An evaluation method and device for the low-temperature performance of steel slag asphalt mixture under expansion characteristics

Abstract

The present application provides a method and device for evaluating the low-temperature performance under the expansion characteristics of steel slag asphalt mixture, which relates to the field of road engineering. The method includes: obtaining a first volume expansion rate data set of a target mixture specimen in a first time period according to the type of the target mixture specimen; obtaining a second volume expansion rate data set of the target mixture specimen in a second time period through a volume expansion rate prediction model according to the first volume expansion rate data set; obtaining data of low-temperature performance evaluation indexes corresponding to the expansion rate of the target mixture specimen through a target test device according to the first and second volume expansion rate data sets; optimizing the main components of the index data through the principal component analysis method, and obtaining data of low-temperature performance evaluation indexes corresponding to the optimized expansion rate; and performing a low-temperature performance evaluation operation through the data of low-temperature performance evaluation indexes corresponding to the optimized expansion rate. The present application solves the problem that the existing low-temperature performance test method has inaccurate judgment of low-temperature performance.

Description

Technical Field

[0001] This application relates to the field of road engineering, and particularly to an evaluation method and device for the low-temperature performance of steel slag asphalt mixture under the expansion characteristics. Background Art

[0002] Nowadays, steel slag asphalt mixture is widely used in infrastructure construction such as roads, bridges, and airport runways. Its immersion expansion characteristics are particularly crucial in practical applications. The immersion expansion of steel slag asphalt mixture may lead to the instability of the road surface structure, causing problems such as cracking, potholes, and spalling. This not only reduces the service life of the road surface but also poses a threat to driving safety. Since low-temperature environments may cause the asphalt mixture to shrink and crack, further exacerbating the damage to the road surface, it has become an important research topic to test its crack resistance ability in low-temperature environments.

[0003] Currently, the common low-temperature performance test methods for steel slag asphalt mixture are usually beam bending test and direct tension test. These methods control the temperature of standard specimens to drop to the set low-temperature environment, then apply external forces and measure multiple evaluation indexes. However, since these methods rely on multiple evaluation indexes to evaluate the low-temperature crack resistance performance of steel slag asphalt mixture under expansion characteristics, the reliability of each evaluation index under different test conditions cannot be verified. If different evaluation indexes show large differences or there are data deviations, the reliability of the test results will be reduced, thereby affecting the accurate determination of the low-temperature performance of steel slag asphalt mixture under expansion characteristics.

[0004] Therefore, there is an urgent need for an evaluation method and device for the low-temperature performance of steel slag asphalt mixture under the expansion characteristics to overcome the deficiencies of existing methods and improve the accuracy and reliability of test results. Summary of the Invention

[0005] This application provides an evaluation method and device for the low-temperature performance of steel slag asphalt mixture under the expansion characteristics, which solves the problem of inaccurate judgment of low-temperature performance in the existing evaluation method for low-temperature performance under the expansion characteristics.

[0006] In the first aspect of this application for the evaluation of the low-temperature performance of steel slag asphalt mixture under the expansion characteristics, an evaluation method for the low-temperature performance of steel slag asphalt mixture under the expansion characteristics is provided

[0007] Evaluation of low-temperature performance under the expansion characteristics of steel slag asphalt mixture: Obtain target mixture specimens, and use a preset method to obtain the first volume expansion rate data set of the target mixture specimens in the first time period; According to the first volume expansion rate data set, obtain the second volume expansion rate data set of the target mixture specimens in the second time period through a volume expansion rate prediction model, where the second time period is after the first time period; According to the first volume expansion rate data set and the second volume expansion rate data set, obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimens through a target test device. The low-temperature performance evaluation index data includes freeze-breaking temperature data, transformation point temperature data, freeze-breaking strength data, and temperature stress growth slope data; Optimize the main components of the target mixture specimens through the principal component analysis method, and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate. The low-temperature performance evaluation index data corresponding to the optimized expansion rate is at least one of the evaluation index data in the low-temperature performance evaluation index data; Perform a low-temperature performance evaluation operation on the asphalt mixture through the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

[0008] Optionally, when the specimen type is a steel slag asphalt mixture specimen, the preset method is the water immersion weighing method. Obtain the first volume expansion rate data set of the steel slag asphalt mixture specimen in the first time period through the water immersion weighing method, which specifically includes: S11, obtain the air mass data and water weight data corresponding to the steel slag asphalt mixture specimen; S12, determine the bulk specific gravity data of the steel slag asphalt mixture specimen before and after immersion according to the air mass data and water weight data; S13, calculate the volume change data of the steel slag asphalt mixture specimen before and after immersion according to the bulk specific gravity data; S14, indirectly calculate the first volume expansion rate data corresponding to the steel slag asphalt mixture specimen according to the volume change data; S15, divide the first time period into multiple preset time nodes; S16, obtain multiple first volume expansion rate data corresponding to multiple preset time nodes by repeating steps S11 to S14, where one preset time node corresponds to one first volume expansion rate data; S17, use the multiple first volume expansion rate data as the first volume expansion rate data set.

[0009] Optionally, indirectly calculate the first volume expansion rate data corresponding to the target mixture specimen according to the volume change data, which specifically includes: Calculate the first volume expansion rate data according to the following formula:

[0010] ;

[0011] Where, is the bulk specific gravity data of the steel slag asphalt mixture specimen after immersion, is the air mass data, is the water weight data, is the mass data corresponding to the target mixture specimen when the surface water is wiped dry. is the volume change data. is the first volume expansion rate data. is the volume data corresponding to the steel slag asphalt mixture specimen before immersion in water. is the volume data corresponding to the steel slag asphalt mixture specimen after immersion in water.

[0012] Optionally, regularization constraints are adopted in the low-dimensional projection process, so that the principal component vectors in the principal component analysis method satisfy the following relational expression:

[0013] ;

[0014] Wherein, is the principal component vector, is the principal component matrix, is the original data sample, is the regularization parameter, is the Frobenius norm, is the principal component matrix in the elements, is the number of elements, is the principal component matrix is the transpose matrix, is the weight factor.

[0015] Optionally, according to the first volume expansion rate data set, the second volume expansion rate data set of the target mixture specimen in the second time period is obtained through the volume expansion rate prediction model, specifically including: dividing the second time period into multiple second time nodes; obtaining the volume expansion rate change characteristics corresponding to the target mixture specimen through the first volume expansion rate data set; according to the volume expansion rate change characteristics, obtaining multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model; and taking the multiple second volume expansion rate data as the second volume expansion rate data set.

[0016] Optionally, obtaining multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model specifically includes: predicting the second volume expansion rate data set through the hidden state in the volume expansion rate prediction model:

[0017]

[0018] Wherein, is the predicted second volume expansion rate data set, is the weight matrix of the output layer, is the bias of the output layer.

[0019] Optionally, the principal component analysis method is used to optimize the principal components of the target mixture specimen, and the low-temperature performance evaluation index data corresponding to the optimized expansion rate is obtained, specifically including: obtaining the principal component data corresponding to the target mixture specimen and the target low-temperature performance evaluation index data, where the target low-temperature performance evaluation index data is any one of the evaluation index data on the influence of the expansion rate on the low-temperature performance; projecting the target low-temperature performance evaluation index data in the selected direction of the principal component data, and calculating the contribution rate score of the target mixture specimen on the principal component data; judging whether the contribution rate score meets the decision tree condition by the least squares method; if the contribution rate score meets the decision tree condition, the target low-temperature performance evaluation index data is used as the low-temperature performance evaluation index data corresponding to the optimized expansion rate of the target mixture specimen.

[0020] In the second aspect of the present application, a low-temperature performance evaluation device for the expansion characteristics of steel slag asphalt mixture is provided. The device includes a prediction module, a principal component analysis module, and an output module, where,

[0021] The prediction module is configured to obtain a target mixture specimen, and use a preset method to obtain a first volume expansion rate data set of the target mixture specimen in a first time period; according to the first volume expansion rate data set, obtain a second volume expansion rate data set of the target mixture specimen in a second time period through a volume expansion rate prediction model, and the second time period is after the first time period.

[0022] The principal component analysis module is configured to, according to the first volume expansion rate data set and the second volume expansion rate data set, obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through a target test device, and the low-temperature performance evaluation index data includes freeze-breaking temperature data, transformation point temperature data, freeze-breaking strength data, and temperature stress growth slope data; optimize the principal components of the target mixture specimen by the principal component analysis method, and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate, and the low-temperature performance evaluation index data corresponding to the optimized expansion rate is at least one of the low-temperature performance evaluation index data corresponding to the expansion rate.

[0023] The output module is configured to perform a low-temperature performance evaluation operation on the asphalt mixture through the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

[0024] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of the above.

[0025] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the method as described in any one of the above.

[0026] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0027] 1. Obtain a target mixture specimen. According to the specimen type corresponding to the target mixture specimen, use a preset method to obtain the first volume expansion rate data set of the target mixture specimen in the first time period; according to the first volume expansion rate data set, obtain the second volume expansion rate data set of the target mixture specimen in the second time period through a volume expansion rate prediction model; according to the first and second volume expansion rate data sets, obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through a target testing device; optimize the principal components of the target mixture specimen through the principal component analysis method, and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate; perform low-temperature performance test operations on the steel slag asphalt mixture under the expansion characteristics through the low-temperature performance evaluation index data corresponding to the optimized expansion rate. Not only can the volume expansion rate data set of the target mixture specimen in the future time period, that is, the second time period, be predicted through the volume expansion rate prediction model, significantly reducing the time required to obtain a complete expansion data set, but also the principal components of the target mixture specimen can be optimized through the principal component analysis method, and the low-temperature performance evaluation index data corresponding to the optimized expansion rate can be obtained, so as to use the more reliable low-temperature performance evaluation index data as the key input for the low-temperature performance test of the asphalt mixture, reduce the interference of redundant information, improve the quality of the adaptability evaluation of the mixture in a low-temperature environment, and further improve the accuracy of the low-temperature test results.

[0028] 2. Obtain the principal component data corresponding to the target mixture specimen and the target low-temperature performance evaluation index data, and project the target low-temperature performance evaluation index data in the selected direction of the principal component data to calculate the contribution rate score of the target mixture specimen on the principal component data; judge whether the contribution rate score meets the decision tree condition through the least squares method; if the contribution rate score meets the decision tree condition, then use the target low-temperature performance evaluation index data as the low-temperature performance evaluation index data corresponding to the optimized expansion rate under the expansion characteristics of the target mixture specimen, thereby improving the effectiveness of the low-temperature performance evaluation index data, reducing the interference of redundant information, ensuring the rationality of the key evaluation index, and providing more representative evaluation data for the subsequent low-temperature performance test under the expansion characteristics. Description of the Drawings

[0029] Figure 1 It is a schematic flowchart of a method for evaluating the low-temperature performance of a steel slag asphalt mixture under expansion characteristics provided by an embodiment of the present application;

[0030] Figure 2 Schematic diagram of the relationship curve between the specimen expansion rate and the immersion time for restraining the temperature stress of the specimen

[0031] Figure 3a Schematic diagram of a curve showing the variation of the freezing fracture temperature with the immersion time and the expansion rate

[0032] Figure 3b Schematic diagram of a curve showing the variation of the transformation point temperature with the immersion time and the expansion rate

[0033] Figure 3c Schematic diagram of a curve showing the variation of the freezing fracture strength with the immersion time and the expansion rate

[0034] Figure 3d Schematic diagram of a curve showing the variation of the temperature stress growth slope with the immersion time and the expansion rate

[0035] Figure 4a Schematic diagram of the relationship between the evaluation index data and the average coefficient of variation of the freezing fracture index

[0036] Figure 4b Schematic diagram of the relationship between the evaluation index data and the index weight coefficient

[0037] Figure 5 Schematic diagram of the modules of an evaluation device for the low-temperature performance under the expansion characteristics of steel slag asphalt mixture provided by an embodiment of the present application

[0038] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application

[0039] Explanation of the reference numerals: 51, prediction module; 52, principal component analysis module; 53, output module; 601, processor; 602, communication bus; 603, user interface; 604, network interface; 605, memory Detailed implementation manners

[0040] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments

[0041] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0042] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0043] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0044] Please refer to Figure 1 , which shows a schematic flowchart of a test method for the low-temperature performance of steel slag asphalt mixture under expansion characteristics provided by the embodiments of the present application. The flowchart mainly includes the following steps: S101 to S105.

[0045] Step S101, obtain a target mixture specimen and obtain a first volume expansion rate data set of the target mixture specimen in the first time period by using a preset method.

[0046] Specifically, when the user performs a low-temperature performance test operation on the steel slag asphalt mixture under expansion characteristics, the target mixture specimen is prepared by grading and screening. The target mixture specimen is: a steel slag asphalt mixture specimen (a prism specimen with a size of 220 mm × 40 mm × 40 mm formed by cutting a rutting plate by the wheel rolling method). The steps for obtaining the first volume expansion rate data set are as follows:

[0047] First, place the target mixture specimen in a constant temperature water bath at 60 °C, and then, according to different times, namely 0 h, 48 h, 96 h, and 144 h, place the soaked target mixture specimen at room temperature and let it dry naturally for 72 h. Then, obtain the first volume expansion rate data set of each target mixture specimen in the first time period. The first time period is the early time period in 0 h to 144 h. The embodiments of the present application will give a detailed description of the first time period in step S102.

[0048] In a possible implementation, step S101 further includes: obtaining a target mixture specimen, where the specimen type corresponding to the target mixture specimen is: a steel slag asphalt mixture specimen. The preset method is the water immersion weight method. In the embodiments of the present application, for the steel slag asphalt mixture specimen type, the water immersion weight method is used to obtain the first volume expansion rate data set of the target mixture specimen in the first time period. The specific steps are as follows:

[0049] S11, obtain the air mass data and water weight data corresponding to the steel slag asphalt mixture specimen; S12, determine the bulk specific gravity data of the steel slag asphalt mixture specimen before and after immersion according to the air mass data and water weight data; S13, calculate the volume change data of the steel slag asphalt mixture specimen before and after immersion according to the bulk specific gravity data; S14, indirectly calculate the first volume expansion rate data corresponding to the steel slag asphalt mixture specimen according to the volume change data; S15, divide the first time period into multiple preset time nodes; S16, by repeating steps S11 to S14, obtain multiple first volume expansion rate data corresponding to multiple preset time nodes, and one preset time node corresponds to one first volume expansion rate data; S17, use the multiple first volume expansion rate data as the first volume expansion rate data set.

[0050] Specifically, for S11, obtain the air mass data and water weight data corresponding to the steel slag asphalt mixture specimen: First, obtain the air mass data and water weight data of the specimen. The air mass refers to the mass of the specimen weighed in the air, and the water weight is the weight obtained after immersing the specimen in water. These data provide the necessary basis for subsequent calculations, especially for calculating the volume of the specimen; for S12, determine the bulk specific gravity data of the steel slag asphalt mixture specimen before and after immersion according to the air mass data and water weight data: Calculate the bulk specific gravity data of the steel slag asphalt mixture specimen according to the obtained air mass and water weight data. The bulk specific gravity is calculated from the air mass and water weight data, and its formula is:

[0051] ;

[0052] where is the bulk specific gravity data of the steel slag asphalt mixture specimen after immersion, is the air mass data, is the water weight data, is the mass data corresponding to the target mixture specimen when the surface water is dried. S13. According to the bulk density data, calculate the volume change data of the steel slag asphalt mixture specimen before and after immersion: Through the bulk density data, further calculate the volume change data of the steel slag asphalt mixture specimen. The volume change data refers to the volume difference of the specimen before and after immersion, reflecting the expansion of the material after absorbing water in water. The calculation formula of the volume change data is as follows:

[0053] ;

[0054] Among them, is the volume change data; S14. According to the volume change data, indirectly calculate the first volume expansion rate data corresponding to the steel slag asphalt mixture specimen: According to the volume change data, indirectly calculate the first volume expansion rate of the steel slag asphalt mixture specimen. The expansion rate refers to the volume change rate of the specimen during water immersion, and its formula is:

[0055] ;

[0056] Among them, is the first volume expansion rate data, is the volume data corresponding to the steel slag asphalt mixture specimen before immersion, is the volume data corresponding to the steel slag asphalt mixture specimen after immersion; S15. Divide the first time period into multiple preset time nodes: Divide the early time period in 0h~144h, that is, the first time period, into multiple preset time nodes, which can be 0h, 24h, 48h, 72h, 96h or 0h, 24h, 48h, 72h, etc., and can be adjusted appropriately according to the experimental needs; S16. By repeating steps S11 to S14, obtain multiple first volume expansion rate data corresponding to multiple preset time nodes: Each time node corresponds to a new expansion rate data, so that the expansion change of the specimen during the whole soaking process can be completely recorded; S17. Take the multiple first volume expansion rate data as the first volume expansion rate data group: Finally, summarize the multiple expansion rate data obtained at each time node to form a complete first volume expansion rate data group. This data group will be used for subsequent analysis to evaluate the low-temperature performance of the steel slag asphalt mixture under the expansion characteristics.

[0057] Step S102. According to the first volume expansion rate data group, obtain the second volume expansion rate data group of the target mixture specimen in the second time period through the volume expansion rate prediction model.

[0058] Specifically, based on the first set of volume expansion rate data obtained in step S102, the established volume expansion rate prediction model is used to predict the second set of volume expansion rate data of the target mixture specimen in the second time period, where the second time period is after the first time period. For example, assuming the first time period is 0h - 72h, then the second time period is the time period after 0h - 72h, that is, 72h - 144h. Through the volume expansion rate prediction model, and based on the expansion rate change law in the first time period, as well as relevant physical and chemical properties, such as factors like temperature, humidity, and material properties, fitting and training are carried out, so as to be able to infer the expansion rate performance in the future time period. It can greatly reduce the time required for experiments and facilitate the low-temperature performance test.

[0059] In a possible implementation manner, step S102 further includes: dividing the second time period into multiple second time nodes; obtaining the volume expansion rate change characteristics corresponding to the target mixture specimen through the first set of volume expansion rate data; according to the volume expansion rate change characteristics, obtaining multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model; and using the multiple second volume expansion rate data as the second set of volume expansion rate data.

[0060] Specifically, within the time T, that is, within the second time period, the complete recursive process of the volume expansion rate prediction model is shown in the following formula:

[0061]

[0062] Among them, is the output of the forget gate, and the value range is [0, 1], which is used to control the forgetting degree of the memory at the previous moment of, is the activation function, represents the input data at the current time step of, , represent the weight matrices corresponding to the forget gate, , represent the weight matrices corresponding to the input gate, , represent the weight matrices corresponding to the candidate memory unit, , represent the weight matrices corresponding to the output gate, represents the bias vector corresponding to the forget gate, represents the bias vector corresponding to the input gate, represents the bias vector corresponding to the candidate memory unit, the bias vector of the output gate, represents the hidden state at the represents the The hidden state at a moment, is the output of the input gate at the th moment, represents the candidate memory, represents the state of the memory cell at the current time step, represents the memory at the previous moment, represents the output of the output gate, represents element-wise multiplication. Among them, the volume expansion rate change feature refers to the statistical, dynamic, and regular attributes presented by the volume expansion rate of the target mixture specimen over time during the first time period. This feature reflects the macroscopic volume response behavior of the material under the action of specific external environments (such as temperature, humidity, load changes, etc.) and internal composition conditions (such as aggregate gradation, asphalt content, steel slag activity, interface bonding ability, etc.). The second volume expansion rate data set is predicted through the hidden state in the volume expansion rate prediction model, and the prediction formula is as follows:

[0063]

[0064] Wherein, is the predicted second volume expansion rate data set, is the weight matrix of the output layer, is the bias of the output layer. Please refer to Figure 2 , Figure 2 is a schematic diagram of the relationship curve between the temperature stress specimen expansion rate and the immersion time of the constrained specimen provided in the embodiment of the present application.

[0065] Step S103, according to the first volume expansion rate data set and the second volume expansion rate data set, obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through the target test device.

[0066] Specifically, according to the first and second volume expansion rate data sets, obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through the target test device, where the target test device is the Italian MATEST-PAVTEST-B282-10 test device. The low-temperature performance evaluation index data includes freeze-breaking temperature data, transition point temperature data, freeze-breaking strength data, and temperature stress growth slope data.

[0067] Step S104, perform principal component optimization on the target mixture specimen through principal component analysis, and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

[0068] Specifically, by adopting principal component analysis (PCA) under the MSE model to analyze the reliability at low temperatures of the expansion characteristics of steel slag asphalt mixture, it can effectively process high-dimensional, multi-variable, and strongly correlated data, that is, optimize the data of the low-temperature performance evaluation index corresponding to the expansion rate. The data of the low-temperature performance evaluation index corresponding to the expansion rate is at least one of the evaluation index data of the low-temperature performance evaluation index corresponding to the expansion rate: Using the MSE model and the principal component analysis method (PCA), the correlated indexes of the freeze-breaking temperature data FT, the transformation point temperature data TPT, the freeze-breaking strength data FS, and the temperature stress growth slope data TSTG of the constrained specimen temperature stress test are compressed into several principal components, reducing the dimension and complexity of the data. PCA dimensionality reduction can remove redundant information, reduce noise, and improve data processing efficiency, thereby providing a more stable and accurate input for the MSE model and improving the accuracy and reliability of the low-temperature performance evaluation analysis corresponding to the expansion rate.

[0069] In a possible implementation manner, step S104 further includes: obtaining the principal component data corresponding to the target mixture specimen and the target low-temperature performance evaluation index data, where the target low-temperature performance evaluation index data is any one of the evaluation index data of the low-temperature performance evaluation index corresponding to the expansion rate; projecting the target low-temperature performance evaluation index data in the selected direction of the principal component data, and calculating the contribution rate score of the target mixture specimen on the principal component data; judging whether the contribution rate score meets the decision tree condition through the least squares method; if the contribution rate score meets the decision tree condition, then use the target low-temperature performance evaluation index data as the low-temperature performance evaluation index data corresponding to the optimized expansion rate of the target mixture specimen.

[0070] Specifically, for each target low-temperature performance evaluation index data, that is, the freeze-breaking temperature data FT, the transformation point temperature data TPT, the freeze-breaking strength data FS, and the temperature stress growth slope data TSTG, first standardize the data using the Z-score standardization method:

[0071] ;

[0072] Among them, is the standardized data matrix, is the original target low-temperature performance evaluation index data, is the mean of the original target low-temperature performance evaluation index data, is the standard deviation of the original target low-temperature performance evaluation index data. Then its covariance matrix can be calculated by the following formula:

[0073] ;

[0074] Among them, is the covariance matrix, is the number of samples, represents the matrix transpose operation. Calculate the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition:

[0075] ;

[0076] where, are the eigenvalues, are the eigenvectors. Then, project the target low-temperature performance evaluation index data onto the selected direction of the principal component data, and calculate the contribution rate score of the target mixture specimen on the principal component data:

[0077] ;

[0078] where, is the contribution rate score of the th data point, is the standardized data of the th data point. When applying the least squares method, the error can be minimized through an optimization process. In PCA, this means optimizing the selection of principal components by minimizing the data reconstruction error. Use MSE to optimize the principal components to reconstruct the data:

[0079]

[0080] where, are the evaluation index data after reconstruction. The decision tree formula is generally:

[0081]

[0082] where, represents the Gini index at node , represents the probability of class in node , and class means that the class of the low-temperature performance evaluation index data is in number. In this application, it is 4: freeze-breaking temperature data FT, transition point temperature data TPT, freeze-breaking strength data FS, and temperature stress growth slope data TSTG. Please refer to Figure 3a , Figure 3a is a schematic diagram of the curve of freeze-breaking temperature varying with immersion time and expansion rate. Please refer to Figure 3b , Figure 3b is a schematic diagram of the curve of transition point temperature varying with immersion time and expansion rate. Please refer to Figure 3c , Figure 3c is a schematic diagram of the curve of freeze-breaking strength varying with immersion time and expansion rate. Please refer to Figure 3d , Figure 3dIt is a schematic diagram of the curve of the temperature stress growth slope varying with the immersion time and the expansion rate. According to the diagram, the variation laws of the transformation point temperature (TPT) and the freeze-thaw strength (FS) of the three kinds of steel slag asphalt mixtures with the immersion time and the expansion rate are basically the same as those of the freeze-thaw temperature (FT), and the variation law presented by the freeze-thaw strength (FS) index is the most obvious. However, the variation law of the temperature stress growth slope (TSTG) index is quite different from that of the other three indexes. From the decision tree formula, we know that:

[0083] ;

[0084] Among them, represents the entire sample set, represents the th subset generated after the decision tree splitting, represents the entropy of the sample set, represents the entropy of the th subset. Among the steel slag aggregates, except for the freeze-thaw temperature, the correlation of each index is very low, indicating that the indexes of freeze-thaw strength (FS), temperature stress growth slope (TSTG), and transformation point temperature (TPT) are selected well and there is less redundant information. For the linear SVM, the optimization objective is the maximum classification margin, that is:

[0085] ;

[0086] Among them, is the objective function used to optimize the SVM, represents the weight vector, represents the bias term. At the same time, it satisfies the constraint condition . is the label of the th sample. Therefore, after optimizing the principal component analysis method (PCA) with the SVM model, the average coefficient of variation (CV) of the freeze-thaw strength (FS) and the temperature stress growth slope (TSTG) is greater than that of the other two indexes, indicating that the freeze-thaw temperature (FT) and the transformation point temperature (TPT) are more stable in evaluating the low-temperature performance of the steel slag mixture. The minimum mean square error (MSE) reconstruction principal component analysis method (PCA) can be expressed as:

[0087] ;

[0088] Among them, represents the th original data sample (the sample data before dimensionality reduction), is the data sample of the samples after reconstruction by the principal component analysis (PCA). In order to improve the discrimination ability of the principal components, a weight factor is introduced in :

[0089] ;

[0090] In the above - improved , by introducing a weight factor , the number of categories that also represent the evaluation index data of low - temperature performance is (in this application, it is 4). To further optimize the dimensionality reduction effect, regularization constraints are adopted in the low - dimensional projection process, so that the principal component vectors satisfy:

[0091] ;

[0092] Among them, is the principal component vector, is the regularization parameter, is the Frobenius norm, is an element in the principal component matrix . This optimization term can sparsify the principal component loading matrix during the dimensionality reduction process, thereby retaining the dominant role of key variables. is the transpose matrix of the principal component matrix . Based on this optimization objective, a weighted principal component score matrix is constructed:

[0093] ;

[0094] Among them, is the weighted principal component score matrix, is the diagonalized weight matrix, so that variables with a greater impact on low - temperature performance occupy higher weights during the dimensionality reduction process. Then, a constrained regression model is used to establish the contribution relationship of different types of low - temperature performance evaluation index data to low - temperature performance:

[0095] ;

[0096] Among them represents the contribution relationship of the - th low - temperature performance evaluation index data to low - temperature performance, is the intercept term, is the regression coefficient vector corresponding to the weighted principal component score matrix, is the regression coefficient vector corresponding to the original data sample, is the error term. According to the constrained regression model, it can be calculated that the freeze - off temperature (FT) and the transition point temperature (TPT) have a greater contribution to low - temperature performance and can be used as accurate indicators to evaluate the low - temperature performance of steel - slag asphalt mixtures. That is, it is confirmed that the freeze - off temperature (FT) has a certain reliability as an indicator to evaluate the low - temperature performance of steel - slag asphalt mixtures. Combining Figure 4a and Figure 4bThe coefficient of variation of the temperature stress growth slope (TSTG) and the freezing fracture temperature (FT) is relatively large, and it is not suitable to be used alone as an evaluation index for the low-temperature performance of steel slag asphalt mixture. It is confirmed that the freezing fracture temperature (FT) has a certain reliability in evaluating the low-temperature performance of steel slag asphalt mixture. Please refer to Figure 4a , Figure 4a Figure showing the relationship between the data of an evaluation index and the average coefficient of variation of the freezing fracture index. Please refer to Figure 4b , Figure 4b Figure showing the relationship between the data of an evaluation index and the index weight coefficient.

[0097] Step S105: Perform a low-temperature performance evaluation operation on the asphalt mixture by using the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

[0098] Specifically, perform a low-temperature performance test operation on the expansion characteristics of the steel slag asphalt mixture by using the low-temperature performance evaluation index data corresponding to the optimized expansion rate, that is, perform a low-temperature performance evaluation operation on the expansion characteristics of the steel slag asphalt mixture by using the freezing fracture temperature data.

[0099] By adopting the above method, this application obtains target mixture specimens, and according to the specimen type corresponding to the target mixture specimens, obtains the first volume expansion rate data set of the target mixture specimens in the first time period by using a preset method; according to the first volume expansion rate data set, obtains the second volume expansion rate data set of the target mixture specimens in the second time period through a volume expansion rate prediction model; according to the first and second volume expansion rate data sets, obtains the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimens through a target test device; performs principal component optimization on the target mixture specimens through the principal component analysis method, and obtains the low-temperature performance evaluation index data corresponding to the optimized expansion rate; performs a low-temperature performance evaluation operation on the expansion characteristics of the steel slag asphalt mixture by using the low-temperature performance evaluation index data corresponding to the optimized expansion rate. Not only can the volume expansion rate data set of the target mixture specimens in the future time period, that is, the second time period, be predicted through the volume expansion rate prediction model, greatly reducing the time required to obtain a complete expansion data set, but also the principal component optimization of the target mixture specimens is performed through the principal component analysis method, and the low-temperature performance evaluation index data corresponding to the optimized expansion rate is obtained, so as to use the low-temperature performance evaluation index data corresponding to a higher reliability expansion rate as the key input for the low-temperature performance test of the steel slag asphalt mixture expansion characteristics, reduce the interference of redundant information, improve the quality of the adaptability evaluation of the steel slag mixture expansion characteristics in a low-temperature environment, and further improve the accuracy of the low-temperature test results.

[0100] Please refer to Figure 5, which shows a schematic module diagram of a test device for evaluating the low-temperature performance of steel slag asphalt mixture under the expansion characteristics provided by the embodiments of the present application. The device includes a prediction module 51, a principal component analysis module 52, and an output module 53. Among them,

[0101] The prediction module 51 is configured to obtain a target mixture specimen and acquire a first volume expansion rate data set of the target mixture specimen in a first time period by using a preset method; according to the first volume expansion rate data set, obtain a second volume expansion rate data set of the target mixture specimen in a second time period through a volume expansion rate prediction model, and the second time period is after the first time period.

[0102] The principal component analysis module 52 is configured to obtain low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through the target test device according to the first volume expansion rate data set and the second volume expansion rate data set. The low-temperature performance evaluation index data includes freeze-breaking temperature data, transformation point temperature data, freeze-breaking strength data, and temperature stress growth slope data; perform principal component optimization on the target mixture specimen through the principal component analysis method, and obtain low-temperature performance evaluation index data corresponding to the optimized expansion rate. The low-temperature performance evaluation index data corresponding to the optimized expansion rate is at least one of the evaluation index data in the low-temperature performance evaluation index data.

[0103] The output module 53 is configured to perform a low-temperature performance evaluation operation on the steel slag asphalt mixture under the expansion characteristics through the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

[0104] In a possible implementation manner, when the specimen type is a steel slag asphalt mixture specimen, the preset method is the water immersion weighing method. The prediction module 51 is configured to obtain a first volume expansion rate data set of the steel slag asphalt mixture specimen in a first time period through the water immersion weighing method, which specifically includes: S11, obtaining the air mass data and the water weight data corresponding to the steel slag asphalt mixture specimen; S12, determining the bulk density data of the steel slag asphalt mixture specimen before and after immersion according to the air mass data and the water weight data; S13, calculating the volume change data of the steel slag asphalt mixture specimen before and after immersion according to the bulk density data; S14, indirectly calculating the first volume expansion rate data corresponding to the steel slag asphalt mixture specimen according to the volume change data; S15, dividing the first time period into multiple preset time nodes; S16, obtaining multiple first volume expansion rate data corresponding to multiple preset time nodes by repeating steps S11 to S14, and one preset time node corresponds to one first volume expansion rate data; S17, taking the multiple first volume expansion rate data as the first volume expansion rate data set.

[0105] In a possible implementation, the prediction module 51 is used to indirectly calculate the first volume expansion rate data corresponding to the target mixture specimen according to the volume change data, specifically including: calculating the first volume expansion rate data according to the following formula:

[0106] ;

[0107] Among them, is the bulk density data corresponding to the steel slag asphalt mixture specimen after immersion in water, is the air mass data, is the weight data in water, is the mass data corresponding to the target mixture specimen when the surface water is dried, is the volume change data, is the first volume expansion rate data, is the volume data corresponding to the steel slag asphalt mixture specimen before immersion in water, is the volume data corresponding to the steel slag asphalt mixture specimen after immersion in water.

[0108] In a possible implementation, the prediction module 52 is used to adopt regularization constraints in the low-dimensional projection process, so that the principal component vectors in the principal component analysis method satisfy the following relational expression:

[0109] ;

[0110] Among them, is the principal component vector, is the principal component matrix, is the original data sample, is the regularization parameter, is the Frobenius norm, is the principal component matrix in the elements, is the number of elements, is the principal component matrix of the transposed matrix, is the weight factor.

[0111] In a possible implementation, the prediction module 52 is used to obtain multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model, specifically including:

[0112] Predict the second volume expansion rate data group through the hidden state in the volume expansion rate prediction model:

[0113]

[0114] Among them, is the predicted second volume expansion rate data group, is the weight matrix of the output layer, is the bias of the output layer.

[0115] In a possible implementation, the prediction module 52 is configured to obtain a second set of volume expansion rate data of the target mixture specimen in the second time period through a volume expansion rate prediction model according to the first set of volume expansion rate data, specifically including: dividing the second time period into multiple second time nodes; obtaining the volume expansion rate change characteristics corresponding to the target mixture specimen through the first set of volume expansion rate data; obtaining multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes according to the volume expansion rate change characteristics; and using the multiple second volume expansion rate data as the second set of volume expansion rate data.

[0116] In a possible implementation, the principal component analysis module 52 is configured to perform principal component optimization on the target mixture specimen through principal component analysis and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate, specifically including: obtaining the principal component data corresponding to the target mixture specimen and the target low-temperature performance evaluation index data, where the target low-temperature performance evaluation index data is any one of the evaluation index data of the low-temperature performance evaluation index data corresponding to the expansion rate; projecting the target low-temperature performance evaluation index data in the selected direction of the principal component data and calculating the contribution rate score of the target mixture specimen on the principal component data; judging whether the contribution rate score meets the decision tree condition through the least squares method; and if the contribution rate score meets the decision tree condition, using the target low-temperature performance evaluation index data as the low-temperature performance evaluation index data corresponding to the optimized expansion rate of the target mixture specimen.

[0117] It should be noted that when the device provided in the above embodiments realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0118] This application also provides an electronic device. Referring to Figure 6 , Figure 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 601, at least one communication bus 602, a user interface 603, at least one network interface 604, and a memory 605.

[0119] Among them, the communication bus 602 is used to realize the connection and communication between these components.

[0120] Among them, the user interface 603 may include a display screen and a camera. Optionally, the user interface 603 may further include a standard wired interface and a wireless interface.

[0121] Among them, the network interface 604 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0122] Among them, the processor 601 may include one or more processing cores. The processor 601 connects various parts within the entire server through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and by calling the data stored in the memory 605, the processor 601 performs various functions of the server and processes data. Optionally, the processor 601 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 601 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 601 and may be implemented separately by a single chip.

[0123] Among them, the memory 605 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 605 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 605 may further be at least one storage device located far from the aforementioned processor 601. Refer toFigure 6 In the memory 605, which is a computer storage medium, an operating system, a network communication module, a user interface module, and an application program for evaluating the low-temperature performance under the expansion characteristics of steel slag asphalt mixture can be included.

[0124] In Figure 6 In the electronic device shown, the user interface 603 is mainly used to provide an interface for the user to input data and obtain the data input by the user; while the processor 601 can be used to call the application program for evaluating the low-temperature performance under the expansion characteristics of steel slag asphalt mixture stored in the memory 605. When executed by one or more processors 601, the electronic device performs one or more of the methods as described in the above embodiments. It should be noted that for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0125] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, the electronic device performs one or more of the methods as described in the above embodiments.

[0126] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0127] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0130] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0131] The above are only exemplary embodiments disclosed in the present application, and the scope of the present application disclosed cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings disclosed in the present application still fall within the scope covered by the present application. Those skilled in the art will readily think of other implementation schemes of the present application after considering the specification and the disclosure of the practical truth.

[0132] The present application aims to cover any variations, uses, or adaptive changes of the present application, and these variations, uses, or adaptive changes follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not recorded in the present application.

Claims

1. An evaluation method for the low-temperature performance of steel slag asphalt mixture under the expansion characteristics, characterized in that, The method includes: Obtaining a target mixture specimen, and obtaining a first volume expansion rate data set of the target mixture specimen in a first time period by using a preset method; According to the first volume expansion rate data set, obtaining a second volume expansion rate data set of the target mixture specimen in a second time period through a volume expansion rate prediction model, where the second time period is after the first time period; According to the first volume expansion rate data set and the second volume expansion rate data set, obtaining low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through a target test device, where the low-temperature performance evaluation index data includes freeze-breaking temperature data, transformation point temperature data, freeze-breaking strength data, and temperature stress growth slope data; Performing principal component optimization on the target mixture specimen through principal component analysis, and obtaining low-temperature performance evaluation index data corresponding to an optimized expansion rate, where the low-temperature performance evaluation index data corresponding to the optimized expansion rate is at least one of the evaluation index data in the low-temperature performance evaluation index data; Performing a low-temperature performance evaluation operation on the asphalt mixture through the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

2. The method according to claim 1, characterized in that The specific steps of obtaining the first volume expansion rate data set of the target mixture specimen in the first time period by using the preset method include: S11. Obtaining the air mass data and water weight data corresponding to the target mixture specimen; S12. Determining the bulk volume density data of the target mixture specimen before and after immersion according to the air mass data and the water weight data; S13. Calculating the volume change data of the target mixture specimen before and after immersion according to the bulk volume density data; S14. Indirectly calculating the first volume expansion rate data corresponding to the target mixture specimen according to the volume change data; S15. Dividing the first time period into multiple preset time nodes; S16. By repeating steps S11 to S14, obtaining multiple first volume expansion rate data corresponding to the multiple preset time nodes, where one preset time node corresponds to one first volume expansion rate data; S17. Using the multiple first volume expansion rate data as the first volume expansion rate data set.

3. The method according to claim 2, wherein The specific steps of indirectly calculating the first volume expansion rate data corresponding to the target mixture specimen according to the volume change data include: Calculating the first volume expansion rate data according to the following formula: ； Among them, is the bulk specific gravity data corresponding to the target mixture specimen after immersion in water, is the mass data in air, is the weight data in water, is the mass data corresponding to the target mixture specimen when the surface water is wiped dry, is the volume change data, is the first volume expansion rate data, is the volume data corresponding to the target mixture specimen before immersion in water, is the volume data corresponding to the target mixture specimen after immersion in water.

4. The method according to claim 1, characterized in that, Using regularization constraints in the low-dimensional projection process to make the principal component vectors in the principal component analysis satisfy the following relational expression: ； Among them, is the principal component vector, is the principal component matrix, is the original data sample, is the regularization parameter, is the Frobenius norm, is the principal component matrix in the elements, is the number of elements, is the principal component matrix transpose matrix, is the weight factor.

5. The method according to claim 1, characterized in that, The specific steps of obtaining the second volume expansion rate data set of the target mixture specimen in the second time period through the volume expansion rate prediction model according to the first volume expansion rate data set include: Dividing the second time period into multiple second time nodes; Obtaining the volume expansion rate change characteristics corresponding to the target mixture specimen through the first volume expansion rate data set; According to the characteristics of the volume expansion rate change, obtain multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model; Take the multiple second volume expansion rate data as the second volume expansion rate data set.

6. The method according to claim 5, characterized in that The obtaining of multiple second volume expansion rate data corresponding to the target mixture specimen at multiple second time nodes through the volume expansion rate prediction model specifically includes: Predict the second volume expansion rate data set through the hidden state in the volume expansion rate prediction model: ； Among them, is the predicted second volume expansion rate data set, is the weight matrix of the output layer, represents the hidden state at the time, and is the bias of the output layer.

7. The method according to claim 1, characterized in that The performing of principal component optimization on the target mixture specimen by the principal component analysis method and obtaining the low-temperature performance evaluation index data corresponding to the optimized expansion rate specifically includes: Obtain the principal component data corresponding to the target mixture specimen and the target low-temperature performance evaluation index data, where the target low-temperature performance evaluation index data is any one of the evaluation index data in the low-temperature performance evaluation index data; Project the target low-temperature performance evaluation index data in the selected direction of the principal component data, and calculate the contribution rate score of the target mixture specimen on the principal component data; Judge whether the contribution rate score meets the decision tree condition through the least squares method; If the contribution rate score meets the decision tree condition, take the target low-temperature performance evaluation index data as the low-temperature performance evaluation index data corresponding to the optimized expansion rate of the target mixture specimen.

8. An evaluation device for the low-temperature performance of steel slag asphalt mixture under expansion characteristics, characterized in that The device includes a prediction module, a principal component analysis module, and an output module, where, The prediction module is used to obtain a target mixture specimen, and obtain a first volume expansion rate data set of the target mixture specimen in a first time period by a preset method; according to the first volume expansion rate data set, obtain a second volume expansion rate data set of the target mixture specimen in a second time period through a volume expansion rate prediction model, where the second time period is after the first time period; The principal component analysis module is used to obtain the low-temperature performance evaluation index data corresponding to the expansion rate of the target mixture specimen through a target test device according to the first volume expansion rate data set and the second volume expansion rate data set, where the low-temperature performance evaluation index data includes freeze-breaking temperature data, transformation point temperature data, freeze-breaking strength data, and temperature stress growth slope data; perform principal component optimization on the target mixture specimen by the principal component analysis method, and obtain the low-temperature performance evaluation index data corresponding to the optimized expansion rate, where the low-temperature performance evaluation index data corresponding to the optimized expansion rate is at least one of the evaluation index data in the low-temperature performance evaluation index data; The output module is used to perform a low-temperature performance evaluation operation on the asphalt mixture through the low-temperature performance evaluation index data corresponding to the optimized expansion rate.

9. An electronic device, characterized in that, It includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions. When the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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