A method for establishing an asphalt material database management system

Abstract

The present invention relates to the technical field of asphalt material data processing, and in particular to a method for establishing an asphalt material database management system. The method comprises the following steps: using a scanning electron microscope, an atomic force microscope, and an X-ray tomography device to perform imaging scans on asphalt material samples of different gradations, different modifier types, and different aging states, respectively, to obtain multi-scale asphalt material microscopic image data; analyzing the microscopic characteristics of the material samples based on the pre-processed multi-scale asphalt material microscopic image data to obtain asphalt material microscopic characteristic parameters; and creating an asphalt material database management system for the asphalt material microscopic characteristic parameters based on a relational database management system. The present invention creates an asphalt material database for multi-dimensional query by efficiently associating the microstructure and macroscopic properties of asphalt materials, thereby achieving efficient storage and rapid query of asphalt material data.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt material data processing, and in particular to a method for establishing an asphalt material database management system. Background Art

[0002] Asphalt is a complex composite material whose performance is influenced by numerous factors, including asphalt type, aggregate type, additives, grading design, and microstructure. Within the asphalt materials sector, several database management systems have emerged to store and manage asphalt material performance data. These systems typically employ a relational database model, storing various asphalt material parameters as attributes within the database and enabling data retrieval and analysis through query statements. However, when dealing with complex materials like asphalt, these traditional database management systems typically manage the material ID number as a separate field. This approach tends to overlook highly correlated, subtle characteristic information and the complex relationships between asphalt material performance indicators and their microstructural characteristics. Consequently, when material performance screening is required, the microstructural factors that are critical to performance cannot be effectively captured, resulting in low query efficiency. This is especially true when performing complex conditional queries to screen asphalt materials that simultaneously meet multiple performance indicators. This significantly increases query time, making it difficult to meet the needs of practical applications. Summary of the Invention

[0003] Based on this, the present invention proposes a method for establishing an asphalt material database management system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a method for establishing an asphalt material database management system includes the following steps:

[0005] Using a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, asphalt material samples of different gradations, different modifier types, and different aging states are imaged and scanned to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image, and an asphalt material three-dimensional structure image;

[0006] Performing denoising, contrast enhancement, and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data;

[0007] Analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain microscopic characteristic parameters of the asphalt material;

[0008] An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0009] This invention uses advanced microscopic imaging technology to acquire multi-scale microstructural data of asphalt materials, including surface morphology, roughness, and three-dimensional structure. This provides a foundation for a deeper understanding of the material's internal structure, component distribution, and interactions. This microstructural information is often a key factor in determining the macroscopic properties of asphalt materials. Microscopic characteristic parameters of the asphalt material are extracted from the preprocessed microscopic image data. These parameters encompass the material's morphological characteristics, such as particle size, shape, and distribution, as well as surface roughness and porosity. These microstructural parameters are closely related to the asphalt material's macroscopic properties, such as mechanical properties, durability, and high-temperature stability. These microscopic characteristic parameters are combined with asphalt material preparation parameters and macroscopic performance data and integrated into an asphalt material database based on a relational database management system. Unlike traditional methods that manage only material IDs as independent fields, the new system incorporates microscopic characteristics into the database as key attributes, thereby establishing a correlation between the material's microstructure, preparation parameters, and macroscopic properties. This multi-dimensional database design enables the system to not only store and manage large amounts of data but also facilitates complex query conditions. For example, users can simultaneously set multiple conditions, such as preparation parameters (such as asphalt type, aggregate gradation, additive type, etc.), microstructural characteristics (such as porosity and roughness), and macro-performance indicators (such as compressive strength and elastic modulus), for screening, thereby quickly finding asphalt materials that meet specific requirements. By incorporating micro-feature data into the query conditions, asphalt materials with specific microstructures can be more accurately screened. Therefore, the present invention provides a method for establishing an asphalt material database management system. This method uses a scanning electron microscope, an atomic force microscope, and an X-ray tomography device to obtain multi-scale microscopic image data of asphalt materials, and then analyzes and pre-processes the data to obtain micro-feature parameters. Based on a relational database management system, a multi-dimensional query asphalt material database is created by combining preparation parameters, micro-feature parameters, and macro-performance data.

[0010] Preferably, the imaging scan comprises the following steps:

[0011] According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are subjected to pre-treatment operations such as cutting and polishing to obtain sample surfaces that meet the test requirements;

[0012] Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, and determining at least five different imaging areas according to the sample size and target magnification;

[0013] Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by using a scanning electron microscope; the surface roughness image of the asphalt material is obtained by using an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by using an X-ray tomography device.

[0014] The present invention ensures the representativeness and consistency of the samples by presetting the gradation range, modifier type and aging conditions, and performing sample preparation and pretreatment. This enables the acquired image data to truly reflect the microstructural characteristics of different types of asphalt materials. Furthermore, scanning electron microscopes, atomic force microscopes and X-ray tomography equipment are selected to observe microscopic features of different scales, respectively, forming a complementary relationship, which can comprehensively characterize the microstructural characteristics of asphalt materials. At least 5 different imaging areas are determined during the scanning process to ensure the coverage and representativeness of the scan and avoid the influence of the randomness of local samples on the results. The scanning image data of each area is scanned and saved one by one, providing a rich data basis for subsequent analysis and research, and ensuring the comprehensiveness and reliability of the data.

[0015] Preferably, the microscopic characteristic analysis of the material sample comprises the following steps:

[0016] Identifying pore areas based on the three-dimensional structural image of the asphalt material, and performing pore feature analysis to obtain pore feature data;

[0017] Identifying aggregate particle contours based on the asphalt material surface topography image and the asphalt material three-dimensional structure image, and performing aggregate contour feature analysis to obtain aggregate feature data;

[0018] Segment the asphalt film based on the asphalt material surface morphology image, and analyze the asphalt interface state through the asphalt material surface roughness image to obtain the asphalt film characteristic data;

[0019] The pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to obtain the microscopic characteristic parameters of the asphalt material.

[0020] The present invention identifies pore areas from three-dimensional structural images and performs characterization analysis to obtain key information such as porosity, pore size distribution, and pore connectivity. These pore characteristic data are crucial for understanding the macroscopic properties of asphalt materials, such as permeability and durability. Secondly, based on the surface morphology image and the three-dimensional structural image, the outline of the aggregate particles is identified and characteristic analysis is performed to obtain information such as the size, shape, distribution, and adhesion of the aggregate to asphalt. By segmenting the asphalt film and analyzing the asphalt interface state in combination with the surface roughness image, information such as the thickness, uniformity, roughness, and interface bonding state of the asphalt film with the aggregate can be obtained. The extracted pore characteristic data, aggregate characteristic data, and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to ultimately obtain the microscopic characteristic parameters of the asphalt material. This quantitative processing method converts the originally qualitative image information into quantifiable numerical parameters, making the description of the microstructure of the asphalt material more accurate and objective. Establishing the relationship between the characteristic parameters reveals the intrinsic connection between different microscopic characteristics, which provides a deeper understanding of the microstructure of the asphalt material. For example, the distribution of pores is affected by aggregate gradation, and the thickness of asphalt film is related to the type of asphalt.

[0021] Preferably, the pore characterization analysis comprises the following steps:

[0022] grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image;

[0023] Based on the grayscale image, a segmentation threshold interval is set as [T1, T2], pixels in the image with grayscale values lower than T1 are identified as pore areas, and pixels with grayscale values higher than T2 are identified as non-pore areas. The image is converted into a binary image to obtain asphalt pore binary image data; wherein T1 is set to 20% of the average grayscale value of the three-dimensional structural image of the asphalt material, and T2 is set to 80% of the average grayscale value of the three-dimensional structural image of the asphalt material; for pixels with grayscale values between T1 and T2, secondary attribution discrimination is performed based on the grayscale values of their neighboring pixels;

[0024] Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and scan layer thickness in the asphalt material three-dimensional structure image, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample;

[0025] Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material;

[0026] Calculating the distance between each pore pixel and the nearest non-pore pixel based on the asphalt pore binary image data, thereby obtaining pore size distribution data;

[0027] Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster;

[0028] The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore connectivity.

[0029] The present invention grayscales the three-dimensional structure image and sets a threshold interval for binarization processing, which can effectively identify and segment the pore area, laying the foundation for subsequent pore feature analysis. Compared with simple threshold segmentation, the introduction of secondary attribution discrimination can more accurately process pixels whose grayscale values are within the threshold interval, reduce misjudgment, and improve segmentation accuracy, thereby obtaining more accurate pore binary image data. Based on the pore binary image data and image pixel size and scanning layer thickness information, the volume corresponding to each pore pixel is calculated and accumulated, and the total pore volume of the sample can be accurately calculated, thereby obtaining porosity data, which can more accurately reflect the characteristics of the three-dimensional pore structure. By calculating the distance from each pore pixel to the nearest non-pore pixel, the pore size distribution data can be obtained, revealing the changing pattern of pore size and its impact on material properties. By extracting the central skeleton line of the pore area and counting the number of connected paths and the average path length, the pore connectivity can be effectively characterized.

[0030] Preferably, the aggregate profile feature analysis comprises the following steps:

[0031] The Canny edge detection algorithm is used to detect the edges of aggregate particles in the asphalt material surface topography image to obtain asphalt aggregate particle edge data; wherein the Gaussian filter standard deviation σ is set to 1.5, the high threshold is set to 80% of the maximum grayscale value of the asphalt material three-dimensional structure image, and the low threshold is set to 40% of the maximum grayscale value of the asphalt material three-dimensional structure image;

[0032] Tracking the closed contour line of aggregate particles based on the edge data of the asphalt aggregate particles;

[0033] Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data;

[0034] Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data;

[0035] The area and equivalent diameter of each aggregate particle are calculated based on the closed contour line of the aggregate particles to obtain aggregate size distribution data.

[0036] The present invention utilizes the Canny edge detection algorithm to perform edge detection on the surface morphology image of the asphalt material and extract the edge data of the aggregate particles. The Canny algorithm is selected and the appropriate Gaussian filter standard deviation and high and low thresholds are set to ensure the accuracy and efficiency of the edge detection, effectively reduce the influence of noise, and accurately identify the outline of the aggregate particles. By tracking the closed contour lines of the aggregate particles, the complete segmentation and extraction of the aggregate particles are achieved. Based on the morphological analysis of the closed contour lines, the shape of the aggregate particles can be determined, thereby obtaining the aggregate shape data. The shape of the aggregate has an important influence on the gradation design, density, internal friction angle, etc. of the asphalt mixture, and thus affects its deformation resistance. In addition, by fitting the minimum circumscribed rectangle of each aggregate particle and calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, the aggregate direction data can be obtained. The direction of the aggregate has an important influence on the mechanical properties and anisotropy of the asphalt mixture. For example, the directional arrangement of the aggregate will affect the crack resistance and fatigue resistance of the mixture.

[0037] Preferably, the asphalt interface state analysis includes the following steps:

[0038] Segmenting the asphalt film and the aggregate area according to the asphalt material surface topography image and the asphalt material three-dimensional structure image;

[0039] Performing a three-phase contact point analysis based on the segmented asphalt film area and the segmented aggregate area to obtain contact characteristic data of the asphalt sample;

[0040] Setting the interface transition region distance according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film;

[0041] Taking the closed contour line of the aggregate particles as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness at that position;

[0042] Calculating the film thickness average, standard deviation, and coefficient of variation based on the asphalt film thickness, and performing asphalt film uniformity evaluation to obtain asphalt film uniformity evaluation data;

[0043] The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

[0044] The present invention segments the asphalt film and aggregate regions based on surface topography and three-dimensional structural images, laying the foundation for subsequent asphalt film characteristic analysis. Accurate segmentation results ensure the reliability and accuracy of subsequent analysis. Three-phase contact point analysis, based on the segmentation results, can obtain contact characteristic data between the asphalt, aggregate, and pore phases. Through three-phase contact point analysis, contact characteristic data for the asphalt sample is obtained. Three-phase contact points are points where the asphalt film, aggregate particles, and pores intersect. The location and number of these points reflect the contact between the asphalt film and aggregate, as well as the influence of pores on adhesion. Furthermore, based on the contact characteristic data, the interfacial transition region between the aggregate and the asphalt film is determined. The interfacial transition region refers to a region of transition between the properties of the asphalt film and the aggregate. Within this region, the properties of the asphalt film are affected by the surface properties of the aggregate, thereby changing the performance of the asphalt film. Using the closed contour line of the aggregate particles as the baseline, the vertical distance from the baseline to the interfacial transition region is measured and used as the asphalt film thickness at that location. This method can accurately quantify the thickness distribution of the asphalt film. The uniformity of the asphalt film was quantitatively evaluated by calculating the mean, standard deviation and coefficient of variation of the film thickness and performing asphalt film uniformity evaluation.

[0045] Preferably, the interface region roughness analysis comprises the following steps:

[0046] Performing image mapping between the interface transition region and the surface roughness image of the asphalt material;

[0047] Divide the roughness value of the interface transition area by the roughness value of the divided aggregate area, and then divide the result by the roughness value of the divided aggregate area to obtain the roughness change rate of the interface area;

[0048] Set roughness threshold and asphalt film thickness threshold;

[0049] If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good; otherwise, it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

[0050] The present invention subtracts the roughness value of the interface transition area from the roughness value of the divided aggregate area, and then divides it by the roughness value of the divided aggregate area to obtain the roughness change rate of the interface area. The roughness change rate reflects the change in roughness relative to the aggregate surface in the interface transition area. A high roughness change rate usually means that the asphalt film fills the unevenness of the aggregate surface well, forming a tighter bond. Setting a roughness threshold and an asphalt film thickness threshold provides a standard for evaluating the interface bonding state. Comparing the interface area roughness change rate with the set threshold, and combining it with the judgment of the asphalt film thickness, the quality of the interface bonding can be comprehensively evaluated. When the interface area roughness change rate is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state of the asphalt film and the aggregate is good. This indicates that the asphalt film can wet and fill the aggregate surface well, forming a good physical bond and mechanical interlocking. Otherwise, it is determined that the interface bonding state is poor.

[0051] Preferably, the three-phase contact point analysis includes the following steps:

[0052] Superimposing the edges of the segmented asphalt film area and the segmented aggregate area, and comparing edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points;

[0053] Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and determining that the candidate contact point is a three-phase contact point if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold;

[0054] fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain asphalt film tangent line fitting data;

[0055] Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle based on the asphalt film tangent fitting data to obtain the asphalt film contact angle;

[0056] The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

[0057] The present invention uses edge superposition and simultaneous comparison of edge coordinate information to screen out pixel pairs whose distance is less than a preset contact threshold and use these pixel pairs as candidate contact points. This is an efficient and automated candidate contact point identification method that can quickly locate potential three-phase contact point locations. By determining whether there are grayscale value changes in at least three directions within the neighborhood that exceed a preset grayscale value change threshold, pseudo contact points can be effectively eliminated, ensuring the accuracy of the analysis. After determining the three-phase contact point, the tangent line of the asphalt film at the contact point is fitted with the three-phase contact point as the center. The tangent fitting provides geometric information about the asphalt film at the contact point, laying the foundation for the subsequent contact angle calculation. Subsequently, the normal direction of the aggregate surface at the three-phase contact point is calculated, and the contact angle is calculated based on the asphalt film tangent fitting data. By screening effective angles and calculating the average contact angle, more representative contact characteristic data for the asphalt sample can be obtained, avoiding the influence of the randomness of a single contact angle on the analysis results. The size of the contact angle directly affects the properties of the interface transition region. Good wettability generally means a more uniform interface transition region and a tighter bond.

[0058] Preferably, the asphalt material database management system is created by:

[0059] Defining preparation parameter fields for the asphalt material sample and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, and aging condition, and setting a composite primary key; the composite primary key is composed of the gradation type code, the modifier type code, and the aging condition code;

[0060] Obtaining macroscopic mechanical property data of asphalt materials; wherein the macroscopic mechanical property data includes dynamic modulus, creep stiffness and fatigue life data;

[0061] A multidimensional index structure is constructed based on the preparation parameter data table, the macroscopic mechanical property data of the asphalt material, and the microscopic characteristic parameters of the asphalt material, with the preparation parameters used as the first-level screening index, the microscopic characteristic parameters used as the second-level screening index, and the macroscopic mechanical properties used as the third-level screening index, thereby obtaining a multidimensional index structure for asphalt;

[0062] An asphalt material database management system is created for the asphalt material samples based on the asphalt multidimensional index structure.

[0063] The present invention constructs an efficient and flexible database platform that enables multi-dimensional association and query of preparation parameters, micro-characteristic parameters, and macro-performance data, greatly improving the efficiency of asphalt material research and application. The process first defines the preparation parameter fields of the asphalt material sample and creates a preparation parameter data table in the relational database. By defining key parameters such as gradation type, modifier type, aging conditions, and setting a composite primary key, the uniqueness and association of the data are ensured. Constructing a multi-dimensional index structure is a key step in the present invention. Using preparation parameters, micro-characteristic parameters, and macro-mechanical performance data as primary, secondary, and tertiary screening indexes, respectively, enables rapid retrieval of data. When a user performs a query, the database can quickly locate the data that meets the conditions based on the index, avoiding a full table scan and significantly improving query efficiency. The design of this multi-dimensional index structure fully considers the diversity and complexity of asphalt material data and can meet the user's multi-faceted query needs. Based on the asphalt multi-dimensional index structure, an asphalt material database management system was created. This system can efficiently store, manage, and query large amounts of asphalt material data and supports complex queries with multiple conditions.

[0064] Preferably, the multidimensional index structure construction includes the following steps:

[0065] Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type, and aging condition as joint keys;

[0066] An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions;

[0067] Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys;

[0068] The three-level indexes are integrated into a logical association table through a database view.

[0069] The present invention constructs a B+ tree index for the preparation parameter data table, and uses the grading type, modifier type and aging condition as joint keys, which can efficiently support queries based on the preparation parameters. The B+ tree index is suitable for equivalence queries and range queries, and can quickly locate records that meet the conditions. In particular, when there are many types of preparation parameters and a large amount of data, the advantages are more obvious. An R-tree index is constructed for the microscopic characteristic parameters of asphalt materials, and porosity, aggregate size distribution and asphalt film thickness are used as spatial dimensions, which can efficiently support range queries based on microscopic characteristic parameters. The R-tree index is suitable for spatial data queries, and can quickly find data within a specific spatial range, such as finding asphalt materials with porosity within a certain range. A hash index is constructed for the macroscopic mechanical properties data of asphalt materials, and dynamic modulus, creep stiffness and fatigue life are used as hash keys, which can efficiently support equivalence queries based on macroscopic mechanical properties data. The hash index is suitable for equivalence queries, and can quickly locate records with specific macroscopic mechanical properties values, such as finding asphalt materials with a dynamic modulus equal to a certain value. The three-level index is integrated into a logical association table through the database view. A view is a virtual table that links and integrates data from multiple tables, allowing users to query data from multiple tables as if it were a single table. Database views allow logical associations between preparation parameters, microscopic characteristic parameters, and macroscopic mechanical property data, enabling complex, multi-dimensional queries. Users can perform joint queries based on preparation parameters, microscopic characteristics, and macroscopic properties to find asphalt materials that meet specific requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic flow chart of the steps of the method for establishing an asphalt material database management system according to the present invention;

[0071] Figure 2 This is a schematic flow chart of the material imaging scanning implementation steps in the method for establishing an asphalt material database management system of the present invention;

[0072] Figure 3 This is a flowchart of the steps for implementing the asphalt material database creation in the asphalt material database management system establishment method of the present invention;

[0073] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0074] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0076] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for establishing an asphalt material database management system, which specifically includes the following steps:

[0078] Using a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, asphalt material samples of different gradations, different modifier types, and different aging states are imaged and scanned to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image, and an asphalt material three-dimensional structure image;

[0079] Performing denoising, contrast enhancement, and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data;

[0080] Analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain microscopic characteristic parameters of the asphalt material;

[0081] An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0082] In an embodiment of the present invention, the method for establishing an asphalt material database management system specifically includes the following steps:

[0083] Step S1: Using a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, imaging and scanning asphalt material samples of different gradations, different modifier types, and different aging states are performed to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image, and an asphalt material three-dimensional structure image;

[0084] In an embodiment of the present invention, for example, asphalt mixture samples of three different gradations, AC-13, AC-20, and AC-25, were prepared in advance. Each gradation was modified using two different types of modifiers, SBS and SBR, and samples were prepared in three states: unaged, short-term aged, and long-term aged. The surface morphology of the asphalt samples was imaged using a scanning electron microscope (SEM), with an accelerating voltage set to 15 kV and magnifications of 500x, 1000x, and 2000x, respectively, to obtain images of the asphalt material surface morphology at different magnifications. The asphalt sample surface was scanned using an atomic force microscope (AFM), with a scanning range set to 5 μm × 5 μm and a scanning rate set to 1 Hz. Surface roughness image data of the asphalt material was obtained, and surface roughness parameters of the sample, such as root mean square roughness and average roughness, were calculated. Asphalt samples were scanned in three dimensions using X-ray tomography (X-CT) equipment at a scan voltage of 120 kV, a current of 100 μA, and a resolution of 5 μm. Three-dimensional images of the asphalt material were reconstructed and analyzed for porosity and pore size distribution to determine the characteristic parameters of the asphalt material's internal structure. All image data was saved in TIFF format, along with corresponding sample information, including gradation type, modifier type, and aging status.

[0085] Step S2: performing denoising, contrast enhancement and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data;

[0086] In an embodiment of the present invention, the acquired SEM, AFM and X-CT image data are imported into ImageJ software for preprocessing. First, the median filtering algorithm is used to denoise the image to remove random noise in the image, and the filter window size is set to 3×3 pixels. Then, the histogram equalization method is used to enhance the image contrast, improve the image clarity and the visualization of detail information. In view of the geometric distortion existing in the SEM image, the polynomial fitting method is used to correct the geometric distortion to ensure the geometric accuracy of the image. For X-CT images, the grayscale threshold segmentation method is used to distinguish asphalt and pores, and three-dimensional reconstruction is performed to obtain the three-dimensional structure of the asphalt material. All preprocessed image data are saved in TIFF format, and the corresponding preprocessing parameters are recorded.

[0087] Step S3: analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain microscopic characteristic parameters of the asphalt material;

[0088] In the present embodiment, the preprocessed SEM images were analyzed using ImageJ software to extract characteristic parameters of the asphalt material surface morphology, such as asphaltene particle size, distribution, and pore morphology. The preprocessed AFM images were analyzed using Gwyddion, an AFM image analysis software, to calculate the asphalt material surface roughness parameters. The preprocessed X-CT 3D images were analyzed using VGStudioMAX software to extract characteristic parameters of the asphalt material 3D structure, such as porosity, pore connectivity, pore size distribution, and skeletal structure.

[0089] Step S4: creating an asphalt material database management system for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0090] In this embodiment of the present invention, a MySQL relational database management system is used to create an asphalt material database. The database contains multiple data tables, such as a "sample information table," a "SEM image data table," a "AFM image data table," a "X-CT image data table," and a "micro-characteristic parameter table." The "sample information table" stores asphalt sample preparation parameters, including gradation type, modifier type, and aging status. The "SEM image data table," the "AFM image data table," and the "X-CT image data table" each store the corresponding image data file path and associated imaging parameters. The "micro-characteristic parameter table" stores micro-characteristic parameters extracted from image analysis, such as asphaltene particle size, surface roughness, and porosity. The various data tables are linked using sample IDs. The database management system provides a user-friendly graphical interface that supports multi-dimensional queries based on preparation parameters, micro-characteristic parameters, and macro-performance data. For example, users can enter a specific gradation type, modifier type, and aging status to query the micro-characteristic parameters and image data of asphalt samples that meet these criteria. Users can also query information for asphalt samples that meet these criteria based on a porosity range. The database management system will output asphalt material data that meets the conditions, including sample information, image data, and microscopic characteristic parameters, and display them in the form of tables or charts.

[0091] Preferably, the imaging scan specifically includes the following steps:

[0092] According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are subjected to pre-treatment operations such as cutting and polishing to obtain sample surfaces that meet the test requirements;

[0093] Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, and determining at least five different imaging areas according to the sample size and target magnification;

[0094] Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by using a scanning electron microscope; the surface roughness image of the asphalt material is obtained by using an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by using an X-ray tomography device.

[0095] As an example of the present invention, refer to Figure 2 FIG. 1 is a flow chart showing the steps for implementing material imaging scanning. In an embodiment of the present invention, the imaging scanning specifically includes the following steps:

[0096] Step S11: preparing asphalt material samples of different gradations, different modifier types, and different aging conditions according to the preset gradation range, modifier type, and aging conditions, and performing pretreatment operations such as cutting and polishing on the asphalt material samples to obtain sample surfaces that meet the test requirements;

[0097] In this embodiment of the present invention, three aggregates with different gradations were selected based on a preset gradation range (e.g., AC-13, AC-20, and AC-25), and the optimal asphalt content was determined using the Marshall mix design method. Two different modifiers, such as styrene-butadiene-styrene block copolymer (SBS) and styrene-butadiene rubber (SBR), were added to the base asphalt at ratios of 3%, 5%, and 7% by mass, respectively, to prepare modified asphalt. The aggregates and modified asphalt were mixed according to the designed mix ratio, and asphalt mixture specimens with a diameter of 100 mm and a height of 63.5 mm were prepared using a gyratory compactor. To simulate different aging conditions, some specimens were placed in a rotary thin-film oven for short-term aging (85°C, 5 days) and long-term aging (100°C, 5 days). All prepared asphalt mixture specimens were cut into 10 mm × 10 mm × 10 mm cubes using a diamond cutter. The cut sample blocks were then polished using 300-, 600-, 1000-, and 2000-grit sandpaper in a sequential manner until a smooth surface was achieved to meet the requirements of subsequent microscopic testing. During the polishing process, coolant was used to cool the sample to prevent softening and deformation of the asphalt. Finally, the polished sample was rinsed with anhydrous ethanol and dried with compressed air to prevent surface contamination.

[0098] Step S12: fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, and determining at least five different imaging areas according to the sample size and target magnification;

[0099] In an embodiment of the present invention, for SEM testing, the pretreated asphalt material sample is fixed on an aluminum sample stage with conductive glue, and a thin gold film is sprayed on the sample surface using an ion sputtering device to improve the conductivity of the sample and prevent charging effects. For AFM testing, the sample is fixed on the AFM sample stage with double-sided tape. For X-CT testing, the sample is placed directly on the sample rotation stage of the X-CT device. Based on the sample size (10mm×10mm×10mm) and the target magnification, at least 5 different imaging areas are selected on each sample surface. When selecting the imaging area, the representativeness of the sample is considered to avoid selecting areas at the edge or with obvious defects. For SEM testing, the target magnifications selected are 500x, 1000x, and 2000x, respectively. For AFM testing, a scanning range of 5μm×5μm is selected. For X-CT testing, a three-dimensional scanning area that includes the entire sample is selected.

[0100] Step S13: Start the scanning electron microscope, atomic force microscope and X-ray tomography equipment, scan the imaging areas one by one, and save the scanning image data of each area to obtain the surface morphology image of the asphalt material, the surface roughness image of the asphalt material and the three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by the scanning electron microscope; the surface roughness image of the asphalt material is obtained by the atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by the X-ray tomography equipment.

[0101] In this embodiment of the present invention, a scanning electron microscope (SEM) is activated, with the acceleration voltage set to 15 kV and the working distance set to 10 mm. Each imaging area is scanned individually according to a predetermined imaging region and magnification, and the scanned image data for each area is saved to obtain an image of the asphalt material surface topography in TIFF format. An atomic force microscope (AFM) is activated, with the tapping mode selected, the scan rate set to 1 Hz, and the scanning range set to 5 μm × 5 μm. Each imaging area is scanned individually according to a predetermined imaging region, and the scanned image data for each area is saved to obtain an image of the asphalt material surface roughness in TIFF format. An X-ray tomography (X-CT) device is activated, with the scanning voltage set to 120 kV, the scanning current set to 100 μA, and the scanning resolution set to 5 μm. A predetermined sample is scanned in three dimensions, and the scan data is saved. The scanned data is then reconstructed in three dimensions using X-CT image reconstruction software to obtain a three-dimensional structural image of the asphalt material in DICOM or TIFF format. All image data is named and stored according to sample number, imaging region, and magnification.

[0102] Preferably, the microscopic characteristic analysis of the material sample specifically includes the following steps:

[0103] Identifying pore areas based on the three-dimensional structural image of the asphalt material, and performing pore feature analysis to obtain pore feature data;

[0104] Identifying aggregate particle contours based on the asphalt material surface topography image and the asphalt material three-dimensional structure image, and performing aggregate contour feature analysis to obtain aggregate feature data;

[0105] Segment the asphalt film based on the asphalt material surface morphology image, and analyze the asphalt interface state through the asphalt material surface roughness image to obtain the asphalt film characteristic data;

[0106] The pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to obtain the microscopic characteristic parameters of the asphalt material.

[0107] In one embodiment of the present invention, VGStudio MAX software is used to identify pore regions in a preprocessed 3D structural image (X-CT image) of asphalt material. First, the image is binarized by setting a grayscale threshold to distinguish the pore regions from the asphalt matrix. The threshold can be automatically determined through grayscale histogram analysis or the Otsu method to ensure accurate identification of the pore regions. Morphological operations, such as erosion and dilation, are then performed on the binarized image to remove noise and isolated pixels and connect broken pore regions to make them more complete. Next, a 3D reconstruction is performed on the identified pore regions to obtain 3D pore structural information. Based on the reconstructed 3D pore structure, pore characteristic data can be calculated, such as porosity (the ratio of pore volume to total volume), pore volume distribution (the volume fraction of pores of different sizes), pore surface area, pore connectivity (the proportion of interconnected pores), average pore diameter, and pore shape factor (a measure of pore shape complexity, such as sphericity or circularity). Aggregate particle contours are identified and analyzed by combining asphalt surface topography images (SEM images) and three-dimensional structural images (X-CT images). First, edge detection is performed on the SEM image, such as the Canny edge detection algorithm, to extract edge information of the aggregate particles. Next, image segmentation techniques, such as the watershed algorithm, are used to segment the aggregate particles from the asphalt matrix. For X-CT images, similar image processing methods can be used, or grayscale threshold segmentation can be used to distinguish aggregate particles from the asphalt matrix and pores. After identifying the aggregate particle contours, aggregate contour feature analysis is performed to extract aggregate characteristic data. Aggregate characteristic data includes aggregate size, aggregate shape (e.g., aspect ratio, roundness, angularity), aggregate quantity, aggregate distribution (e.g., aggregate spacing, aggregate orientation), and aggregate surface texture. Aggregate size can be measured directly from the segmented image. Aggregate shape can be quantified using shape factors. Aggregate distribution can be determined by calculating the centroid coordinates of the aggregates and then analyzing the distribution of these centroid coordinates. Aggregate surface texture can be analyzed using methods such as the gray-level co-occurrence matrix (GLCM). Segmentation of the SEM image is required to separate the asphalt film from the aggregate and pore regions. Common segmentation methods include grayscale-based threshold segmentation, edge-based region growing, and segmentation methods combined with morphological operations. Since the grayscale value of the asphalt film is generally between that of the aggregate and the pores, an appropriate threshold range can be set to segment the asphalt film. The asphalt material surface roughness image (AFM image) is then used to analyze the interfacial state of the asphalt film. Calculate the roughness parameters of the asphalt film surface, such as root mean square roughness (Rq), average roughness (Ra), and peak-to-valley value (Rpv). Furthermore, characteristics such as the thickness distribution, uniformity, and adhesion of the asphalt film to aggregate particles can be analyzed.The acquired pore characteristic data, aggregate characteristic data, and asphalt film characteristic data are quantified. For example, the pore size distribution is converted into discrete numerical intervals and corresponding volume fractions; the aggregate shape factor is converted into specific numerical values; and the asphalt film thickness distribution is converted into average thickness and standard deviation. Statistical methods, such as correlation analysis and regression analysis, are then used to establish relationships between characteristic parameters. For example, the Pearson correlation coefficient or Spearman rank correlation coefficient can be calculated between different characteristic parameters to assess linear or rank correlations. For example, the correlation coefficient between porosity and asphalt film thickness can be calculated to analyze the impact of changes in porosity on asphalt film thickness. The correlation coefficient between aggregate shape factor and asphalt film surface roughness can also be calculated to analyze the influence of aggregate shape on asphalt film microstructure. For example, a regression model can be established between porosity and asphalt mixture fatigue resistance to predict the fatigue life of asphalt mixtures at different porosities. Regression models can also be established between aggregate characteristic parameters (such as size distribution and shape factor) and asphalt mixture mechanical properties (such as compressive strength and tensile strength) to analyze the impact of aggregate characteristics on asphalt mixture mechanical properties. For example, asphalt samples with different gradations, different modifier types and different aging states can be clustered according to pore characteristics, aggregate characteristics and asphalt film characteristics to find sample groups with similar microstructures.

[0108] Preferably, the pore characteristic analysis specifically comprises the following steps:

[0109] grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image;

[0110] Based on the grayscale image, a segmentation threshold interval is set as [T1, T2], pixels in the image with grayscale values lower than T1 are identified as pore areas, and pixels with grayscale values higher than T2 are identified as non-pore areas. The image is converted into a binary image to obtain asphalt pore binary image data; wherein T1 is set to 20% of the average grayscale value of the three-dimensional structural image of the asphalt material, and T2 is set to 80% of the average grayscale value of the three-dimensional structural image of the asphalt material; for pixels with grayscale values between T1 and T2, secondary attribution discrimination is performed based on the grayscale values of their neighboring pixels;

[0111] Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and scan layer thickness in the asphalt material three-dimensional structure image, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample;

[0112] Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material;

[0113] Calculating the distance between each pore pixel and the nearest non-pore pixel based on the asphalt pore binary image data, thereby obtaining pore size distribution data;

[0114] Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster;

[0115] The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore connectivity.

[0116] In an embodiment of the present invention, a three-dimensional structural image of asphalt material obtained by X-ray tomography (X-CT) (typically stored as a DICOM sequence or multi-layer TIFF file) is loaded into professional image processing software, such as ImageJ (with the Fiji plug-in package), Avizo, or VGStudio MAX. These software programs provide functions for processing and analyzing three-dimensional image data. If the original image is in color (e.g., RGB format), it must be converted to a grayscale image. All pixels in the three-dimensional grayscale image are traversed, and the sum of all pixel grayscale values is calculated. This sum is then divided by the total number of pixels to obtain the average grayscale value. The 20% and 80% values of the average grayscale value are then calculated, respectively, to obtain T1 and T2. For each pixel in the image, the following determination is made: If the pixel grayscale value is less than T1, the pixel is marked as a pore pixel, and the corresponding pixel value in the binarized image is set to 1 (or 255, depending on the specific software and data format). If the pixel grayscale value is greater than T2, the pixel is marked as a non-pore pixel, and the corresponding pixel value in the binarized image is set to 0. If the pixel grayscale value is between T1 and T2, a secondary attribution determination is required. A 3x3x3 or 5x5x5 cubic neighborhood (in 3D images) centered on the pixel is selected. The average grayscale value of all pixels within this neighborhood is calculated. If the neighborhood average grayscale value is less than (T1+T2) / 2, the pixel is marked as a pore pixel; otherwise, it is marked as a non-pore pixel. The pixel size and slice thickness of the X-ray tomography device are required. Pixel size refers to the actual physical dimensions represented by a pixel in an XCT image, for example, 10 microns x 10 microns. Slice thickness refers to the thickness of each slice scanned during an XCT scan, for example, 10 microns. A pixel represents a small volume unit in a 3D structural image, and its volume can be calculated using the pixel size and slice thickness. For binary asphalt pore image data, pixels with a grayscale value of 0 represent pore areas. Therefore, the binary image needs to be traversed to count the number of pixels with a grayscale value of 0. The statistical method can scan the image row by row or column by column, and accumulate the number of pixels with a grayscale value of 0. Assume that the number of pixels with a grayscale value of 0 obtained by statistics is N. Then, the number of pore pixels N is multiplied by the volume represented by each pixel to obtain the total pore volume data of the sample. The calculation formula is: total pore volume of the sample = N × (pixel size × pixel size × scanning layer thickness). For example, the threshold-based segmentation method. Based on the grayscale image, a threshold is set to identify the pixels in the image with grayscale values higher than the threshold as asphalt samples, and the pixels with grayscale values lower than the threshold as other parts, thereby realizing the segmentation of the asphalt sample. Compare it with the obtained total pore volume data of the sample to calculate the porosity data of the asphalt material. Porosity is defined as the ratio of the total pore volume to the total volume of the sample, usually expressed as a percentage.The calculation formula is: Porosity = (total pore volume of the sample / total volume of the asphalt sample) × 100%. For each pore pixel (grayscale value 0) in the binary asphalt pore image, the nearest non-pore pixel (grayscale value 255) must be found. The Euclidean distance transform is a commonly used distance transform algorithm that calculates the straight-line distance from each pixel to the nearest non-zero pixel. On a binary image, the Euclidean distance transform calculates the Euclidean distance from each pixel to the nearest non-pore pixel. The calculation process iterates over each pixel in the image. For each pixel, the distance to all non-pore pixels is calculated, and the minimum distance is taken as the distance from that pixel to the nearest non-pore pixel. The pore region is simplified to a curve centered at the pore center, preserving the primary topological structure of the pores. Common skeletonization algorithms include thinning and distance transform skeletonization. The thinning algorithm gradually removes the boundary pixels of the pore region until only the central skeleton line remains. The distance transform skeletonization algorithm extracts local maxima as skeleton points based on the distance transform results. Select a skeletonization algorithm and apply it to the binary image of asphalt pores. After processing, a skeleton line will appear at the center of the pore region in the resulting image. This skeleton line consists of a series of pixels that connect to form the central pore structure. Connected skeleton line segments are then defined as a connected pore cluster. The connected domain analysis algorithm identifies connected skeleton line segments. Starting from a pixel in the image, the algorithm recursively labels all pixels connected to that pixel as belonging to the same region. Skeleton line segments that are continuous belong to the same connected pore cluster. For each connected pore cluster, the number of connected paths and the average path length are calculated. The number of connected paths refers to the number of mutually accessible paths within a connected pore cluster. The average path length refers to the average length of all paths within a connected pore cluster. Graph theory algorithms can be used to calculate the number of connected paths and the average path length. Each skeleton line segment can be considered a node in a graph. When two skeleton line segments are connected, an edge is established between the nodes. Calculating the number of connected paths is equivalent to counting the number of edges connecting two nodes in the graph. The average path length can be calculated by summing the lengths of all paths and dividing it by the number of connected paths.

[0117] Preferably, the aggregate profile feature analysis specifically includes the following steps:

[0118] The Canny edge detection algorithm is used to detect the edges of aggregate particles in the asphalt material surface topography image to obtain asphalt aggregate particle edge data; wherein the Gaussian filter standard deviation σ is set to 1.5, the high threshold is set to 80% of the maximum grayscale value of the asphalt material three-dimensional structure image, and the low threshold is set to 40% of the maximum grayscale value of the asphalt material three-dimensional structure image;

[0119] Tracking the closed contour line of aggregate particles based on the edge data of the asphalt aggregate particles;

[0120] Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data;

[0121] Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data;

[0122] The area and equivalent diameter of each aggregate particle are calculated based on the closed contour line of the aggregate particles to obtain aggregate size distribution data.

[0123] In this embodiment of the present invention, the Canny edge detection algorithm is used to process an asphalt material surface topography image (SEM image) to detect the edges of aggregate particles and obtain asphalt aggregate particle edge data. The Canny edge detection algorithm is a multi-step image processing algorithm that aims to identify locations within an image where intensity changes dramatically, i.e., edges. The image is smoothed using a Gaussian filter to remove noise. The standard deviation σ of the Gaussian filter is set to 1.5 pixels. The convolution kernel size of the Gaussian filter is typically automatically determined based on the standard deviation, typically rounded to (6×σ+1), which is approximately 10×10 pixels in this case. The gradient magnitude and direction are calculated for each pixel in the image. The gradient magnitude indicates the speed and intensity of the grayscale change in the image, while the gradient direction indicates the direction of the grayscale change. The gradient magnitude and direction can be calculated using the Sobel operator, Prewitt operator, or more advanced gradient operators. These operators essentially perform a convolution operation on the image. By differentiating the image, they calculate the grayscale change rate in the horizontal and vertical directions, thereby obtaining the gradient magnitude and direction. Using dual thresholding, edge pixels are classified as strong edge pixels, weak edge pixels, and non-edge pixels. Two thresholds are set: a high threshold and a low threshold. The high threshold is typically greater than the low threshold. If the gradient magnitude of a pixel is greater than the high threshold, it is marked as a strong edge pixel. If the gradient magnitude of a pixel is less than the low threshold, it is marked as a non-edge pixel. If the gradient magnitude of a pixel is between the high and low thresholds, it is marked as a weak edge pixel. Weak edge pixels are then connected to form a complete edge. The principle of edge connection is that if a weak edge pixel is adjacent to a strong edge pixel, the weak edge pixel is also marked as an edge pixel. This method can connect broken edges caused by factors such as noise or uneven illumination to form a complete edge. An edge pixel is randomly selected from the edge data as the starting point. Then, the next edge pixel is searched along the edge direction. The search principle is to find pixels that meet the edge conditions within the neighborhood of the current pixel. Neighboring pixels can be defined using a 4-neighborhood or an 8-neighborhood. Edge connections can be determined using heuristics, such as ensuring that the gradient direction of the next pixel is consistent with that of the current pixel. If the next edge pixel is found, it is added to the contour and used as the new current pixel. The above process is repeated until the starting point is reached, or the next edge pixel cannot be found. If the starting point is reached, a closed contour is formed. If the next edge pixel cannot be found, it indicates a break in the edge. In the case of a break in the edge, the nearest edge pixel to the current pixel is searched within the neighborhood of the current pixel. If such a pixel is found, the two pixels are connected.The goal of morphological analysis is to extract shape features from the contours of aggregate particles, thereby describing and classifying their shapes. Aggregate shape can be described from multiple perspectives. For example, a minimum enclosing rectangle (MER) is fitted to the closed contours of the aggregate particles. The aspect ratio is the ratio of the long side to the short side of the MER. Roundness is the ratio of the area of the aggregate particle to the square of its perimeter, reflecting the degree of circularity. Angularity is the degree of concavity of the aggregate particle contour, reflecting the degree of angularity. Calculating angularity requires first calculating the curvature of the aggregate particle contour. This can be calculated using a difference-based method. Convexity is the ratio of the area of the aggregate particle to the area of its convex hull, reflecting the degree of concavity of the aggregate particle. The convex hull is the smallest convex polygon that contains all the pixels of the aggregate particle. A MAR is fitted to the closed contours of each aggregate particle. The MAR is the rectangle with the smallest area that contains the aggregate particle contour. The minimum enclosing rectangle of the aggregate particles can be calculated using methods such as the rotating calculus algorithm or the least squares method, as well as the major axis of the rectangle. The angle between the major axis of the rectangle and a preset reference coordinate system can be calculated. The choice of reference coordinate system can be determined based on the actual situation. In SEM images, the horizontal direction of the image is usually selected as the reference coordinate system. The angle between the major axis of the rectangle and the horizontal direction can be calculated. The inverse tangent function, arctan(), can be used to calculate this angle. The distribution of aggregate particle orientations can be statistically analyzed. Aggregate orientations can be divided into several intervals, such as 0-10 degrees, 10-20 degrees, etc. The number of aggregate particles falling within each interval is then counted, and the statistical results are plotted as a histogram. For each closed contour line of an aggregate particle, the number of pixels within the contour line is counted. The area of each pixel can be obtained based on the pixel size of the SEM image. For example, if the pixel size of the SEM image is 10 microns x 10 microns, the area of each pixel is 100 square microns. The area of the aggregate particle is equal to the number of internal pixels multiplied by the area of the pixels. Calculate the equivalent diameter of the aggregate particle. The equivalent diameter is the diameter of a circle with the same area as the aggregate particle.

[0124] Preferably, the asphalt interface state analysis specifically includes the following steps:

[0125] Segmenting the asphalt film and the aggregate area according to the asphalt material surface topography image and the asphalt material three-dimensional structure image;

[0126] Performing a three-phase contact point analysis based on the segmented asphalt film area and the segmented aggregate area to obtain contact characteristic data of the asphalt sample;

[0127] Setting the interface transition region distance according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film;

[0128] Taking the closed contour line of the aggregate particles as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness at that position;

[0129] Calculating the film thickness average, standard deviation, and coefficient of variation based on the asphalt film thickness, and performing asphalt film uniformity evaluation to obtain asphalt film uniformity evaluation data;

[0130] The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

[0131] In an embodiment of the present invention, SEM and XCT images are preprocessed to improve segmentation accuracy. Preprocessing operations include denoising, contrast enhancement, and image correction. Denoising can be performed using methods such as mean filtering, median filtering, or non-local mean filtering to remove noise from the image. Contrast enhancement can be performed using methods such as histogram equalization to improve the visual quality of the image. Three-phase contact points are detected in the binary images of the segmented asphalt film and aggregate regions. Three-phase contact points are defined as the intersection of the asphalt film, aggregate, and pore regions. By traversing the pixels of the binary image, each pixel is determined to determine whether it meets the definition of a three-phase contact point. This determination can be based on neighborhood analysis. For example, a 3x3 neighborhood is selected. If an asphalt film pixel, an aggregate pixel, and a pore pixel exist within the neighborhood, the pixel is considered a three-phase contact point. To avoid misjudgments, a certain tolerance can be set. For example, the absence of a phase within the neighborhood can be tolerated and still be identified as a three-phase contact point. The number and distribution of three-phase contact points are calculated. The number of three-phase contact points refers to the total number of pixels in the image identified as three-phase contact points. The distribution of three-phase contact points can be described by counting their positional coordinates. For example, the coordinates of the centroids of the three-phase contact points can be calculated and plotted in the image. Alternatively, the image can be divided into several regions and the number of three-phase contact points in each region can be counted to analyze the distribution of three-phase contact points in the asphalt mixture. For example, a denser distribution of three-phase contact points indicates a wider interface transition zone. A mathematical model can be established to link contact characteristic data with the interface transition zone distance. For example, the interface transition zone distance can be set proportional to the average distance between the three-phase contact points. A series of points are selected along the closed contour line of the aggregate particles as measurement points. The measurement points can be selected at equal intervals or randomly. For example, measurement points can be selected at regular intervals along the contour line. The perpendicular distance from each measurement point to the interface transition zone is calculated. The interface transition zone is the area where the aggregate surface meets the asphalt film. Based on the set interface transition zone distance, the distance to the interface transition zone is measured perpendicular to the closed contour line of the aggregate particles. If the interface transition region distance is set to a fixed value, such as 5 microns. For each measurement point on the contour line, move 5 microns outward (i.e., in the direction of the asphalt film) in a direction perpendicular to the contour line. The position after the movement is the interface transition region. Measuring the distance from the point to the contour line will give the thickness of the asphalt film at that measurement point. Repeat the above process to measure the asphalt film thickness at all measurement points. Perform statistical analysis on the obtained asphalt film thickness measurement data to calculate the mean, standard deviation, and coefficient of variation of the asphalt film thickness. For example, the coefficient of variation can be set as uniform if less than 10%, general if 10%-20%, and non-uniform if greater than 20%. Extract the asphalt film-aggregate interface region from the AFM image. The definition of the interface region depends on the boundary between the asphalt film and the aggregate.Perform a roughness analysis on the interface area. Commonly used roughness parameters include arithmetic mean roughness, root mean square roughness, and the sum of the maximum peak height and maximum valley depth of the profile curve. Based on the calculated roughness parameters, the bonding state of the asphalt film-aggregate interface is evaluated. The degree of roughness is related to the adhesion of the interface. Higher roughness increases the adhesion of the interface, thereby improving the strength and durability of the asphalt mixture. By correlating these roughness parameters with the performance indicators of the asphalt mixture, a relationship between the interface bonding state and performance can be established. Asphalt film-aggregate interface bonding state data is obtained.

[0132] Preferably, the analysis of the roughness state of the interface region specifically includes the following steps:

[0133] Performing image mapping between the interface transition region and the surface roughness image of the asphalt material;

[0134] Divide the roughness value of the interface transition area by the roughness value of the divided aggregate area, and then divide the result by the roughness value of the divided aggregate area to obtain the roughness change rate of the interface area;

[0135] Set roughness threshold and asphalt film thickness threshold;

[0136] If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good; otherwise, it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

[0137] In an embodiment of the present invention, it is necessary to determine the correspondence between the SEM image and the AFM image. Since the SEM image and the AFM image are obtained on different devices and there are differences in the scanned areas, image registration is required. There are many methods for image registration, and a feature point-based registration method or a region-based registration method can be used. In the asphalt mixture scenario, aggregate particles can be selected as feature points to identify the same aggregate particles in the SEM image and the AFM image, and a correspondence between these feature points can be established. The determined interface transition area is mapped to the AFM image using the transformation relationship of image registration. The interface transition area is usually determined based on the contour lines of the aggregate particles. By mapping the contour lines of the aggregate particles to the AFM image according to the transformation relationship obtained by image registration, the corresponding aggregate particle contour lines on the AFM image can be obtained. Then, the interface transition area is determined on the AFM image based on the distance setting of the determined interface transition area. For example, if the distance of the interface transition region is set to 5 microns, then the corresponding interface transition region on the AFM image is obtained by moving 5 microns outward (toward the asphalt film) perpendicular to the aggregate particle contour. The roughness value of the segmented aggregate region needs to be calculated. The segmented aggregate region refers to the area on the aggregate particle surface adjacent to the interface transition region in the AFM image. The roughness value of the segmented aggregate region is extracted. The calculation method is the same as that for the interface transition region, using the same roughness parameters. The interface region roughness change rate is calculated using the formula: Roughness change rate = (Interface transition region roughness value - segmented aggregate region roughness value) / segmented aggregate region roughness value × 100%. A roughness threshold is set. The roughness threshold is used to determine whether the interface region roughness change rate is sufficiently large, indicating good bonding between the asphalt film and the aggregate. The roughness threshold can be set based on experimental results and experience. Asphalt mixture samples with different bonding states can be prepared, their interface roughness change rates measured, and their mechanical properties tested. Then, according to the mechanical properties, set an appropriate roughness threshold. For example, if the roughness change rate is greater than 50%, it is considered that the bonding state between the asphalt film and the aggregate is good. If the roughness change rate is less than 20%, it is considered that the bonding state between the asphalt film and the aggregate is poor. Determine whether the roughness change rate of the interface area is greater than the roughness threshold. If the roughness change rate of the interface area is greater than the roughness threshold, it means that the interface transition area is rougher than the aggregate surface, indicating that the bonding between the asphalt film and the aggregate is better. Then, determine whether the asphalt film thickness is greater than the asphalt film thickness threshold. Asphalt film thickness is an important indicator for evaluating the performance of asphalt mixtures. If the asphalt film thickness is greater than the asphalt film thickness threshold, it means that the asphalt film has sufficient thickness and can provide sufficient bonding force to enhance the bonding between the asphalt film and aggregate particles.If the interface roughness change rate is greater than the roughness threshold and the asphalt film thickness is greater than the asphalt film thickness threshold, the interface bonding between the asphalt film and the aggregate is considered good. A good bonding condition can improve the strength, durability, and water damage resistance of the asphalt mixture. Otherwise, the interface bonding between the asphalt film and the aggregate is considered poor. Poor bonding can lead to a decline in the performance of the asphalt mixture.

[0138] Preferably, the three-phase contact point analysis specifically includes the following steps:

[0139] Superimposing the edges of the segmented asphalt film area and the segmented aggregate area, and comparing edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points;

[0140] Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and determining that the candidate contact point is a three-phase contact point if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold;

[0141] fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain asphalt film tangent line fitting data;

[0142] Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle based on the asphalt film tangent fitting data to obtain the asphalt film contact angle;

[0143] The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

[0144] In an embodiment of the present invention, the edge data of the asphalt film area and the edge data of the aggregate area are superimposed. The superposition operation is to merge the edge data of the asphalt film area and the edge data of the aggregate area to form a new data set. The coordinate information of the edge data is compared. The merged edge pixels are traversed, and the distance between the asphalt film edge pixels and the aggregate edge pixels is calculated respectively. The distance can be calculated using Euclidean distance, etc. Pixel pairs with a distance less than a preset contact threshold are filtered. The preset contact threshold is an empirical value that represents the maximum distance between the edge pixels of two areas that can be considered to be in contact. The selection of the contact threshold needs to be adjusted according to the actual situation and can usually be set to a distance of several pixels. For example, if the contact threshold is set to 2 pixels, only pixel pairs with a distance less than 2 pixels are retained. If the distance between two pixels is less than the preset contact threshold, the two pixels are regarded as candidate contact points. The neighborhood radius needs to be determined. The neighborhood radius refers to a circular or square area selected with the candidate contact point as the center. The size of the neighborhood radius needs to be adjusted according to the image resolution and the actual situation. For example, the neighborhood radius can be set to 3 pixels or 5 pixels. Next, the grayscale value change rate of pixels within the candidate contact point's neighborhood is calculated. The grayscale value change rate refers to the speed and direction of the pixel grayscale value change within the neighborhood. Various methods can be used to calculate the grayscale value change rate. One method is to calculate the gradient magnitude of the pixels within the neighborhood. The gradient magnitude reflects the magnitude of the grayscale value change, while the gradient direction reflects the direction of the grayscale value change. Another method is to calculate the grayscale value difference of pixels within the neighborhood. The grayscale value difference reflects the grayscale value difference between adjacent pixels. A grayscale value change threshold is set. The grayscale value change threshold is an empirical value used to determine whether a pixel is a three-phase contact point. The grayscale value change threshold setting needs to be adjusted based on actual conditions. For example, the grayscale value change threshold can be set to 10% or 20% of the grayscale value range based on the image's grayscale value range. If the grayscale value change in at least three directions within the candidate contact point's neighborhood exceeds the preset grayscale value change threshold, the candidate contact point is determined to be a three-phase contact point. This means that there is an obvious change in grayscale value around the candidate contact point, indicating that there is an intersection point of the three phases of asphalt film, aggregate and pores. If this condition is met, the candidate contact point can be considered to be a three-phase contact point. It is necessary to determine the fitting range of the tangent. Since the tangent is a description of the local area, it is necessary to determine the pixel points used for fitting. The fitting range can be selected within the neighborhood centered on the three-phase contact point. The size of the neighborhood needs to be adjusted according to the resolution of the image and the actual situation. Tangent fitting is performed on the pixel points of the asphalt film in the neighborhood. There are many methods for tangent fitting, such as linear fitting, polynomial fitting, etc. Considering the computational efficiency, a linear fitting method can be selected. The linear fitting method is to fit the pixel points of the asphalt film in the neighborhood into a straight line. The parameters of the straight line can be solved using methods such as the least squares method.The slope and intercept of the tangent can be determined using the least squares method. The slope of the tangent indicates the degree of inclination of the asphalt film at the contact point. The intercept of the tangent indicates the position of the tangent in the coordinate system. It is necessary to calculate the normal direction of the aggregate surface at the three-phase contact point. The normal direction of the aggregate surface is the direction perpendicular to the aggregate surface. This can be done by extracting the edge pixels of the aggregate at the three-phase contact point, calculating the gradient direction at that point, and defining its normal direction. For a given three-phase contact point, edge information of that point must be extracted from the aggregate region. This edge information provides the local orientation of the aggregate surface at that point. An edge detection operator, such as the Sobel operator, can be used to calculate the gradient direction of the aggregate at the contact point. The gradient direction is the direction with the greatest change in grayscale value. If the normal direction is perpendicular to the gradient direction, the normal direction of the aggregate surface at that point can be calculated based on the gradient direction. The contact angle is the angle between the tangent direction of the asphalt film and the normal direction of the aggregate surface. The contact angle can be calculated using the vector dot product method. Contact angle can be calculated using the following formula: Contact angle = arccos((normal vector · tangent vector) / (normal vector length × tangent vector length)). Range filtering can be used. Set a valid range for contact angles. For example, set the valid range for contact angles from 0 to 180 degrees. Remove contact angle values outside this range. Statistical filtering can also be used. For example, calculate the mean and standard deviation of the contact angles and remove contact angle values outside the mean ±3 times the standard deviation.

[0145] Preferably, the creation of the asphalt material database management system specifically includes the following steps:

[0146] Defining preparation parameter fields for the asphalt material sample and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, and aging condition, and setting a composite primary key; the composite primary key is composed of the gradation type code, the modifier type code, and the aging condition code;

[0147] Obtaining macroscopic mechanical property data of asphalt materials; wherein the macroscopic mechanical property data includes dynamic modulus, creep stiffness and fatigue life data;

[0148] A multidimensional index structure is constructed based on the preparation parameter data table, the macroscopic mechanical property data of the asphalt material, and the microscopic characteristic parameters of the asphalt material, with the preparation parameters used as the first-level screening index, the microscopic characteristic parameters used as the second-level screening index, and the macroscopic mechanical properties used as the third-level screening index, thereby obtaining a multidimensional index structure for asphalt;

[0149] An asphalt material database management system is created for the asphalt material samples based on the asphalt multidimensional index structure.

[0150] As an example of the present invention, refer to Figure 3FIG. 1 is a flow chart showing the steps for creating an asphalt material database. In an embodiment of the present invention, the steps for creating an asphalt material database specifically include the following steps:

[0151] Step S21: defining preparation parameter fields for the asphalt material sample and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, and aging condition, and setting a composite primary key; the composite primary key is composed of the gradation type code, the modifier type code, and the aging condition code;

[0152] In this embodiment of the present invention, the preparation parameter field is used to describe the preparation process and material composition of the asphalt material. These parameters directly affect the performance of the asphalt material. The preparation parameter field includes at least: gradation type, modifier type, and aging condition. The gradation type describes the proportion of aggregates of different particle sizes in the asphalt mixture. Industry standards such as AC-13, AC-16, and AC-20 can be used to represent gradation types with different particle size ranges. The modifier type describes the type of modifier used in the asphalt material. Modifiers such as SBS (styrene-butadiene-styrene block copolymer), SBR (styrene-butadiene rubber), and EVA (ethylene-vinyl acetate copolymer) can improve the performance of the asphalt material. The aging condition describes the degree of aging experienced by the asphalt material during service. Aging conditions include short-term aging and long-term aging. Short-term aging generally simulates the aging of asphalt materials during the mixing and paving processes, while long-term aging generally simulates the aging of asphalt materials during road service. For example, the rotating thin film oven test (RTFOT) can be used for short-term aging, while the pressure aging autoclave test (PAV) can be used for long-term aging. Combine the gradation type code, modifier type code, and aging condition code to form a composite primary key. For example, if the gradation type is AC-13, the modifier is SBS, and the aging condition is RTFOT, the composite primary key might be 01-01-01. This composite primary key ensures that each asphalt material sample has a unique identifier.

[0153] Step S22: obtaining macroscopic mechanical property data of the asphalt material; wherein the macroscopic mechanical property data includes dynamic modulus, creep stiffness and fatigue life data;

[0154] In an embodiment of the present invention, macroscopic mechanical property data of the material is mined by publicly available literature on experimental data on the mechanical properties of asphalt materials. The macroscopic mechanical property data include dynamic modulus, creep stiffness and fatigue life data. The dynamic modulus describes the stiffness of the asphalt material under cyclic load. The dynamic modulus can be obtained through a dynamic modulus test (for example, a repeated loading creep test or a dynamic bending test). Creep stiffness describes the deformation characteristics of the asphalt material under continuous load. Creep stiffness can be obtained through a creep test. Creep stiffness decreases with time. Creep stiffness can be measured at different temperatures and different loading times. Fatigue life describes the fatigue resistance of the asphalt material under cyclic load. Fatigue life can be obtained through fatigue testing. Fatigue testing is to apply cyclic loads to asphalt material samples and record their fatigue life at different stress levels.

[0155] Step S23: constructing a multidimensional index structure based on the preparation parameter data table, the macroscopic mechanical property data of the asphalt material, and the microscopic characteristic parameters of the asphalt material, using the preparation parameters as the first-level screening index, the microscopic characteristic parameters as the second-level screening index, and the macroscopic mechanical properties as the third-level screening index, thereby obtaining the asphalt multidimensional index structure;

[0156] In an embodiment of the present invention, preparation parameters are used as the first-level screening index. Preparation parameters include gradation type, modifier type, and aging conditions. The purpose of the first-level screening index is to quickly narrow the data range according to the user's screening conditions. For example, if a user wants to query asphalt material samples with a gradation type of AC-13 and a modifier type of SBS, the first-level screening index can filter according to the gradation type and modifier type to narrow the data range. Microscopic characteristic parameters are used as the second-level screening index. Microscopic characteristic parameters describe the microstructural characteristics of asphalt materials, such as porosity, aggregate size, asphalt film thickness, etc. The purpose of the second-level screening index is to further narrow the data range based on the first-level screening index. For example, if a user wants to query asphalt material samples with a porosity of less than 5%, the second-level screening index can filter according to the porosity to narrow the data range. Macroscopic mechanical properties are used as the third-level screening index. Macroscopic mechanical properties describe the mechanical properties of asphalt materials, such as dynamic modulus, creep stiffness, fatigue life, etc. The purpose of the third-level screening index is to finally determine the asphalt material samples that meet the user's query conditions based on the second-level screening index. For example, if a user wants to search for asphalt samples with a dynamic modulus greater than 3000 MPa at 20°C and 10 Hz, the three-level screening index can filter based on the dynamic modulus to ultimately identify asphalt samples that meet the criteria. Constructing a multidimensional index structure requires establishing relationships between different indexes. For example, by linking the preparation parameter, microscopic characteristic parameter, and macroscopic mechanical property data tables through foreign keys, a multidimensional index structure can be achieved.

[0157] Step S24: creating an asphalt material database management system for the asphalt material sample based on the asphalt multidimensional index structure.

[0158] In this embodiment of the present invention, a suitable relational database management system (RDBMS) is selected, such as MySQL, PostgreSQL, or another. Within the RDBMS, the database table structure is designed, creating multiple asphalt material data tables to store information such as preparation parameter data, microscopic characteristic parameter data, macroscopic mechanical property data, and image data paths. The data tables are linked by sample number, and a data import program is written to import various asphalt material data into the database. This data import program must ensure data accuracy and consistency. A user-friendly graphical user interface (GUI) or web interface is developed to allow users to query the database by entering query criteria (e.g., preparation parameters, microscopic characteristic parameter ranges, macroscopic mechanical property ranges). Various query functions are provided, such as queries based on preparation parameters, microscopic characteristic parameter ranges, macroscopic mechanical property ranges, and combined queries based on multiple criteria. Query results should be displayed in tables, charts, and other formats. The query function should be based on a multidimensional index structure to ensure fast query response. The database management system is tested and optimized to ensure its performance and stability, and finally, the database management system is created.

[0159] Preferably, the multidimensional index structure construction specifically includes the following steps:

[0160] Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type, and aging condition as joint keys;

[0161] An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions;

[0162] Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys;

[0163] The three-level indexes are integrated into a logical association table through a database view.

[0164] In an embodiment of the present invention, fields that need to be indexed in the preparation parameter data table are selected, namely, gradation type, modifier type, and aging condition. These three fields together constitute a joint key that can uniquely identify an asphalt material sample. Then, based on the joint key, a B+ tree index is constructed on the preparation parameter data table. Fields that need to be indexed in the microscopic characteristic parameters of the asphalt material are selected, namely, porosity, aggregate size distribution, and asphalt film thickness. Since these parameters all have numerical attributes, they can be regarded as coordinates in a multidimensional space. Then, an R-tree index is constructed on the microscopic characteristic parameter data based on porosity, aggregate size distribution, and asphalt film thickness. Fields that need to be indexed in the macroscopic mechanical properties data are selected, namely, dynamic modulus, creep stiffness, and fatigue life. These fields can be used as hash keys. A hash index is constructed on the macroscopic mechanical properties data based on dynamic modulus, creep stiffness, and fatigue life. Three basic tables are created. These three basic tables correspond to the preparation parameter data table, the microscopic characteristic parameter data table, and the macroscopic mechanical properties data table, respectively. A database view is created. A database view organizes data from base tables according to a specific logic to form a logically linked table. This view also consolidates three indexes into a logically linked table. Logically linked tables can integrate data from different tables based on user needs, simplifying query operations and improving query efficiency.

[0165] This application involves imaging and scanning asphalt samples with different gradations, modifier types, and aging conditions. The resulting data captures multi-scale microscopic image data, including surface morphology, roughness, and three-dimensional structure. This allows for precise extraction of asphalt material microscopic characteristic parameters, such as porosity, pore size distribution, aggregate shape, aggregate size distribution, asphalt film thickness, and interfacial bonding. By combining these microscopic characteristic parameters with macroscopic performance data, the constructed asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters, and macroscopic performance data. Users can flexibly set multiple criteria based on preparation conditions (e.g., gradation type, modifier type, aging conditions), microstructural characteristics (e.g., porosity, roughness), and macroscopic performance indicators (e.g., dynamic modulus, fatigue life), enabling rapid identification of asphalt materials that meet specific requirements. This multi-dimensional design significantly enhances data management flexibility and query efficiency.

[0166] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0167] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for establishing an asphalt material database management system, characterized in that: The following steps are involved: Using scanning electron microscopes, atomic force microscopes, and X-ray tomography equipment, asphalt samples with different gradations, different modifier types, and different aging states were imaged and scanned to obtain multi-scale asphalt material microscopic image data. The multi-scale asphalt material microscopic image data includes asphalt material surface morphology images, asphalt material surface roughness images, and asphalt material three-dimensional structure images. Perform denoising, contrast enhancement and geometric distortion correction preprocessing operations on multi-scale asphalt material microscopic image data; Analyze the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain the microscopic characteristic parameters of the asphalt material; wherein the microscopic characteristic analysis of the material sample includes the following steps: Identifying pore areas based on the three-dimensional structural image of the asphalt material, and performing pore feature analysis to obtain pore feature data; Identifying aggregate particle contours based on the asphalt material surface topography image and the asphalt material three-dimensional structure image, and performing aggregate contour feature analysis to obtain aggregate feature data; Segment the asphalt film based on the asphalt material surface morphology image, and analyze the asphalt interface state through the asphalt material surface roughness image to obtain the asphalt film characteristic data; Quantifying the pore characteristic data, aggregate characteristic data, and asphalt film characteristic data, and establishing a relationship between the characteristic parameters to obtain microscopic characteristic parameters of the asphalt material; An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

2. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The imaging scan comprises the following steps: According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are cut, polished, cooled, cleaned and treated with blow-through treatment to obtain a sample surface that meets the test requirements; Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope, and an X-ray tomography device, and determining at least five different imaging areas according to the sample size and target magnification; Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by using a scanning electron microscope; the surface roughness image of the asphalt material is obtained by using an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by using an X-ray tomography device.

3. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The pore characteristic data includes asphalt material porosity data, pore size distribution data, and pore connectivity data. The pore characteristic analysis includes the following steps: grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image; Based on the grayscale image, the segmentation threshold interval is set to [T1, T2]. Pixels with grayscale values lower than T1 in the image are identified as pore areas, and pixels with grayscale values higher than T2 are identified as non-pore areas. The image is converted into a binary image to obtain asphalt pore binary image data. Among them, T1 is set to 20% of the average grayscale value of the asphalt material 3D structural image, and T2 is set to 80% of the average grayscale value of the asphalt material 3D structural image. For pixels with grayscale values between T1 and T2, secondary attribution discrimination is performed based on the grayscale values of their neighboring pixels. Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and scan layer thickness in the asphalt material three-dimensional structure image, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample; Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material; Calculating the distance between each pore pixel and the nearest non-pore pixel based on the asphalt pore binary image data, thereby obtaining pore size distribution data; Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster; The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore connectivity.

4. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The aggregate characteristic data includes aggregate shape data, aggregate direction data and aggregate size distribution data, and the aggregate profile characteristic analysis includes the following steps: The Canny edge detection algorithm is used to detect the edges of aggregate particles in the asphalt material surface topography image to obtain asphalt aggregate particle edge data; wherein the Gaussian filter standard deviation σ is set to 1.5, the high threshold is set to 80% of the maximum grayscale value of the asphalt material three-dimensional structure image, and the low threshold is set to 40% of the maximum grayscale value of the asphalt material three-dimensional structure image; Tracking the closed contour line of aggregate particles based on the edge data of the asphalt aggregate particles; Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data; Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data; The area and equivalent diameter of each aggregate particle are calculated based on the closed contour line of the aggregate particles to obtain aggregate size distribution data.

5. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The asphalt film characteristic data includes asphalt film thickness, asphalt film uniformity evaluation data and asphalt film-aggregate interface bonding state data. The asphalt interface state analysis includes the following steps: Segment the asphalt film and the aggregate region according to the asphalt material surface topography image and the asphalt material three-dimensional structure image to obtain the asphalt film region and the aggregate region respectively; Performing a three-phase contact point analysis on the asphalt film area and the aggregate area to obtain contact characteristic data of the asphalt sample; Setting the interface transition region distance according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film; Taking the closed contour line of the aggregate particles as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness in the interface transition area; Calculating the film thickness average, standard deviation, and coefficient of variation based on the asphalt film thickness, and performing asphalt film uniformity evaluation to obtain asphalt film uniformity evaluation data; The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

6. The method for establishing an asphalt material database management system according to claim 5, characterized in that: The interface region roughness analysis comprises the following steps: Performing image mapping between the interface transition region and the surface roughness image of the asphalt material; Divide the roughness value of the interface transition area by the roughness value of the aggregate area, and then divide the result by the roughness value of the aggregate area to obtain the roughness change rate of the interface area; Set roughness threshold and asphalt film thickness threshold; If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good; otherwise, it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

7. The method for establishing an asphalt material database management system according to claim 5, characterized in that: The three-phase contact point analysis includes the following steps: Superimposing the edges of the asphalt film area and the aggregate area, and comparing the edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points; Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and determining that the candidate contact point is a three-phase contact point if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold; fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain asphalt film tangent line fitting data; Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle based on the asphalt film tangent fitting data to obtain the asphalt film contact angle; The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

8. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The asphalt material database management system is created by the following steps: Defining preparation parameter fields for the asphalt material sample and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, and aging condition, and setting a composite primary key; the composite primary key is composed of the gradation type code, the modifier type code, and the aging condition code; Obtaining macroscopic mechanical property data of asphalt materials; wherein the macroscopic mechanical property data includes dynamic modulus, creep stiffness and fatigue life data; An asphalt multidimensional index structure is constructed based on the preparation parameter data table, the macroscopic mechanical property data of the asphalt material, and the microscopic characteristic parameters of the asphalt material, wherein the preparation parameters in the preparation parameter data table serve as the primary screening index, the microscopic characteristic parameters in the microscopic characteristic parameters of the asphalt material serve as the secondary screening index, and the macroscopic mechanical properties in the macroscopic mechanical property data of the asphalt material serve as the tertiary screening index; An asphalt material database management system is created for the asphalt material samples based on the asphalt multidimensional index structure.

9. The method for establishing an asphalt material database management system according to claim 8, characterized in that: The multidimensional index structure construction includes the following steps: Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type, and aging condition as joint keys; An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions; Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys; The joint key, the spatial dimension and the hash key are integrated as a three-level index into a logical association table through a database view to obtain an asphalt multidimensional index structure.

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