shown in Figure 5 the step S104 comprises:

[0101] S501, according to the asphalt film thickness and the modulus of the innermost sublayer, the predicted value of the asphalt mortar modulus of different distribution functions is calculated when the mineral powder volume concentration changes;

[0102] S502, the predicted value of the asphalt mortar modulus is compared with the measured asphalt mortar modulus, the distribution function with the highest prediction accuracy is selected, and the modulus distribution of the asphalt-mineral powder interface is predicted by the distribution function with the highest prediction accuracy;

[0103] S503. Under different volume concentrations, compare the distribution function with the uniform model with the one that has the highest prediction accuracy, and output the volume concentration with the largest difference in prediction accuracy between the distribution function and the uniform model as the working concentration.

[0104] It should be noted that the asphalt film thickness is combined with the distribution function in Table 1. Figure 2 Based on the modulus of the innermost sublayer, the following pattern can be observed: the faster the rate of increase of the modulus of the innermost sublayer at different frequencies, the smaller the calculated asphalt film thickness, and the larger the maximum value of the modulus of the innermost sublayer (e.g., the Wang function); the slower the rate of decrease of the modulus of the innermost sublayer at different frequencies, the larger the calculated asphalt film thickness, and the smaller the maximum value of the modulus of the innermost sublayer (e.g., the linear function).

[0105] Understandably, the asphalt film thickness and the maximum modulus of the adsorbed asphalt layer are substituted into the model to predict the modulus of asphalt mastic at other volume concentrations (25%, 40%). The predicted results are then compared with the measured modulus to calculate the prediction error under different modulus distribution functions. The prediction results for all distribution functions are as follows: Figure 6 , Figure 7 As shown in Table 3.

[0106] The asphalt film thickness and the modulus of the innermost sublayer were substituted into the model to predict the asphalt mastic modulus at other volume concentrations (25%, 40%). The predicted results were compared with the measured modulus to calculate the prediction error under different modulus distribution functions. The prediction results for all distribution functions are as follows: Figure 6 , Figure 7 As shown in Table 3. Figure 6 In the table, a represents the prediction result (25%) under the SL function distribution, b represents the prediction result (25%) under the power function distribution, c represents the prediction result (25%) under the Wang function distribution, and d represents the prediction result (25%) under the linear function distribution. Figure 7 In the table, a represents the prediction result (40%) under the SL function distribution, b represents the prediction result (40%) under the power function distribution, c represents the prediction result (40%) under the Wang function distribution, and d represents the prediction result (40%) under the linear function distribution.

[0107]

[0108] Table 3 Prediction accuracy under different distribution functions

[0109] The results show that the prediction accuracy of different distribution functions at different volume concentrations is significantly different. The S-L distribution function shows the highest prediction accuracy at different volume concentrations, followed by the power exponent function, while the Wang function and the linear function have significant errors at certain volume concentrations. In summary, the S-L distribution function shows high prediction accuracy at all concentrations, indicating that its modulus decreasing property is more consistent with the trend of modulus gradient of the adsorption layer.

[0110] Stress is transmitted from the mineral powder particles to the asphalt matrix through the adsorbed asphalt layer, forming a stress gradient field. At a concentration of 25%, the thickness of the adsorption layer is large, and its modulus distribution directly affects the stress transmission path and the prediction accuracy of the modulus. The S-L distribution function has the highest prediction accuracy, and its smooth decreasing modulus property effectively reduces stress concentration, allowing stress to gradually decay in the high modulus region, ensuring the contribution of the adsorption layer to the overall modulus. This indicates that the actual adsorption layer modulus distribution may decrease slightly faster near the particle surface and gradually flatten away from the particle surface, forming a gradual transition mode to minimize stress concentration. At a concentration of 40%, the particle spacing decreases, the adsorption layer thickness decreases, and the inter-particle contact effect increases. The characterization of the modulus distribution function for the volume of the high modulus region directly affects the prediction accuracy. The predicted value under the S-L distribution function is lower than that at a concentration of 25%, but it still maintains high accuracy. This is because its smooth decreasing property ensures that the volume of the high modulus region is moderate, reducing the negative impact of the low modulus region. The actual adsorption layer modulus distribution may be similar to the S-L distribution function, which remains smooth in the case of a thin adsorption layer, avoiding a sharp decrease in modulus and ensuring that the adsorption layer can maintain a high modulus contribution even with a reduced thickness.

[0111] In some embodiments of the present application, the distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface. The model is referred to as the contact surface gradient mortar model. To verify the theoretical rationality of the proposed contact surface gradient mortar model, a four-phase model based on the assumption of uniform contact surface is selected for comparative study. Based on the same test data set (mineral powder volume concentration 25%, 40% measured mortar modulus), the prediction accuracy difference of the two models in the wide frequency range is compared and analyzed. Since the scatter plot of the predicted values of the two models expressed in logarithmic coordinates is too close to see the difference, the prediction results are shown in linear coordinates as shown in Figure 8 The left graph shows the prediction results at a mineral powder volume concentration of 25%, and the right graph shows the prediction results at a mineral powder volume concentration of 40%.

[0112] It can be understood that Table 4 shows the prediction accuracy of two types of models at different volume concentrations, and Table 4 is a comparison of the prediction accuracy of different models. From the comparison of the prediction results, when the volume concentration of the mineral powder is 25%, the prediction accuracy of the contact surface gradient mortar model of the application and the four-phase model is small. This shows that under the condition of low concentration, the stress distribution of the interface has limited influence on the overall shear modulus, and the uniform contact surface assumption of the traditional model can still approximately describe the material behavior. However, when the volume concentration is increased to 40%, the model of the application shows a significant accuracy advantage, and the prediction accuracy R2 of the prediction value and the experimental data is increased by about 8% compared with the four-phase model, especially in the high frequency area (>1x103 Hz), the prediction deviation of the two shows a clear differentiation trend. This shows that as the concentration increases, the gradient distribution characteristics of the interface will significantly change the local stress field distribution, thereby the reinforcing effect on the material is increasingly significant.

[0113]

[0114] Table 4 comparison of prediction accuracy of different models

[0115] The embodiment is aimed at the interface modulus distribution of the asphalt mineral powder with uneven interface, the modulus of the mortar at different volume concentrations is predicted, the predicted value of the modulus of the mortar at different modulus distribution functions is calculated, and the predicted value of the modulus of the mortar is compared with the measured modulus of the mortar, the relative error is calculated, the function distribution with the highest prediction accuracy is selected as the contact surface gradient mortar model. The prediction accuracy of the traditional four-phase model is compared with the prediction accuracy of the contact surface gradient mortar model of the application, and the advantages of the contact surface gradient mortar model in predicting the modulus of the mortar are shown. For the uneven contact surface of the asphalt mineral powder, the best contact surface gradient mortar model is selected, which provides theoretical support for the calculation of the modulus distribution of the uneven interface of the asphalt-mineral powder.

[0116] In some embodiments of the application, step S502 comprises:

[0117] The prediction accuracy of different distribution functions at the same volume concentration is compared, and the distribution function with the highest prediction accuracy at each concentration is output.

[0118] When the distribution function with the highest prediction accuracy at each volume concentration is the same function, the distribution function with the highest prediction accuracy is selected.

[0119] It should be noted that the S-L distribution function has high prediction accuracy at the volume concentrations of 25% and 40%, the error is the lowest at the volume concentration of 25%, the error slightly increases at the concentration of 40%, but still maintains good fitting degree. The power exponent distribution function has high prediction accuracy at the concentration of 25%, but the prediction error slightly increases at the concentration of 40%, which reflects that it is insufficient in adaptability to the modulus change under the condition of high concentration. The Wang distribution function performs well at the concentration of 25%, but has large error at the volume concentration of 40%. The linear distribution function performs stably at the volume concentration of 40%, but has certain error at the concentration of 25%. In summary, the S-L distribution function has high prediction accuracy under the condition of various concentrations, and the S-L distribution function is selected as the distribution function with the highest prediction accuracy.

[0120] In order to improve the prediction accuracy of the non-uniform interface modeling on the asphalt mortar contact surface shear modulus, the asphalt mineral powder interface modulus distribution calculation method is proposed based on the multi-layer spherical inclusion model. The asphalt film thickness and the maximum adsorption modulus are brought into several common asphalt shear modulus distribution functions, the predicted value of the mortar modulus obtained by the method is compared with the measured value of the asphalt modulus and the measured value of the mortar shear modulus, the most accurate shear modulus function is obtained through the accuracy comparison.

[0121] In order to better implement the asphalt mineral powder interface modulus distribution calculation method in the embodiment of the application, on the basis of the asphalt mineral powder interface modulus distribution calculation method, corresponding, as shown in Figure 9 The asphalt mineral powder interface modulus distribution calculation system 900 includes:

[0122] The model construction module 901 is configured to divide the asphalt mineral powder interface into a preset number of sub-layers; wherein the sub-layers are connected to the mineral powder from the inside to the outermost sub-layer, and the outermost sub-layer is connected to the asphalt.

[0123] The data assumption module 902 is configured to assume that the asphalt film thickness is a preset thickness, assume that the modulus of the innermost sub-layer is the mineral powder modulus, and calculate the mortar modulus prediction value at different frequencies according to the material Poisson's ratio, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration and the mineral powder layer radius in combination with the distribution function.

[0124] The data determination module 903 is configured to compare the mortar modulus prediction value with the measured mortar modulus, and calculate the relative error, and output the asphalt film thickness and the modulus of the innermost sub-layer when the relative error is less than a preset error.

[0125] The modulus output module 904 is configured to calculate the prediction accuracy of different distribution functions when the volume concentration of the mineral powder changes according to the asphalt film thickness and the modulus of the innermost sublayer, and select the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface.

[0126] The asphalt-mineral powder interface modulus distribution calculation system 900 provided by the above embodiment can implement the technical solutions described in the above asphalt-mineral powder interface modulus distribution calculation method embodiments, and the principles of the implementation of the above modules or units can be referred to the corresponding content in the above asphalt-mineral powder interface modulus distribution calculation method embodiments, which will not be described here again.

[0127] As shown in Figure 10 The present application also provides an electronic device 1000 accordingly. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Figure 10 Only part of the components of the electronic device 1000 are shown, but it should be understood that all the shown components are not required, and more or less components can be alternatively implemented.

[0128] The processor 1001 can be a central processing unit (CPU), a microprocessor, or other data processing chip in some embodiments, configured to run the program codes stored in the memory 1002 or process data, such as the asphalt-mineral powder interface modulus distribution calculation method in the present application.

[0129] In some embodiments, the processor 1001 can be a single server or a group of servers. The group of servers can be centralized or distributed. In some embodiments, the processor 1001 can be local or remote. In some embodiments, the processor 1001 can be implemented in a cloud platform. In an embodiment, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0130] The memory 1002 can be an internal storage unit of the electronic device 1000 in some embodiments, such as a hard disk or a memory of the electronic device 1000. The memory 1002 can also be an external storage device of the electronic device 1000 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1000.

[0131] Further, the memory 1002 can include both the internal storage unit and the external storage device of the electronic device 1000. The memory 1002 is configured to store application software and various data installed on the electronic device 1000.

[0132] Display 1003 can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. in some embodiments. Display 1003 is used to display information of electronic device 1000 and to display visualized user interfaces. Components 1001-1003 of electronic device 1000 communicate with each other through a system bus.

[0133] In an embodiment, the following steps can be implemented when processor 1001 executes the magnetic resonance image optimization program in memory 1002:

[0134] The pitch mineral powder interface equal-thickness profile is divided into a preset number of sub-layers; wherein the sub-layers are connected from the innermost layer of the sub-layers to the mineral powder, and the outermost layer of the sub-layers is connected to the pitch;

[0135] Assuming the pitch film thickness is a preset thickness, assuming the modulus of the innermost layer of the sub-layers is the modulus of the mineral powder, according to the Poisson's ratio of the material, the pitch modulus, the mineral powder modulus, the pitch layer radius, the mineral powder volume concentration and the mineral powder layer radius, the predicted value of the mortar modulus at different frequencies is calculated in combination with the distribution function;

[0136] The predicted value of the mortar modulus is compared with the measured mortar modulus, and the relative error is calculated, and when the relative error is less than a preset error, the pitch film thickness and the modulus of the innermost layer of the sub-layers are outputted;

[0137] According to the pitch film thickness and the modulus of the innermost layer of the sub-layers, the prediction accuracy of different distribution functions when the mineral powder volume concentration changes is calculated, and the distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the pitch mineral powder interface.

[0138] It should be understood that: in addition to the above functions, processor 1001 can also implement other functions when executing the magnetic resonance image optimization program in memory 1002, which can be specifically referred to the description of the corresponding method embodiments.

[0139] Further, the embodiments of the present application do not make specific limitation on the type of the electronic device 1000 mentioned above, and the electronic device 1000 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of the portable electronic device include, but are not limited to, a portable electronic device running an IOS, an android, a microsoft, or other operating system. The portable electronic device can also be other portable electronic devices, such as a laptop computer having a touch-sensitive surface (e.g., a touch panel), and the like. It should also be understood that in some other embodiments of the present application, the electronic device 1000 can also not be a portable electronic device, but a desktop computer having a touch-sensitive surface (e.g., a touch panel).

[0140] Correspondingly, the embodiments of the present application also provide a computer readable storage medium for storing computer readable programs or instructions, which can realize the steps or functions in the asphalt and mineral powder interface modulus distribution calculation method provided by the above-mentioned method embodiments when executed by a processor.

[0141] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware (such as a processor, a controller, etc.) to complete, and the computer program can be stored in a computer readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0142] The above provides a detailed description of the asphalt and mineral powder interface modulus distribution calculation method, device, electronic device, and storage medium provided by the present application. The principles and implementation manners of the present application are described by applying specific examples in this paper. The above embodiment is only used to help understand the method and core idea of the present application. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for calculating asphalt filler interfacial modulus distribution, characterized in that, The method comprises: dividing the asphalt and mineral powder interface into a preset number of sub-layers; wherein the innermost sub-layer of the sub-layers is connected with the mineral powder, and the outermost sub-layer is connected with the asphalt; assuming that the asphalt film thickness is a preset thickness, and assuming that the modulus of the innermost sub-layer is the modulus of the mineral powder, calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and a distribution function combined with the mineral powder layer radius; comparing the predicted value of the mortar modulus with the measured mortar modulus, and calculating the relative error, and outputting the asphalt film thickness and the modulus of the innermost sub-layer when the relative error is less than a preset error; calculating the prediction accuracy of different distribution functions when the mineral powder volume concentration changes, and selecting the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt and mineral powder interface according to the asphalt film thickness and the modulus of the innermost sub-layer.

2. The asphalt filler interface modulus distribution calculation method of claim 1, wherein, After the step of assuming that the asphalt film thickness is a preset thickness, and assuming that the modulus of the innermost sub-layer is the modulus of the mineral powder, calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and a distribution function combined with the mineral powder layer radius, the method comprises: assuming that the asphalt film thickness is a preset thickness, calculating the radius of the outermost sub-layer in combination with the mineral powder volume concentration, and calculating the radius of each sub-layer according to the number of sub-layers, wherein the modulus of the outermost sub-layer is the asphalt modulus; assuming that the modulus of the innermost sub-layer is the modulus of the mineral powder, solving the parameters of different distribution functions according to the asphalt modulus and the mineral powder modulus, and calculating the shear modulus of all sub-layers based on the distribution functions and the sub-layer radius; wherein the distribution functions include power exponential function, S-L function, Wang function, and linear function; calculating the predicted value of the mortar modulus at different frequencies according to the shear modulus and the Poisson's ratio of the material.

3. The asphalt mineral filler interfacial modulus distribution calculation method of claim 2, wherein, After the step of comparing the predicted value of the mortar modulus with the measured mortar modulus, and calculating the relative error, the method comprises: when the relative error at any frequency is greater than a preset threshold, controlling the modulus of the innermost sub-layer to decrease by a preset modulus; performing the step of calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and a distribution function combined with the mineral powder layer radius.

4. The asphalt mineral filler interfacial modulus distribution calculation method of claim 3, wherein, After the step of comparing the predicted value of the mortar modulus with the measured mortar modulus, and calculating the relative error, the method comprises: when the relative error at any frequency is greater than a preset threshold, and the modulus of the innermost sub-layer is less than the asphalt modulus, controlling the asphalt film thickness to increase by a preset thickness; performing the step of calculating the predicted value of the mortar modulus at different frequencies according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the mineral powder volume concentration, and a distribution function combined with the mineral powder layer radius.

5. The asphalt mineral filler interfacial modulus distribution calculation method of claim 4, wherein, After the step of comparing the predicted value of the mortar modulus with the measured mortar modulus, and calculating the relative error, the method comprises: when the relative error at each frequency is greater than a preset threshold, terminating the iteration and outputting the parameters; When the asphalt film thickness is greater than a preset maximum value and the relative error is greater than a preset threshold, the data is recorded as being unable to be optimized.

6. The asphalt mineral filler interfacial modulus distribution calculation method of claim 2, wherein, The step of calculating the prediction accuracy of different distribution functions when the volume concentration of the mineral powder changes according to the asphalt film thickness and the modulus of the innermost sublayer, and selecting the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface, comprises: According to the asphalt film thickness and the modulus of the innermost sublayer, the modulus prediction values of the mortar at different volume concentrations of the mineral powder are calculated according to different distribution functions; The modulus prediction values of the mortar are compared with the measured modulus of the mortar, the distribution function with the highest prediction accuracy is selected, and the modulus distribution of the asphalt-mineral powder interface is predicted by the distribution function with the highest prediction accuracy; The distribution function with the highest prediction accuracy is compared with the uniform model at different volume concentrations, and the volume concentration with the maximum difference in prediction accuracy between the distribution function and the uniform model is output as the working concentration.

7. The asphalt mineral filler interfacial modulus distribution calculation method of claim 6, wherein, The step of comparing the modulus prediction values of the mortar with the measured modulus of the mortar and selecting the distribution function with the highest prediction accuracy comprises: The prediction accuracies of different distribution functions at the same volume concentration are compared, and the distribution function with the highest prediction accuracy at each concentration is output; When the distribution function with the highest prediction accuracy at each volume concentration is the same function, the distribution function is selected as the distribution function with the highest prediction accuracy.

8. An asphalt filler interface modulus distribution calculation system characterized by, The system is used to perform the modulus distribution calculation method of the asphalt-mineral powder interface as claimed in any one of claims 1 to 7, and the system comprises: A model construction module is configured to divide an asphalt-mineral powder interface into a preset number of sublayers according to equal thickness; wherein the sublayers are connected to mineral powder from the innermost sublayer to the outside, and the outermost sublayer is connected to asphalt; A data assumption module is configured to assume that the asphalt film thickness is a preset thickness, and the modulus of the innermost sublayer is the modulus of the mineral powder; and according to the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the asphalt layer radius, the volume concentration of the mineral powder, and the mineral powder layer radius, the modulus prediction values of the mortar at different frequencies are calculated according to a distribution function; A data determination module is configured to compare the measured modulus of the mortar with the modulus prediction values of the mortar, and calculate the relative error; when the relative error is less than a preset error, the asphalt film thickness and the modulus of the innermost sublayer are output. A modulus output module is configured to calculate the prediction accuracy of different distribution functions when the volume concentration of the mineral powder changes according to the asphalt film thickness and the modulus of the innermost sublayer, and select the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface.

9. An electronic device, comprising: It comprises a memory and a processor, wherein The memory is used to store programs; The processor is coupled with the memory and is used to execute the programs stored in the memory to realize the steps in the modulus distribution calculation method of the asphalt-mineral powder interface as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer-readable program or instruction is stored, and the program or instruction can realize the steps in the modulus distribution calculation method of the asphalt-mineral powder interface as claimed in any one of claims 1 to 7 when executed by a processor.

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