Ultra-compact asphalt mixture pavement layer detection method
By using X-ray CT scanning and nonlinear curve fitting technology to detect the porosity and interconnected porosity of asphalt mixtures, the problem of difficulty in evaluating the waterproof performance of asphalt mixture pavement layers in existing technologies has been solved, thus achieving effective protection of steel bridge decks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies are insufficient to effectively test the waterproof performance of asphalt mixture pavement layers, leading to the rapid development of defects in steel bridge decks and making it impossible to assess their protective capabilities in a timely manner.
X-ray CT scanning technology combined with the large law algorithm and the ring segmentation method is used to detect the void ratio and connected void ratio of asphalt mixture. The relationship between void ratio and connected void ratio is established by nonlinear curve fitting, the design void ratio threshold is calculated, and it is determined whether the asphalt mixture is in an ultra-dense state.
It enables the testing of the waterproof performance of asphalt mixture pavement layers, allowing for timely assessment of their protective capabilities for steel bridge decks and extending the service life of steel bridge deck plates.
Smart Images
Figure CN121702967A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of road engineering bridge deck paving technology, and in particular to a method for testing ultra-dense asphalt mixture paving layers. Background Technology
[0002] A steel bridge deck pavement structure typically includes steel plates, a pavement layer, and the interface between the two. The steel plates form the bridge deck; the interface is a waterproof adhesive layer laid on the surface of the steel plates; the pavement layer is usually formed by compacting epoxy asphalt mixture and laid on top of the waterproof adhesive layer to further prevent water from seeping into the steel plates.
[0003] Asphalt mixtures are typically formed by mixing asphalt with granular aggregates of different sizes in a certain proportion and then compacting the mixture. Since the asphalt cannot completely fill the spaces between the aggregate particles, voids are formed between them. The presence of voids is usually expressed by the porosity, which is the percentage of void volume to the total volume of the specimen.
[0004] To achieve better waterproofing performance, the porosity of epoxy asphalt mixtures is generally designed to be less than 3%. At this point, the permeability coefficient of the pavement surface is 0 mL / min. Therefore, theoretically, rainwater is unlikely to seep into the pavement layer, let alone reach the steel plate. This effectively prevents rainwater from penetrating the steel bridge surface and corroding the steel plate.
[0005] However, in practice, it has been found that steel bridge decks with pavement layers having a porosity of less than 3% still frequently experience defects such as micro-cracks, pumping, exposed aggregate, and potholes caused by water seepage into the pavement layer within 2-3 years of opening to traffic, making it impossible to effectively protect the steel bridge deck. At the same time, there is currently a lack of testing methods to assess the protective ability of asphalt mixture pavement layers against the above-mentioned defects in a short period of time. This makes it difficult to effectively test and evaluate the protective ability of the pavement layer after its completion, thus making it difficult to effectively protect the steel plates of the steel bridge deck. Summary of the Invention
[0006] Based on this, the purpose of this invention is to test the waterproof performance of asphalt mixture pavement layers and to provide a method for testing ultra-dense asphalt mixture pavement layers.
[0007] A method for testing ultra-dense asphalt mixture pavement layers includes the following steps: S1: Collect asphalt mixture samples from multiple locations on the pavement layer; S21: Perform X-ray CT scans on each of the asphalt mixture samples to obtain multiple sets of tomographic images; S22A: Based on the fault images described in each group, the porosity of each asphalt mixture sample is detected respectively; S22B: Based on the fault images described in each group, detect the connectivity porosity of each asphalt mixture sample; S3: Using a nonlinear curve fitting method, a correspondence between porosity and interconnected porosity is established based on the porosity and interconnected porosity of each asphalt mixture sample; the relationship between interconnected porosity and porosity satisfies an exponential function as follows: , in, The interconnected porosity of asphalt mixtures. The porosity of asphalt mixture. , and The parameters are for fitting the function; S3B5: Calculate the design void ratio threshold of the asphalt mixture based on the corresponding relationship; the design void ratio threshold is the void ratio when the connected void ratio is zero. S41: Establish the design void ratio threshold function for asphalt mixtures based on the aforementioned correspondence; the design void ratio threshold function is: , in, To design the porosity threshold, , and The parameters are for fitting the function, and , ; S42: Calculate the standard deviation and estimated value of each of the function fitting parameters according to their confidence intervals, and calculate the variance and standard deviation of the design porosity threshold in combination with the function of the design porosity threshold; S43: The design porosity is calculated based on the porosity error distribution and the design porosity threshold. S5: Detect the measured void ratio of each of the asphalt mixture samples and compare it with the design void ratio threshold. If the measured void ratio is less than the design void ratio of the asphalt mixture, the detected asphalt mixture is ultra-dense asphalt.
[0008] Compared with the prior art, the present invention provides a method for detecting whether an asphalt mixture pavement layer is in an ultra-dense state, that is, it can detect the waterproof performance of the asphalt mixture pavement layer against gaseous water, and thus accurately assess the protective ability of the asphalt mixture pavement layer for steel bridge decks.
[0009] Furthermore, in step S2, tomographic imaging is performed by combining the large-scale algorithm and the ring-shaped segmentation method.
[0010] Furthermore, the specific imaging method involves dividing the complete image into continuous but non-overlapping annular sub-images, and then using the Big Law algorithm to segment each annular region to form a complete tomographic image.
[0011] Furthermore, the porosity is obtained by statistically analyzing the ratio of the porosity area to the total area in a single CT tomographic image, and then by calculating the average porosity of all tomographic images. The expression for this average is: , in, The porosity of a certain specimen. For the first The area of the gaps in a CT scan tomographic image. For the first The total area of the CT scan tomographic images. This represents the number of CT scan tomographic images.
[0012] Further, in step S22B, each set of tomographic images is converted into an inner binary image, and the gap pixels and other pixels are marked with different gray values; the horizontal connectivity of the gaps in each set of tomographic images is analyzed layer by layer.
[0013] Furthermore, step S22B specifically includes: S22B01: Convert all the aforementioned tomographic images into binary image data; S22B02: Take the tomographic image located at the top layer; S22B03: Check the gray value of the current pixel. If the gray value of the current pixel is 0, proceed to step S22B04. If the gray value of the current pixel is not 0, check the gray value of the next pixel. Continue until all pixels have been traversed, then proceed to step S22B05. S22B04: Check the connectivity of the current pixel's 8 neighborhoods. If there are pixels with a grayscale value of 0 within the 8 neighborhoods, mark these pixels with a grayscale value of 0 as the same gap object. The 8 neighborhoods are the remaining 8 pixels within a 3x3 grid centered on the current pixel. S22B05: Determine whether the pixels of the current tomographic image have been traversed. If the current pixel is the last pixel, proceed to step S22B06; if the current pixel is not the last pixel, repeat steps S22B03 to S22B04. S22B06: Determine whether the current tomographic image is the bottom layer tomographic image. If yes, proceed to step S22B07; otherwise, take the tomographic image located in the next layer and repeat steps S22B03 to S22B05. S22B07: Obtain the tomographic image located at the top layer; S22B08: Retrieves a gap object in the current tomographic image; S22B09: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the gap object in the next layer of tomographic image. If so, mark them as vertically connected objects. S22B010: Determine whether the void objects in the current fault image have been traversed. If not, take the next void object and repeat step S22B09; if the traversal is completed, execute step S22B11. S22B11: Determine whether the current fault image is the bottom fault image. If yes, proceed to step S22B12; otherwise, take the fault image located in the next layer and repeat steps S22B09 to S22B10. S22B12: Acquire tomographic images of the underlying layer; S22B13: Retrieves a gap object in the current tomographic image; S22B14: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the gap object in the previous layer tomographic image. If they exist, mark them as connected; if they do not exist, mark them as closed. S22B015: Determine whether the void objects in the current fault image have been traversed. If not, take the next void object and repeat step S22B13; if the traversal is completed, execute step S22B16. S22B016: Determine whether the current fault image is the top fault image. If yes, proceed to step S22B17. If not, take the fault image located in the previous layer and repeat steps S22B13 to S22B12. S22B17: Check whether there is a connection mark in the gaps of each top fault image. If there is, mark the gap with the gap mark as a connected gap. If there is no connection mark, mark it as a closed gap. S22B18: Calculate the connectivity porosity based on the connectivity gaps.
[0014] Furthermore, in step S1, the method for collecting asphalt mixture samples is to set the spacing between measuring points in the direction perpendicular to the station number to 1m, and the spacing between measuring points along the station number to 1m.
[0015] Furthermore, the number of measured points is no less than 400.
[0016] Furthermore, the variance formula for the design porosity threshold function is: , in, To design the variance of the porosity threshold function, , , , , and The parameters are respectively the function fitting parameters. , and The standard deviation within the confidence interval.
[0017] Based on the same inventive concept, the present invention also includes an electronic device comprising a processor; a memory for storing a computer program executed by the processor; wherein, when the processor executes the computer program, it implements the method for detecting ultra-dense asphalt mixture pavement layers as described above.
[0018] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description
[0019] Figure 1 This is a flowchart of the asphalt design method of the present invention; Figure 2 This is a schematic diagram of a tomographic image; Figure 3 Here is a flowchart showing the specific steps of step S22B; Figure 4 This is a diagram showing the correspondence between porosity and connectivity porosity in this embodiment. Detailed Implementation
[0020] The inventors carefully studied the current situation where asphalt mixtures cannot effectively prevent the steel plates of steel bridge decks from being corroded. They found that the current asphalt mixtures can only prevent liquid water penetration, but in reality, gaseous water in the environment can also diffuse into the asphalt mixture pavement layer and reach the interface of the steel bridge deck. Under the influence of changes in environmental conditions, it condenses into liquid water and eventually covers the surface of the steel plate, causing an electrochemical reaction that leads to rusting of the steel plate.
[0021] Further research by the inventors revealed that most diffusion phenomena in nature are non-steady-state diffusions, meaning that the diffusion phenomenon changes with both time and space. However, asphalt mixtures continuously undergo water vapor diffusion during long-term service, especially for asphalt mixtures used in steel bridge deck pavements in cross-river and cross-sea environments, where the mixtures are essentially saturated. The gaseous water diffusion in the asphalt mixture of steel bridge deck pavements is a steady-state penetrating diffusion, thus revealing the change in water concentration over time: , Where C is the concentration of water.
[0022] Simultaneously, combining Fick's first and second laws, as well as the diffusion boundary conditions of asphalt mixtures, the diffusion model of gaseous water in asphalt mixtures is obtained as follows: , Where D is the water diffusion coefficient of the asphalt mixture, C is the water concentration, and x, y, and z are the three directions of the asphalt mixture.
[0023] At the same time, combining the two formulas about diffusion flux in Fick's first law: , and , in, For diffusion flux.
[0024] Solving the gaseous water diffusion model yields the water diffusion coefficient of the asphalt mixture as follows: , Where D is the water diffusion coefficient of the asphalt mixture, C is the water concentration, and x, y, and z represent the three directions of the asphalt mixture. For diffusion flux.
[0025] According to the equivalence principle, the diffusion coefficient of a homogeneous and isotropic asphalt mixture can be expressed as: , According to fundamental thermodynamic theory, at a constant temperature, the concentration of water can be expressed as: , Where D is the water diffusion coefficient of the asphalt mixture, C is the water concentration, and x, y, and z represent the three directions of the asphalt mixture. For diffusion flux, The vapor pressure of water. Atmospheric pressure. The molar density of air, This represents the molecular mass of gaseous water. The vapor pressure of water and the relative humidity of water vapor are also considered. satisfy , The saturated vapor pressure of gaseous water at 25ºC is 3169 Pa; The molecular weight of gaseous water is 18.015. .
[0026] From the above formulas, the concentration difference of gaseous water humidity at one end and the other end of the infinitesimal element can be obtained as follows: , At the same time, according to Fick's second law, the formula for the mass change of matter within a infinitesimal element is: , Based on the relationship between changes in mass and concentration, the initial mass within the infinitesimal element is: After time Afterwards, the concentration of matter within the infinitesimal element becomes The formula for the change in mass of matter within a infinitesimal element is obtained as follows: , The combined results are: , By incorporating the concentration difference, the water diffusivity and diffusion flux of the asphalt mixture under the simplified homogeneous isotropic condition can be determined. The expression for the water diffusivity is: , The expression for the diffusion flux of the asphalt mixture is: , Where D is the water diffusion coefficient of the asphalt mixture, in m² / s; R is the universal gas constant, 8.314. T is the Kelvin temperature of 298.15 K. The distance of water vapor diffusion from the specimen is the specimen thickness, in meters (m); A is the self-measured diffusion area, in square meters (m²). This represents the rate of change of the device's mass loss over time, as measured in the water vapor diffusion experiment. The relative humidity difference at both ends of the asphalt mixture specimen is used to drive water vapor diffusion. This represents the molecular mass of gaseous water.
[0027] Subsequently, the interconnected porosity variable of asphalt mixture is introduced into the model of water vapor diffusion coefficient and diffusion flux, thereby quantifying the relationship between the diffusion coefficient and the interconnected porosity structure inside the asphalt mixture. This yields a model for the water vapor diffusion coefficient and diffusion flux of asphalt mixture after correction for the influence of the internal porosity structure. The expressions for this water vapor diffusion coefficient and diffusion flux are as follows: , and , in, Let be the interconnected void ratio of the asphalt mixture, and m and k be the model coefficients, respectively.
[0028] Please see Figure 1 , Figure 1 This is a graph showing the functional relationship between the diffusion flux and the connectivity porosity. The inventors then studied the water vapor diffusion performance of 100 EA-10 Marshall specimens of epoxy asphalt mixture obtained from on-site core samples of a typical steel bridge deck pavement, based on the aforementioned model of the water vapor diffusion coefficient and diffusion flux of the asphalt mixture. A nonlinear curve fitting method was used to construct the diffusion flux of the connectivity porosity of the asphalt mixture, obtaining the functional relationship between the diffusion flux and the connectivity porosity. The expression for this functional relationship is: , Where a and b are both function fitting parameters, The connected void ratio of the asphalt mixture is given in Table 1 below: Table 1
[0029] get , The solution is obtained based on the relationship between the diffusion flux and the water diffusion coefficient: , The water diffusion coefficient is calculated using Fick's first and second laws as follows: , Where D is the water diffusion coefficient model of asphalt mixture. It represents the interconnected void ratio of asphalt mixtures.
[0030] The inventors discovered that while dense-graded asphalt mixtures have a low porosity, they still contain randomly distributed voids that are partially or completely interconnected. This allows moisture from the natural environment to diffuse from top to bottom across the steel bridge deck pavement, preventing the existing pavement from achieving the expected waterproofing performance. Based on a water diffusion model, the inventors proposed an ultra-dense asphalt mixture. By achieving a zero interconnected porosity, the water diffusion coefficient of the asphalt mixture is reduced to zero, placing it in an ultra-dense state and effectively preventing the diffusion of gaseous water within the asphalt mixture.
[0031] Based on this, the inventors provide a method for testing ultra-dense asphalt pavement layers, which can detect the waterproof performance of asphalt pavement layers against gaseous water, effectively detect whether the asphalt pavement layer is in an ultra-dense state, and thus take timely measures to extend the service life of steel bridge deck steel plates.
[0032] Please see Figure 1 , Figure 1 This is a flowchart of the asphalt design method of the present invention. An asphalt pavement layer inspection method of the present invention includes: S1: Collect asphalt mixture samples from multiple locations on the pavement layer. In this embodiment, the spacing between measuring points perpendicular to the station number is set to 1m, and the spacing between measuring points along the station number is also set to 1m, with a minimum of 400 measuring points. The station number direction refers to the direction in which the highway mileage increases or decreases, i.e., the road direction. In this embodiment, a 100mm diameter, 35mm thick EA-10 Marshall specimen of epoxy asphalt mixture for steel bridge deck is used as an example for testing.
[0033] S2: X-ray CT tomography is used to perform tomographic imaging on each asphalt mixture sample, and the tomographic images of each sample are analyzed. In X-ray CT tomography, X-rays undergo photoelectric and Compton scattering and pair production at the molecular or atomic level when penetrating the medium, causing a significant attenuation of X-ray energy. Since the degree of X-ray attenuation is related to the properties of the medium, the grayscale distribution on the CT image reflects the distribution of the attenuation coefficient of X-rays at different energy levels within the object under test. In X-ray CT tomography, X-rays are used to perform layered detection of the sample. A tomographic image is formed at certain depths along the top to bottom of the sample, ultimately obtaining multiple tomographic images from different depths of the same sample. The specific steps for performing X-ray CT tomography on each asphalt mixture sample in step S2 are as follows: Please see Figure 2 , Figure 2 This is a schematic diagram of a tomographic image. S21: X-ray CT scans are performed on each of the aforementioned asphalt mixture samples to obtain multiple sets of tomographic images. The X-ray CT scan uses a modified Big Rule algorithm based on the ring segmentation method for imaging, that is, the complete image is divided into continuous but non-overlapping circular sub-images, and then the Big Rule algorithm is used to segment each circular region to form a complete tomographic image. The basic idea of the Big Rule algorithm is to maximize the inter-class variance. When detecting asphalt mixtures, the multi-phase classification problem of asphalt mixtures is transformed into multiple one-to-two classifications, sequentially segmenting the background, aggregates, asphalt slurry, and voids in the tomographic image to achieve threshold segmentation of the asphalt mixture phases; however, since asphalt mixtures usually have uneven brightness in the radial direction in CT scan images, it is easy to cause black bad spots to appear in the CT image, affecting the phase segmentation quality. The ring segmentation method divides the CT image into circular regions with different brightness levels. Therefore, this application combines the Big Rule algorithm and the ring segmentation method to effectively improve the imaging quality of X-ray CT scans of asphalt mixtures. By performing tomographic scanning on each asphalt mixture sample, a set of tomographic images is formed for each asphalt mixture sample. Each set of tomographic images includes multiple tomographic images with uniform depth intervals from the top to the bottom of the asphalt mixture. For example... Figure 2 As shown, Figure 2 (a) is a schematic diagram of the ring-shaped blocks. Figure 2 (b) is a schematic diagram of the DaLaw algorithm for image segmentation. Figure 2 (c) is a tomographic image of the improved large law algorithm of the circumferential segmentation method of the present invention.
[0034] S22A: Based on the tomographic images of each group, the porosity of each asphalt mixture sample is detected. The porosity is obtained by statistically analyzing the ratio of the porosity area to the total area in a single CT tomographic image, and further by calculating the mean porosity of all scanned tomographic images. The expression is as follows: , in, The porosity of a certain specimen. For the first The area of the gaps in a CT scan tomographic image. For the first The total area of the CT scan tomographic images. This represents the number of CT scan tomographic images.
[0035] Please see Figure 3 , Figure 3 The flowchart below shows the specific steps of step S22B. S22B: Based on the tomographic images of each group, the connectivity porosity of each asphalt mixture sample is detected. The connectivity porosity is a channel composed of completely interconnected voids from one end face to the other in the asphalt mixture. The connectivity porosity is determined by converting each tomographic image into a binary image, marking the grayscale value of void pixels as 0 and the grayscale value of pixels in other phases as 1, and then analyzing the horizontal connectivity layer by layer. This step S22B uses Matlab for connectivity porosity analysis, and the specific steps include: S22B01: Convert all the said tomographic images into binary image data.
[0036] S22B02: Take the tomographic image located at the top layer.
[0037] S22B03: Check the gray value of the current pixel. If the gray value of the current pixel is 0, proceed to step S22B04. If the gray value of the current pixel is not 0, check the gray value of the next pixel. Continue until all pixels have been traversed, then proceed to step S22B05.
[0038] S22B04: Check the connectivity of the current pixel's 8-neighborhood. If there are pixels with a grayscale value of 0 within the 8-neighborhood, mark these pixels with a grayscale value of 0 as the same gap object. The 8-neighborhood refers to the remaining 8 pixels within a 3x3 grid centered on the current pixel.
[0039] S22B05: Determine whether the pixels of the current tomographic image have been traversed. If the current pixel is the last pixel, proceed to step S22B06; if the current pixel is not the last pixel, repeat steps S22B03 to S22B04.
[0040] S22B06: Determine whether the current tomographic image is the bottom layer tomographic image. If yes, proceed to step S22B07; otherwise, select the tomographic image located in the next layer and repeat steps S22B03 to S22B05.
[0041] S22B07: Obtain the tomographic image located at the top layer.
[0042] S22B08: Get a gap object in the current tomographic image.
[0043] S22B09: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the gap object in the next layer of tomographic image. If so, mark them as vertically connected objects.
[0044] S22B010: Determine whether the gap objects in the current fault image have been traversed. If not, take the next gap object and repeat step S22B09; if the traversal is completed, execute step S22B11.
[0045] S22B11: Determine whether the current fault image is the bottom fault image. If yes, proceed to step S22B12. If not, take the fault image located in the next layer and repeat steps S22B09 to S22B10.
[0046] S22B12: Obtain the tomographic image located at the bottom layer.
[0047] S22B13: Get a gap object in the current tomographic image.
[0048] S22B14: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the object in the previous layer of the tomographic image. If they exist, mark them as connected; otherwise, mark them as closed.
[0049] S22B015: Determine whether the gap objects in the current fault image have been traversed. If not, take the next gap object and repeat step S22B13; if the traversal is completed, execute step S22B16.
[0050] S22B016: Determine whether the current fault image is the top-level fault image. If yes, proceed to step S22B17; otherwise, take the fault image located on the previous level and repeat steps S22B13 to S22B12.
[0051] S22B17: Check whether there is a connection mark in the gaps of each top fault image. If there is, mark the gap with the gap mark as a connected gap. If there is no connection mark, mark it as a closed gap.
[0052] S22B18: Calculate the connectivity porosity based on the connectivity gaps.
[0053] S3: A nonlinear curve fitting method is used to establish the correspondence between porosity and connected porosity for each asphalt mixture sample. The designed porosity threshold function is a functional relationship between the connected porosity (when it is zero) and the total porosity, including multiple function fitting parameters. Please refer to [link to relevant documentation]. Figure 4 , Figure 4 This diagram illustrates the relationship between porosity and connectivity porosity in this embodiment. The connectivity porosity and porosity satisfy an exponential function relationship, established based on the following function: , in, The interconnected porosity of asphalt mixtures. The porosity of asphalt mixture. , and These are the parameters for fitting the function.
[0054] The function fitting parameters , and The range of values for is shown in Table 2: Table 2
[0055] S4: Calculate the design void ratio threshold of the asphalt mixture based on the aforementioned correspondence. The void ratio threshold is the void ratio when the connected void ratio is 0. This step S4 specifically includes: S41: Establish the design void ratio threshold function for asphalt mixtures based on the aforementioned correspondence. The design void ratio threshold function is: , in, To design the porosity threshold, , and The parameters are for fitting the function, and , .
[0056] S42: Calculate the standard deviation and estimated value of each function fitting parameter based on its confidence interval, and calculate the variance and standard deviation of the design porosity threshold using the function of the design porosity threshold. The design porosity threshold is the maximum allowable porosity when the connected porosity is 0, i.e., when the measured porosity of the asphalt mixture is lower than the design porosity threshold, the connected porosity is 0. Refer to the function fitting parameters in Table 1. , and The prediction range is defined as the range within which the estimated value fluctuates by 1.96 standard deviations. In this embodiment, the standard deviations and estimated values of each function fitting parameter are calculated based on the 95% confidence interval. The function fitting parameters are then used as the basis for the prediction. For example, its prediction range is expressed as Therefore, its standard deviation is obtained by dividing the tail value of the lower limit of the 95% confidence interval by 1.96. The estimated value is Similarly, calculate the standard deviation of the fitting parameters for the other two functions to obtain... and The estimated values are respectively and Therefore, the estimated value of the design porosity threshold is calculated as follows: , in, , and These are the fitting parameters. , and The estimated value is 1.178.
[0057] The variance of the design porosity threshold is further calculated using the Delta method, and the variance formula is as follows: , in, , , , , and The parameters are respectively the function fitting parameters. , and The standard deviation within the 95% confidence interval; based on the estimated values of each fitted parameter, the following is obtained: ,
[0058] and Therefore, the variance of the design porosity threshold is calculated to be 0.03326, and the standard deviation of the design porosity threshold is further obtained. .
[0059] S43: Calculate the design void ratio threshold based on the variance and standard deviation of the design void ratio threshold. In this embodiment, based on the estimated value, variance, and standard deviation of the design void ratio threshold, the predicted range of the design void ratio threshold that guarantees a 95% connectivity void ratio of 0 in the asphalt mixture is obtained as follows: , in, To estimate the porosity threshold, The standard deviation is used to design the porosity threshold.
[0060] The predicted range for the design porosity threshold with a 95% guarantee rate is [0.821%, 1.535%]. Based on the relationship between the connected porosity and the porosity, the design porosity threshold is set at 1.535%. This means that when the measured porosity of the asphalt mixture is less than 1.535%, there is a 95% probability that the connected porosity of the asphalt mixture will be zero, allowing the asphalt mixture to reach an ultra-dense state and effectively preventing water vapor from diffusing within the asphalt mixture.
[0061] S5: Detect the measured void ratio of each asphalt mixture sample and compare it with the design void ratio threshold. If the measured void ratio is less than the design void ratio of the asphalt mixture, the detected asphalt mixture is ultra-dense asphalt. In this embodiment, when the average measured void ratio of the detected asphalt mixture is less than 1.535% of the design void ratio, the asphalt mixture is in an ultra-dense state.
[0062] Compared with the prior art, the present invention provides a method for detecting whether an asphalt mixture pavement layer is in an ultra-dense state, that is, it can detect the waterproof performance of the asphalt mixture pavement layer against gaseous water, and thus accurately assess the protective ability of the asphalt mixture pavement layer for steel bridge decks.
[0063] This application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.
[0064] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments and claims of this application are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that, unless otherwise stated, “a plurality” means two or more; the terms “first,” “second,” “third,” etc., are used only to distinguish and not to describe a particular order or sequence, nor should they be construed as indicating or implying relative importance. The term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items. When the above description relates to drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0065] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and the present invention also intends to include these modifications and variations.
Claims
1. A method for testing ultra-dense asphalt mixture pavement layers, characterized in that, Includes the following steps: S1: Collect asphalt mixture samples from multiple locations on the pavement layer; S21: Perform X-ray CT scans on each of the asphalt mixture samples to obtain multiple sets of tomographic images; S22A: Based on the fault images described in each group, the porosity of each asphalt mixture sample is detected respectively; S22B: Based on the fault images described in each group, detect the connectivity porosity of each asphalt mixture sample; S3: Using a nonlinear curve fitting method, a correspondence between porosity and interconnected porosity is established based on the porosity and interconnected porosity of each asphalt mixture sample; the relationship between interconnected porosity and porosity satisfies an exponential function as follows: ， in, The interconnected porosity of asphalt mixtures. The porosity of asphalt mixture. , and The parameters are for fitting the function; S41: Establish the design void ratio threshold function for asphalt mixtures based on the aforementioned correspondence; the design void ratio threshold function is: ， in, To design the porosity threshold, , and The parameters are for fitting the function, and , ; S42: Calculate the standard deviation and estimated value of each of the function fitting parameters according to their confidence intervals, and calculate the variance and standard deviation of the design porosity threshold in combination with the function of the design porosity threshold; S43: Calculate the design porosity threshold based on the variance and standard deviation of the design porosity threshold; S5: Detect the measured void ratio of each of the asphalt mixture samples and compare it with the design void ratio threshold. If the measured void ratio is less than the design void ratio of the asphalt mixture, the detected asphalt mixture is ultra-dense asphalt.
2. The method for testing ultra-dense asphalt mixture pavement layers according to claim 1, characterized in that, In step S2, tomographic imaging is performed by combining the large-scale algorithm and the ring-shaped segmentation method.
3. The method for testing ultra-dense asphalt mixture pavement layers according to claim 2, characterized in that, The specific imaging method involves dividing the complete image into continuous but non-overlapping annular sub-images, and then using the DaLaw algorithm to segment each annular region to form a complete tomographic image.
4. The method for testing ultra-dense asphalt mixture pavement layers according to claim 1, characterized in that, The porosity is obtained by statistically analyzing the ratio of the porosity area to the total area in a single CT tomographic image. The mean porosity of all tomographic images is then calculated. The expression for this mean is: ， in, The porosity of a certain specimen. For the first The area of the gaps in a CT scan tomographic image. For the first The total area of the CT scan tomographic images. This represents the number of CT scan tomographic images.
5. The method for testing ultra-dense asphalt mixture pavement layers according to claim 1, characterized in that, Step S22B converts each set of tomographic images into internal binary images, and marks the gap pixels and other pixels with different gray values; the horizontal connectivity of the gaps is analyzed layer by layer in each set of tomographic images.
6. The method for testing ultra-dense asphalt mixture pavement layers according to claim 5, characterized in that, Step S22B is as follows: S22B01: Convert all the aforementioned tomographic images into binary image data; S22B02: Take the tomographic image located at the top layer; S22B03: Check the gray value of the current pixel. If the gray value of the current pixel is 0, proceed to step S22B04. If the gray value of the current pixel is not 0, check the gray value of the next pixel. Continue until all pixels have been traversed, then proceed to step S22B05. S22B04: Check the connectivity of the current pixel's 8 neighborhoods. If there are pixels with a grayscale value of 0 within the 8 neighborhoods, mark these pixels with a grayscale value of 0 as the same gap object. The 8 neighborhoods are the remaining 8 pixels within a 3x3 grid centered on the current pixel. S22B05: Determine whether the pixels of the current tomographic image have been traversed. If the current pixel is the last pixel, proceed to step S22B06; if the current pixel is not the last pixel, repeat steps S22B03 to S22B04. S22B06: Determine whether the current tomographic image is the bottom layer tomographic image. If yes, proceed to step S22B07; otherwise, take the tomographic image located in the next layer and repeat steps S22B03 to S22B05. S22B07: Obtain the tomographic image located at the top layer; S22B08: Retrieves a gap object in the current tomographic image; S22B09: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the gap object in the next layer of tomographic image. If so, mark them as vertically connected objects. S22B010: Determine whether the void objects in the current fault image have been traversed. If not, take the next void object and repeat step S22B09; if the traversal is completed, execute step S22B11. S22B11: Determine whether the current fault image is the bottom fault image. If yes, proceed to step S22B12; otherwise, take the fault image located in the next layer and repeat steps S22B09 to S22B10. S22B12: Obtain the tomographic image located at the bottom layer; S22B13: Retrieves a gap object in the current tomographic image; S22B14: Check if there are any pixels with a gray value of 0 in the 8-neighborhood of the gap object in the previous layer tomographic image. If they exist, mark them as connected; if they do not exist, mark them as closed. S22B015: Determine whether the void objects in the current fault image have been traversed. If not, take the next void object and repeat step S22B13; if the traversal is completed, execute step S22B16. S22B016: Determine whether the current fault image is the top fault image. If yes, proceed to step S22B17. If not, take the fault image located in the previous layer and repeat steps S22B13 to S22B12. S22B17: Check whether there is a connection mark in the gaps of each top fault image. If there is, mark the gap with the gap mark as a connected gap. If there is no connection mark, mark it as a closed gap. S22B18: Calculate the connectivity porosity based on the connectivity gaps.
7. The method for testing ultra-dense asphalt mixture pavement layers according to claim 1, characterized in that, In step S1, the method for collecting asphalt mixture samples is to set the spacing between measuring points in the direction perpendicular to the station number to 1m, and the spacing between measuring points along the station number to 1m.
8. The method for testing ultra-dense asphalt mixture pavement layers according to claim 7, characterized in that, The number of measuring points shall not be less than 400.
9. The method for testing ultra-dense asphalt mixture pavement layers according to claim 1, characterized in that, The variance formula for the design porosity threshold function is: ， in, To design the variance of the porosity threshold function, , , , , and The parameters are respectively the function fitting parameters. , and The standard deviation within the confidence interval.
10. An electronic device, characterized in that, processor; Memory for storing computer programs executed by the processor; The processor executes the computer program to implement the method for detecting ultra-dense asphalt mixture pavement as described in any one of claims 1-9.