Asphalt pavement pore network model construction method

Abstract

The invention relates to the technical field of microscopic characterization of pavement material pore structures, in particular to a pore network model construction method of asphalt pavement pores, which comprises the following steps: firstly, measuring the three-dimensional size of a pavement structure material sample, and setting scanning parameters of computed tomography equipment according to the data of the three-dimensional size to obtain a two-dimensional slice image; then processing the two-dimensional slice image through image processing so as to improve the image quality of the two-dimensional slice image, obtaining a three-dimensional image through three-dimensional modeling, and then identifying and separating the three-dimensional image by adopting an adaptive threshold segmentation algorithm so as to reserve a pore structure in the three-dimensional image; and finally, identifying and retaining mutually communicated pores in the three-dimensional image through connectivity analysis, namely, providing clear image materials for the construction of a subsequent pore network structure through image processing, threshold segmentation and connectivity analysis, and finally, obtaining the pore network structure through a maximum sphere algorithm. Therefore, the pore network structure model of the communicated pores can be accurately and completely constructed.

Description

Technical Field

[0001] The invention relates to the technical field of microscopic characterization of pore structure of pavement materials, and in particular to a method for constructing a pore network model of pores in asphalt pavement. Background Art

[0002] As the requirements for asphalt pavement performance in the field of road engineering increase, the understanding of the internal structure of asphalt mixtures has gradually deepened. The mechanical properties of asphalt pavement are closely related to its microscopic pore structure, and traditional macroscopic testing methods cannot accurately reflect these microscopic properties. Computed tomography (CT) technology, as a non-destructive testing method, can provide a three-dimensional image of the internal pores of asphalt mixtures, thereby achieving quantitative characterization of the pore microstructure. The microscopic pore structure of asphalt mixtures includes parameters such as pore distribution, pore size, pore shape and pore connectivity, which have an important influence on the water stability, durability and mechanical properties of asphalt pavements.

[0003] The quantitative characterization method of the pore microstructure of asphalt pavement based on computer tomography (CT) scanning can reveal the heterogeneity of the 3D pore morphology of porous asphalt mixture materials, providing a reference for a better understanding of the flow law of pores in drainage pavements. This method collects data on the distribution, size and complexity of the pore structure in asphalt mixtures through CT scanning and digital image processing technology, and then evaluates high-temperature deformation, low-temperature cracking and water damage resistance. The quantitative characterization method of the pore microstructure of asphalt pavement based on CT scanning can not only provide an in-depth understanding of the internal structure of asphalt mixtures, but also has important significance for optimizing asphalt pavement design, improving pavement performance and extending service life. At present, there is no complete and accurate characterization method for the pore microstructure of asphalt pavement in the field of microscopic characterization of the pore structure of pavement materials. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for constructing a pore network model of asphalt pavement pores, thereby providing parameters for the microscopic characterization of the pore structure of pavement materials by accurately and completely constructing the pore network model.

[0005] A first aspect of an embodiment of the present invention discloses a method for constructing a pore network model of asphalt pavement pores, comprising:

[0006] S1. Obtain a pavement structure material sample and measure the three-dimensional size of the pavement structure material sample;

[0007] S2, setting scanning parameters of a computer tomography device according to the three-dimensional size of the pavement structure material sample and scanning the pavement structure material sample to obtain a two-dimensional slice image, performing image processing on the two-dimensional slice image, and performing three-dimensional stereo modeling on the two-dimensional slice image after image processing to obtain a three-dimensional image;

[0008] S3, using an adaptive threshold segmentation algorithm to identify and separate pores in the three-dimensional image;

[0009] S4, performing connectivity analysis on the pores in the three-dimensional image, identifying and retaining the interconnected pores in the three-dimensional image;

[0010] S5. Use the maximum sphere algorithm to construct a pore network structure model of the interconnected pores in the three-dimensional image.

[0011] Preferably, obtaining the pavement structure sample in step S1 includes:

[0012] S11. Select representative road surface areas for sample collection;

[0013] S12, observing, numbering and recording the collected samples and their collection information and characteristic information;

[0014] S13. Properly preserve the recorded samples.

[0015] Preferably, the image processing in step S2 includes image enhancement processing, noise suppression processing, contrast optimization processing and edge detection processing.

[0016] Preferably, the adaptive threshold analysis algorithm in step S3 includes setting a threshold according to the contrast between pores and other solids, applying the threshold to the three-dimensional image, marking areas with pixel values ​​higher than the threshold as pores, and marking areas with pixel values ​​lower than the threshold as other solids.

[0017] Preferably, the connectivity analysis in step S4 comprises determining the shape factor of the pores:

[0018]

[0019] Among them, when G is less than 0.3, the pores are connected pores; 0.3<G<0.7, the pores are semi-connected pores; G>0.7, the pores are closed pores; G is the shape factor of the pores, V is the volume of the pores, and S is the surface area of ​​the pores.

[0020] Preferably, the maximum sphere algorithm in step S5 includes:

[0021] S51, define the largest sphere:

[0022] For any point p0 in the three-dimensional space of the three-dimensional image, the set of points p that satisfy Dist(p,p0)≤R is defined as the largest sphere B with p0 as the center and radius R R (p0),

[0023] in,

[0024] R is the radius of the largest sphere, Dist(p,p0) represents the distance from point p to point p0, the coordinates of point p are (x,y,z), and the coordinates of point p0 are (x0,y0,z0);

[0025] S52, determine the upper limit R of the radius of the largest sphere uppe :

[0026] R uppe =Dist 2 (p0,p g )=(x g -x0) 2 +(y g -y0) 2 +(z g -z0) 2

[0027] Among them, R uppe Represents the distance from the center of the sphere p0 to the matrix point p with the shortest distance g The square of the distance, the coordinates of point p0 are (x0, y0, z0), p g The coordinates of the point are (x g ,y g ,z g );

[0028] S53, distinguishing between pores and throats:

[0029] All points in the three-dimensional space of the three-dimensional image are converted into several maximum spheres according to step S51, and the upper limit of the radius R of each maximum sphere is obtained by step S52. uppe , and in descending order of the radius upper limit, the maximum balls with the same radius upper limit are divided into a group, the maximum balls adjacent to or overlapping any maximum ball in each group are grouped together to form a maximum ball cluster, and the ball with the largest radius in the multi-cluster is selected as the ancestor of the multi-cluster; each multi-cluster ancestor represents a pore, and when a maximum ball has two multi-cluster ancestors, the maximum ball corresponds to a throat, and the multi-cluster between the throat maximum ball and the pore maximum ball is defined as a pore throat chain; the pores and throats are connected to form a pore network structure model.

[0030] Preferably, the maximum sphere algorithm in step S5 further includes:

[0031] S54, determine the lower limit R of the radius of the largest sphere lower :

[0032] R lower = max{Dist 2 (p v ,p0)|Dist2 (p v ,p0)<R upper ,p0∈M,p v ∈M}

[0033] Among them, the lower limit of the radius of the largest sphere is R lower Indicates that the radius is R upper The distance from the point pv in the pore body farthest from the sphere center p0 to the sphere center p0, Dist 2 (p v ,p0)=(x v -x0) 2 +(y v -y0) 2 +(z v -z0) 2 , the coordinates of point p0 are (x0, y0, z0), p v The coordinates of the point are (x v ,y v ,z v ), M is the pore space;

[0034] S55, handling redundant balls:

[0035] If Dist(p A ,p B )<R LA +R LB , then the maximum ball B is completely contained in the maximum ball A, delete the maximum ball B,

[0036] in, The center p of the largest sphere A A The coordinates of the point are (x A ,y A ,z A ), the center p of the largest ball B B The coordinates of the point are (x B ,y B ,z B );R LA is the lower limit of the radius of the largest sphere A and R LB is the lower limit of the radius of the largest sphere B.

[0037] Preferably, the sample collection information includes the sample collection location or the sample collection time.

[0038] Preferably, the characteristic information of the sample includes the size of the sample, the shape of the sample or the flatness of the sample.

[0039] The present invention firstly measures the three-dimensional size of a pavement structure material sample, sets scanning parameters of a computer tomography device according to the three-dimensional size data, and performs scanning by setting appropriate scanning parameters, thereby improving the image quality of a two-dimensional slice image. Then, the two-dimensional slice image is processed by image processing, thereby further improving the image quality of the two-dimensional slice image. After obtaining a three-dimensional image by three-dimensional modeling, an adaptive threshold segmentation algorithm is used to identify and separate the three-dimensional image according to the grayscale value, that is, firstly identify pores and other solids (such as crushed stone, stone chips, mineral powder, etc.) in the three-dimensional image, then separate the pore structure and other solid structures in the three-dimensional image, and only retain the pore structure in the three-dimensional image. Finally, the interconnected pores in the three-dimensional image are identified and retained by connectivity analysis, that is, clear and accurate image materials are provided for the subsequent construction of a pore network structure through image processing, threshold segmentation, and connectivity analysis. Finally, by using a maximum sphere algorithm, due to the advantage of the maximum sphere algorithm having an accurate algorithm, a pore network structure model of connected pores is accurately and completely constructed, thereby providing parameters for the microscopic characterization of the pore structure of the pavement material. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 It is a flow chart of a method for constructing a pore network model of asphalt pavement pores from a first perspective disclosed in a first embodiment of the present invention;

[0042] Figure 2 It is a representation diagram of the pore network structure model of the asphalt pavement pores disclosed in the first embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the present invention, the directions or positional relationships indicated by "upper", "lower", "outer", etc. are based on the directions or positional relationships shown in the drawings. These terms are mainly used to better describe the present invention and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific direction, or to be constructed and operated in a specific direction.

[0045] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those skilled in the art, the specific meanings of these terms in the present invention can be understood according to specific circumstances.

[0046] In addition, the terms "installed", "set", "provided with", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] In addition, the terms "first", "second", etc. are mainly used to distinguish different devices, elements or components (the specific types and structures may be the same or different), and are not used to indicate or imply the relative importance and quantity of the indicated devices, elements or components. Unless otherwise specified, "plurality" means two or more.

[0048] See also Figure 1 As shown, a method for constructing a pore network model of asphalt pavement pores proposed in the first embodiment of the present invention includes:

[0049] S1. Obtain a pavement structure material sample and measure the three-dimensional dimensions of the pavement structure material sample. Specifically, when the pavement structure material sample is in the shape of a cuboid, the collected three-dimensional dimensions may include the length, width and height of the pavement structure material sample;

[0050] S2, setting scanning parameters of a computer tomography device according to the three-dimensional size of the pavement structure material sample and scanning the pavement structure material sample to obtain a two-dimensional slice image, performing image processing on the two-dimensional slice image, and performing three-dimensional stereo modeling on the two-dimensional slice image after image processing to obtain a three-dimensional image;

[0051] Specifically, before scanning, it can be confirmed again whether the pavement structure material sample is clean and dust-free, etc., to avoid image interference during the scanning process. Fix the pavement structure material sample on a special tray or bracket of the computer tomography device to ensure that the pavement structure material sample does not move during the scanning process. According to the three-dimensional size of the pavement structure material sample, set appropriate scanning parameters, such as the voltage and current of the X-ray, the scanning layer thickness, the scanning speed, etc. Start the computer tomography device, the X-ray source rotates around the pavement structure material sample, and the detector captures the X-ray signal passing through the pavement structure material sample. The computer tomography device generates a series of two-dimensional slice images, which represent the internal structure of the pavement structure material sample at different angles. After the scanning is completed, all the two-dimensional slice image data are collected, which will be used for subsequent three-dimensional reconstruction and analysis. Ensure the integrity and accuracy of the data for accurate image reconstruction. During the entire scanning process, ensure compliance with radiation safety regulations to protect operators and the surrounding environment from radiation damage. Through these steps, detailed internal structure information of the pavement structure material sample can be effectively obtained, providing important data for further analysis and evaluation of the pavement structure material sample.

[0052] S3, using an adaptive threshold segmentation algorithm to identify and separate pores in the three-dimensional image;

[0053] S4, performing connectivity analysis on the pores in the three-dimensional image, identifying and retaining the interconnected pores in the three-dimensional image;

[0054] S5. Use the maximum sphere algorithm to construct a pore network structure model of the interconnected pores in the three-dimensional image.

[0055] In this embodiment, firstly, the three-dimensional dimensions of the pavement structure material sample are measured, and the scanning parameters of the computer tomography device are set according to the three-dimensional dimension data. By setting appropriate scanning parameters for scanning, the image quality of the two-dimensional slice image can be improved. Then, the two-dimensional slice image is processed by image processing to further improve the image quality of the two-dimensional slice image. After the three-dimensional image is obtained by three-dimensional modeling, an adaptive threshold segmentation algorithm is used to identify and separate the three-dimensional image according to the grayscale value, that is, firstly the pores and other solids (such as gravel, stone chips, mineral powder, etc.) in the three-dimensional image are identified, and then the pore structure and other solid structures in the three-dimensional image are separated. The pore structure is retained in the three-dimensional image, and finally the interconnected pores in the three-dimensional image are identified and retained through connectivity analysis, that is, clear and accurate image materials are provided for the subsequent construction of the pore network structure model through image processing, threshold segmentation, and connectivity analysis. Finally, since all calculations of the maximum sphere algorithm are performed on the subvoxel precise distance field, no approximation of the discrete sphere is performed, which improves the accuracy of the algorithm. At the same time, clear and accurate image materials are provided through image acquisition and processing. Therefore, by processing the above image materials through the maximum sphere algorithm, more detailed pore and throat features can be identified, thereby realizing the accurate and complete construction of the pore network structure model of connected pores, such as Figure 2 As shown, the pore network structure model constructed by the above method can clearly and completely display the pore and throat characteristics.

[0056] At the same time, the maximum sphere algorithm has the following advantages: for example, the maximum sphere algorithm can extract pore and throat networks from volume data, realize sub-voxel accurate pore network extraction, and improve the speed and memory efficiency of the algorithm; compared with digital image processing technology, the maximum sphere algorithm can provide a three-dimensional pore network model, which can more comprehensively characterize the spatial pore structure characteristics of the pavement structure, while digital image processing technology can only obtain the pore distribution characteristics within the plane; the spatial pore network structure model of the pavement structure established based on the maximum sphere algorithm can not only obtain the pore size distribution of the soil sample, but also quantitatively characterize the pore connectivity, shape factor, etc.

[0057] Specifically, through the pore network structure model, the pore volume fraction can be extracted (the extracted pore volume fraction refers to the ratio of the volume of pores in the material to the total volume); pore surface area (pore surface area refers to the total area of ​​the pore surface in the porous material); pore volume ratio (pore volume ratio refers to the ratio of the pore volume in the material to the total volume of the material); pore connection path length (pore connection path length refers to the length of the path connecting the pores in the porous material); porosity (porosity refers to the sum of the air-permeable pores and water-holding pores in the matrix, with the pore volume as the total volume of the matrix) The key parameters such as the percentage of the pores in the asphalt pavement are expressed as a percentage); the above key parameters are used for quantitative analysis to further fully and accurately characterize the microstructure of the pores in the asphalt pavement. For example, the method for obtaining key parameters such as the volume fraction is as follows: the CT model has strong multi-physical field coupling, scans the image, and extracts the pores in the structure after image processing to obtain the pavement structure information including porosity, pore size distribution, pore throat size distribution and connectivity between pores; after establishing the pore network structure model, the pore spacing in the pore network structure model is calculated to determine the spatial coordinates of each pore. Through the statistical analysis of the parameters of the pore network structure model, the parameters such as the pore radius, throat radius, pore coordination number, etc. can be obtained, and then the pore volume fraction can be calculated.

[0058] At the same time, since the pore network structure model is established based on the physical statistical parameters of the pore characteristics of porous media, it can improve the calculation efficiency on the basis of reflecting the statistical characteristics of the pore structure; the pore network structure model is close to the actual pore structure, so that the obtained parameters have high precision and do not destroy the sample; the pore network has the strong cross-scale information interactivity, which enables the model to handle physical phenomena at different scales and provide a more comprehensive analysis, so it can also make the obtained quantitative analysis parameter results more accurate.

[0059] like Figure 1 As shown, in order to facilitate the comprehensive scanning and analysis of the sample by the computer tomography device, the second embodiment of the present invention proposes a pore network model construction method for asphalt pavement pores, and on the basis of the first embodiment, the pavement structure sample obtained in step S1 includes:

[0060] S11. Select representative road surface areas for sample collection;

[0061] S12, observing the collected samples. Specifically, the observation may include observing the appearance of the samples, such as shape, whether the surface is flat, whether it is damaged, etc., marking and recording the sample collection information by number. Specifically, the collection information includes information such as the sample collection location, the sample collection time, and characteristic information. Specifically, the characteristic information includes the size of the sample, the shape of the sample, the flatness of the sample, etc.);

[0062] S13. The recorded samples should be properly stored. Specifically, the temperature, humidity, sealing and other conditions should be paid attention to during storage:

[0063] In this embodiment, firstly, by selecting a representative road surface area for sample collection, it can be ensured that the sample can reflect the actual road surface usage and material properties. Secondly, the appearance of the sample after collection is observed, such as the size and shape of the sample, whether the surface is flat, whether it is damaged, etc., to meet the needs of subsequent experiments and tests. Ensure that the surface of the sample is flat and has no obvious defects to reduce the test error. Then the samples are numbered and marked, and the information such as the sample collection location, sample collection time, sample size, sample shape, sample flatness, etc. is recorded to facilitate subsequent data analysis and tracing. Finally, the recorded samples are properly preserved. Specifically, the temperature, humidity, sealing and other conditions should be paid attention to during preservation to avoid moisture, contamination or damage to the sample, and ensure the integrity and reliability of the sample. If the sample needs to be sent to other laboratories for testing, it is necessary to ensure the safety of the sample during transportation to avoid the impact of severe vibration or temperature changes on the sample. Through the above steps, we can prepare the scanned object for the computer tomography device to scan it so that it can be comprehensively analyzed and evaluated.

[0064] like Figure 1 As shown, in order to ensure the accuracy and reliability of the subsequent analysis process, the third embodiment of the present invention proposes a pore network model construction method for asphalt pavement pores, and on the basis of the first embodiment, the image processing in step S2 includes image enhancement processing, noise suppression processing, contrast optimization processing and edge detection processing.

[0065] In this embodiment, the brightness of the image is first adjusted by image enhancement processing, the contrast between different materials in the material sample is increased, and certain features in the image are made more obvious. Then, a suitable filter is set for the image by noise suppression processing to reduce random noise in the image, which may be caused by the quantum properties of X-rays, the electronic noise of the detector, etc., so as to retain the image details while reducing the noise; secondly, the window width and window position of the image and the contrast of the image are adjusted by contrast optimization processing, and the parameters are adjusted according to the contrast between different materials in the sample to optimize the visual effect of the image, so that the difference between different structures in the image is more obvious, and finally, the edge detection processing is performed to accurately identify the pore edges, cracks and other features in the image. In summary, the image quality can be improved through the above-mentioned image processing steps, thereby ensuring the accuracy and reliability of the subsequent analysis process.

[0066] like Figure 1As shown, in order to identify and distinguish the pore structure and other solids in the image, the fourth embodiment of the present invention proposes a method for constructing a pore network model of asphalt pavement pores. On the basis of the first embodiment, the adaptive threshold analysis algorithm in step S3 includes setting a threshold according to the contrast between pores and other solids, applying the threshold to the three-dimensional image output in step S2, marking the area with a pixel value higher than the threshold as pores, and marking the area with a pixel value lower than the threshold as other solids.

[0067] In this embodiment, since the contrasts of different objects in the three-dimensional image are different, for example, the contrasts between pores and other solids (such as crushed stones, stone chips, mineral powder, etc.) are different, so first select an appropriate threshold according to different contrasts, apply the threshold to the three-dimensional image, mark the area with a pixel value higher than the threshold as pores, and mark the area with a pixel value lower than the threshold as other solids. By comparing the threshold with the pixel value, the pores and other solids in the three-dimensional image can be identified and separated.

[0068] As Figure 1 shown, in order to analyze the connected pores in the image, the fifth embodiment of the present invention proposes a method for constructing a pore network model of asphalt pavement pores. On the basis of the first embodiment, the connectivity analysis in step S4 includes determining the shape factor of the pores:

[0069]

[0070] where, when G < 0.3, the pore is a connected pore; 0.3 < G < 0.7, the pore is a semi-connected pore; G > 0.7, the pore is a closed pore; G - the shape factor of the pore, V - the volume of the pore, S - the surface area of the pore.

[0071] In this embodiment, first calculate the shape factor G of the pores by collecting parameters such as the pore volume V and the pore surface area S, and refer to the relevant parameter standards for the pore distribution shape (as shown in Table 1). The shape factor G is set within a certain range, that is, G < 0.3 is a connected pore (that is, as shown in Table 1, the pore distribution shape is a connected network or dendritic), 0.3 < G < 0.7 is a semi-connected pore, and G > 0.7 is a closed pore. By the above judgment criteria, the interconnected pores in the three-dimensional image can be accurately identified and retained.

[0072] Table 1 Relevant parameter standards for pore distribution shape

[0073]

[0074] where, E: Euler number, used to reflect the connectivity of the porous medium E = b0 - b1 + b2

[0075] Bett i number is a number that describes the characteristics of topological space and is determined by analyzing the contours and structure of the pores. b0 (zero-order Bett i number): represents the number of connected components in the pore space, that is, the number of isolated pores or pore clusters. b1 (first-order Bett i number): represents the number of "holes" or paths in the pore space, which connect different pores or pore clusters, reflecting the number of connected paths of the pores. b2 (second-order Bett i number): represents the number of cavities or closed spaces in the pore space, that is, the closed area inside the pores.

[0076] like Figure 1 As shown, in order to realize an accurate and complete connected pore network structure model, the sixth embodiment of the present invention proposes a pore network model construction method for asphalt pavement pores, and on the basis of the first embodiment, the maximum sphere algorithm in step S5 includes:

[0077] S51, define the largest sphere:

[0078] For any point p0 in the three-dimensional space of the three-dimensional image output in step S4, the set of points p that satisfy Dist(p,p0)≤R is defined as the largest sphere B with p0 as the center and radius R. R (p0),

[0079] in,

[0080] R is the radius of the largest sphere, Dist(p,p0) represents the distance from point p to point p0, the coordinates of point p are (x,y,z), and the coordinates of point p0 are (x0,y0,z0);

[0081] S52, determine the upper limit R of the radius of the largest sphere uppe :

[0082] R uppe =Dist 2 (p0,p g )=(x g -x0) 2 +(y g -y0) 2 +(z g -z0) 2

[0083] Among them, R uppe It represents the square of the distance from the center p0 to the shortest matrix point pg. The coordinates of point p0 are (x0, y0, z0). g The coordinates of the point are (x g ,y g ,z g );

[0084] S53, distinguishing between pores and throats:

[0085] All points in the three-dimensional space of the three-dimensional image are converted into several maximum spheres according to step S51, and the upper limit of the radius R of each maximum sphere is obtained by step S52. uppe , and in descending order of the radius upper limit, the maximum balls with the same radius upper limit are grouped into one group, the maximum balls adjacent to or overlapping with any maximum ball in each group are grouped together to form a maximum ball multi-cluster, and the ball with the largest radius in the multi-cluster is selected as the ancestor of the multi-cluster;

[0086] The ancestor of each multi-cluster represents a pore, and when a maximum ball has two multi-cluster ancestors, the maximum ball corresponds to a throat, and the multi-cluster between the throat maximum ball and the pore maximum ball is defined as a pore-throat chain;

[0087] The pores and throats are connected to form a pore network structure model.

[0088] like Figure 1 As shown, in order to effectively ensure the accuracy of the constructed connected pore network structure model, the seventh embodiment of the present invention proposes a pore network model construction method for asphalt pavement pores, and based on the sixth embodiment, the maximum sphere algorithm in step S5 also includes:

[0089] S54, determine the lower limit R of the radius of the largest sphere lower :

[0090] R lower = max{Dist 2 (p v ,p0)|Dist 2 (p v ,p0)<R upper ,p0∈M,p v ∈M}

[0091] Among them, the lower limit of the radius of the largest sphere is R lower Indicates that the radius is R upper The distance from the point pv in the pore body farthest from the sphere center p0 to the sphere center p0, Dist 2 (p v ,p0)=(x v -x0) 2 +(y v -y0) 2 +(z v -z0) 2 , the coordinates of point p0 are (x0, y0, z0), p v The coordinates of the point are (x v ,y v ,zv ), M is the pore space;

[0092] S55, handling redundant balls:

[0093] If Dist(p A ,p B )<R LA +R LB , then the maximum ball B is completely contained in the maximum ball A, delete the maximum ball B,

[0094] in, The center p of the largest sphere A A The coordinates of the point are (x A ,y A ,z A ), the center p of the largest ball B B The coordinates of the point are (x B ,y B ,z B );R LA is the lower limit of the radius of the largest sphere A and R LB is the lower limit of the radius of the largest sphere B.

[0095] In this embodiment, by deleting redundant spheres (ie, the largest sphere that is completely contained in another largest sphere), the accuracy of the expression of pore and throat characteristics in the pore network structure model can be effectively improved.

[0096] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the rights of the present invention.

Claims

1. A method for constructing a pore network model of asphalt pavement pores, characterized in that: include: S1. Obtain a pavement structure material sample and measure the three-dimensional size of the pavement structure material sample; S2, setting scanning parameters of a computer tomography device according to the three-dimensional size of the pavement structure material sample and scanning the pavement structure material sample to obtain a two-dimensional slice image, performing image processing on the two-dimensional slice image, and performing three-dimensional stereo modeling on the two-dimensional slice image after image processing to obtain a three-dimensional image; S3, using an adaptive threshold segmentation algorithm to identify and separate pores in the three-dimensional image; S4, performing connectivity analysis on the pores in the three-dimensional image, identifying and retaining the interconnected pores in the three-dimensional image; S5. Use the maximum sphere algorithm to construct a pore network structure model of the interconnected pores in the three-dimensional image.

2. The method for constructing a pore network model of asphalt pavement pores according to claim 1, characterized in that: The step S1 of obtaining the pavement structure sample comprises: S11. Select representative road surface areas for sample collection; S12, observing, numbering and recording the collected samples and their collection information and characteristic information; S13. Properly preserve the recorded samples.

3. The method for constructing a pore network model of asphalt pavement pores according to claim 1, characterized in that: The image processing in step S2 includes image enhancement processing, noise suppression processing, contrast optimization processing and edge detection processing.

4. The method for constructing a pore network model of asphalt pavement pores according to claim 1, characterized in that: The adaptive threshold analysis algorithm in step S3 includes setting a threshold according to the contrast between pores and other solids, applying the threshold to the three-dimensional image, marking areas with pixel values ​​higher than the threshold as pores, and marking areas with pixel values ​​lower than the threshold as other solids.

5. The method for constructing a pore network model of asphalt pavement pores according to claim 1, characterized in that: The connectivity analysis in step S4 includes determining the shape factor of the pores: Among them, when G is less than 0.3, the pores are connected pores; 0.3<G<0.7, the pores are semi-connected pores; G>0.7, the pores are closed pores; G is the shape factor of the pores, V is the volume of the pores, and S is the surface area of ​​the pores.

6. The method for constructing a pore network model of asphalt pavement pores according to claim 1, characterized in that: The maximum sphere algorithm in step S5 includes: S51, define the largest sphere: For any point p0 in the three-dimensional space of the three-dimensional image, the set of points p that satisfy Dist(p,p0)≤R is defined as the largest sphere B with p0 as the center and radius R R (p0), in, R is the radius of the largest sphere, Dist(p,p0) represents the distance from point p to point p0, the coordinates of point p are (x,y,z), and the coordinates of point p0 are (x0,y0,z0); S52, determine the upper limit R of the radius of the largest sphere uppe : R uppe =Dist 2 (p0,p g )=(x g -x0) 2 +(y g -y0) 2 +(z g -z0) 2 Among them, R uppe Represents the distance from the center of the sphere p0 to the matrix point p with the shortest distance g The square of the distance, the coordinates of point p0 are (x0, y0, z0), p g The coordinates of the point are (x g ,y g ,z g ); S53, distinguishing between pores and throats: All points in the three-dimensional space of the three-dimensional image are converted into several maximum spheres according to step S51, and the upper limit of the radius R of each maximum sphere is obtained by step S52. uppe , and in descending order of the radius upper limit, the maximum balls with the same radius upper limit are grouped into one group, the maximum balls adjacent to or overlapping with any maximum ball in each group are grouped together to form a maximum ball multi-cluster, and the ball with the largest radius in the multi-cluster is selected as the ancestor of the multi-cluster; The ancestor of each multi-cluster represents a pore, and when a maximum ball has two multi-cluster ancestors, the maximum ball corresponds to a throat, and the multi-cluster between the throat maximum ball and the pore maximum ball is defined as a pore-throat chain; The pores and throats are connected to form a pore network structure model.

7. The method for constructing a pore network model of asphalt pavement pores according to claim 6, characterized in that: The maximum ball algorithm in step S5 also includes: S54, determine the lower limit R of the radius of the largest sphere lower : R lower =max{Dist 2 (p v ,p0)∣Dist 2 (p v ,p0)<R upper ,p0∈M,p v ∈M} Among them, the lower limit of the radius of the largest sphere is R lower Indicates that the radius is R upper The point p in the pore body that is within the range and farthest from the sphere center p0 v The distance to the center of the sphere p0, Dist 2 (p v ,p0)=(x v -x0) 2 +(y v -y0) 2 +(z v -z0) 2 , the coordinates of point p0 are (x0, y0, z0), p v The coordinates of the point are (x v ,y v ,z v ), M is the pore space; S55, handling redundant balls: If Dist(p A ,p B )<R LA +R LB , then the maximum ball B is completely contained in the maximum ball A, delete the maximum ball B, in, The center p of the largest sphere A A The coordinates of the point are (x A ,y A ,z A ), the center p of the largest ball B B The coordinates of the point are (x B ,y B ,z B );R LA is the lower limit of the radius of the largest sphere A and R LB is the lower limit of the radius of the largest sphere B.

8. The method for constructing a pore network model of asphalt pavement pores according to claim 2, characterized in that: The sample collection information includes the sample collection location or the sample collection time.

9. The method for constructing a pore network model of asphalt pavement pores according to claim 2, characterized in that: The characteristic information of the sample includes the size of the sample, the shape of the sample or the flatness of the sample.

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