A method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, and its system and electronic equipment

Abstract

The present invention discloses a method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, a system thereof, and electronic equipment. These methods belong to the technical field of material testing or analysis and include: obtaining the splitting tensile strength and cohesive strength of asphalt mixture; performing data fitting processing on a training set to obtain an asphalt mixture tensile strength training model; generating an asphalt mixture tensile strength prediction model by testing a test set; inputting one or more asphalt mixtures into the asphalt mixture tensile strength prediction model; setting a prediction temperature range; and obtaining a model fitting effect. The embodiments of the present application can save a significant amount of testing costs, improve evaluation efficiency, and efficiently and quickly obtain the tensile strength value of the asphalt mixture.

Description

Technical Field

[0001] The embodiments of the present application relate to a method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, a system thereof, and electronic equipment, and belong to the technical field of material testing or analysis. Background Art

[0002] To ensure the superior performance of asphalt pavements, asphalt mixtures must meet certain road performance requirements before they can be used in engineering applications. Common mechanical properties of asphalt mixtures include shear strength, flexural strength, tensile strength, and compressive strength. Among these, tensile strength plays a particularly important role in asphalt mixtures, particularly influencing the low-temperature crack resistance and durability of asphalt pavements. Higher tensile strength enables asphalt mixtures to better withstand external tensile stresses, reducing cracking and thereby extending the service life of asphalt pavements.

[0003] Bending beam tests, direct tensile tests, and indirect tensile tests are all used to evaluate the tensile properties of asphalt mixtures. The indirect tensile test, also known as the splitting test, is widely used in asphalt mixture performance evaluation and asphalt pavement analysis. During the splitting test, as the load increases, the stress state in the middle of the specimen closely resembles the stress state in the underlying pavement when the load is applied. This test plays a crucial role in the performance evaluation and engineering application of asphalt mixtures, particularly in terms of low-temperature crack resistance, fatigue resistance, and durability. It not only provides data support for asphalt mixture mix design but also ensures the stability and safety of asphalt pavements over the long term.

[0004] To address the problem of cracking in asphalt mixtures, asphalt mixtures need to be evaluated. Existing tests for evaluating the tensile strength of asphalt mixtures require molded specimens, long test cycles, and high-precision testing equipment. Therefore, how to more efficiently and cost-effectively measure the tensile strength of asphalt mixtures has become a pressing issue. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, and its system and electronic equipment, so as to solve the problems existing in the prior art such as the long test cycle of the tensile strength of asphalt mixture and the high precision requirements of the test instruments, and to address the shortcomings of the prior art.

[0006] The present application provides the following solutions:

[0007] A method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar is used to generate an asphalt mixture tensile strength prediction model, including:

[0008] Obtain splitting tensile strength of asphalt mixture and cohesive strength of asphalt mortar;

[0009] The data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength are divided into a training set and a test set. The training set is subjected to data fitting processing to obtain an asphalt mixture tensile strength training model. The asphalt mixture tensile strength training model includes: an independent variable is asphalt mortar cohesive strength, a dependent variable is asphalt mixture splitting tensile strength, and the dependent variable is the natural logarithm of the independent variable;

[0010] The asphalt mixture tensile strength training model is tested using the test set to detect the relative error between the test value and the actual test value. If the relative error is less than the preset threshold, an asphalt mixture tensile strength prediction model is generated.

[0011] Furthermore, the method of obtaining the splitting tensile strength of the asphalt mixture and the cohesive strength of the asphalt mortar further includes:

[0012] Prepare asphalt mixture specimens, conduct asphalt mixture splitting tests at different temperatures, and obtain the asphalt mixture splitting tensile strength;

[0013] Prepare asphalt mortar, conduct pull-out tests at corresponding temperatures to obtain the asphalt mortar cohesive strength. Specifically, keep the content of each grade of aggregate and asphalt content in the mortar consistent with the original mixture, prepare asphalt mortar, and conduct pull-out tests to obtain the cohesive strength of the asphalt mortar at different temperatures.

[0014] The asphalt mixture splitting test conditions match those of the asphalt mortar pull-out test, both using water bath insulation and uniform loading.

[0015] According to the obtained splitting tensile strength of asphalt mixture and cohesive strength of asphalt mortar, a training set and a test set are formed, and the asphalt mixture tensile strength prediction model is obtained by fitting the training set.

[0016] Furthermore, in the process of preparing asphalt mortar, the aggregate distribution of the mortar is converted according to the gradation of the asphalt mixture, and the asphalt dosage is determined by the specific surface area method;

[0017] The size of the slab, pulling rate, and pulling head diameter required for the pulling test were set, the pulling head and the slab were heated, the same mass of asphalt mortar was applied to the groove of the pulling head, the pulling head was pressed against the slab until it made contact with the slab, and the pulling head was allowed to cool to room temperature;

[0018] After controlling the temperature in a water bath, a pull-out test was performed, the maximum tensile force when the asphalt mortar was destroyed was read, and the average value was taken as the experimental result.

[0019] Furthermore, the data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength are divided into a training set and a test set, and the training set is subjected to data fitting processing to obtain an asphalt mixture tensile strength training model, further comprising:

[0020] The independent variable in the asphalt mixture tensile strength training model is the asphalt mortar cohesive strength, and the dependent variable is the asphalt mixture splitting tensile strength. The dependent variable is the natural logarithm of the independent variable and satisfies the following formula:

[0021] y=ln(C+Ax)

[0022] Among them, the cohesive strength of asphalt mortar is the independent variable x, the splitting tensile strength of asphalt mixture is the dependent variable y, C is a constant, and A is the coefficient of the dependent variable.

[0023] Furthermore, different functions are fitted in the process of data fitting the training set, and the corresponding model parameters are adjusted;

[0024] The asphalt mixture tensile strength training model was iterated multiple times, and the model parameters were adjusted to achieve fitting convergence under the logarithmic function. The asphalt mixture tensile strength training model was used to predict the test set to obtain the corresponding predicted value.

[0025] The relative error between the predicted value and the actual test value is calculated until the asphalt mixture tensile strength training model meets the error threshold, and the asphalt mixture tensile strength prediction model is generated.

[0026] More preferably, it also includes:

[0027] One or more asphalt mixtures are input into the asphalt mixture tensile strength prediction model, a prediction temperature range is set, a model fitting effect is obtained, and the tensile strength of the asphalt mixture is obtained.

[0028] Still further preferably, the method further includes: obtaining an asphalt mixture sample, generating a corresponding asphalt mixture image and performing corresponding binarization processing, selecting a threshold to distinguish the asphalt mortar and aggregate areas, and obtaining a rectangular slice color image of the asphalt mixture sample using a sample slicing method and digital image scanning technology;

[0029] The asphalt mortar area and the mineral area are separated by the connected region labeling algorithm to form different region labels. The distribution of the asphalt mortar area is analyzed by calculating the pixel value histogram of the asphalt mortar area in the image.

[0030] Performing image center cropping processing on the rectangular slice color image, setting a representative volume element as a sliding window, obtaining a processed rectangular slice color image, and performing calculation based on the self uniformity and relative uniformity of the asphalt mixture sample;

[0031] The normal distribution curves of different asphalt mixtures are fitted, the normal distribution curves of the selected asphalt mixtures are used as a benchmark for calculation, the KLD divergence values ​​of the different asphalt mixtures are obtained, and the uniformity of the asphalt mixture samples is determined according to the KLD divergence values.

[0032] More preferably, the step of obtaining an asphalt mixture sample, generating a corresponding asphalt mixture image and performing corresponding binarization processing, and obtaining a rectangular slice color image of the asphalt mixture sample using a sample slicing method and a digital image scanning technique further includes:

[0033] The rectangular slice color image is preprocessed, and brightness unevenness and image noise are eliminated through image enhancement technology and median filtering algorithm, and the mortar area and coarse aggregate area in the grayscale image are defined as different colors using an image binarization algorithm.

[0034] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0035] A computer program product comprises a computer program, which implements the method when executed by a processor.

[0036] Compared with the prior art, the embodiments of the present application have the following advantages:

[0037] (1) The embodiment of the present application is based on data such as the splitting tensile strength of asphalt mixture and the cohesive strength of asphalt mortar. It performs data fitting processing through a training set and establishes a corresponding asphalt mixture tensile strength training model. It then tests the asphalt mixture tensile strength training model through a test set, and generates a corresponding asphalt mixture tensile strength prediction model based on the comparison result between the relative error and the preset threshold. The embodiment of the present application establishes a mathematical model to establish a mapping relationship between the cohesive strength of asphalt mortar and the splitting tensile strength of asphalt mixture. It only needs to perform a pull-out test on the asphalt mortar to predict the tensile strength of the mixture, which can save a lot of test costs and improve evaluation efficiency. It is not necessary to provide a molded specimen every time the asphalt mixture tensile strength test is performed, which shortens the test cycle and reduces the accuracy requirements of the test instrument. It can obtain the tensile strength value of the asphalt mixture efficiently and economically.

[0038] (2) This application achieves a model fitting effect by reasonably setting the prediction temperature range. For example, when the base asphalt and modified asphalt are used as asphalt mixtures and the prediction temperature range is 10°C-25°C, the model fitting effect is better than 95%. By applying the asphalt mixture tensile strength prediction model to asphalt mixtures, the purpose of quickly and efficiently calculating the tensile strength of asphalt mixtures is achieved.

[0039] (3) The embodiment of the present application also realizes the processing of asphalt mixture images through image enhancement technology and application of median filtering algorithm, and can measure the uniformity of mortar and aggregate distribution of different asphalt mixture samples through KLD divergence value, thereby evaluating the reliability and accuracy of the asphalt mixture tensile strength prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of the method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar.

[0042] Figure 1A It is a flow chart of the optimization technical solution from step S11 to step S13.

[0043] Figure 1B It is a flow chart of the optimization technical solution from step S121 to step S123.

[0044] Figure 2 This is the architecture diagram of the asphalt mixture tensile strength prediction system based on the cohesive strength of asphalt mortar.

[0045] Figure 3 It is a flowchart of a specific application of the asphalt mixture tensile strength prediction model.

[0046] Figure 4 It is a coordinate diagram of tensile strength and splitting strength.

[0047] Figure 5 It is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions of the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present application, not all of them. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the embodiments of the present application.

[0049] like Figure 1 The asphalt mixture tensile strength prediction method based on the cohesive strength of asphalt mortar is used to generate an asphalt mixture tensile strength prediction model, including:

[0050] Step S1, obtaining the splitting tensile strength of asphalt mixture and the cohesive strength of asphalt mortar; in step S1, the asphalt mixture can be split The test was used to obtain the splitting tensile strength of asphalt mixture, and the pull-out test was used to obtain the cohesive strength of asphalt mortar at different temperatures.

[0051] Step S2: divide the data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength into a training set and a test set, perform data fitting processing on the training set, and obtain an asphalt mixture tensile strength training model. The asphalt mixture tensile strength training model includes: the independent variable is the asphalt mortar cohesive strength, the dependent variable is the asphalt mixture splitting tensile strength, and the dependent variable is the natural logarithm of the independent variable.

[0052] Step S3, testing the asphalt mixture tensile strength training model through the test set, detecting the relative error between the test value and the actual test value, and generating an asphalt mixture tensile strength prediction model if the relative error is less than a preset threshold.

[0053] The technical solution provided in steps S1 to S3 is intended to predict the tensile strength of asphalt mixture. The splitting tensile strength of asphalt mixture and the cohesive strength of asphalt mortar are obtained through experiments, and then these data are divided into a training set and a test set. The training set data is then used to establish an asphalt mixture tensile strength prediction model. The asphalt mixture tensile strength prediction model uses the cohesive strength of asphalt mortar as the independent variable and the natural logarithm of the splitting tensile strength of asphalt mixture as the dependent variable. The test set is used to verify the model based on the asphalt mixture tensile strength prediction model. If the relative error between the model prediction results and the actual test values ​​is within an acceptable range, then this model can be used as an effective prediction tool for evaluating the tensile strength of asphalt mixture.

[0054] The inventors have discovered that the splitting tensile strength of asphalt mixtures is primarily composed of asphalt mortar strength and asphalt-aggregate adhesion, but a correlation function model between the two has not yet been proposed. The technical solutions provided in steps S1 to S3 are established based on this technical problem, aiming to reveal the relationship between splitting tensile strength, asphalt mortar strength, and asphalt-aggregate adhesion. The technical solutions provided in steps S1 to S3 enable the establishment of a mathematical model for the tensile strength of asphalt mixtures. This mathematical model experimentally obtains key physical performance parameters, namely the splitting tensile strength of the asphalt mixture and the cohesive strength of the asphalt mortar, and converts these physical performance parameters into an asphalt mixture tensile strength prediction model that can predict the tensile strength of the asphalt mixture. This asphalt mixture tensile strength prediction model reveals the relationship between asphalt mortar strength and asphalt mixture tensile performance, and can also quantify the impact of asphalt-aggregate adhesion on overall performance.

[0055] The asphalt mixture tensile strength prediction model enhances the generalization ability of the model by dividing the data set into a training set and a test set. The training set is used to build the model, while the test set is used to verify the model's prediction accuracy. The correct establishment of the training set and test set makes the asphalt mixture tensile strength prediction model not only theoretically valid, but also highly reliable in practical applications. When the relative error between the model prediction results and the actual test values ​​is within an acceptable range, the tensile strength of the asphalt mixture can be evaluated under different environments and conditions, providing a scientific basis for road design and construction, improving the efficiency and accuracy of asphalt mixture performance evaluation, significantly reducing dependence on traditional destructive tests, reducing testing costs, and accelerating the process of asphalt material research and development and asphalt material performance improvement, optimizing the asphalt mixture ratio and construction process, which can significantly improve the durability and reliability of the road, reduce maintenance costs, and extend the service life of the road, providing a strong guarantee for promoting process improvements in road engineering.

[0056] like Figure 1A As shown, in step S1, obtaining the splitting tensile strength of the asphalt mixture and the cohesive strength of the asphalt mortar further includes:

[0057] Step S11: preparing asphalt mixture specimens, performing asphalt mixture splitting tests at different temperatures, and obtaining the asphalt mixture splitting tensile strength.

[0058] Step S12, prepare asphalt mortar, perform a pull-out test at a corresponding temperature, and obtain the asphalt mortar cohesive strength of the asphalt mortar. Specifically, the content of each grade of aggregate and the asphalt content in the mortar are kept consistent with the original mixture, and the asphalt mortar is prepared. The pull-out test is performed to obtain the cohesive strength of the asphalt mortar at different temperatures.

[0059] Step S13: The asphalt mixture splitting test conditions match the asphalt mortar pull-out test conditions, and both use water bath insulation and uniform speed loading test methods.

[0060] Step S14: forming a training set and a test set based on the obtained splitting tensile strength of the asphalt mixture and the cohesive strength of the asphalt mortar, and obtaining an asphalt mixture tensile strength prediction model by fitting the training set.

[0061] The optimization technology provided in steps S11 to S14 systematically obtains the key mechanical performance parameters of asphalt mixture and asphalt mortar through experimental methods, and then constructs a mathematical model that can predict the tensile strength of asphalt mixture. First, the optimization technology provided in steps S11 to S14 adopts data-driven model construction, and splitting tests are conducted at different temperatures to prepare asphalt mixture specimens to obtain their splitting tensile strength. The actual data obtained from the experiments provide a solid foundation for the model, ensuring that the model's prediction results are consistent with the actual situation. The influence of temperature factors is fully considered. Tests are conducted at different temperatures, considering the impact of ambient temperature on the performance of asphalt mixture, and enhancing the adaptability of the model under different environmental conditions. The experimental results are combined with mathematical modeling to improve the accuracy of the prediction, provide a scientific basis for the design and construction of asphalt mixtures, reduce dependence on actual destructive tests, thereby reducing testing costs and improving R&D efficiency. The establishment of a corresponding prediction model based on detailed and rigorous experimental data helps to better understand the relationship between the cohesive strength of asphalt mortar and the splitting tensile strength of asphalt mixtures, optimize the mix ratio of asphalt mixtures, and improve the performance of asphalt mixtures.

[0062] In the optimization technical solution provided in steps S11 to S14, the aggregate content of the mortar is consistent with that of the mixture, so that the prediction model has a principle to follow, because not any asphalt mortar can predict the performance of the asphalt mixture, to prevent the situation where the prediction model has no principle to follow. Because the mortar, as a part of the asphalt mixture, bears the main tensile strength, the prepared mortar must comply with the grading and the asphalt dosage must be consistent with the mixture.

[0063] like Figure 1B As shown, preferably, in the process of preparing asphalt mortar in step S12, the following steps are further included:

[0064] In step S121, the asphalt dosage is determined using the specific surface area method based on the maximum particle size of the fine aggregate in the mortar. In step S121, the maximum particle size of the fine aggregate can be set to 0.6 mm. Alternatively, a range of values ​​around 0.6 mm can be selected to ensure that the aggregate content and asphalt content in the mortar remain consistent with those in the original mixture, and that the mixing time and temperature are consistent with those of the original mixture.

[0065] In step S122, the pulling head and slab size, pulling rate, and pulling head diameter, consistent with the fine aggregate lithology required for the pullout test, are set. The pulling head and slab are heated, the mixed asphalt mortar is applied to the grooves of the pulling head, and the pulling head is pressed against the slab until it contacts the slab. The pulling head is then allowed to cool to room temperature. In step S122, the pulling head and slab can be heated in an oven at approximately 160°C for approximately one hour.

[0066] In step S123, after the water bath treatment, a pull-out test is performed to read the maximum tensile force at which the asphalt mortar is destroyed, and the average value is taken as the experimental result. In step S123, the water bath is kept at the experimental temperature or room temperature for 1 hour. During the averaging process, each experiment is repeated 4 times and the average value is taken as the experimental result.

[0067] The optimization technology provided by steps S121 to S123 prepares asphalt mortar through a series of carefully controlled experimental steps and accurately measures its cohesive strength. By precisely controlling the asphalt dosage and test conditions, the consistency and repeatability of the asphalt mortar samples are ensured, making the experimental data reliable and accurate. By simulating the temperature and pressure conditions in actual construction, the experimental results can better reflect the behavior of the asphalt mortar in actual application. By measuring the maximum tensile force of the asphalt mortar in the pull-out test, its cohesive properties can be directly evaluated, and the mechanical characteristics and durability data of the asphalt material can be accurately obtained. A large number of tests are also efficiently conducted through standardized experimental processes and conditions, thereby quickly accumulating data and optimizing material design. Repeating experiments and taking average values ​​reduces the impact of random errors and improves the reliability of the experimental results. In short, steps S121 to S123 provide a scientific, accurate, and efficient experimental method for evaluating the performance of asphalt mortar.

[0068] In step S2, the data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength are divided into a training set and a test set, and the training set is subjected to data fitting processing to obtain an asphalt mixture tensile strength training model, further comprising:

[0069] The independent variable in the asphalt mixture tensile strength training model is the asphalt mortar cohesive strength, and the dependent variable is the asphalt mixture splitting tensile strength. The dependent variable is the natural logarithm of the independent variable and satisfies the following formula:

[0070] y=ln(C+Ax)……(1)

[0071] Among them, the cohesive strength of asphalt mortar is the independent variable x, the splitting tensile strength of asphalt mixture is the dependent variable y, C is a constant, and A is the coefficient of the dependent variable.

[0072] In the process of data fitting of the training set, different functions are fitted and the corresponding model parameters are adjusted;

[0073] The asphalt mixture tensile strength training model was iterated multiple times, and the model parameters were adjusted to achieve fitting convergence under the logarithmic function. The asphalt mixture tensile strength training model was used to predict the test set to obtain the corresponding predicted value.

[0074] The relative error between the predicted value and the actual test value is calculated until the asphalt mixture tensile strength training model meets the error threshold, and the asphalt mixture tensile strength prediction model is generated.

[0075] The core content of step S2 is to establish an asphalt mixture tensile strength training model that can predict the splitting tensile strength of asphalt mixture through mathematical modeling and data fitting methods. The model uses the cohesive strength of asphalt mortar as the independent variable and the natural logarithm of the splitting tensile strength of asphalt mixture as the dependent variable. The relationship between the two is described by determining the constant C and coefficient A in the model. The asphalt mixture tensile strength training model divides the data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength into a training set and a test set. Using the training set data, a prediction model for asphalt mixture tensile strength is constructed by fitting different functions and adjusting parameters. The model is then iterated multiple times and the model parameters are continuously optimized until the relative error between the model's predicted value for the test set and the actual test value reaches an acceptable range, achieving model fitting convergence and forming an asphalt mixture tensile strength prediction model.

[0076] The asphalt mixture tensile strength prediction model uses data fitting to form a natural logarithm-based prediction model. This model more accurately captures the relationship between the cohesive strength of asphalt mortar and the splitting tensile strength of asphalt mixture, providing more accurate prediction results. The model also ensures good generalization and adaptability to diverse data and conditions. Finally, through multiple iterations and parameter adjustments, the model finds the optimal parameter combination, thereby improving prediction accuracy and reliability. Compared with traditional experimental methods, the prediction model can significantly reduce the number and cost of experiments, improve the efficiency of research and development, and evaluate the results. It provides a scientific basis for the design and construction of asphalt mixtures and helps optimize material selection and construction processes.

[0077] like Figure 2 The asphalt mixture tensile strength prediction system architecture diagram based on asphalt mortar cohesive strength includes:

[0078] Asphalt material data acquisition module, used to obtain the splitting tensile strength of asphalt mixture and the cohesive strength of asphalt mortar.

[0079] The asphalt mixture tensile strength training model generation module divides the data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength into a training set and a test set, performs data fitting processing on the training set, and obtains the asphalt mixture tensile strength training model. The asphalt mixture tensile strength training model includes: the independent variable is the asphalt mortar cohesive strength, the dependent variable is the asphalt mixture splitting tensile strength, and the dependent variable is the natural logarithm of the independent variable.

[0080] The asphalt mixture tensile strength prediction model generation module tests the asphalt mixture tensile strength training model through the test set, detects the relative error between the test value and the actual test value, and generates the asphalt mixture tensile strength prediction model if the relative error is less than the preset threshold.

[0081] It is worth noting that although only some basic functional modules are disclosed in the embodiments of the present application, it does not mean that the composition of the present system is limited to the above basic functional modules. On the contrary, what the embodiments of the present application want to express is: on the basis of the above basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the existing technology to form an infinite number of embodiments or technical solutions. In other words, the present system is open rather than closed. Just because the embodiments of the present application only disclose individual basic functional modules, it cannot be considered that the scope of protection of the claims of the embodiments of the present application is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above devices are described in terms of functions, which are divided into various units and modules. Of course, when implementing the embodiments of the present application, the functions of each unit and module can be implemented in the same or one or more software and / or hardware.

[0082] like Figure 3 The content shown is a specific application of the asphalt mixture tensile strength prediction model obtained above, including:

[0083] In step T1, one or more asphalt mixtures are input into the asphalt mixture tensile strength prediction model, a prediction temperature range is set, and the model fit is obtained to determine the tensile strength of the asphalt mixture. In step T1, the multiple asphalt mixtures can be base asphalt and modified asphalt, and the optimal model fit is greater than 95%. The tensile strength of the asphalt mixture obtained using the asphalt mixture tensile strength prediction model is a predicted value, and its reliability requires further verification.

[0084] In step T2, an asphalt mixture sample is obtained, and a corresponding asphalt mixture image is generated and binarized. A rectangular color image of the asphalt mixture sample is obtained using a sample slicing method and digital image scanning technology. In step T2, an appropriate threshold is selected to distinguish between the asphalt mortar and aggregate areas.

[0085] Step T3, performing image center cropping processing on the rectangular slice color image, setting the representative volume element RVE as a sliding window, obtaining the processed rectangular slice color image, and performing calculation based on the self-uniformity and relative uniformity of the asphalt mixture sample.

[0086] Step T4, fitting normal distribution curves of different asphalt mixtures, selecting the normal distribution curve of the asphalt mixture as a benchmark for calculation, obtaining KLD divergence values ​​of the different asphalt mixtures, and determining the uniformity of the asphalt mixture sample according to the KLD divergence values.

[0087] In step T2, the asphalt mortar area can be separated from the mineral area by the connected component labeling algorithm to form different area labels. The distribution of the asphalt mortar area in the image can be analyzed by calculating the pixel value histogram of the asphalt mortar area in the image. The connected component labeling algorithm is a technical means used in the field of image processing and computer vision to identify and label connected areas in images. Connected areas refer to sets of pixels in the image that have the same attributes (such as color, intensity or texture) and are connected to each other. These areas can be foreground objects, background parts or any other meaningful image features. The connected component labeling algorithm provides a basis for image analysis and understanding. By labeling and analyzing connected areas, image data can be better understood and processed.

[0088] Optionally, step T2 can use a rotation sampling method to evaluate the directional uniformity of the mortar, with the center of the image as the coordinate origin and the vertical upward direction defined as 0°. By setting a sampling line every 10°, calculating the ratio of the number of mortar pixels to the total number of sampling line pixels, and calculating the coefficient of variation (Coefficient of Variation of Orientation, COV) in different directions, the directional uniformity of the mortar distribution is quantified to form an evaluation of the positional uniformity. In the evaluation of positional uniformity, the mortar distribution is analyzed by the four-quadrant method. Similarly, the image center is used as the coordinate origin, and the image is divided into four quadrants using a rectangular coordinate system. The centroid coordinates of the mortar in each quadrant are calculated separately, and the coefficient of variation (Coefficient of Variation of Position, COP) of the centroid coordinates of the mortar in these four quadrants is calculated to evaluate the positional uniformity of the mortar distribution.

[0089] The evaluation of positional uniformity also comprehensively considers the directional and positional uniformity of mortar distribution, defining the Uniformity Index of Mortar (UIM). Its calculation formula is the sum of the Coefficient of Performance (COP) and the Coefficient of Value (COV). The UIM provides a comprehensive metric for evaluating the uniformity of mortar distribution in asphalt mixtures, offering a new perspective for asphalt mixture design and performance optimization. The UIM enables a more comprehensive understanding and control of the microstructure of asphalt mixtures, thereby improving their macroscopic performance.

[0090] In step T3, the representative volume element (RVE) is used as a sliding window concept. Its main application areas are materials science and image analysis. For example, it can be used to analyze the microstructure of multiphase materials (such as asphalt mixture).

[0091] A representative volume element (RVE) refers to a volume unit that is large enough to contain all the microstructural features inside the material and can represent the statistical properties of the entire material or structure. In image analysis, the representative volume element (RVE) can be regarded as a virtual window that is controlled to slide on the two-dimensional or three-dimensional image of the material to analyze the structural characteristics inside the material. By using the representative volume element (RVE) as a sliding window, local information such as aggregate distribution, void ratio, material phase interface, etc. can be extracted in different areas of the image. In asphalt mixture analysis, the representative volume element (RVE) window can be used to evaluate the uniformity of the mixture. By sliding the window of the R representative volume element (VR) on the image, parameters such as the distribution and area ratio of aggregates in each window area can be calculated to quantify the uniformity of the mixture and evaluate the spatial distribution of different components.

[0092] In actual operation, the size and shape of the representative volume element RVE window can be determined according to the characteristics of the material and the purpose of analysis. For example, in asphalt mixtures, the representative volume window RVE can be set to contain a certain number of aggregate particles to ensure statistical significance. By sliding the representative volume element RVE window on the image and calculating the relevant parameters at each position, a detailed distribution map of the internal structure of the material can be obtained, which provides an observation method for analyzing the microstructure of asphalt materials for studying asphalt materials. It can be used to reduce errors caused by local variability when evaluating the uniformity and performance of asphalt materials, and provide more accurate asphalt material characteristic analysis.

[0093] The technical solution provided in steps T1 to T4 inputs different types of asphalt mixtures (including base asphalt and modified asphalt) into a prediction model by configuring and operating the model, including temperature setting, image acquisition and processing, image cropping and uniformity evaluation, and normal distribution curve fitting and uniformity determination, and sets a prediction temperature range to obtain the fitting effect of the model, ensuring that the best fitting effect is greater than 95%. The color image of the asphalt mixture sample is obtained by sample slicing method and digital image scanning technology, and binarized to prepare for subsequent image analysis. The image is center cropped and the representative volume element RVE is used as a sliding window to process the image. The self-uniformity and relative uniformity index of the asphalt mixture sample are calculated based on image analysis, the normal distribution curves of different asphalt mixtures are fitted, and the KLD divergence value is calculated based on the normal distribution curve of the asphalt mixture, and finally the uniformity of the asphalt mixture sample is determined. The predicted tensile strength of the asphalt mixture obtained by the aforementioned tensile strength prediction model is further combined with the uniformity of the asphalt mixture sample obtained from the KLD divergence value. The reliability and accuracy of the asphalt mixture tensile strength prediction model are evaluated from two perspectives: the predicted value and the uniformity of the actual image. If the uniformity of the actual image is not ideal, that is, if the KLD divergence is greater than 5, steps S1-S3 need to be re-executed to verify or correct the asphalt mixture tensile strength prediction model.

[0094] The technical solution provided in steps T1 to T4 compares the differences in aggregate distribution uniformity between different asphalt mixture samples by determining the uniformity of the asphalt mixture samples. This is quantified using statistical methods such as the Kullback-Leibler divergence (KLD divergence), measuring the distance between two probability distributions. The KLD divergence can be used to fit the normal distribution of the aggregate area ratios of multiple mixture samples and is an important parameter for evaluating the quality of asphalt mixtures. By evaluating the uniformity of the mixture, the performance and durability of asphalt pavements can be predicted and guaranteed, and the preparation and construction processes of the asphalt mixture can be optimized to improve the performance of the final asphalt product, which helps to improve the quality of road projects, extend the service life of roads, and reduce maintenance costs. This provides a comprehensive, accurate, and efficient technical solution for the performance evaluation of asphalt mixtures.

[0095] Preferably, in step T2, the rectangular slice color image is preprocessed, and the brightness unevenness and image noise are eliminated by image enhancement technology and median filtering algorithm, and the mortar area and coarse aggregate area in the grayscale image are defined as different colors by using an image binarization algorithm. For example, the mortar area can be defined as black, and the coarse aggregate can be defined as white.

[0096] During the optimization and improvement of step T2, the color images of the asphalt mixture samples were preprocessed using image processing techniques to improve the accuracy and reliability of subsequent image analysis. This improved image quality made the detailed features of the asphalt mixture samples more distinct, facilitating subsequent analysis. A median filter algorithm was applied to eliminate uneven brightness and noise in the image, reducing random interference and further improving image clarity and readability. An image binarization algorithm was used to convert the grayscale image into an image containing only two colors. By defining the mortar and coarse aggregate regions as different colors, the different components in the asphalt mixture can be distinguished, providing a foundation for the automated processing and analysis of asphalt mixture images and contributing to the automation and standardization of asphalt mixture performance evaluation.

[0097] As a further improvement, the process of image enhancement and median filtering algorithm processing satisfies the following formula:

[0098]

[0099] ……(2)

[0100] Among them: I original (x,y) represents the pixel value of the original image at coordinate (x,y).

[0101] I enhanced (x,y) represents the pixel value of the enhanced image at coordinate (x,y).

[0102] L represents the contrast stretching factor, which is used to adjust the contrast of the image. The value of L is usually a positive number and is used to control the degree of contrast enhancement.

[0103] α represents a gain factor, which is used to control the adjustment of the overall image brightness. α can be any real number greater than 0.

[0104] β represents the weight factor of the median filter, which is used to control the degree of influence of the median filter on the final image. The value of β is between 0 and 1.

[0105] M(I original (x,y)) represents the pixel value at coordinate (x,y) in the image after median filtering. Median filtering is a nonlinear filtering technique used to replace each pixel value with the median of its neighboring pixel values, thereby reducing noise.

[0106] γ represents the offset used to adjust the brightness level of the image. The value of γ can be any real number. A positive value increases the brightness, while a negative value decreases the brightness.

[0107] By applying formula (1), contrast enhancement can be achieved. The logarithmic function can expand the pixel value range of low-contrast areas, making the dark details of the image more obvious. Brightness adjustment is achieved through α and γ. α can enlarge or reduce the brightness range of the image, while γ can increase or decrease the overall brightness of the image. Finally, median filtering is implemented through β·M(Ioriginal(x,y)) to remove salt and pepper noise in the image while maintaining edge information.

[0108] Formula (1) combines the effects of contrast enhancement, brightness adjustment, and median filtering to achieve an overall improvement in image quality. By adjusting the values ​​of α, β, and γ, it can be optimized for different images and noise conditions.

[0109] like Figure 4 As shown, the present application provides an implementation method in a specific application scenario. In this implementation method, the asphalt mixture tensile strength prediction model establishes a mapping relationship between the cohesive strength of asphalt mortar and the splitting tensile strength of asphalt mixture. It only needs to conduct a pull-out test on the asphalt mortar to predict the tensile strength of the mixture, which can save a lot of test costs and improve evaluation efficiency. The specific steps of the method are as follows:

[0110] Step 1: Obtain a prediction model for the tensile strength of an asphalt mixture, wherein the prediction model includes a functional relationship between the cohesive strength of the asphalt mortar and the tensile strength of the mixture;

[0111] Step 2: Design the asphalt mixture, calculate the aggregate and asphalt content of the mortar to be consistent with the mixture, and prepare the asphalt mortar;

[0112] Step 3: Conduct a pull-out test on the mortar at a selected temperature to obtain the cohesive strength;

[0113] Step 4: Input the obtained cohesive strength into the prediction model and calculate the tensile strength of the asphalt mixture using the functional relationship.

[0114] The methods for constructing the prediction model include the following:

[0115] A variety of asphalts were selected to prepare multiple groups of asphalt mixture specimens with the same gradation and keep the porosity consistent. The asphalt mixture splitting tests were carried out at different temperatures to obtain the splitting tensile strength.

[0116] The content of each grade of aggregate and asphalt content in the mortar are kept consistent with the original mixture to prepare asphalt mortar, and the pull-out test is used to obtain the cohesive strength of the asphalt mortar at different temperatures.

[0117] The above-obtained data sets of mortar cohesive strength and asphalt mixture splitting tensile strength were divided into training set and test set. The training set was fitted to obtain the asphalt mixture tensile strength prediction model: y = ln(-11.209 + 6.652x), where y is the asphalt mixture splitting tensile strength (MPa) and x is the asphalt mortar cohesive strength (MPa).

[0118] Use the model to predict the test set and calculate the relative error between the predicted value and the actual test value. If the error is less than the error threshold of 10%, the training is completed and the model is determined.

[0119] The prediction model is applicable to a variety of asphalt mixtures (including base asphalt and modified asphalt). When the prediction temperature range is 10℃-25℃, the model fitting effect is best greater than 95%.

[0120] In the process of preparing mortar, the maximum particle size of fine aggregate selected for mortar preparation is 0.6mm, and the asphalt dosage is determined by the specific surface area method to ensure that the content of each grade of aggregate and asphalt content in the mortar are consistent with the original mixture, and the mixing time and temperature are kept the same as the mixture.

[0121]

[0122] P b砂浆 -Oil-to-stone ratio of mortar: %;

[0123] SA - total specific surface area of ​​aggregate in the mixture: m 2 / kg;

[0124] SA 0.6 Total specific surface area of ​​aggregate below -0.6: m 2 / kg;

[0125] P b -Optimal oil-stone ratio of mixture: %;

[0126] The steps for performing a pull-out test are as follows:

[0127] The pulling test used a 10cm*10cm stone slab with the same lithology as the fine aggregate, a pulling rate of 0.7MPa / s, and a pulling head diameter of 20mm.

[0128] Heat the pulling head and the slate in an oven at 160°C for 1 hour, apply the mixed asphalt mortar to the groove of the pulling head, press the pulling head against the slate until it contacts the slate, and cool to room temperature.

[0129] Place the test tube in a water bath at the experimental temperature for 1 hour, perform a pull-out test, read the maximum tensile force at failure, repeat each experiment 4 times, and take the average value as the experimental result.

[0130] The asphalt mixture splitting test conditions matched those of the asphalt mortar pull-out test, using a water bath for 2 h and a uniform loading rate of 50 mm / min.

[0131] The obtained data sets of mortar cohesive strength and asphalt mixture splitting tensile strength were fitted with different functions, and the model parameters were adjusted. It was found that the fitting convergence was achieved after 6 iterations under the logarithmic function.

[0132] Table 1 - Model data training set

[0133] serial number Temperature / ℃ Asphalt Type Tensile strength / Mpa Splitting strength / Mpa 1 10 Base asphalt 3.410 2.230 2 10 Base asphalt 3.010 2.137 3 15 Base asphalt 2.700 2.030 4 15 Base asphalt 3.170 2.234 5 25 Base asphalt 2.060 0.822 6 25 Base asphalt 2.090 0.832 7 10 Modified asphalt 1 4.510 2.822 8 10 Modified asphalt 1 3.980 2.678 9 15 Modified asphalt 1 3.560 2.560 10 15 Modified asphalt 1 3.640 2.604 11 25 Modified asphalt 1 2.220 1.226 12 25 Modified asphalt 1 2.200 1.188 13 10 Modified asphalt 2 4.210 2.790 14 10 Modified asphalt 2 4.150 2.785 15 15 Modified asphalt 2 3.600 2.413 16 15 Modified asphalt 2 3.020 2.440 17 25 Modified asphalt 2 2.180 1.340 18 25 Modified asphalt 2 2.190 1.416

[0134] Table 2 - Error analysis of model training set

[0135] Input value Output predicted value Actual test value Relative error 4.300 2.839 2.960 4.08％ 2.99 2.148 2.190 1.91％ 2.30 1.405 1.502 6.48％

[0136] Relative error is an important indicator of model prediction accuracy. The data in Table 2 demonstrates that the model's predictions are very close to the actual test values, demonstrating high accuracy. Even though the maximum relative error is 6.48%, significantly larger than the previous two, it remains within an acceptable range, especially considering the natural variability of material properties and experimental error. The data in Table 2 demonstrates that the prediction model demonstrates high accuracy and reliability in predicting the tensile strength of asphalt mixtures. The prediction model can provide valuable insights, aiding data-driven decision-making, and fulfilling its decision-making support function.

[0137] like Figure 5 As shown, the embodiment of the present application provides a method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, and also provides corresponding electronic equipment and computer program products:

[0138] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0139] A computer program product comprises a computer program, which implements the method when executed by a processor.

[0140] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 5The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application. The electronic device can typically be a device in an electronic product based on the asphalt mixture tensile strength prediction method based on the asphalt mortar cohesive strength in the above embodiment. Figure 5As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, one or more processing units or processors 516, memory 528, and a bus 518 connecting various system components (including memory 528 and processor 516). Bus 518 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus. Electronic device 500 typically includes a variety of computer-readable media. These media can be any available media accessible by electronic device 500, including volatile and non-volatile media, removable and non-removable media. Memory 528 may include computer-readable media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. The electronic device 500 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 534 may be used to read and write non-removable, non-volatile magnetic media (not shown in the figure, commonly referred to as a "hard drive"). Although not shown in the figure, the storage system 534 may provide a disk drive for reading and writing a removable non-volatile disk (e.g., a floppy disk, a mobile hard disk, a hot-swappable storage medium), and an optical drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media). In these cases, each drive may be connected to the bus 518 via one or more data medium interfaces. The memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application. A program / utility 540 having a set (at least one) of program modules 542 may be stored in, for example, a memory 528. Such program modules 542 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each of these examples or some combination may include the implementation of a network environment. The program modules 542 typically perform the functions and / or methods in the embodiments described in the embodiments of the present application. The electronic device 500 may also communicate with one or more external devices 514 (e.g., keyboards, pointing devices, displays 524, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.).Such communication can be performed through an input / output (I / O) interface 522. In addition, the electronic device 500 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 520. The network adapter 520 communicates with other modules of the electronic device 500 through a bus 518. It should be understood that, although not shown in the figure, those skilled in the art can use other hardware and / or software modules in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems. The processor 516 executes various functional applications and data processing by running programs stored in the memory 528, such as implementing the methods provided in any one or more embodiments of the present application.

[0141] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features that are included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, any one of the embodiments claimed in the claims may be used in any combination in the present invention.

[0143] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0144] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including corresponding claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including corresponding claims, abstracts and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present application, rather than to limit them. Although the embodiments of the present application have been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar, which is used to generate an asphalt mixture tensile strength prediction model, characterized in that: include: Obtain splitting tensile strength of asphalt mixture and cohesive strength of asphalt mortar; The data sets of asphalt mixture splitting tensile strength and asphalt mortar cohesive strength are divided into training set and test set. The training set is processed with data fitting to obtain the asphalt mixture tensile strength training model. The independent variable in the asphalt mixture tensile strength training model is the asphalt mortar cohesive strength, and the dependent variable is the asphalt mixture splitting tensile strength. The dependent variable is the natural logarithm of the independent variable and satisfies the following formula: y=ln(C+Ax) Among them, the cohesive strength of asphalt mortar is the independent variable x, the splitting tensile strength of asphalt mixture is the dependent variable y, C is a constant, and A is the coefficient of the dependent variable; The asphalt mixture tensile strength training model is tested using a test set to detect the relative error between the test value and the actual test value. If the relative error is less than a preset threshold, an asphalt mixture tensile strength prediction model is generated. The generated asphalt mixture tensile strength prediction model is then used to predict the tensile strength of one or more input asphalt mixtures.

2. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 1, characterized in that: The method of obtaining the splitting tensile strength of the asphalt mixture and the cohesive strength of the asphalt mortar further comprises: Prepare asphalt mixture specimens, conduct asphalt mixture splitting tests at different temperatures, and obtain the asphalt mixture splitting tensile strength; Prepare asphalt mortar, conduct pull-out tests at corresponding temperatures to obtain the asphalt mortar cohesive strength. Specifically, keep the content of each grade of aggregate and asphalt content in the mortar consistent with the original mixture, prepare asphalt mortar, and conduct pull-out tests to obtain the cohesive strength of the asphalt mortar at different temperatures. The asphalt mixture splitting test conditions match the asphalt mortar pull-out test conditions, and both use water bath insulation and uniform loading test methods.

3. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 2, characterized in that: In the process of preparing asphalt mortar, the aggregate distribution of the mortar is converted according to the gradation of the asphalt mixture, and the asphalt dosage is determined by the specific surface area method; The size of the slab, pulling rate, and pulling head diameter required for the pulling test were set, the pulling head and the slab were heated, the same mass of asphalt mortar was applied to the groove of the pulling head, the pulling head was pressed against the slab until it made contact with the slab, and the pulling head was allowed to cool to room temperature; After controlling the temperature in a water bath, a pull-out test was performed, the maximum tensile force when the asphalt mortar was destroyed was read, and the average value was taken as the experimental result.

4. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 1, characterized in that: In the process of data fitting of the training set, different functions are fitted and the corresponding model parameters are adjusted; The asphalt mixture tensile strength training model was iterated multiple times, and the model parameters were adjusted to achieve fitting convergence under the logarithmic function. The asphalt mixture tensile strength training model was used to predict the test set to obtain the corresponding predicted value. The relative error between the predicted value and the actual test value is calculated until the asphalt mixture tensile strength training model meets the error threshold, and the asphalt mixture tensile strength prediction model is generated.

5. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 4, characterized in that: Also includes: One or more asphalt mixtures are input into the asphalt mixture tensile strength prediction model, a prediction temperature range is set, a model fitting effect is obtained, and the tensile strength of the asphalt mixture is obtained.

6. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 5, characterized in that: Also includes: Obtain asphalt mixture samples, generate corresponding asphalt mixture images and perform corresponding binarization processing, select thresholds to distinguish asphalt mortar and aggregate areas, and use sample slicing method and digital image scanning technology to obtain rectangular slice color images of asphalt mixture samples; The asphalt mortar area and the mineral area are separated by the connected region labeling algorithm to form different region labels. The distribution of the asphalt mortar area is analyzed by calculating the pixel value histogram of the asphalt mortar area in the image. Performing image center cropping processing on the rectangular slice color image, setting a representative volume element as a sliding window, obtaining a processed rectangular slice color image, and performing calculation based on the self uniformity and relative uniformity of the asphalt mixture sample; The normal distribution curves of different asphalt mixtures are fitted, the normal distribution curves of the selected asphalt mixtures are used as a benchmark for calculation, the KLD divergence values ​​of the different asphalt mixtures are obtained, and the uniformity of the asphalt mixture samples is determined according to the KLD divergence values.

7. The method for predicting the tensile strength of asphalt mixture based on the cohesive strength of asphalt mortar according to claim 6, characterized in that: The method of obtaining an asphalt mixture sample, generating a corresponding asphalt mixture image and performing corresponding binarization processing, and obtaining a rectangular slice color image of the asphalt mixture sample using a sample slicing method and a digital image scanning technology further includes: The rectangular slice color image is preprocessed, and brightness unevenness and image noise are eliminated through image enhancement technology and median filtering algorithm, and the mortar area and coarse aggregate area in the grayscale image are defined as different colors using an image binarization algorithm.

8. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method according to any one of claims 1 to 7.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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