Asphalt concrete structure performance quantitative mapping method fusing multi-source parameters

Abstract

The invention discloses an asphalt concrete structure performance quantitative mapping method fusing multi-source parameters, and particularly relates to the field of road engineering materials, and the method comprises the following steps: S1, selecting asphalt concrete test pieces with uniform gradation and the same cementing material type and compaction mode, and carrying out a loading test by adopting a semicircular bending-tensile test, a UTM test system is combined with a DIC system to obtain mechanical response indexes including a macroscopic peak load Fmax and a microscopic maximum level principal strain Exxmax, and a test piece mechanical property response database is constructed; and S2, performing high-resolution image acquisition on the section of the test piece by using a digital image processing technology, and combining gray enhancement, edge detection and morphological operation. According to the method, the technical problems that structural parameter analysis is isolated, a coupling mechanism between parameters is missing, the prediction modeling capability is insufficient, and performance optimization cannot be performed under a fixed material system can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of road engineering materials, and more specifically, to a quantitative mapping method for the structural performance of asphalt concrete that integrates multiple source parameters. Background Technology

[0002] As a typical multiphase composite material, the mechanical properties of asphalt concrete are influenced not only by the properties of its constituent materials (aggregates, asphalt, and mineral powder) but also by the spatial distribution of its internal structure. During service, this material exhibits complex nonlinear, non-uniform, and multi-scale failure behaviors. In particular, its internal structural characteristics play a decisive role in stress-induced cracking, debonding, and fatigue damage. Therefore, deeply elucidating the coupling relationship between the internal structural parameters and mechanical properties of asphalt concrete has become an important research direction in the field of asphalt materials.

[0003] However, existing research on the description of the internal structure of asphalt concrete remains at a relatively coarse statistical level, making it difficult to achieve high-precision, quantitative, and systematic structure-performance mapping. Traditional research has made some progress in quantifying aggregate contact characteristics (such as the number of contact points, contact length, and contact direction) and pore structure characteristics (such as porosity, pore volume, and connectivity), but these parameters are mostly analyzed in isolation, with few systematic studies exploring the interrelationship between the two and their coupling effect on macroscopic performance. The geometric relationship of aggregate contact may directly affect the morphology and connectivity of pores, while the distribution of the pore network, in turn, affects stress paths and failure modes. Currently, there is a lack of a complete framework to explain the interaction between contact structure and pore structure, especially how they jointly affect fracture behavior. On the one hand, traditional testing methods such as two-dimensional image analysis or empirical formulas cannot fully characterize the three-dimensional features of aggregate contact state and pore space structure; on the other hand, existing mechanical performance analysis mainly focuses on the causal relationship between external loading and response, lacking a systematic analysis of the "structure-behavior-performance" evolution mechanism. Therefore, there is an urgent need to develop a multi-source fusion method that integrates image processing, 3D modeling, data dimensionality reduction, and statistical modeling to achieve a refined quantitative relationship between internal structural parameters and performance indicators.

[0004] Against this backdrop, the concept of materials genomics has been introduced into asphalt mixture research. Its aim is to systematically mine the dominant structural factors influencing key performance characteristics by constructing an "internal structural parameter gene library," thereby establishing a "gene-performance" mapping relationship. Unlike traditional empirical design, this method emphasizes data-driven and feature-driven approaches, starting from the microstructure level to achieve an integrated transformation of performance prediction, material regulation, and structural optimization. Therefore, there is an urgent need to develop an integrated method that combines image recognition, 3D modeling, principal component extraction, and performance modeling to establish a quantitative mapping mechanism between the internal structural parameters of asphalt concrete and its mechanical properties. This would enable engineers to predict and optimize material performance by controlling aggregate contact states and pore distribution characteristics without altering the basic material composition. The goal of this study is to construct a "structural gene unit-performance" mapping model within the framework of materials genomics, providing technical support for the structural optimization design and performance visualization prediction of asphalt mixtures. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a quantitative mapping method for the performance of asphalt concrete structures by integrating multi-source parameters, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A quantitative mapping method for the performance of asphalt concrete structures that integrates multiple source parameters includes the following steps:

[0008] Step S1: Select asphalt concrete specimens with uniform gradation, the same binder type and compaction method, and conduct loading tests using the semi-circular bending tensile test (SCB). Obtain mechanical response indicators, including macroscopic peak load Fmax and microscopic maximum horizontal principal strain Exxmax, through the UTM test system combined with the DIC system (digital image correlation technology), and construct a specimen mechanical performance response database.

[0009] Step S2: High-resolution image acquisition of the specimen cross-section is performed using digital image processing (DIP) technology, combined with grayscale enhancement, edge detection and morphological operations;

[0010] Step S3: The internal three-dimensional pore structure of the specimen is reconstructed using X-ray CT scanning technology. Six key pore structure parameters, including porosity, equivalent pore diameter, pore volume, connectivity, pore surface area, and tortuosity, are calculated using the reconstructed data to construct a subset of three-dimensional pore distribution features.

[0011] Step S4: Combine the contact parameters and pore parameters obtained in Steps 2 and 3 to form a complete internal structural feature set. After Z-score standardization, use Principal Component Analysis (PCA) to reduce the dimensionality of the high-dimensional structural parameters and extract three principal components C1, C2, and C3 that mainly characterize the aggregate contact behavior, pore morphology, and overall structural state. Record the cumulative variance contribution rate of each principal component.

[0012] Step S5: Using principal components C1, C2, and C3 as independent variables and Fmax and Exxmax as dependent variables, establish two independent multiple linear regression models to quantitatively describe the influence of internal structural parameters on the macroscopic mechanical properties and microscopic strain response of asphalt concrete.

[0013] In a preferred embodiment, in step S1, all asphalt concrete samples are formed using the same gradation, binder type, and Superpave Gyratory Compactor (SGC) compaction method. Superpave Gyratory Compactor (SGC) is a rotary compaction device used for asphalt mixture design. It evaluates the compaction performance of the mixture by simulating actual road climate and load conditions to ensure consistent initial conditions. During the semi-circular bending tensile test, the loading speed is set to 50 mm / min, and the spacing between the support rollers is 0.8 times the diameter of the specimen to obtain a stable fracture path and structural response.

[0014] The DIC system includes: an industrial camera, an LED frequencyless flash source, DIC analysis software (GOMCorrelate), and a calibration scale and optical correction plate;

[0015] The DIC analysis software (GOMCorrelate) is used for image matching and strain field reconstruction; the calibration scale and optical calibration plate are used for system intrinsic parameter calibration.

[0016] Before the test, the loading surface of the specimen was treated. First, a white matte primer was sprayed as a background, and then a black high-contrast speckle pattern was randomly distributed using a micro-spraying device to ensure that the image feature points were evenly distributed, which would facilitate the subsequent extraction of displacement and strain fields.

[0017] In a preferred embodiment, in step S1, during the loading process of the semi-circular bending tensile test, the DIC system and the UTM test system are linked and triggered simultaneously to record the load, displacement, and strain response: the macroscopic mechanical index Fmax (peak load) is recorded in real time by the UTM test system; the temporal evolution of the horizontal principal strain Exx is calculated by the DIC image sequence; the image sampling frequency is set to 2-5Hz to meet the monitoring requirements of high strain gradient changes before and after nonlinear fracture; the DIC system measures 0.05%-1000% strain, with a displacement resolution of 0.01 pixels, fully capturing the evolution of the crack tip and the development of the strain concentration zone. In summary, the above loading-acquisition-extraction process ensures that the strain data acquired by the DIC is completely synchronized with the UTM loading process, possessing high spatial resolution and deformation tracking capabilities, providing a solid experimental foundation and highly reliable data support for establishing a quantitative mapping relationship between structure and performance.

[0018] In a preferred embodiment, in step S2, a professional flatbed scanner with a resolution of 762 dpi (approximately 0.0334 mm / pixel) is used to acquire images of the loaded cross-section of the fractured specimen to ensure the continuity of the fine aggregate boundaries and contact areas. The obtained images are first preprocessed using a median filter to remove isolated noise points caused by material surface particles or scanning errors, and to maintain the smoothness and integrity of the aggregate boundaries. The image grayscale is standardized using a maximum value filter (H-max) to ensure uniform overall brightness distribution. Subsequently, a grayscale inversion operation (255 - grayscale value) is performed to enhance the aggregate boundary area into a high-contrast feature area, which is beneficial for subsequent edge extraction and aggregate body identification. After completing the grayscale enhancement processing, the image enters the edge extraction stage. A method based on the watershed transformation algorithm combined with threshold segmentation is used to accurately divide the adjacent aggregate boundaries in the image. The watershed algorithm can construct a topological "ridge" based on the grayscale gradient, effectively distinguishing aggregate agglomeration from the actual contact area.

[0019] Based on clearly defined aggregate boundaries, morphological calculation tools were used to identify the following key contact parameters:

[0020] Based on the measurement results of contact point number (NC), contact angle (θC), contact distance (DC), aggregate quantity (NA), aggregate orientation (θA), and aggregate equivalent diameter (DA), some indices describing the mechanical structure of asphalt concrete skeleton are calculated, including: aggregate contact number (ACN), average contact length (Lave), average aggregate contact length (ACL), total vertical projection of contact length (LV), internal structure index (ISI), vertical and horizontal projections of aggregate equivalent diameter (DV, DH), and average aggregate angle (θave). A subset of contact structure features is constructed by calculation, and the definitions of these parameters are as follows.

[0021] In a preferred embodiment, in step S3, a Zeiss Xradia 620 Versa high-resolution industrial CT scanning system is used. This system has sub-micron level image resolution and can complete multi-channel scanning without damaging the specimen structure. To balance imaging quality and computational cost, the scanning resolution is set to 0.075 mm / pixel, which is sufficient to identify the three-dimensional structural features of typical pore sizes (0.1~2 mm) in asphalt mixtures. The system operating voltage is set to 580 kV and the operating current is 1 mA. The original image acquisition obtains a sequence of 256 grayscale slice images (0-255), where black represents low-density areas (such as pores) and white represents high-density areas (such as aggregates). After acquisition, the system's built-in volume rendering reconstruction algorithm is used to overlay and reconstruct the continuous slice images to form a complete three-dimensional visualization model of the asphalt mixture specimen. This method preserves the topological structure and geometric boundary integrity of the pore channels through light projection and density fusion, providing basic data support for subsequent structural parameter extraction.

[0022] In a preferred embodiment, in step S3, the voxel grayscale values ​​of the reconstructed image are factored based on the Otsu automatic thresholding method. The segmentation point that maximizes the inter-class variance in the grayscale histogram is automatically identified as the optimal segmentation threshold for the entire image. After setting the threshold, areas with grayscale values ​​below the threshold are marked as pores, and areas with grayscale values ​​above the threshold are defined as background solid material (aggregate + asphalt skeleton). The SeparateObject module of the Watershed algorithm is used to further separate closely adjacent but physically independent pore structures. This module uses gradient information to mark object boundaries, achieving boundary expansion and segmentation, preventing adjacent pores from being mistaken for individual cells. Each identified pore structure is displayed in the software platform as an 8-color cyclically numbered layer, facilitating subsequent individual cell parameter extraction and topology network identification. All data is saved in structured voxel format.

[0023] In a preferred embodiment, in step S4, to eliminate the influence of dimensions, all structural parameters are first subjected to Z-score normalization, and the normalization formula is as follows:

[0024] ;

[0025] Perform covariance analysis on the standardized matrix Z to generate a p×p covariance matrix S, where p is the number of structural parameters. The calculation formula is as follows:

[0026] ;

[0027] Where: n represents the number of samples; Standardized covariance estimation matrix;

[0028] Eigenvalue decomposition of the covariance matrix S yields:

[0029] ;

[0030] Where: V represents the eigenvector matrix (principal component direction); Λ corresponds to the eigenvalue diagonal matrix, reflecting the variance contribution of each principal component.

[0031] In a preferred embodiment, step S5 uses the IBM SPSS Statistics 27 platform for modeling. The modeling process uses Enter mode (i.e., all variables are entered into the model at once) to avoid structural feature distortion caused by variable removal during stepwise regression. The model form is as follows:

[0032] Y = β0 + β1 C1 + β2 C2 + β3 C3 + ε

[0033] Y is represented as Fmax;

[0034] C1, C2, and C3 represent the standardized principal component variables;

[0035] β represents the regression constant term;

[0036] ε represents the random error term.

[0037] The modeling results are evaluated using the following core metrics:

[0038] Goodness of fit (R²): Measures the extent to which principal components explain the mechanical properties; Fmax model: R² = 0.925, indicating that it can explain approximately 92.5% of the load response variability; Exxmax model: R² = 0.889, indicating that it can explain approximately 88.9% of the mesoscopic deformation behavior. Variance inflation factor (VIF): VIF < 1.0 for all principal component variables, indicating that there is no risk of collinearity in the model. Residual analysis: The standardized residuals follow an approximately normal distribution, and no structural bias is observed in the residual plot, verifying the robustness of the model assumptions.

[0039] The technical effects and advantages of this invention's quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters are as follows:

[0040] 1. The internal structure of asphalt concrete is divided into two categories: "aggregate contact characteristics" and "pore structure characteristics". Based on DIP and X-ray CT technologies, relevant parameters are extracted respectively, and a multi-scale and multi-dimensional structural parameter system is constructed, covering seven key indicators including contact point density (CPD), contact direction (AOD), maximum contact length (MCL), porosity (PA), tort, and connectivity (CN), laying a quantitative foundation for revealing the structural control performance.

[0041] 2. Principal component analysis (PCA) is introduced to compress and reconstruct the weights of high-dimensional structural parameters, extract principal component variables that reflect contact behavior, pore morphology and comprehensive structural performance, and construct prediction models for peak load Fmax and principal strain Exxmax through multiple linear regression. The constructed models achieve prediction accuracies of R²=0.925 and R²=0.889, respectively, which are significantly better than the traditional single-factor correlation analysis path and have good stability and generalization ability.

[0042] 3. The constructed structure-performance mapping model not only possesses predictive capabilities but can also be used to infer the "optimal internal structural state." Utilizing the PCA weight matrix, this invention achieves a quantitative ranking of the contribution of structural factors, thereby identifying performance-sensitive structural variables and realizing a closed-loop structural control system centered on "structural input – performance output – structural counter-control." This approach allows engineers to optimize mechanical performance by controlling structural states such as "lower polygonality, higher contact density, and more uniform directional distribution" without changing the gradation or material type. Engineers can identify performance-sensitive structural factors (such as lower pore curvature, more consistent contact direction, and higher contact density) based on the model's regression coefficients and principal component loads. Without changing the gradation or binder, mechanical performance can be improved through structural fine-tuning, breaking through the limitations of traditional design paths that rely on material replacement.

[0043] 4. It can be embedded in the early structural evaluation stage of engineering projects to realize a closed-loop analysis process from image acquisition to parameter extraction, PCA modeling, performance prediction and structural optimization. It is suitable for the structural design and performance evaluation of newly built, heavy-load and high-speed traffic roads, and provides technical support for the engineering application of materials genome technology in road engineering. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a method for quantitatively mapping the performance of asphalt concrete structures by integrating multiple source parameters.

[0045] Figure 2 This is a schematic diagram of a quantitative mapping system for the performance of asphalt concrete structures that integrates multiple source parameters. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] Reference Figure 1 This invention provides a quantitative mapping method for the performance of asphalt concrete structures by integrating multiple source parameters, specifically including the following steps:

[0049] Step S1: Select asphalt concrete specimens with uniform gradation, the same binder type and compaction method, and conduct loading tests using the semi-circular bending tensile test (SCB). Obtain mechanical response indicators, including macroscopic peak load Fmax and microscopic maximum horizontal principal strain Exxmax, through the UTM test system combined with the DIC system (digital image correlation technology), and construct a specimen mechanical performance response database.

[0050] Step S2: High-resolution image acquisition of the specimen cross-section is performed using digital image processing (DIP) technology, combined with grayscale enhancement, edge detection and morphological operations;

[0051] Step S3: The internal three-dimensional pore structure of the specimen is reconstructed using X-ray CT scanning technology. Six key pore structure parameters, including porosity, equivalent pore diameter, pore volume, connectivity, pore surface area, and tortuosity, are calculated using the reconstructed data to construct a subset of three-dimensional pore distribution features.

[0052] Step S4: Combine the contact parameters and pore parameters obtained in Steps 2 and 3 to form a complete internal structural feature set. After Z-score standardization, use Principal Component Analysis (PCA) to reduce the dimensionality of the high-dimensional structural parameters and extract three principal components C1, C2, and C3 that mainly characterize the aggregate contact behavior, pore morphology, and overall structural state. Record the cumulative variance contribution rate of each principal component.

[0053] Step S5: Using principal components C1, C2, and C3 as independent variables and Fmax and Exxmax as dependent variables, establish two independent multiple linear regression models to quantitatively describe the influence of internal structural parameters on the macroscopic mechanical properties and microscopic strain response of asphalt concrete.

[0054] In step S1, all asphalt concrete samples were formed using the same gradation, binder type, and Superpave Gyratory Compactor (SGC) compaction method. Superpave Gyratory Compactor (SGC) is a rotary compaction device used for asphalt mixture design. It evaluates the compaction performance of the mixture by simulating actual road climate and load conditions to ensure consistent initial conditions. During the semi-circular bending tensile test, the loading speed was set to 50 mm / min, and the spacing between the support rollers was 0.8 times the diameter of the specimen to obtain the stable fracture path and structural response.

[0055] The DIC system includes: an industrial camera, an LED frequencyless flash source, DIC analysis software (GOMCorrelate), and a calibration scale and optical correction plate;

[0056] The DIC analysis software (GOMCorrelate) is used for image matching and strain field reconstruction; the calibration scale and optical calibration plate are used for system intrinsic parameter calibration.

[0057] Before the test, the loading surface of the specimen was treated. First, a white matte primer was sprayed as a background, and then a black high-contrast speckle pattern was randomly distributed using a micro-spraying device to ensure that the image feature points were evenly distributed, which would facilitate the subsequent extraction of displacement and strain fields.

[0058] In step S1, during the loading process of the semi-circular bending tensile test, the DIC system and the UTM test system are linked and triggered simultaneously to record the load, displacement, and strain response: the macroscopic mechanical index Fmax (peak load) is recorded in real time by the UTM test system; the temporal evolution of the horizontal principal strain Exx is calculated from the DIC image sequence; the image sampling frequency is set to 2-5Hz to meet the monitoring requirements of high strain gradient changes before and after nonlinear fracture; the DIC system measures 0.05%-1000% strain, with a displacement resolution of 0.01 pixels, fully capturing the evolution of the crack tip and the development of the strain concentration zone. In summary, the above loading-acquisition-extraction process ensures that the strain data acquired by the DIC is completely synchronized with the UTM loading process, possessing high spatial resolution and deformation tracking capabilities, providing a solid experimental foundation and highly reliable data support for establishing a quantitative mapping relationship between structure and performance.

[0059] In step S2, a professional flatbed scanner with a resolution of 762 dpi (approximately 0.0334 mm / pixel) is used to acquire images of the loaded cross-section of the fractured specimen to ensure the continuity of the fine aggregate boundaries and contact areas. The obtained images are first preprocessed using a median filter to remove isolated noise points caused by material surface particles or scanning errors, and to maintain the smoothness and integrity of the aggregate boundaries. The image grayscale is standardized using a maximum value filter (H-max) to ensure uniform overall brightness distribution. Subsequently, a grayscale inversion operation (255 - grayscale value) is performed to enhance the aggregate boundary area into a high-contrast feature area, which is beneficial for subsequent edge extraction and aggregate body identification. After completing the grayscale enhancement processing, the image enters the edge extraction stage. A method based on the watershed transformation algorithm combined with threshold segmentation is used to accurately divide the adjacent aggregate boundaries in the image. The watershed algorithm can construct a topological "ridge" based on the grayscale gradient, effectively distinguishing aggregate agglomeration from the actual contact area.

[0060] Based on clearly defined aggregate boundaries, morphological calculation tools were used to identify the following key contact parameters:

[0061] Based on the measurements of contact point number (NC), contact angle (θC), contact distance (DC), aggregate quantity (NA), aggregate orientation (θA), and aggregate equivalent diameter (DA), several indices describing the mechanical structure of the asphalt concrete skeleton are calculated, including: aggregate contact number (ACN), average contact length (Lave), average aggregate contact length (ACL), total vertical projection of contact length (LV), internal structure index (ISI), vertical and horizontal projections of aggregate equivalent diameter (DV, DH), and average aggregate angle (θave). A subset of contact structure features is constructed, and the definitions of these parameters are as follows:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] For the number of contacts;

[0071] Number of aggregates;

[0072] The contact distance of the surrounding pixel i;

[0073] The orientation of the surrounding pixel i;

[0074] Let be the equivalent diameter of aggregate i;

[0075] Let i be the orientation of aggregate i.

[0076] In step S3, a Zeiss Xradia 620 Versa high-resolution industrial CT scanning system was used. This system possesses sub-micron image resolution capabilities and can perform multi-channel scanning without damaging the specimen structure. To balance imaging quality and computational cost, the scanning resolution was set to 0.075 mm / pixel, sufficient to identify the three-dimensional structural features of typical pore sizes (0.1-2 mm) in asphalt mixtures. The system operating voltage was set to 580 kV, and the operating current to 1 mA. The original image acquisition yielded a sequence of 256 grayscale slice images (0-255), with black representing low-density areas (such as pores) and white representing high-density areas (such as aggregates). After acquisition, the system's built-in volume rendering reconstruction algorithm was used to overlay and reconstruct the continuous slice images, forming a complete three-dimensional visualization model of the asphalt mixture specimen. This method, through light projection and density fusion, preserves the topological structure and geometric boundary integrity of the pore channels, providing fundamental data support for subsequent structural parameter extraction.

[0077] In step S3, the Otsu automatic thresholding method is used to factorize the voxel gray values ​​of the reconstructed image. The segmentation point that maximizes the inter-class variance in the gray-level histogram is automatically identified as the optimal segmentation threshold for the entire image. After setting the threshold, areas with gray values ​​below the threshold are marked as pores, while those above the threshold are defined as background material (aggregate + asphalt skeleton). The SeparateObject module of the Watershed algorithm is used to further separate closely adjacent but physically independent pore structures. This module uses gradient information to mark object boundaries, achieving boundary expansion and segmentation, preventing adjacent pores from being mistaken for individual cells. Each identified pore structure is displayed in the software platform as an 8-color cyclically numbered layer, facilitating subsequent individual cell parameter extraction and topology network identification. All data is saved in structured voxel format.

[0078] This invention extracts the following six three-dimensional pore structure feature parameters, which are defined as follows:

[0079] Porosity: Porosity = Vtotal / Vpore; where Vpore is the sum of the volumes of all pore voxels in the image, and Vtotal is the reconstructed volume of the specimen.

[0080] The equivalent pore diameter assumes that each pore is an equivalent sphere, and its spherical diameter is inferred from the pore volume.

[0081] Pore ​​volume: The voxel counting method directly counts the number of voxels in each independent pore multiplied by the volume of a single voxel.

[0082] Connectivity Index, based on three-dimensional topological connectivity analysis, is defined as the proportion of pores that have at least one connected path intersecting with the specimen boundary, reflecting permeation potential and path complexity.

[0083] The pore surface area is calculated using a 3D boundary extraction algorithm (such as MarchingCubes) to measure the outer surface area of ​​each individual pore, and the triangulation of high curvature regions is automatically optimized.

[0084] Tortuosity is calculated as the ratio of the actual length of the pore connectivity path to the ideal straight-line distance, describing the path complexity and structural deviation during gas-liquid flow.

[0085] In summary, the acquisition process, 3D reconstruction, image segmentation, and structural parameter extraction of the X-ray CT scan section all possess verifiable and standardized pathways. The extracted pore structure feature parameters not only have geometrical significance but are also closely related to the interfacial failure behavior, load-bearing stability, and durability of asphalt mixtures, providing a solid data foundation for the subsequent "structural state-performance response" mapping model.

[0086] In step S4, to eliminate the influence of dimensions, all structural parameters are first standardized using Z-score. The standardization formula is as follows:

[0087] ;

[0088] Perform covariance analysis on the standardized matrix Z to generate a p×p covariance matrix S, where p is the number of structural parameters. The calculation formula is as follows:

[0089] ;

[0090] Where: n represents the number of samples; Standardized covariance estimation matrix;

[0091] Eigenvalue decomposition of the covariance matrix S yields:

[0092] ;

[0093] Where: V represents the eigenvector matrix (principal component direction); Λ corresponds to the eigenvalue diagonal matrix, reflecting the variance contribution of each principal component.

[0094] In step S5, the IBM SPSS Statistics 27 platform is used for modeling. The modeling process uses Enter mode (i.e., all variables are entered into the model at once) to avoid structural feature distortion caused by variable removal during stepwise regression. The model form is as follows:

[0095] Y = β0 + β1 C1 + β2 C2 + β3 C3 + ε

[0096] Y is represented as Fmax;

[0097] C1, C2, and C3 represent the standardized principal component variables;

[0098] β represents the regression constant term;

[0099] ε represents the random error term.

[0100] The modeling results are evaluated using the following core metrics:

[0101] Goodness of fit (R²): Measures the extent to which principal components explain the mechanical properties; Fmax model: R² = 0.925, indicating that it can explain approximately 92.5% of the load response variability; Exxmax model: R² = 0.889, indicating that it can explain approximately 88.9% of the mesoscopic deformation behavior. Variance inflation factor (VIF): VIF < 1.0 for all principal component variables, indicating that there is no risk of collinearity in the model. Residual analysis: The standardized residuals follow an approximately normal distribution, and no structural bias is observed in the residual plot, verifying the robustness of the model assumptions.

[0102] Significance tests (F-test and t-test):

[0103] The overall model was significant by the F-test (p<0.01); all principal component regression coefficients were significantly different from zero within the 95% confidence interval.

[0104] Unlike traditional modeling methods that rely on raw variables, this invention effectively mitigates parameter redundancy and noise interference by reconstructing structural principal control factors, while simultaneously improving the model's physical interpretability. Using principal component scores as input facilitates mapping complex structural representations into low-dimensional structural control factors with behavioral-driven significance, thereby: achieving a three-dimensional logical mapping of "state-behavior-performance"; providing quantitative basis for screening performance-sensitive parameters and formulating structural optimization strategies; and supporting early-stage identification and performance grading evaluation of non-destructive structural states in engineering projects.

[0105] The present invention also has the following technical features:

[0106] The DIC image acquisition system has a resolution of 2448×2048, a calibration point spacing of 1.0 mm, and a loading rate of 50 mm / min. It is used to synchronously acquire the full-field strain distribution on the surface of a semi-circular specimen.

[0107] The X-ray CT scanning equipment has a resolution of 20μm, a reconstructed voxel spacing of 0.02mm³, and an image threshold segmentation accuracy of no less than 98%, which is used to extract multi-scale pore morphology data.

[0108] Both PCA analysis and regression modeling were performed using IBM SPSS Statistics 27 software. The principle for retaining principal components was that the cumulative variance contribution rate was greater than 90%, and the data were uniformly processed using the zero-mean normalization method.

[0109] Example 2

[0110] This embodiment discloses a specific implementation process for a quantitative mapping method between the internal structural parameters and mechanical properties of asphalt concrete based on principal component analysis, such as... Figure 2 As shown, the main steps include:

[0111] In this embodiment, 20 asphalt concrete specimens with the same aggregate type, gradation composition and compaction process were selected. The specimens were AC-20 gradation and loaded using the semi-circular bending test (SCB) method. The macroscopic peak load (Fmax) and full-field strain distribution of each specimen were acquired simultaneously by combining a universal testing machine (UTM) and a digital image correlation system (DIC). The maximum horizontal principal strain value (Exxmax) was extracted to construct a performance response dataset.

[0112] Digital image processing (DIP) technology was used to enhance the cross-section of the specimen, perform edge detection and target segmentation, and construct a contact network graph model. Seven typical contact structure parameters were extracted, including average number of contact points (ACN), average contact length (ACL), standard deviation of contact direction (SDO), maximum contact length (MCL), contact point density (CPD), contact ratio (ACR), and internal structure index (ISI), to generate a set of contact feature parameters.

[0113] High-resolution X-ray CT scanning system was used to perform tomographic imaging and 3D reconstruction of each group of specimens. Using image segmentation and voxel analysis, six pore structure features were extracted, including apparent porosity (PA), equivalent pore size (DE), pore volume (PV), connectivity (CN), surface area (SA), tort, and morphological complexity (CI), to establish a 3D pore parameter database.

[0114] After normalizing the contact parameter set and the pore parameter set, principal component analysis (PCA) was performed. Based on the criterion of "cumulative variance contribution rate ≥ 90%" for principal component extraction, three representative principal components, PC1, PC2, and PC3, were extracted and categorized as "contact geometry," "pore space," and "structural comprehensive performance," respectively. Simultaneously, characteristic load matrix analysis was used to identify the weight contributions of different original variables in the principal components, thereby revealing the performance-controlling structural factors (such as ACL, Tort, CN, etc.), providing a scientific basis for accurate performance prediction and structural back-engineering optimization.

[0115] Two multiple linear regression models were established using the scores of the three principal components as independent variables and Fmax and Exxmax as dependent variables. The prediction results show that the goodness-of-fit R² for Fmax is 0.925 and for Exxmax is 0.889. The model residuals are reasonably distributed, the predictive performance is stable, and it has good engineering adaptability and generalizability.

[0116] The DIC test resolution is 2448×2048, the calibration point spacing is 1mm, and the spatial accuracy of the obtained principal strain distribution is ±0.003.

[0117] The CT image resolution is 20μm, the 3D reconstruction accuracy is 0.02mm³, and the segmentation algorithm error is controlled within 2%.

[0118] The PCA and regression model construction process used SPSS Statistics 27 software, and the data normalization adopted the Z-score standardization method.

[0119] The established regression model can not only be used for performance prediction, but also to infer the optimal internal structural state (such as minimum Tort and most consistent ACD) to guide structural optimization.

[0120] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0121] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and other division methods may exist in actual implementation. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections can be indirect couplings or communication connections between devices or units through some interfaces, and can be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quantitative mapping method for the performance of asphalt concrete structures integrating multi-source parameters, characterized in that, Includes the following steps: Step S1: Select asphalt concrete specimens with uniform gradation, the same binder type and compaction method, and conduct loading tests using a semi-circular bending tensile test. Obtain mechanical response indicators, including macroscopic peak load Fmax and microscopic maximum horizontal principal strain Exxmax, through the UTM test system in conjunction with the DIC system, and construct a specimen mechanical performance response database. Step S2: High-resolution images of the specimen cross-section are acquired using digital image processing technology, combined with grayscale enhancement, edge detection and morphological operations; Step S3: The internal three-dimensional pore structure of the specimen is reconstructed using X-ray CT scanning technology. Six key pore structure parameters, including porosity, equivalent pore diameter, pore volume, connectivity, pore surface area, and tortuosity, are calculated using the reconstructed data to construct a subset of three-dimensional pore distribution features. Step S4: Combine the contact parameters and pore parameters obtained in Steps 2 and 3 to form a complete internal structural feature set. After Z-score standardization, use principal component analysis algorithm to reduce the dimensionality of the high-dimensional structural parameters and extract three principal components C1, C2, and C3 that mainly characterize the aggregate contact behavior, pore morphology, and overall structural state. Record the cumulative variance contribution rate of each principal component. Step S5: Using principal components C1, C2, and C3 as independent variables and Fmax and Exxmax as dependent variables, establish two independent multiple linear regression models to quantitatively describe the influence of internal structural parameters on the macroscopic mechanical properties and microscopic strain response of asphalt concrete.

2. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 1, characterized in that: In step S1, all asphalt concrete samples were formed using the same gradation, binder type and SGC compaction method to ensure consistent initial conditions. During the semi-circular bending tensile test, the loading speed was set to 50 mm / min and the spacing between the support rollers was 0.8 times the diameter of the specimen to obtain a stable fracture path and structural response. The DIC system includes: an industrial camera, an LED non-frequency flash source, DIC analysis software, and a calibration scale and optical correction plate; DIC analysis software is used for image matching and strain field reconstruction; calibration scales and optical calibration plates are used for system intrinsic parameter calibration. Before the test, the loading surface of the specimen was treated. First, a white matte primer was sprayed as a background, and then a black high-contrast speckle pattern was randomly distributed using a micro-spraying device to ensure that the image feature points were evenly distributed, which would facilitate the subsequent extraction of displacement and strain fields.

3. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 2, characterized in that: In step S1, during the loading process of the semi-circular bending tensile test, the DIC system and the UTM test system are linked and triggered to synchronously record the load, displacement and strain response: the macroscopic mechanical index Fmax is recorded in real time by the UTM test system; the temporal evolution of the horizontal principal strain Exx is calculated by the DIC image sequence; the image sampling frequency is set to 2-5Hz to meet the monitoring requirements of high strain gradient changes before and after nonlinear fracture; the DIC system has a measurement range of 0.05%-1000% strain and a displacement resolution of 0.01 pixels, which fully captures the evolution of the crack tip and the development process of the strain concentration zone.

4. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 3, characterized in that: In step S2, a flatbed scanner is used to acquire images of the loaded cross-section of the fractured specimen to ensure the continuity of the micro-aggregate boundaries and contact areas. The obtained images are first preprocessed using a median filter to remove isolated noise points caused by material surface particles or scanning errors, and to maintain the smoothness and integrity of the aggregate boundaries. The image grayscale is standardized using a maximum value filter to ensure uniform overall brightness distribution. Subsequently, a grayscale inversion operation is performed to enhance the aggregate boundary area into a high-contrast feature area, which is beneficial for subsequent edge extraction and aggregate body recognition. After completing the grayscale enhancement processing, the image enters the edge extraction stage, and the adjacent aggregate boundaries in the image are accurately divided using a watershed transform algorithm combined with threshold segmentation.

5. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 4, characterized in that: In step S3, an industrial CT scanning system is used to acquire a sequence of grayscale slice images from the original images. After acquisition, the continuous slice images are superimposed and reconstructed to form a complete three-dimensional visualization model of the asphalt mixture specimen. This method preserves the topological structure and geometric boundary integrity of the pore channels through light projection and density fusion, providing basic data support for subsequent structural parameter extraction.

6. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 5, characterized in that: In step S3, the voxel gray values ​​of the reconstructed image are factored, and the segmentation point that maximizes the inter-class variance in the gray-level histogram is automatically identified as the best segmentation threshold for the whole image. After setting the threshold, the area with gray values ​​below the threshold is marked as a pore, and the part above the threshold is defined as background solid material. Closely adjacent but physically independent pore structures are further separated, and the object boundary is marked by gradient information to achieve boundary expansion and segmentation, preventing adjacent pores from being mistaken for single objects.

7. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 6, characterized in that: In step S4, to eliminate the influence of dimensions, all structural parameters are first standardized using Z-score. The standardization formula is as follows: ； Perform covariance analysis on the standardized matrix Z to generate a p×p covariance matrix S, where p is the number of structural parameters. The calculation formula is as follows: ； Where: n represents the number of samples; Standardized covariance estimation matrix; Eigenvalue decomposition of the covariance matrix S yields: ; Where: V represents the eigenvector matrix; Λ corresponds to the eigenvalue diagonal matrix, reflecting the variance contribution of each principal component.

8. The quantitative mapping method for asphalt concrete structural performance integrating multi-source parameters according to claim 7, characterized in that: In step S5, modeling is performed using the Enter mode to avoid structural feature distortion caused by variable removal during stepwise regression. The model form is as follows: Y = β0 + β1 C1 + β2 C2 + β3 C3 + ε Y is represented as Fmax; C1, C2, and C3 represent the standardized principal component variables; β represents the regression constant term; ε represents the random error term.

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