Asphalt mineral powder interface modulus distribution calculation method, system and equipment and storage medium

Abstract

The invention discloses an asphalt mineral powder interface modulus distribution calculation method, system and device and a storage medium. The method comprises the following steps: dividing an asphalt mineral powder interface into a preset number of sub-layers with equal thickness; assuming that the thickness of the asphalt film is a preset thickness, assuming that the modulus of the innermost sublayer is a mineral powder modulus, and calculating to obtain rubber cement modulus predicted values of 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 the mineral powder layer radius combined distribution function; and comparing the rubber cement modulus predicted value with the actually measured rubber cement modulus to calculate a relative error, and when the relative error is smaller than a preset error, outputting the asphalt film thickness and the modulus of the innermost sub-layer so as to calculate the prediction precision of different distribution functions when the volume concentration of the mineral powder changes. The prediction precision of different distribution functions is compared, the most suitable distribution function is selected, and the modulus distribution of the asphalt mineral powder interface is predicted through the optimal distribution function.

Description

Technical Field

[0001] This invention relates to the field of micromechanics, and in particular to a method, system, device and storage medium for calculating the interfacial modulus distribution of pitch powder. Background Technology

[0002] Digital design of materials, by constructing a digital mapping relationship between the microscopic information and macroscopic mechanical response of materials, breaks through the limitations of traditional empirical design and achieves accurate prediction of material properties, providing a new path for the development of low-carbon, high-toughness asphalt pavement materials. In the digital design of asphalt mixtures, the cross-scale model is the core theory. It describes the influence of microstructural parameters on macroscopic mechanical behavior through mathematical physics equations, thus determining the reliability of the digital design of materials.

[0003] In asphalt-aggregate mortar systems, a mortar adsorption layer is formed between mineral particles and the asphalt matrix through physicochemical interactions. This adsorption layer exhibits a significant gradient shear modulus characteristic, with its modulus value gradually decreasing radially from the mineral powder surface towards the asphalt matrix. This gradient effect significantly influences the overall shear modulus of the mortar.

[0004] However, traditional paste models typically simplify the adsorbed layer as a homogeneous phase, making it difficult to accurately characterize the spatial gradient distribution of the shear modulus. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, system, device and storage medium for calculating the interfacial modulus distribution of asphalt mineral powder, in order to solve the technical problem that the interfacial modulus distribution cannot be directly and accurately obtained due to the unevenness of the mortar bonding surface at the asphalt mineral powder interface.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for calculating the interfacial modulus distribution of asphalt mineral powder, comprising: The asphalt-mineral powder interface is divided 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. Assuming the asphalt film thickness is a preset thickness, and assuming the modulus of the innermost sublayer 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. The predicted value of the asphalt mortar modulus is compared with the measured value of the asphalt mortar modulus, and the relative error is calculated. When the relative error is less than the preset error, the thickness of the asphalt film and the modulus of the innermost sub-layer are output. Based on the thickness of the asphalt film and the modulus of the innermost sublayer, the prediction accuracy of different distribution functions is calculated when the volume concentration of mineral powder changes. The distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface.

[0007] In one possible implementation, after assuming the asphalt film thickness is a preset thickness, assuming the modulus of the innermost sublayer is the mineral powder modulus, and calculating the predicted values ​​of the mastic modulus at different frequencies based on the material's Poisson's ratio, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration, and the distribution function of the mineral powder layer radius, the following steps are included: Assuming the asphalt film thickness is a preset thickness, the radius of the outermost sublayer is calculated based on the mineral powder volume concentration. The radius of each sublayer is calculated based on the number of sublayers. The modulus of the outermost sublayer is the asphalt modulus. Assuming the modulus of the innermost sublayer is the mineral powder modulus, the parameters of different distribution functions are solved based on the bitumen 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 power function, SL function, Wang function and linear function; The predicted values ​​of the adhesive modulus at different frequencies are calculated based on the shear modulus and the Poisson's ratio of the material.

[0008] In one possible implementation, after the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method further includes: When the relative error at any frequency exceeds a preset threshold, the modulus of the innermost sub-layer is reduced by the preset modulus. The steps involve calculating the predicted values ​​of the binder modulus at different frequencies 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.

[0009] In one possible implementation, after the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method further includes: 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 thickness of the asphalt film is controlled to increase by a preset thickness. The steps involve calculating the predicted values ​​of the binder modulus at different frequencies 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.

[0010] In one possible implementation, after the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method further includes: When the relative error at each frequency is greater than the preset threshold, the iteration is terminated and the parameters are output. When the thickness of the asphalt film is greater than the preset maximum value and the relative error is greater than the preset threshold, it is recorded as data that cannot be optimized.

[0011] In one possible implementation, the step of calculating the prediction accuracy of different distribution functions based on the thickness of the asphalt film and the modulus of the innermost sublayer 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-mineral powder interface, includes: Based on the thickness of the asphalt film and the modulus of the innermost sublayer, the predicted values ​​of the mastic modulus for different distribution functions are calculated when the mineral powder volume concentration changes. The predicted value of the binder modulus is compared with the measured binder modulus, and the distribution function with the highest prediction accuracy is selected. The modulus distribution of the asphalt-mineral powder interface is then predicted using 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 largest difference in prediction accuracy between the distribution function and the uniform model is output as the working concentration.

[0012] In one possible implementation, the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and selecting the distribution function with the highest prediction accuracy includes: 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. 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.

[0013] On the other hand, the present invention also proposes a calculation system for the interfacial modulus distribution of asphalt powder with a non-uniform interface, comprising: The model building module is used to divide the asphalt-mineral powder interface into a preset 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. The data assumption module is used to assume that the thickness of the asphalt film is a preset thickness and that the modulus of the innermost sub-layer is the mineral powder modulus. 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, the predicted values ​​of the mastic modulus at different frequencies are calculated. The data determination module is used to compare the measured modulus of the asphalt mortar with the predicted modulus of the asphalt mortar, and calculate the relative error. When the relative error is less than the preset error, the module outputs the thickness of the asphalt film and the modulus of the innermost sub-layer. The modulus output module is used to calculate the prediction accuracy of different distribution functions when the mineral powder volume concentration changes, based on the thickness of the asphalt film 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.

[0014] In a second aspect, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the bitumen powder interface modulus distribution calculation method described in any of the above implementations.

[0015] Thirdly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the method for calculating the interfacial modulus distribution of asphalt mineral powder as described in any of the above implementations.

[0016] The beneficial effects of this invention are as follows: The method for calculating the interfacial modulus distribution of asphalt and mineral powder provided by this invention uniformly divides the asphalt-mineral powder interface phase into multiple equal parts, each part being equivalent to a uniform interface with the same material properties. Assuming the asphalt film thickness is a preset thickness and the modulus of the innermost sublayer is the mineral powder modulus, the Poisson's ratio of the known materials, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration, and mineral powder layer radius are combined with a distribution function to calculate the predicted values ​​of the mastic modulus at different frequencies. By comparing the measured mastic modulus with the predicted values, the modulus of the innermost sublayer and the asphalt film thickness are calculated. Based on the asphalt film thickness and the modulus of the innermost sublayer, 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. Furthermore, since this invention is based on a calculation method using a multi-layer spherical inclusion model, the thickness of the asphalt film and the modulus of the innermost sub-layer are substituted into several common distribution functions. By comparing the accuracy, the distribution function with the most accurate prediction is obtained, thereby improving the accuracy of the asphalt mineral powder interface modulus distribution calculated based on the distribution function. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of an embodiment of the method for calculating the interfacial modulus distribution of asphalt mineral powder provided by the present invention; Figure 2 The modulus of the innermost sublayer of different distribution functions provided by this invention; Figure 3 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 4 For the present invention Figure 1A schematic diagram of an embodiment of S103; Figure 5 For the present invention Figure 1 A schematic diagram of an embodiment of S104; Figure 6 The model prediction results for different distribution functions at a concentration of 25% provided by this invention; Figure 7 The model prediction results for different distribution functions at a concentration of 40% provided by this invention; Figure 8 A comparison of prediction results from different models provided by this invention; Figure 9 A schematic diagram of an embodiment of the asphalt mineral powder interface modulus distribution calculation system provided by the present invention; Figure 10 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0020] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Before demonstrating the embodiments, the following terms will be explained.

[0023] Modulus: Also known as shear modulus (G), it is a quantitative indicator of a material's ability to resist shear deformation under shear stress, defined as the ratio of shear stress to shear strain. For asphalt mixtures, its value directly reflects the material's resistance to shear flow under dynamic loads and is a core parameter for evaluating high-temperature rutting resistance.

[0024] 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.

[0025] 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.

[0026] 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: 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. 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. 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. 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.

[0027] 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 Poisson's ratio of asphalt is a value obtained from experiments, varying with frequency. .

[0028] In some embodiments of the present invention, N layers of composite spheres are placed in an infinitely large equivalent medium (the (N+1)th phase material), where the infinitely large equivalent medium represents the entire mortar and has a modulus of [missing value]. This is the value that needs to be predicted in this application, called the predicted value of the adhesive modulus. This value is also measured by the experiment and recorded as the measured adhesive modulus. A composite sphere (phases 1 to N) consisting of N concentric spherical shells, with phase 1 as the core and phase N as the outermost layer, is embedded in an infinitely large equivalent medium (phase N+1), making the mechanical behavior of the composite sphere in this medium consistent with that of an actual multilayer structure.

[0029] Given that the Poisson's ratio of each layer of material is The asphalt modulus is The modulus of mineral powder is The radius of the asphalt layer is With the radius of the mineral powder layer The asphalt modulus at different frequencies was obtained experimentally, and the mineral powder volume concentration... Set to 15%, the mineral powder volume concentration refers to the percentage of mineral powder volume in the total volume of solid particles (aggregate + mineral powder) in the asphalt mixture. Assuming the asphalt film thickness is a preset thickness, which serves as the starting point for subsequent iterative solutions, the asphalt film thickness will gradually increase until the predicted and measured binder modulus values ​​match. Approximately. Assuming the modulus of the innermost sublayer of the adsorbed asphalt layer is equal to that of mineral powder, the modulus of the innermost sublayer will gradually decrease during the optimization process. Based on the above parameters and the distribution function, the predicted values ​​of the shear modulus of the binder at different frequencies are calculated.

[0030] Measured modulus of adhesive Predicted value of adhesive modulus The comparisons are made, and the relative errors are calculated. Error analysis proceeds from high frequency to low frequency, calculating the relative error at each frequency in turn: The preset error can be 5%. 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 with different distribution functions will be output.

[0031] Understandably, through inverse calculations using different distribution functions, the results of the asphalt film thickness and the modulus of the innermost sublayer are presented in Table 1 and... Figure 2 :

[0032] Table 1. Thickness of asphalt film under different distribution functions It should be noted that by substituting the thickness of the asphalt film and the modulus of the innermost sublayer into the model, the interfacial modulus of asphalt-mineral powder at other volume concentrations (25%, 40%) is predicted, and the prediction results are compared with the measured modulus to calculate the prediction accuracy under different distribution functions. The distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface.

[0033] This invention studies the mechanical model of an asphalt-mineral powder interface with a 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. Assuming the initial asphalt film thickness is a preset thickness and the modulus of the innermost sublayer is the mineral powder modulus, the predicted values ​​of the mastic modulus at different frequencies are calculated using the Poisson's ratio, asphalt modulus, mineral powder modulus, asphalt layer radius, and mineral powder volume concentration combined with the mineral powder layer radius using a distribution function. By comparing the measured mastic modulus with the predicted values, the modulus of the innermost sublayer and the asphalt film thickness are calculated. Based on the asphalt film thickness and the modulus of the innermost sublayer, 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 using the optimal distribution function.

[0034] In some embodiments of the present invention, such as Figure 3 As shown, step S102 includes: S301. Assuming the asphalt film thickness is the preset thickness, calculate the radius of the outermost sub-layer based on the mineral powder volume concentration, and calculate the radius of each sub-layer based on the number of sub-layers. The modulus of the outermost sub-layer is the asphalt modulus. S302. Assuming the modulus of the innermost sublayer is the mineral powder modulus, solve for the parameters of different distribution functions based on the bitumen modulus and the mineral powder modulus, and calculate the shear modulus of all sublayers based on the distribution function and the sublayer radius; where the distribution function includes the power function, the SL function, the Wang function, and the linear function; S303. Based on the shear modulus and Poisson's ratio of the material, the predicted values ​​of the adhesive modulus at different frequencies are calculated.

[0035] Understandably, the preset thickness can be 0.5 micrometers. Assuming the asphalt film thickness is 0.5 micrometers, the volume concentration of the asphalt film, i.e., the proportion of adsorbed asphalt, is calculated using geometric relationships. This is achieved by inputting the mineral powder volume concentration. and asphalt volume concentration (1- The radius of the mineral powder layer is calculated using the following formula. and the radius of the asphalt layer , in, Represents the volume concentration of mineral powder. This represents the volume fraction of the k-th layer material. Substituting the adsorbed asphalt ratio calculated based on the asphalt film thickness into the above formula, the radius of the adsorbed asphalt layer is calculated. The radius of each layer in the adsorbed asphalt layer is calculated based on the fixed number of layers and the preset quantity n=20. ~ ).

[0036] It should be noted that the amount of components in asphalt that can be adsorbed by fillers is limited, typically accounting for 25%-60% of the asphalt volume. Fillers have a maximum adsorption potential; when they reach saturation, 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 adsorption layer modulus as a function of thickness are used for calculation, as shown in Table 2.

[0037]

[0038] Table 2 Different functions of interfacial modulus distribution of mineral powder asphalt in, This represents the radius of the outermost boundary of the adsorbed asphalt layer; This represents the innermost boundary radius of the adsorbed asphalt layer; Indicates the modulus at the surface of mineral powder particles; The modulus is represented by the value closest to the asphalt; the rest are function coefficients.

[0039] In some embodiments of the present invention, the modulus distribution in the adsorbed asphalt profile is initially assumed to be one of the four functions mentioned above, with the modulus gradually decreasing from the innermost to the outermost layer of the adsorbed asphalt layer, that is, from the assumed innermost sublayer modulus... The modulus tends towards the asphalt modulus along the function's direction. The modulus of the outermost sublayer of the adsorbed asphalt is equal to the asphalt modulus. Assuming the modulus of the innermost sublayer is the mineral powder modulus, the parameters of the modulus distribution function are solved. Based on the distribution function and the sublayer radius of each layer, the shear modulus of each sublayer in the adsorbed asphalt layer is calculated. .

[0040] It should be noted that, based on the Poisson's ratio of each layer of material in the split layer, The asphalt modulus is The modulus of mineral powder is The radius of the asphalt layer is The radius of the mineral powder layer is The radius of each sublayer ( ~ and the shear modulus of each sublayer The coefficients A, B, and C are expressed by the parameters of the recursion matrix as follows: Substitute the coefficients A, B, and C obtained from the above calculations into the equation: The predicted values ​​of the adhesive modulus at different frequencies were calculated using the implicit equation solving method. .

[0041] In this embodiment of the invention, the predicted values ​​of the mastic modulus at different frequencies are calculated by assuming that the initial thickness of the asphalt film is a preset thickness and that the modulus of the innermost sublayer is the mineral powder modulus. Given the Poisson's ratio of the material, the asphalt modulus, the mineral powder modulus, the radius of the asphalt layer, the volume concentration of the mineral powder, and the distribution function of the mineral powder layer radius.

[0042] In some embodiments of the present invention, such as Figure 4 As shown, step S103 includes: S401. When the relative error is greater than a preset threshold at any frequency, control the modulus of the innermost sub-layer to decrease by a preset modulus. 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 thickness of the asphalt film is controlled to increase the preset thickness. S403. When the relative error of each frequency is greater than the preset threshold, terminate the iteration and output the parameters. S404. When the asphalt film thickness is greater than the preset maximum value and the relative error is greater than the preset threshold, it is recorded as data that cannot be optimized.

[0043] In some embodiments of the present invention, if the relative error at a certain frequency exceeds a preset threshold of 5%, the system iteratively optimizes the modulus of the innermost sublayer of the adsorbed asphalt layer by adjusting the modulus of the innermost sublayer, reducing the modulus of the innermost sublayer of the adsorbed asphalt layer by a step size 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 resolved, and the coefficients A, B, and C are recalculated before prediction, thereby calculating the relative error. 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 size (such as 0.02 times the mineral powder modulus) can be used.

[0044] In some embodiments of the present invention, if the relative error is still greater than 5% after adjusting the modulus of the innermost sublayer at a certain frequency to the asphalt modulus, failing to meet the accuracy requirements, the asphalt film thickness is increased by a step size of 0.01 micrometers. Then, the sublayer radius and shear modulus of each layer of the adsorbed asphalt layer are recalculated, and coefficients A, B, and C are recalculated before prediction. After calculating the coefficients of the prediction formula, the process returns to the previous steps, sequentially calculating the predicted value of the adhesive modulus, the relative error, and the adjustment of the modulus of the innermost sublayer.

[0045] In some embodiments of the present invention, if the prediction error at all frequency points is less than the threshold, the iteration is terminated and the optimized parameters are output.

[0046] In some embodiments of the present invention, if the film thickness exceeds the preset maximum value of 1.5 micrometers, and a certain frequency still cannot meet the accuracy requirements, and the relative error is still greater than 5%, then the situation where optimization is not possible is recorded.

[0047] This embodiment analyzes the error between the predicted and measured modulus values ​​of a specific volume concentration of adhesive mortar to calculate the asphalt film thickness and the modulus of the innermost sublayer. Assuming a preset asphalt film thickness, the adsorbed asphalt ratio is calculated. Assuming the modulus of the innermost sublayer is the mineral powder modulus, the parameters of each distribution function are calculated. The asphalt-mineral powder interface is divided into a preset number of sublayers. The radius of each sublayer is calculated. Based on the sublayer radius and distribution function, the shear modulus of all sublayers is calculated. Coefficients A, B, and C are determined based on the shear modulus of all sublayers. The predicted adhesive modulus value is calculated and compared with the measured adhesive modulus. When the relative error exceeds a preset threshold of 5%, the modulus of the innermost sublayer is reduced by 0.05 times the mineral powder modulus. The calculation of the parameters of each distribution function and subsequent steps are repeated until the relative error is less than the preset threshold of 5%, then the next distribution function is used. If the relative error still exceeds the preset threshold of 5% when the modulus of the innermost sublayer is equal to that of the asphalt, then switch to the test conditions with the lowest temperature and the highest frequency, and increase the thickness of the asphalt film by 0.01 micrometers. Repeat the calculation of the adsorbed asphalt ratio and subsequent steps until the relative error is less than the preset threshold of 5%.

[0048] In some embodiments of the present invention, such as Figure 5 As shown, step S104 includes: S501. Based on the thickness of the asphalt film and the modulus of the innermost sublayer, calculate the predicted values ​​of the mastic modulus for different distribution functions when the mineral powder volume concentration changes. S502. Compare the predicted value of the mortar modulus with the measured mortar modulus, select the distribution function with the highest prediction accuracy, and predict the modulus distribution of the asphalt-mineral powder interface using the distribution function with the highest prediction accuracy. 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.

[0049] It should be noted that the asphalt film thickness is combined with the distribution function in Table 1. Figure 2Based 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).

[0050] 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.

[0051] 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.

[0052]

[0053] Table 3 Prediction accuracy under different distribution functions The results show that different distribution functions exhibit significant differences in prediction accuracy at various volume concentrations. The SL distribution function demonstrates the highest prediction accuracy across all volume concentrations, followed by the power function, while the Wang function and linear function show significant errors at certain volume concentrations. In summary, the SL distribution function exhibits high prediction accuracy under all concentration conditions, indicating that its modulus-decreasing characteristics closely match the changing trend of the adsorption layer modulus gradient.

[0054] Stress is transferred from mineral powder particles to the asphalt matrix through the adsorbed asphalt layer, forming a stress gradient field. At a concentration of 25%, the adsorbed layer is relatively thick, and its modulus distribution directly affects the stress transfer path and the accuracy of modulus prediction. The SL distribution function has the highest prediction accuracy. Its smoothly decreasing modulus characteristics effectively reduce stress concentration, allowing stress to gradually decay in the high-modulus region, ensuring the contribution of the adsorbed layer to the overall modulus. This indicates that the actual modulus distribution of the adsorbed layer may decrease slightly faster near the particle surface, and gradually level off further away from the particle surface, forming a gradual transition mode to minimize stress concentration. At a concentration of 40%, the particle spacing decreases, the adsorbed layer thickness decreases, and the interparticle contact effect increases. The modulus distribution function's characterization of the volume of the high-modulus region directly affects the prediction accuracy. The predicted value under the SL distribution function is lower than that at 25% concentration, but still maintains high accuracy. This is because its smooth decreasing characteristics ensure that the volume of the high-modulus region is moderate, reducing the negative impact of the low-modulus region. The actual modulus distribution of the adsorption layer may resemble the SL distribution function, maintaining a smooth transition even as the adsorption layer thins, thus avoiding a sharp drop in modulus and ensuring that the adsorption layer can maintain a high modulus contribution even as its thickness decreases.

[0055] In some embodiments of the present invention, the distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the bitumen powder interface; this model is called 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 a uniform contact surface is selected for comparative study. Based on the same experimental dataset (measured mortar modulus at 25% and 40% mineral powder volume concentrations), the prediction accuracy differences between the two models in a wide frequency range are compared and analyzed. Since the scatter plots of the predicted values ​​from the two models are too close when expressed on logarithmic axes to show differences, the prediction results are displayed on linear axes as follows: Figure 8 As shown, the left figure compares the prediction results at a mineral powder volume concentration of 25%, and the right figure compares the prediction results at a mineral powder volume concentration of 40%.

[0056] As is understandable, Table 4 shows the prediction accuracy of the two models at different volume concentrations, and Table 4 compares the prediction accuracy of different models. The comparison of prediction results shows that when the mineral powder volume concentration is 25%, the difference in prediction accuracy between the contact surface gradient mortar model and the four-phase model is small. This indicates that under low concentration conditions, the stress distribution at the interface has a limited impact 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 increases to 40%, the model of this application exhibits a significant accuracy advantage. Its prediction accuracy R² compared to the experimental data is about 8% higher than that of the four-phase model, especially in the high-frequency region (>1×10³ Hz), where the prediction deviations of the two models show a clear divergence trend. This indicates that as the concentration increases, the interface gradient distribution characteristics significantly change the local stress field distribution, thus making the reinforcing effect on the material more significant.

[0057]

[0058] Table 4 Comparison of prediction accuracy of different models This embodiment studies the interfacial modulus distribution of asphalt-mineral powder with a non-uniform interface. By predicting the modulus of the binder at different volume concentrations, the predicted modulus values ​​under different modulus distribution functions are calculated. The predicted modulus values ​​are compared with the measured modulus values ​​to calculate the relative error. The function distribution with the highest prediction accuracy is selected as the contact surface gradient binder model. A comparison of the prediction accuracy of the traditional four-phase model with that of the contact surface gradient binder model in this application demonstrates the advantages of the contact surface gradient binder model in predicting binder modulus. For the non-uniform contact surface of asphalt-mineral powder, the optimal contact surface gradient binder model is selected, providing theoretical support for the calculation of the non-uniform interface modulus distribution of asphalt-mineral powder.

[0059] In some embodiments of the present invention, step S502 includes: 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. When the distribution function with the highest prediction accuracy for all volume concentrations is the same function, the distribution function with the highest prediction accuracy shall be selected.

[0060] It should be noted that the SL distribution function exhibits high prediction accuracy at both 25% and 40% volume concentrations, with the lowest error at 25% volume concentration. The error increases slightly at 40% concentration, but the good fit is still maintained. The power distribution function shows high prediction accuracy at 25% concentration, but the prediction error increases slightly at 40% concentration, reflecting its insufficient adaptability to modulus changes under high concentration conditions. The Wang distribution function performs well at 25% concentration, but has a larger error at 40% volume concentration. The linear distribution function is relatively stable at 40% volume concentration, but exhibits some error at 25% concentration. In summary, the SL distribution function shows high prediction accuracy under all concentration conditions, and is therefore selected as the distribution function with the highest prediction accuracy.

[0061] To improve the accuracy of predicting the shear modulus of asphalt mastic interface using non-uniform interface modeling, this invention proposes a method for calculating the interface modulus distribution of asphalt-mineral powder based on a multilayer spherical inclusion model. This method involves inputting the asphalt film thickness and maximum adsorption modulus into several common distribution functions of asphalt shear modulus, and then comparing the resulting predicted mastic modulus value with the measured values ​​of asphalt modulus and mastic shear modulus. Through this accuracy comparison, the most accurate shear modulus function is obtained.

[0062] To better implement the method for calculating the interfacial modulus distribution of asphalt mineral powder in this embodiment of the invention, based on the method for calculating the interfacial modulus distribution of asphalt mineral powder, the corresponding method is as follows: Figure 9 As shown, this embodiment of the invention also provides a system for calculating the interfacial modulus distribution of asphalt and mineral powder. The asphalt and mineral powder interfacial modulus distribution calculation system 900 includes: The model construction module 901 is used to divide the asphalt-mineral powder interface into a preset number of sub-layers of equal thickness; wherein, from the inside out, the innermost sub-layer connects to the mineral powder and the outermost sub-layer connects to the asphalt. The data assumption module 902 is used to assume that the thickness of the asphalt film is a preset thickness and that the modulus of the innermost sub-layer is the mineral powder modulus. 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, the predicted values ​​of the mastic modulus at different frequencies are calculated. The data determination module 903 is used to compare the predicted value of the asphalt modulus with the measured value of the asphalt modulus and calculate the relative error. When the relative error is less than the preset error, it outputs the thickness of the asphalt film and the modulus of the innermost sub-layer. The modulus output module 904 is used to calculate the prediction accuracy of different distribution functions when the volume concentration of mineral powder changes, based on the thickness of the asphalt film 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.

[0063] The asphalt-mineral powder interface modulus distribution calculation system 900 provided in the above embodiments can realize the technical solutions described in the above embodiments of the asphalt-mineral powder interface modulus distribution calculation method. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the asphalt-mineral powder interface modulus distribution calculation method, which will not be repeated here.

[0064] like Figure 10 As shown, the present invention also provides an electronic device 1000. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Figure 10 Only some components of the electronic device 1000 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0065] In some embodiments, processor 1001 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 1002 or process data, such as the bitumen powder interface modulus distribution calculation method of the present invention.

[0066] In some embodiments, processor 1001 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 1001 may be local or remote. In some embodiments, processor 1001 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0067] In some embodiments, memory 1002 may be an internal storage unit of electronic device 1000, such as a hard disk or memory of electronic device 1000. In other embodiments, memory 1002 may also be an external storage device of electronic device 1000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 1000.

[0068] Furthermore, the memory 1002 may include both internal storage units of the electronic device 1000 and external storage devices. The memory 1002 is used to store application software and various types of data installed on the electronic device 1000.

[0069] In some embodiments, display 1003 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1003 is used to display information from electronic device 1000 and to display a visual user interface. Components 1001-1003 of electronic device 1000 communicate with each other via a system bus.

[0070] In one embodiment, when the processor 1001 executes the magnetic resonance image optimization program in the memory 1002, the following steps can be implemented: The asphalt-mineral powder interface is divided 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. Assuming the asphalt film thickness is a preset thickness, and assuming the modulus of the innermost sublayer 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. The predicted value of the asphalt mortar modulus is compared with the measured value of the asphalt mortar modulus, and the relative error is calculated. When the relative error is less than the preset error, the thickness of the asphalt film and the modulus of the innermost sub-layer are output. Based on the thickness of the asphalt film and the modulus of the innermost sublayer, the prediction accuracy of different distribution functions is calculated when the volume concentration of mineral powder changes. The distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface.

[0071] It should be understood that when the processor 1001 executes the magnetic resonance image optimization program in the memory 1002, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0072] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 1000 mentioned. The electronic device 1000 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1000 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0073] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the asphalt powder interface modulus distribution calculation method provided in the above-described method embodiments.

[0074] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0075] The foregoing has provided a detailed description of the method, apparatus, electronic device, and storage medium for calculating the interfacial modulus distribution of asphalt mineral powder provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for calculating the interfacial modulus distribution of asphalt powder, characterized in that, The method includes: The asphalt-mineral powder interface is divided 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. Assuming the asphalt film thickness is a preset thickness, and assuming the modulus of the innermost sublayer 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. The predicted value of the asphalt mortar modulus is compared with the measured value of the asphalt mortar modulus, and the relative error is calculated. When the relative error is less than the preset error, the thickness of the asphalt film and the modulus of the innermost sub-layer are output. Based on the thickness of the asphalt film and the modulus of the innermost sublayer, the prediction accuracy of different distribution functions is calculated when the volume concentration of mineral powder changes. The distribution function with the highest prediction accuracy is selected to predict the modulus distribution of the asphalt-mineral powder interface.

2. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 1, characterized in that, The steps following the assumption that the asphalt film thickness is a preset thickness, and that the modulus of the innermost sub-layer is the mineral powder modulus, and calculating the predicted values ​​of the mastic modulus at different frequencies based on the material's Poisson's ratio, asphalt modulus, mineral powder modulus, asphalt layer radius, mineral powder volume concentration, and the distribution function of the mineral powder layer radius, include: Assuming the asphalt film thickness is a preset thickness, the radius of the outermost sublayer is calculated based on the mineral powder volume concentration. The radius of each sublayer is calculated based on the number of sublayers. The modulus of the outermost sublayer is the asphalt modulus. Assuming the modulus of the innermost sublayer is the mineral powder modulus, the parameters of different distribution functions are solved based on the bitumen 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 power function, SL function, Wang function and linear function; The predicted values ​​of the adhesive modulus at different frequencies are calculated based on the shear modulus and the Poisson's ratio of the material.

3. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 2, characterized in that, After the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method includes: When the relative error at any frequency exceeds a preset threshold, the modulus of the innermost sub-layer is reduced by the preset modulus. The steps involve calculating the predicted values ​​of the mastic modulus at different frequencies 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 a distribution function.

4. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 3, characterized in that, After the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method includes: 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 thickness of the asphalt film is controlled to increase by a preset thickness. The steps involve calculating the predicted values ​​of the mastic modulus at different frequencies 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 a distribution function.

5. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 4, characterized in that, After the step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and calculating the relative error, the method includes: When the relative error at each frequency is greater than the preset threshold, the iteration is terminated and the parameters are output. When the thickness of the asphalt film is greater than the preset maximum value and the relative error is greater than the preset threshold, it is recorded as data that cannot be optimized.

6. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 2, characterized in that, The step of calculating the prediction accuracy of different distribution functions based on the thickness of the asphalt film and the modulus of the innermost sublayer when the volume concentration of mineral powder changes, and selecting the distribution function with the highest prediction accuracy to predict the modulus distribution of the asphalt-mineral powder interface, includes: Based on the thickness of the asphalt film and the modulus of the innermost sublayer, the predicted values ​​of the mastic modulus for different distribution functions are calculated when the mineral powder volume concentration changes. The predicted value of the binder modulus is compared with the measured binder modulus, and the distribution function with the highest prediction accuracy is selected. The modulus distribution of the asphalt-mineral powder interface is then predicted using the distribution function with the highest prediction accuracy. The distribution function with the highest prediction accuracy at different volume concentrations is compared with the uniform model, and the volume concentration with the largest difference in prediction accuracy between the distribution function and the uniform model is output as the working concentration.

7. The method for calculating the interfacial modulus distribution of asphalt powder as described in claim 6, characterized in that, The step of comparing the predicted value of the adhesive modulus with the measured adhesive modulus and selecting the distribution function with the highest prediction accuracy includes: 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. 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.

8. A system for calculating the interfacial modulus distribution of asphalt powder, characterized in that, The system is used to execute the method for calculating the interfacial modulus distribution of asphalt powder as described in any one of claims 1 to 7, the system comprising: The model building module is used to divide the asphalt-mineral powder interface into a preset 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. The data assumption module is used to assume that the thickness of the asphalt film is a preset thickness and that the modulus of the innermost sub-layer is the mineral powder modulus. 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, the predicted values ​​of the mastic modulus at different frequencies are calculated. The data determination module is used to compare the measured modulus of the asphalt mortar with the predicted modulus of the asphalt mortar, and calculate the relative error. When the relative error is less than the preset error, the module outputs the thickness of the asphalt film and the modulus of the innermost sub-layer. The modulus output module is used to calculate the prediction accuracy of different distribution functions when the mineral powder volume concentration changes, based on the thickness of the asphalt film 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, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for calculating the interfacial modulus distribution of bitumen powder as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, enable the implementation of the steps in the method for calculating the interfacial modulus distribution of bitumen powder as described in any one of claims 1 to 7.

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