Calculation method of thermal expansion coefficient of grouting steel slag asphalt concrete based on microstructure

Abstract

The present invention discloses a method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure, comprising the following steps: S1, test and measure the thermal conductivity of steel slag aggregate, asphalt mortar and grouting material; S2, test and measure the thermal expansion coefficient of steel slag aggregate, asphalt mortar and grouting material; S3, select steel slag aggregate samples, obtain two-dimensional projection images of steel slag aggregate, and create a two-dimensional steel slag aggregate template database; S4, establish a mesostructure digital model of grouting steel slag asphalt concrete; S5, establish a finite element model of grouting steel slag asphalt concrete, set material parameters and boundary conditions, and perform finite element meshing and simulation analysis; S6, calculate the overall thermal expansion coefficient of grouting steel slag asphalt concrete based on the simulation results and theoretical formula. The present invention accurately describes the heterogeneous and complex mesostructure characteristics of grouting steel slag asphalt concrete, and greatly improves the prediction accuracy of thermal expansion coefficient.

Description

Technical Field

[0001] The invention belongs to the technical field of civil engineering materials, and in particular relates to a method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on a microstructure. Background Art

[0002] As a new type of building material, grouting steel slag asphalt concrete has been widely used in road engineering due to its good mechanical properties and durability. Especially in high temperature environment, the thermal stability of this material is particularly outstanding. However, due to the multiphase composition and complex internal structure of grouting steel slag asphalt concrete, the accurate prediction of its thermal expansion coefficient has become a difficult point in engineering applications.

[0003] At present, the main methods for obtaining the thermal expansion coefficient of materials are laboratory testing methods, such as dilatometer measurement method, differential scanning calorimetry, etc. However, these experimental methods are not only time-consuming, but the test results are easily affected by external environmental factors, resulting in large measurement errors. Some empirical models have been used to estimate the thermal expansion coefficient of traditional concrete, but these models ignore the complex interface effects and multiphase composition structures inside steel slag and asphalt mixtures, resulting in large deviations between the calculated results and the actual situation. In addition, although the finite element modeling method can take into account the microstructural characteristics of the material, its calculation complexity is high, it consumes a lot of resources, and it is difficult to quickly apply it to engineering design.

[0004] With the development of materials science and numerical analysis technology, the modeling method based on mesostructure provides a new research direction for solving this problem. By characterizing the internal mesostructure of grouting steel slag asphalt concrete and combining the thermophysical parameters of each component phase of the material, a more accurate thermal expansion coefficient calculation model can be constructed. However, how to effectively characterize the mesostructure of the material and ensure the applicability of the calculation model in complex engineering environments is still a difficulty in current research.

[0005] Therefore, a method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure is developed. By accurately characterizing the internal multiphase structure of steel slag and asphalt mixture, the calculation accuracy of the thermal expansion coefficient can be improved, which is of great significance for improving the design level of road engineering materials. Summary of the invention

[0006] The purpose of the present invention is to provide a method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on a microstructure, so as to solve the problems in the background technology that the calculation of the thermal expansion coefficient of grouting steel slag asphalt concrete is highly complex, consumes a lot of resources, and is difficult to be quickly applied in engineering design.

[0007] To achieve the above object, the present invention provides a method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on a mesostructure, comprising the following steps:

[0008] S1. Test and measure the thermal conductivity of steel slag aggregate, asphalt mortar and grouting materials;

[0009] S2. Test and measure the thermal expansion coefficient of steel slag aggregate, asphalt mortar and grouting materials;

[0010] S3, selecting a steel slag aggregate sample, obtaining a two-dimensional projection image of the steel slag aggregate, and creating a two-dimensional steel slag aggregate template database;

[0011] S4. Establish a mesoscopic structural digital model of grouting steel slag asphalt concrete;

[0012] S5. Based on digital image processing, a finite element model of grouting steel slag asphalt concrete is established, and material parameters and boundary conditions are set, and finite element meshing and simulation analysis are performed;

[0013] S6. Based on the simulation results and theoretical formula, the overall thermal expansion coefficient of grouting steel slag asphalt concrete is calculated.

[0014] In a specific implementation, the specific steps of step S3 are:

[0015] S3.1. Select several steel slag aggregates from different particle size ranges of steel slag aggregates as representative samples; place the selected steel slag aggregates on an image acquisition device to obtain a two-dimensional projection image of each steel slag aggregate;

[0016] S3.2, importing the collected two-dimensional projection image into Matlab, and graying the collected image to remove noise in the image;

[0017] S3.3, performing a binarization operation on the processed grayscale image to convert it into a black and white image, wherein the black area represents the steel slag aggregate and the white area represents the background;

[0018] S3.4. Convert the binarized image into a vector file for subsequent modeling and calculation; ensure the accuracy of the image contour and the edges should be smooth.

[0019] In a specific embodiment, the different particle size ranges in step S3.1 include at least four levels of particle size ranges, and sieves with apertures of 16 mm, 13.2 mm, 9.5 mm, 4.75 mm, and 2.36 mm are used for screening respectively to obtain steel slag aggregates with particle sizes ranging from 2.36 to 4.75 mm, 4.75 to 9.5 mm, 9.5 to 13.2 mm, and 13.2 to 16 mm, and at least 80 steel slag aggregates of different sizes are selected in each particle size range.

[0020] In a specific implementation, the specific steps of step S4 are:

[0021] S4.1, importing the vector file of the two-dimensional projection image obtained in step S3 into discrete element modeling software as a random aggregate template;

[0022] S4.2. Determine the length, width and height dimensions of the model specimen, as well as the mix ratio of the grouting steel slag asphalt concrete skeleton, including the gradation of steel slag aggregate, the oil-stone ratio and the porosity of the specimen; then determine the volumes of the coarse aggregate and asphalt mortar by calculating the volume of the target specimen, and calculate the volume fraction of each grade of coarse aggregate in the entire specimen;

[0023] S4.3, based on the random placement algorithm, placing steel slag aggregates of different particle sizes in the preset placement area;

[0024] S4.4. After all the steel slag aggregates are placed, the digital image of the steel slag aggregate skeleton is exported and imported into MATLAB. The thickness of the asphalt mortar layer is determined according to the parameters of the experimental design, including the mix ratio and porosity. Then, MATLAB is used to expand the steel slag aggregate skeleton outward, and the asphalt mortar layer is obtained through Boolean operations. The remaining part is regarded as the grouting area. This completes the establishment of the digital model of the microstructure of the grouting steel slag asphalt concrete.

[0025] In a specific implementation, in step S4.1, the discrete element modeling software is PFC 2D; in step S4.2, the coarse aggregate is an aggregate with a particle size ≥ 2.36 mm; and the asphalt mortar includes mineral powder, asphalt and fine aggregate with a particle size < 2.36 mm.

[0026] In a specific implementation, in step S4.3, steel slag aggregates of different particle sizes are placed in a preset placement area, and the particle size range should be consistent with the actual design gradation. In the model, the steel slag aggregates are randomly distributed in the placement area; during the placement process, the coarse aggregates are divided into 4 grades from large to small and placed separately. When each grade is placed, it is randomly drawn from the aggregate template in step S4.1. According to the particle size range of this grade, the drawn aggregate will be randomly enlarged or reduced to this range, and will be assigned a random orientation angle and a random placement position; after each aggregate is successfully placed, the volume fraction of the current grade of coarse aggregate will be automatically calculated. If it is less than the calculation result in S4.2, the placement will continue, otherwise the next grade of coarse aggregate will be placed until all coarse aggregates are placed.

[0027] In a specific implementation, the specific steps of step S5 are:

[0028] S5.1, exporting the mesostructure digital model obtained in step S4 into a .png format file, converting it into a vector .dxf format, and importing it into ABAQUS to establish geometric components;

[0029] S5.2. Assign corresponding physical properties to each material based on experimental data, including thermal conductivity and thermal expansion coefficient;

[0030] S5.3. Mesh the entire model. The size of the mesh is set according to the size of the aggregate and the detail requirements of the model.

[0031] S5.4. Set the temperature boundary conditions to define the initial temperature of the model and the change of the external temperature field; set the temperature of the outer boundary of the model to gradually increase to simulate the thermal expansion process in the actual environment.

[0032] In a specific implementation, the specific steps of step S6 are:

[0033] Calculation of overall thermal expansion coefficient: According to the deformation results of the model, the overall thermal expansion coefficient of the composite material is calculated using the following formula:

[0034]

[0035] in: is the overall thermal expansion coefficient, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change.

[0036] In a specific implementation, the specific steps of step S1 are:

[0037] S1.1. Prepare steel slag aggregate 1, asphalt mortar 2 and grouting material 3 as test samples respectively, and prepare at least 3 of each test sample;

[0038] S1.2. Place two samples of the same type on the sample rack of the thermal conductivity tester, place the sensor between the two samples, and tighten the clamp to make the sensor fully contact with the two samples;

[0039] S1.3. Turn on the instrument and open the supporting calculation software to zero the equipment. After zeroing, test the thermal conductivity of the sample, wait for the test to end, and record the thermal conductivity of the sample.

[0040] S1.4. Repeat the above operation. Each sample to be tested needs to be measured 3 times and the average value is taken.

[0041] In a specific implementation, the specific steps of step S2 are:

[0042] S2.1. Install the sample obtained in S1.1 on the test platform of the thermal expansion instrument, ensure that the sample can be heated evenly, and that both ends of the sample are in good contact with the support surface of the instrument, and set the test parameters;

[0043] S2.2, start the equipment and start heating the sample; during the temperature change process, the thermal expansion instrument automatically records the relationship between the temperature and the change in sample length, and generates a corresponding curve;

[0044] S2.3. Calculate the thermal expansion coefficients of the three materials based on the temperature changes and sample length changes in the thermal expansion curves:

[0045] Where: α is the thermal expansion coefficient of the material, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change;

[0046] S2.4. Repeat the above operation. Each sample to be tested needs to be measured 3 times and the average value is taken.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) By constructing two-dimensional projection images and digital models of steel slag aggregate, asphalt mortar and grouting material, the present invention can accurately describe the heterogeneous and complex mesoscopic structural characteristics of grouting steel slag asphalt concrete. This lays a solid foundation for the calculation of thermal expansion coefficient, while traditional methods are difficult to effectively consider the mesoscopic characteristics of different materials, resulting in large errors in the calculation results.

[0049] (2) The present invention combines digital image processing technology with finite element analysis to establish a multi-scale model of grouting steel slag asphalt concrete. By simulating the random distribution and gradation of steel slag aggregate, the thermal expansion behavior of the material can be more realistically reflected. Compared with the traditional homogeneous assumption model, this method greatly improves the accuracy of thermal expansion coefficient prediction.

[0050] (3) The microstructure model of the present invention is combined with finite element analysis to effectively generate thermal expansion simulation data of grouting steel slag asphalt concrete under different temperature conditions. Compared with traditional experimental measurements, the finite element method greatly improves the efficiency of model construction and data acquisition, and provides an efficient tool for predicting the thermal expansion properties of materials.

[0051] (4) The present invention ensures the accuracy of the model input parameters based on the experimentally measured physical properties of steel slag aggregate, asphalt mortar and grouting materials, such as thermal conductivity and thermal expansion coefficient. Combining theoretical formulas and numerical simulation results, the model error is effectively reduced and the reliability of the prediction is improved.

[0052] (5) This method is not only applicable to the calculation of thermal expansion coefficient of grouting steel slag asphalt concrete, but can also be extended to the analysis of thermal expansion performance of other composite materials. The flexibility of the digital modeling method enables this method to adapt to different material systems and has strong versatility and adaptability.

[0053] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention is further described in detail below. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0055] Figure 1 It is a schematic diagram of a flow chart of an embodiment of the present invention;

[0056] Figure 2 It is a schematic diagram of the steel slag two-dimensional aggregate template of the present invention;

[0057] Figure 3 This is a schematic diagram of the steel slag skeleton of the grouting steel slag asphalt concrete in the present invention;

[0058] Figure 4 It is a schematic diagram of a digital model of grouting steel slag asphalt concrete in the present invention;

[0059] Figure 5 It is a schematic diagram of the finite element model of grouting steel slag asphalt concrete in the present invention;

[0060] Figure 6 It is a schematic diagram of deformation length of a finite element model extraction model of grouting steel slag asphalt concrete in the present invention;

[0061] Among them, 1. Steel slag aggregate; 2. Asphalt mortar; 3. Grouting material. DETAILED DESCRIPTION

[0062] The embodiments of the present invention are described in detail below. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] Example 1

[0064] A method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on a mesoscopic structure of the present invention comprises the following steps:

[0065] S1. Test and measure the thermal conductivity of steel slag aggregate 1, asphalt mortar 2, and grouting material 3.

[0066] The specific steps of step S1 are:

[0067] S1.1. Preparation of samples to be tested. Steel slag aggregate 1: Select representative steel slag samples and ensure that the samples are neat and flat. Asphalt mortar 2: Mix asphalt and mineral materials in a predetermined proportion to prepare mortar. Grouting material 3: Mix cement, sand and water in a predetermined proportion, stir evenly, and form samples. Cut the above three materials into strip samples with a thickness of 5 mm and a length of 10 mm. At least 3 samples of each type must be prepared.

[0068] S1.2. Place two samples of the same type on the sample holder of the thermal conductivity tester, place the sensor between the two samples, and tighten the clamp to make the sensor fully contact with the two samples.

[0069] S1.3. Turn on the instrument and open the matching calculation software to perform zero adjustment on the equipment. After zero adjustment, perform the thermal conductivity test of the sample, wait for the test to end, and record the thermal conductivity of the sample.

[0070] S1.4. Repeat the above operation. Each sample needs to be measured 3 times and the average value is taken to improve the accuracy of the experimental results.

[0071] S2. Conduct test measurements on the thermal expansion coefficients of the steel slag aggregate 1, the asphalt mortar 2, and the grouting material 3.

[0072] The specific steps of step S2 are:

[0073] S2.1. Install the sample obtained in S1.1 on the test platform of the thermal expansion instrument, ensuring that the sample can be heated evenly and that both ends of the sample are in good contact with the support surface of the instrument. Set the test temperature range and heating rate. The selected heating rate is 5 degrees Celsius / min, and the test temperature range is 0 degrees Celsius to 60 degrees Celsius.

[0074] S2.2, start the equipment and start heating the sample. During the temperature change process, the thermal expansion instrument automatically records the relationship between the temperature and the change in sample length and generates a corresponding curve.

[0075] S2.3. Calculate the thermal expansion coefficients of the three materials based on the temperature changes and sample length changes in the thermal expansion curves.

[0076]

[0077] Where: α is the thermal expansion coefficient of the material, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change.

[0078] S2.4. Repeat the above operation. Each sample needs to be measured 3 times and the average value is taken to improve the accuracy of the experimental results.

[0079] S3. Select steel slag aggregate samples, obtain a two-dimensional projection image of the steel slag aggregate, and create a two-dimensional steel slag aggregate template database.

[0080] The specific steps of step S3 are:

[0081] S3.1. Select several steel slag aggregates from different particle size ranges of steel slag aggregates as representative samples; place the selected steel slag aggregates on the image acquisition device to obtain a two-dimensional projection image of each steel slag aggregate. The different particle size ranges in step S3.1 include at least four levels of particle size ranges, and sieves with apertures of 16mm, 13.2mm, 9.5mm, 4.75mm, and 2.36mm are used for screening, respectively, to obtain steel slag aggregates with particle size ranges of 2.36~4.75mm, 4.75~9.5mm, 9.5~13.2mm, and 13.2~16mm, and 80 steel slag aggregates of different sizes are selected in each particle size range.

[0082] S3.2. Import the collected two-dimensional projection image into Matlab, convert it into grayscale, and remove the noise in the image.

[0083] S3.3. Binarize the processed grayscale image and convert it into a black and white image, in which the black area represents the steel slag aggregate and the white area represents the background.

[0084] S3.4. Convert the binarized image into a vector file for subsequent modeling and calculation; ensure the accuracy of the image contour and the edges should be smooth.

[0085] S4. Based on the random aggregate method and digital image processing technology, a microstructural digital model of grouted steel slag asphalt concrete is established.

[0086] The specific steps of step S4 are:

[0087] S4.1, importing the vector file of the two-dimensional projection image obtained in step S3 into discrete element modeling software as a random aggregate template. In step S4.1, the discrete element modeling software is PFC 2D.

[0088] S4.2, determine the length, width and height dimensions of the model specimen, as well as the mix ratio of the grouting steel slag asphalt concrete skeleton, including the gradation of steel slag aggregate, the oil-stone ratio and the specimen porosity; then determine the volume of the coarse aggregate and the asphalt mortar by calculating the volume of the target specimen, and calculate the volume fraction of each grade of coarse aggregate in the entire specimen. In step S4.2, the coarse aggregate is an aggregate with a particle size of ≥2.36mm; the asphalt mortar includes mineral powder, asphalt and fine aggregate with a particle size of <2.36mm.

[0089] S4.3. Based on the random placement algorithm, steel slag aggregates of different particle sizes are placed in the preset placement area.

[0090] In the step S4.3, steel slag aggregates of different particle sizes are placed in the preset placement area, and the particle size range should be consistent with the actual design gradation. The steel slag aggregates in the model are randomly distributed in the placement area; during the placement process, the coarse aggregates are divided into 4 grades from large to small and placed separately. When each grade is placed, the aggregate template in step S4.1 is randomly drawn. According to the particle size range of the grade, the drawn aggregate will be randomly enlarged or reduced to the range, and will be assigned a random orientation angle and a random placement position. For example, the first grade of coarse aggregate gradation range is 13.2~16 mm, and the randomly drawn coarse aggregate template is scaled to a particle size of 13.2~16 mm and then placed according to the equivalent radius principle. If the generated model aggregates overlap, it is necessary to iteratively adjust them to make them reasonably distributed. By adjusting the position and rotation angle, ensure that the steel slag aggregates in the model do not touch or overlap each other. After each aggregate is successfully placed, the volume fraction of the current coarse aggregate will be automatically calculated. If it is less than the calculation result in S4.2, the placement will continue. Otherwise, the next coarse aggregate will be placed until all coarse aggregates are placed.

[0091] S4.4. After all the steel slag aggregates are placed, the digital image of the steel slag aggregate skeleton is exported and imported into MATLAB for grayscale processing. The grayscale value of the pixel is 0, which is black and represents the steel slag aggregate. The grayscale value of the rest is 255, which is white and represents the rest.

[0092] According to the parameters of the test design, including the mix ratio and porosity, the thickness of the asphalt mortar layer is determined. Specifically, the actual size represented by each pixel is determined by the size ratio between the digital image and the actual specimen. The volume fraction of the asphalt mortar and steel slag aggregate in the specimen can be calculated based on the mix ratio and porosity, and the volume fraction is simplified to the area fraction in the two-dimensional model.

[0093] Then, MATLAB was used to expand the steel slag aggregate skeleton outward, and the asphalt mortar layer was obtained through Boolean operation, and the rest was regarded as the grouting area.

[0094] Specifically, by using MATLAB to expand each steel slag aggregate area in step S4.4 outward, the steel slag aggregate and asphalt mortar parts can be obtained, and the area fraction after expansion is counted again and compared with the calculated area fraction. When the error is less than 5%, it can be considered to meet the requirements. The asphalt mortar layer can be obtained by Boolean operation of the two digital images before and after expansion. The rest of the parts except steel slag aggregate and asphalt mortar can be regarded as grouting areas. Specifically, the mesostructure digital model of grouting steel slag asphalt concrete is a digital matrix composed of pixel values, and each part is distinguished by different pixel values. For example, a grayscale value of 0 represents the area of ​​steel slag aggregate; a grayscale value of 50 represents the area of ​​asphalt mortar; a grayscale value of 100 represents the grouting material area. That is, the establishment of the mesostructure digital model of grouting steel slag asphalt concrete is completed.

[0095] S5. Based on digital image processing, a finite element model of grouting steel slag asphalt concrete is established, and the material parameters and boundary conditions are set, and finite element meshing and simulation analysis are performed.

[0096] The specific steps of step S5 are:

[0097] S5.1, exporting the mesostructure digital model obtained in step S4 into a .png format file, converting it into a vector .dxf format, and importing it into ABAQUS to establish geometric components;

[0098] S5.2. Assign corresponding physical properties to each material based on experimental data, including thermal conductivity and thermal expansion coefficient;

[0099] S5.3. Mesh the entire model. The size of the mesh is set according to the size of the aggregate and the detail requirements of the model.

[0100] S5.4. Set the temperature boundary conditions, define the initial temperature of the model and the change of the external temperature field; set the temperature of the outer boundary of the model to gradually increase, so as to simulate the thermal expansion process in the actual environment. For example, set the ambient temperature to rise from 0 degrees Celsius to 60 degrees Celsius, and extract the horizontal displacement of the nodes at both ends of the model for calculation.

[0101] S6. Based on the simulation results and theoretical formula, the overall thermal expansion coefficient of grouting steel slag asphalt concrete is calculated.

[0102] The specific steps of step S6 are:

[0103] Calculation of overall thermal expansion coefficient: Based on the deformation results of the model, extract the displacements of the two nodes on the left and right sides of the model in the result file, such as Figure 6 The displacement from node 1 to node 2 is shown, and the overall thermal expansion coefficient of the composite material is calculated using the following formula:

[0104]

[0105] in: is the overall thermal expansion coefficient, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change.

[0106] The key to the present invention is to construct a mesoscopic structural model through digital image processing technology and random placement algorithm to achieve accurate simulation of grouting steel slag asphalt concrete. The experimental measurement data is used to ensure the accuracy of the model parameters, thereby improving the reliability of the thermal expansion coefficient calculation. In addition, the finite element analysis technology is used to simulate the thermal response of the model, reasonably set the boundary conditions, and comprehensively consider the material properties of each component. Finally, the deformation results are combined with the theoretical formula to accurately calculate the overall thermal expansion coefficient. This method can not only effectively deal with complex porous structure characteristics, but also simplifies the analysis process, providing an efficient tool for material performance prediction.

[0107] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure, characterized in that: The following steps are involved: S1. Test and measure the thermal conductivity of steel slag aggregate (1), asphalt mortar (2), and grouting material (3); S2. Test and measure the thermal expansion coefficients of the steel slag aggregate (1), the asphalt mortar (2), and the grouting material (3); S3, selecting a steel slag aggregate sample, obtaining a two-dimensional projection image of the steel slag aggregate, and creating a two-dimensional steel slag aggregate template database; S4. Establish a mesoscopic structural digital model of grouting steel slag asphalt concrete; The specific steps of step S4 are: S4.1, importing the vector file of the two-dimensional projection image obtained in step S3 into discrete element modeling software as a random aggregate template; S4.2, determine the length, width and height dimensions of the model specimen, as well as the mix ratio of the grouting steel slag asphalt concrete skeleton, including the gradation of the steel slag aggregate (1), the oil-stone ratio and the specimen porosity; then determine the respective volumes of the coarse aggregate and the asphalt mortar (2) by calculating the volume of the target specimen, and calculate the volume fraction of each grade of coarse aggregate in the entire specimen; S4.3, placing steel slag aggregates of different particle sizes in a preset placement area based on a random placement algorithm (1); S4.

4. After all the steel slag aggregates (1) are placed, the digital image of the steel slag aggregate skeleton is exported and imported into MATLAB. According to the parameters of the experimental design, including the mix ratio and porosity, the thickness of the asphalt mortar layer is determined; then, the steel slag aggregate skeleton is expanded outwards using MATLAB, and the asphalt mortar layer is obtained through Boolean operations, and the remaining part is regarded as the grouting area; thus, the establishment of the digital model of the microstructure of the grouting steel slag asphalt concrete is completed; S5. Based on digital image processing, a finite element model of grouting steel slag asphalt concrete is established, and material parameters and boundary conditions are set, and finite element meshing and simulation analysis are performed; The specific steps of step S5 are: S5.1, exporting the mesostructure digital model obtained in step S4 into a .png format file, converting it into a vector .dxf format, and importing it into ABAQUS to establish geometric components; S5.

2. Assign corresponding physical properties to each material based on experimental data, including thermal conductivity and thermal expansion coefficient; S5.

3. Mesh the entire model. The size of the mesh is set according to the size of the aggregate and the detail requirements of the model. S5.

4. Set the temperature boundary conditions to define the initial temperature of the model and the change of the external temperature field; set the temperature of the outer boundary of the model to gradually increase, so as to simulate the thermal expansion process in the actual environment; S6. Calculate the overall thermal expansion coefficient of grouting steel slag asphalt concrete based on the simulation results and theoretical formula; The specific steps of step S6 are: Calculation of overall thermal expansion coefficient: According to the deformation results of the model, the overall thermal expansion coefficient of the composite material is calculated using the following formula: Where: α eff is the overall thermal expansion coefficient, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change.

2. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 1 is characterized in that: The specific steps of step S3 are: S3.

1. Selecting a plurality of steel slag aggregates (1) from different particle size ranges of the steel slag aggregates (1) as representative samples; placing the selected steel slag aggregates (1) on an image acquisition device to obtain a two-dimensional projection image of each steel slag aggregate (1); S3.2, importing the collected two-dimensional projection image into Matlab, and graying the collected image to remove noise in the image; S3.3, performing a binarization operation on the processed grayscale image to convert it into a black and white image, wherein the black area represents the steel slag aggregate (1) and the white area represents the background; S3.

4. Convert the binarized image into a vector file for subsequent modeling and calculation.

3. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 2 is characterized in that: The different particle size ranges in step S3.1 include at least four levels of particle size ranges, and sieves with apertures of 16 mm, 13.2 mm, 9.5 mm, 4.75 mm, and 2.36 mm are used for screening, respectively, to obtain steel slag aggregates (1) with particle sizes ranging from 2.36 to 4.75 mm, 4.75 to 9.5 mm, 9.5 to 13.2 mm, and 13.2 to 16 mm. At least 80 steel slag aggregates (1) of different sizes are selected in each particle size range.

4. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 1 is characterized in that: In step S4.1, the discrete element modeling software is PFC 2D; in step S4.2, the coarse aggregate is an aggregate with a particle size of ≥2.36 mm; and the asphalt mortar (2) comprises mineral powder, asphalt and fine aggregate with a particle size of <2.36 mm.

5. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 1 is characterized in that: In step S4.3, steel slag aggregate (1) of different particle sizes is placed in a preset placement area, and the particle size range should be consistent with the actual design gradation. In the model, the steel slag aggregate (1) is randomly distributed in the placement area; during the placement process, the coarse aggregate is divided into 4 grades from large to small according to the particle size of the coarse aggregate and is placed separately. When placing each grade, the aggregate is randomly drawn from the aggregate template in step S4.

1. According to the particle size range of the grade, the drawn aggregate will be randomly enlarged or reduced to the range, and will be assigned a random orientation angle and a random placement position; after each aggregate is successfully placed, the volume fraction of the coarse aggregate in the current grade will be automatically calculated. If it is less than the calculation result in S4.2, the placement will continue. Otherwise, the next grade of coarse aggregate will be placed until all coarse aggregates are placed.

6. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 1 is characterized in that: The specific steps of step S1 are: S1.

1. Prepare steel slag aggregate 1, asphalt mortar 2 and grouting material 3 as samples to be tested, and prepare at least 3 samples of each type of sample to be tested; S1.

2. Place two samples of the same type on the sample rack of the thermal conductivity tester, place the sensor between the two samples, and tighten the clamp to make the sensor fully contact with the two samples; S1.

3. Turn on the instrument and open the supporting calculation software to zero the equipment. After zeroing, test the thermal conductivity of the sample, wait for the test to end, and record the thermal conductivity of the sample. S1.

4. Repeat the above operation. Each sample to be tested needs to be measured 3 times and the average value is taken.

7. The method for calculating the thermal expansion coefficient of grouting steel slag asphalt concrete based on mesostructure according to claim 6 is characterized in that: The specific steps of step S2 are: S2.

1. Install the sample obtained in S1.1 on the test platform of the thermal expansion instrument, ensure that the sample can be heated evenly, and that both ends of the sample are in good contact with the support surface of the instrument, and set the test parameters; S2.2, start the equipment and start heating the sample; during the temperature change process, the thermal expansion instrument automatically records the relationship between the temperature and the change in sample length, and generates a corresponding curve; S2.

3. Calculate the thermal expansion coefficients of the three materials based on the temperature changes and sample length changes in the thermal expansion curves: Where: α is the thermal expansion coefficient of the material, ΔL is the length change of the model, L0 is the initial length, and ΔT is the temperature change; S2.

4. Repeat the above operation. Each sample to be tested needs to be measured 3 times and the average value is taken.

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