Method for determining performance degradation rate of cementing material-mineral aggregate

Abstract

The invention discloses a cementing material-mineral aggregate performance degradation rate determination method which comprises the following steps: acquiring a sample; performing CT scanning on the sample according to a set interval, and obtaining a plurality of two-dimensional scanning images; performing image processing on the two-dimensional scanning image to obtain a gap extraction image, and calculating a layer-by-layer void ratio; carrying out three-dimensional model reconstruction on the gap extraction graph to obtain a sample three-dimensional model, and carrying out statistics on the equivalent diameter of each gap; calculating a void ratio attenuation rate along a direction perpendicular to the two-dimensional scanning graph; and judging each performance attenuation rate according to the void ratio attenuation rate. According to the method, various road properties of the PPUM material can be analyzed through nondestructive testing of the internal gaps of the sample, rapid development and debugging of the PPUM material are facilitated, and the research and development period is shortened. Meanwhile, the method can be used for predicting the service life of the pavement.

Description

Technical Field

[0001] The present invention relates to the technical field of void ratio of cementitious materials, and in particular to a method for determining the performance attenuation rate of cementitious materials and mineral materials. Background Art

[0002] In the field of road engineering, PPU (Polyurethane) binder is often used in some special pavement materials. It has excellent bonding properties and other characteristics. The porous polyurethane mixture formed by mixing with mineral aggregate can be used for paving special functional pavements such as drainage and noise reduction pavement. Its road performance will be affected by factors such as the properties and proportions of the PPU binder and the mineral aggregate. For example, the viscosity and elasticity of the PPU binder and the particle size, shape, and gradation of the mineral aggregate will affect the strength, durability, permeability and other road performance of the PPUM (Porous Polyurethane Mixture).

[0003] When preparing PPUM, its performance needs to be tested. This testing process requires considering actual application scenarios and simulating real-world conditions. This results in a large amount of testing and a long testing cycle. Furthermore, the decay rate of various PPUM properties is a key parameter for predicting the lifespan of PPUM materials. Determining the decay rate of various PPUM properties is a key issue in this field that needs to be addressed. Summary of the Invention

[0004] The present invention aims to provide a method for determining the performance decay rate of binder-mineral aggregate to address the shortcomings of existing technologies. This method can nondestructively detect internal voids in a sample and, based on this information, analyze the various road properties of PPUM materials. This facilitates the rapid development and commissioning of PPUM materials, shortening the R&D cycle. Furthermore, it can be used to predict the lifespan of a pavement.

[0005] The present invention provides a method for determining the performance attenuation rate of a binder-mineral aggregate, which comprises the following steps: Obtaining samples; Perform CT scanning on the sample according to the set interval and obtain multiple two-dimensional scan images; Perform image processing on the two-dimensional scanned image to obtain the void extraction map and calculate the void ratio layer by layer; The three-dimensional model of the sample is reconstructed by performing three-dimensional model reconstruction on the void extraction map, and the equivalent diameter of each void is calculated; The void ratio attenuation rate is calculated along a direction perpendicular to the two-dimensional scanning image; and the attenuation rates of various properties are determined based on the void ratio attenuation rate.

[0006] The method for determining the binder-mineral material performance attenuation rate as described above, wherein, optionally, the sample is cylindrical; the set spacing is 40 to 60 um, and the multiple two-dimensional scanning images are all plane images perpendicular to the center line of the sample.

[0007] In the above-mentioned method for determining the attenuation rate of the binder-mineral aggregate performance, optionally, the method for performing image processing on the two-dimensional scanned image includes: Grayscale the two-dimensional scanned image; Perform image noise reduction on two-dimensional scanned images; Convert the original image grayscale histogram into a form that is evenly distributed across the entire grayscale range; Segment the image according to different grayscale values; Extract the gaps on each 2D scan image according to the set grayscale value.

[0008] In the above-mentioned method for determining the attenuation rate of the binder-mineral aggregate performance, optionally, the calculation formula for the layer-by-layer void ratio is: ; is the layer-by-layer void ratio; It is the area of ​​voxels contained in the gaps within a single CT scan slice; It is the number of voxels contained in the mineral material and glue in a single CT scan slice.

[0009] In the above-mentioned method for determining the performance attenuation rate of the binder-mineral aggregate, optionally, the method for reconstructing the three-dimensional model of the void extraction map to obtain the three-dimensional model of the sample is: Arrange all gap extraction images in sequence according to the set distance; Starting from any gap extraction map, all gaps on the gap extraction map are marked, and gaps on adjacent gap extraction maps are classified into first gaps and second gaps according to whether they can be merged with the previously marked gaps; the first gap is merged with the marked gap, and the second gap is marked; all gap extraction maps are processed in sequence according to the above method to obtain multiple marked three-dimensional gaps, the position of each three-dimensional gap, the gap extraction map corresponding to each three-dimensional gap, and the coordinates of the three-dimensional gap; The area outside the three-dimensional gap is filled to obtain a reconstructed three-dimensional model.

[0010] The method for determining the attenuation rate of the binder-mineral aggregate performance as described above, wherein, optionally, the void equivalent diameter includes a two-dimensional void equivalent diameter and a three-dimensional void equivalent diameter; The calculation formula for the two-dimensional void equivalent diameter is: ; in,d is the equivalent diameter of the two-dimensional void; S is the two-dimensional void area, μm 2 ; N is the number of gaps; The calculation formula for the three-dimensional void equivalent diameter is: ; in, D is the three-dimensional void equivalent diameter; V is the three-dimensional void volume.

[0011] In the above-mentioned method for determining the performance attenuation rate of the binder-mineral aggregate, optionally, the method for determining the attenuation rate of each performance according to the porosity attenuation rate is: Substitute the void ratio into the equation of the relationship between each performance attenuation rate and void ratio attenuation rate for calculation; Among them, the performance attenuation rate includes: splitting strength attenuation rate, compressive strength attenuation rate, low-temperature bending failure strain attenuation rate, dynamic stability attenuation rate and dynamic modulus attenuation rate.

[0012] Compared to existing technologies, this method uses CT to scan the internal image of the binder, generating multiple 2D scans. These 2D scans are then processed to identify voids in a void extraction map and calculate the layer-by-layer void fraction. A 3D model of the specimen is then reconstructed from the void extraction map, and the equivalent diameter of each void is calculated. The void fraction attenuation rate is then used to determine the attenuation rate of various properties. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of the steps of the present invention; Figure 2 It is a two-dimensional scan of the present invention; Figure 3 This is the effect diagram of the two-dimensional scan image after processing with different filtering algorithms; Figure 4 It is a comparison of the 2D scans before and after histogram equalization; Figure 5 This is a comparison of the 2D scan image and the gap extraction image; Figure 6 This is a comparison diagram of the actual sample of the present invention and the three-dimensionally reconstructed one; Figure 7 This is the graph showing the variation of PPUM void ratio with specimen height; Figure 8 This is the graph showing the variation of the number of PPUM voids with specimen height; Figure 9 This is the graph showing the variation of the PPUM void equivalent diameter with the specimen height; Figure 10 The variation of PPUM void ratio with specimen height; Figure 11 This is a correlation analysis chart between the void ratio attenuation rate and various performances. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0015] Embodiments of the present invention: Figure 1 As shown, a method for determining the performance attenuation rate of a binder-mineral aggregate comprises the following steps: S1, obtain the sample. The sample is a cylinder with a size of φ63.5mm*100mm.

[0016] S2: Perform a CT (Computed Tomography) scan of the sample at a set spacing, obtaining multiple two-dimensional scan images. The sample is placed in the center of the test platform for testing. The equipment operating parameters are: scanning voltage 160kV, current 180uA, and resolution 53um. By adjusting the position of the sample stage, the sample is rotated and scanned in 0.24-degree steps. Two-dimensional cross-sectional images are derived at 53um spacing. Over 1200 cross-sectional scan images are obtained and reconstructed into a three-dimensional data volume using software. In some implementations, the set spacing can be any value between 40 and 60um, which is not specifically limited in this disclosure. The multiple two-dimensional scan images are all images of a plane perpendicular to the centerline of the sample.

[0017] CT scanning technology uses X-rays as a radiation source to scan test samples. When the X-rays pass through the test sample, there will be different degrees of attenuation. The attenuation equation of the ray is shown below. When the attenuated X-rays are received by the detector at the other end, the signal received by the detector is converted into a digital matrix through analog-to-digital conversion. That is, the coordinate distribution μ(x, y) for a specific energy value at a certain height section. The obtained digital matrix is ​​then converted by a computer into pixels of different grayscales. It is precisely based on this difference in penetration that after rotating the sample 360° to obtain a full-range slice projection image (i.e., a CT scan image), the image is further processed using a reconstruction algorithm to obtain a three-dimensional visual structure diagram of the test sample.

[0018] The relationship between the incident and outgoing ray intensities is: ; Where: I is the beam intensity after the ray penetrates the object; I0 ​​is the beam intensity before the ray penetrates the object; μ is the linear attenuation coefficient of the penetrated material (cm2 / g); Δx is the thickness of the penetrated material.

[0019] PPUM is a mixture of PPU binder, aggregate and voids. These three parts have different X-ray absorption rates, resulting in different signals received by the back-end detector. Therefore, the binder, aggregate and voids can be distinguished by the signal difference, thereby obtaining a tomographic image of PPUM, such as Figure 2 shown.

[0020] S3: Process the 2D scanned image to obtain a void extraction map and calculate the layer-by-layer void ratio. This step primarily eliminates noise points, uneven grayscale distribution, and fuzzy quality caused by equipment and environmental factors. Based on mathematical algorithms and using a computer with high computing speed and large storage capacity, the acquired image is subjected to noise reduction, enhancement, segmentation, and extraction. The image signal is converted into a digital signal to facilitate image information analysis and recognition, thereby performing processing and analysis. This step can be performed using software on the 2D scanned image of the PPUM.

[0021] Step S3 further includes steps S31 to S35. Because the image acquisition process is affected by various factors, image quality inevitably varies. The primary purpose of image enhancement is to highlight key information, reduce ineffective information, improve the image's visual quality, or make it suitable for computer processing, thereby increasing the image's utility. Spatial and frequency domain methods are commonly used for image enhancement. Spatial domain enhancement modifies the image's grayscale (using a grayscale histogram and low-pass filtering algorithm); frequency domain enhancement modifies the image's Fourier transform values. This is generally not used because pixel quality significantly changes after the conversion.

[0022] S31 grayscales the two-dimensional scanned image. To simplify image information and increase software processing speed, the captured two-dimensional scanned image is grayscaled. The software uses three grayscale image bit rates: 8-bit, 16-bit, and 32-bit. 8-bit grayscale images have limited color expression, while 32-bit grayscale images occupy a large amount of storage space. 16-bit grayscale images offer the advantages of a high number of grayscale levels, rich color expression, and a small storage requirement. Therefore, after importing the PPUM two-dimensional scanned image into the software, it is converted into a 16-bit grayscale image with a grayscale range of 0 to 65535, where 0 represents black, 65535 represents white, and values ​​between 0 and 65535 represent pixels of varying grayscales.

[0023] S32, image noise reduction processing is performed on the two-dimensional scan image; CT scan images are easily affected by factors such as human operation and device signals during the acquisition, storage and transmission process, which leads to noise points in the image, which will directly reduce the accuracy of image information extraction, so noise filtering is required. Fiji software uses different mathematical algorithms to process signals in the spatial domain or frequency domain to eliminate the influence of noise. The filtering methods embedded in the software include Gaussian filtering, mean filtering, minimum filtering, maximum filtering and sharpening filtering. The effects of different filtering methods are as follows: Figure 3 As shown. Among them, Figure 3 (a) is the original image. Figure 3 (b) is mean filtering, Figure 3 (c) is the minimum filter, Figure 3 (d) in the middle is Gaussian filtering, Figure 3 (e) in is the maximum value filter, Figure 3 (f) in the figure represents a sharpening filter. As can be seen from the above figures, different filtering algorithms have different processing emphases, resulting in significantly different noise reduction effects. Mean filtering replaces the central pixel value with the arithmetic mean of the surrounding pixel values ​​to remove noise points. However, if the point is not a noise point but is close to an edge, it will be over-averaged. This filtering algorithm is an image processing method that allows the computer to more clearly distinguish between voids and aggregates, increasing differentiation. This algorithm slightly reduces contrast and is not very effective for removing noise points. The 2D scan image processed with Gaussian filtering appears blurry compared to the original image. This is because the algorithm applies "weighting," with the central pixel receiving a higher weight and the surrounding pixels receiving a lower weight. Although this algorithm blurs the image, it effectively preserves the void features, which facilitates subsequent image segmentation. Compared to the first two linear filtering methods, minimum and maximum filtering (collectively referred to as extremum filtering) are nonlinear filters that replace the central pixel value with the maximum or minimum value of the surrounding pixel. If the minimum value is used instead of the center pixel value, the processing effect will be darker. If there are points with pixel values ​​of 0, the number of gap points will increase. If the maximum value is used instead of the center value, the processing effect will be brighter. Sharpening filtering mainly makes the image brighter, clearer and more vivid by enhancing the edge and detail information of the image and strengthening the contour structure at the image separation point. This algorithm is based on mathematical differential theory. It weakens the areas with relatively small differences in pixel values ​​in the image and enhances the areas with relatively large differences in pixel values, thereby effectively separating the image and obtaining useful information. Based on the above analysis and the noise point processing effect, the present disclosure adopts a combination of Gaussian filtering and sharpening filtering to enhance the two-dimensional scan image of the mixture.

[0024] S33, converting the grayscale histogram of the original image into a form that is evenly distributed throughout the entire grayscale range; the grayscale histogram uses grayscale as the horizontal axis and grayscale frequency as the vertical axis to represent the number of pixels of different grayscale levels in the image, thereby reflecting the frequency of occurrence of different grayscale levels. If the acquired two-dimensional scan image is too bright or too dark, the grayscale histogram will be concentrated at one end of the grayscale range of 0~65535. At this time, the grayscale analysis and processing can be performed using histogram equalization technology. Histogram equalization technology is a method of adjusting the contrast of the image grayscale histogram. Through this method, the grayscale histogram of the original image is converted from a relatively concentrated grayscale interval to a uniform distribution within the entire grayscale range. Based on this, the grayscale histogram of the original image is converted into a form that is evenly distributed throughout the entire grayscale range through pixel grayscale value conversion. This changes the distribution characteristics of the grayscale values ​​of the original image, but does not change the spatial position of the image pixels. Although the grayscale histograms before and after equalization occupy the entire grayscale range, the grayscale histogram after equalization is more uniform and the standard deviation has increased significantly. Please refer to Figure 4 .

[0025] S34, segmenting the image according to different grayscale values.

[0026] Based on the processing requirements and analysis objectives, the image is segmented according to different grayscale values ​​to achieve the division of different parts. For PPUM, which is composed of polyurethane mortar, mineral material, and voids, image segmentation technology is used to extract and separate these three components, thereby obtaining microstructural information such as the structure and quantity of voids within the mixture. Currently, the most widely used algorithm for image segmentation is threshold segmentation. Its essence is to determine the required grayscale value as the critical threshold T0, and convert it through a mathematical algorithm. The formula is as follows: if the pixel point is greater than this critical value, the output grayscale value is 1, and if the pixel point is less than this critical value, the output grayscale value is 0.

[0027] Where: g(x, y) is the output grayscale value; f(x, y) is the input grayscale value; T0 is the critical threshold.

[0028] Based on this, to quantitatively analyze the void characteristics of PPUM after treatment in different water-temperature environments, image segmentation technology is needed to segment and extract the voids in the mixture. The key lies in determining the appropriate threshold. Currently, the most commonly used threshold segmentation method is the histogram threshold method, which performs threshold segmentation based on the grayscale histogram of the image data. Combining this method with image histogram equalization can make segmentation more convenient and faster.

[0029] S35, extract the gaps on each two-dimensional scan image according to the set grayscale value. The present invention performs gap segmentation and extraction on the CT scan image of PPUM, such as Figure 5 shown. Figure 5The blue positions in (b) are the extracted voids.

[0030] Through step S31 and step S35, the position, size and shape of each gap on each two-dimensional scan image can be determined.

[0031] S4, reconstructing the three-dimensional model of the void extraction image to obtain a three-dimensional model of the specimen, and calculating the equivalent diameter of each void; 3D reconstruction involves extracting 3D sequence information from 2D scans, visually displaying the object's structural information through a 3D model. Colors can also be set to distinguish different materials within the object. The goal is to create a visual 3D model of the object. Based on the different geometric information conveyed by the spatial structure, 3D models can be categorized into three types: wireframe, surface, and solid. The wireframe geometry model is established based on points, lines, arcs, etc. In the process of constructing a three-dimensional model, a two-dimensional wireframe is generally outlined first, and then a three-dimensional geometric model is constructed. However, the surface information cannot be effectively constructed during its use, resulting in missing model information, which affects the judgment of the three-dimensional model of the research object; the surface geometry model is mainly based on the model constructed by the external surface information of the research object. The model has a good display effect, but it cannot express the internal structure information of the research object; the solid geometry model is a model constructed by combining computer science and geometry to enclose a spatial body through the boundary of the research object. The establishment of this model mainly uses the constructive solid geometry method (CSG method for short). The CSG method is based on Boolean operations to splice the basic elements of the object to synthesize complex entities, but the intermediate operations cannot use Boolean operations and the operation data is difficult to extract, which makes it difficult to express the operation entity of the intermediate process.

[0032] The three-dimensional visualization model is constructed based on the two-dimensional scanning image of the mixture to visualize the entity data of PPUM, which mainly requires the use of two methods: surface rendering and volume rendering. Surface rendering collects the surface information of the research object without considering its internal structural information. The geometric model constructed by this method is a shell structure, which can only express the surface information of the research object and cannot show its complex internal structure; while volume rendering is to project the complete voxel information of the research object onto the plane, and use the actual collected spatial units and node coordinates for drawing, which can reflect the structural information of any point of the research object. Therefore, the two-dimensional scanning image information of PPUM is superimposed layer by layer, and the PPUM specimen is visualized and modeled through the volume rendering module and the rendering module. For details, please refer to Figure 6 .

[0033] S5, calculating the void ratio attenuation rate in a direction perpendicular to the two-dimensional scanning image; and determining the attenuation rates of various performances based on the void ratio attenuation rate.

[0034] The calculation formula of layer-by-layer void ratio is: ; is the layer-by-layer void ratio; It is the area of ​​voxels contained in the gaps within a single CT scan slice; It is the number of voxels contained in the mineral material and glue in a single CT scan slice.

[0035] The void equivalent diameter includes two-dimensional void equivalent diameter and three-dimensional void equivalent diameter; The calculation formula for the two-dimensional void equivalent diameter is: ; in, d is the equivalent diameter of the two-dimensional void; S is the two-dimensional void area, μm 2 ; N is the number of gaps; The calculation formula for the three-dimensional void equivalent diameter is: ; in, D is the three-dimensional void equivalent diameter; V is the three-dimensional void volume.

[0036] The method for judging the attenuation rate of various properties according to the void ratio attenuation rate is: Substitute the void ratio into the equation of the relationship between each performance attenuation rate and void ratio attenuation rate for calculation; Among them, the performance attenuation rate includes: splitting strength attenuation rate, compressive strength attenuation rate, low-temperature bending failure strain attenuation rate, dynamic stability attenuation rate and dynamic modulus attenuation rate.

[0037] In some implementations, the reconstruction of the three-dimensional model can also be achieved by other methods, such as arranging all gap extraction maps in sequence at a set distance; starting from any gap extraction map, marking all gaps on the gap extraction map, and classifying the gaps on adjacent gap extraction maps into first gaps and second gaps according to whether they can be merged with the previously marked gaps; merging the first gap with the marked gap, and marking the second gap; processing all gap extraction maps in sequence according to the above method to obtain multiple marked three-dimensional gaps, the position of each three-dimensional gap, the gap extraction map corresponding to each three-dimensional gap, and the coordinates of the three-dimensional gaps; filling the area outside the three-dimensional gaps to obtain a reconstructed three-dimensional model. For example, starting from the first gap extraction map, the first gap in the second gap extraction map is merged with the gap on the first gap extraction map, that is, for each first gap in the second gap extraction map, there is a gap corresponding to it on the first gap extraction map, and the two are continuous sections of a three-dimensional gap at different positions. The second void in the second void extraction map is then numbered, and this process is repeated until all void extraction maps are merged to obtain a complete three-dimensional structure, in which all complete void frameworks are formed. Fitting is performed on each numbered three-dimensional void. For each three-dimensional void, multiple two-dimensional void maps are formed along the axis of the specimen, and the position on the axis is fixed. The configuration of the three-dimensional void can be obtained through fitting until all three-dimensional voids have been fully configured. Finally, the parts outside the three-dimensional void positions are filled to obtain a complete three-dimensional specimen model.

[0038] The variation characteristics of the internal voids of PPUM have a significant impact on the bonding effect between PPU binder and mineral aggregate, and can also reflect the integrity of the mixture and the change of road performance. Therefore, this paper studies the variation of PPUM void ratio with the vertical height of the sample in different water-temperature environments, such as Figure 7 shown.

[0039] Figure 7 Figures (a), (b), and (c) show the variation of PPUM void content with specimen height after treatment in different water-temperature environments. The results show consistent changes in void content across freeze-thaw cycles, ambient temperature immersion, and high-temperature immersion, exhibiting an overall "concave" distribution with larger voids at the ends and smaller voids in the middle. This is primarily due to the influence of artificial tamping and loading during specimen preparation, which causes coarser aggregate to accumulate on the specimen surface, resulting in larger voids. PPUM voids are easily affected by aggregate gradation, angularity, surface texture, and internal voids. After forming, the PPUM is compacted and compressed, reducing internal voids.

[0040] Furthermore, the porosity of the PPUM increased with increasing treatment time in all three water temperature environments. The porosity change primarily occurred between 10 and 50 mm. This change is attributed to the surface voids in the PPUM, which serve as channels for water ingress. After the specimens were removed from the water treatment, the larger voids at the ends resulted in faster water mobility, leaving no water on the surface. However, the water retained within the specimens remained for a long time, hindering evaporation and thus increasing the porosity of the PPUM. Due to variations in porosity caused by artifacts during specimen preparation, the initial porosity of the mixtures in the three environments was calculated to be 1.44%, 1.53%, and 1.91%, respectively. The porosity increased with increasing freeze-thaw cycles and days of immersion, with the most significant changes occurring in freeze-thaw cycles and high-temperature immersion. After six freeze-thaw cycles and six days of high-temperature immersion, the porosity of the mixtures reached 2.29% and 3.11%, respectively, representing increases of approximately 59.0% and 62.8% compared to the original specimens. The increase in the porosity of PPUM before and after treatment in the normal temperature water immersion environment was small, and the porosity increased by 45.8% compared with the original specimen, reflecting that this environment has less impact than the other two water temperature environments, but it still has a greater impact on the road performance of PPUM.

[0041] Based on the description of the porosity, it was found that the porosity inside PPUM increases with the increase of freeze-thaw cycles and soaking days, and the change in the number of voids in PPUM will lead to an increase in its porosity. Therefore, the present invention uses two-dimensional slice diagrams to study the change of the number of voids in PPUM specimens after treatment with different water temperature environments with the vertical height of the specimens, and calculates the number of voids in the entire specimen through a three-dimensional visualization model, as shown in Figure 2. Figure 8 shown.

[0042] Figure 8 The variation of the number of voids in PPUM with the specimen height in (a), (b) and (c) is very complex. Figure 8 (d) shows a significant difference in the number of voids in PPUM after freeze-thaw cycles and high-temperature immersion compared to room-temperature immersion. After room-temperature immersion, the number of voids in PPUM increased with the number of days of immersion, but the number of voids in PPUM after freeze-thaw cycles and high-temperature immersion showed an initial increase followed by a decrease over time. After one freeze-thaw cycle and one day of high-temperature immersion, the number of voids per layer of PPUM was approximately 300–500 and 500–850, respectively. However, after three freeze-thaw cycles and three days of high-temperature immersion, the number of voids per layer decreased to 250–450 and 420–610, respectively. This may be due to the continuous generation of new voids in PPUM after freeze-thaw cycles and high-temperature immersion. As the number of voids increases, each void gradually connects to form larger interconnected voids, resulting in the initial increase followed by a decrease in the number of voids.

[0043] According to the analysis of the number of voids, it was found that the number of voids in PPUM showed a trend of increasing first and then decreasing with the extension of water-temperature environment treatment time, indicating that when the voids increase to a certain extent, they will connect with each other to form larger connected voids. By calculating the void diameter in the two-dimensional slice diagram and the void diameter in the three-dimensional visualization model, the calculation results are shown in Figure 2. Figure 9 .

[0044] Figure 9 The study describes the variation of the average equivalent void diameter within PPUM as a function of specimen height under different water-temperature conditions. It shows that the equivalent void diameter initially decreases and then increases with increasing freeze-thaw cycles and immersion days. This is due to the continuous growth of microvoids within the PPUM after exposure to the three water-temperature conditions, which, when averaged, results in a decrease in the equivalent void diameter per layer. Furthermore, as the microvoids continue to grow, they gradually connect and develop into larger, interconnected voids, resulting in a decrease in the number of voids and an increase in their diameter, which in turn leads to an increase in the equivalent void diameter. Overall, the voids in the PPUM exposed to freeze-thaw cycles are generally larger, with an average equivalent diameter of 0.4–0.8 mm. After exposure to ambient and high-temperature immersion conditions, the equivalent void diameters range from 0.3–0.55 mm. This indicates that freeze-thaw cycles not only form microvoids within the mixture but also affect the size of the existing voids through changes in water volume.

[0045] It is difficult to clearly understand the distribution of voids in the mixture by looking at the variation of the average equivalent diameter of voids along the specimen height in the PPUM two-dimensional slice diagram. In order to more clearly express the distribution of voids with different equivalent diameters in PPUM, the number of voids with different equivalent diameters in the three-dimensional model was counted, as shown in Figure 2. Figure 10 shown.

[0046] Depend on Figure 10 The statistical plots of the number of different void diameters in PPUM under the three water-temperature environments all show a normal distribution, with the equivalent diameter of the PPUM voids concentrated between 148 and 607 μm. After one freeze-thaw cycle and one day of immersion in water, the equivalent diameter of the voids generated in the PPUM was generally around 214 μm. For the PPUM treated in the freeze-thaw cycle, the voids after three and six freeze-thaw cycles were concentrated between 345 and 410 μm, representing an increase of approximately 131 to 196 μm compared to the untreated PPUM. After immersion in water at room temperature, the equivalent diameter of the voids was around 214 μm. Due to the relatively slow growth of the voids, there was no significant increase in the equivalent diameter. In the high-temperature immersion environment, the equivalent diameter of the voids in the PPUM mixture was concentrated between 279 and 345 μm, indicating smaller voids within the mixture compared to the freeze-thaw cycle, which is due to the volume change of water during the freeze-thaw cycle.

[0047] Based on the above reasons, the water temperature environment has a greater impact on the void ratio. Therefore, in order to determine the relationship between different void parameters and the road performance of PPUM.

[0048] In some implementations, the relationship between different gap parameters and PPUM road performance is determined. According to the influence of the water temperature environment, three water temperature environments are analyzed to determine the relationship between each road performance and different gap parameters.

[0049] Please refer to Figure 11 , Figure 11 (a) is a schematic diagram showing the changes in the splitting strength decay rate, compressive strength decay rate, low-temperature bending failure strain decay rate, dynamic stability decay rate, and dynamic modulus decay rate with the void ratio decay rate under freeze-thaw cycle conditions; Figure 11 Middle (b) is a schematic diagram of the change of splitting strength attenuation rate, compressive strength attenuation rate, low-temperature bending failure strain attenuation rate, dynamic stability attenuation rate and dynamic modulus attenuation rate with the void ratio attenuation rate under room temperature water immersion conditions. Figure 11 Middle (c) is a schematic diagram of the change of splitting strength attenuation rate, compressive strength attenuation rate, low-temperature bending failure strain attenuation rate, dynamic stability attenuation rate and dynamic modulus attenuation rate with the void ratio attenuation rate under high-temperature water immersion conditions. Figure 11 The functional relationships of various expressions are given in , and the corresponding performance attenuation rate can be calculated through the corresponding functional relationships.

[0050] The relationship between the attenuation rate of various properties and the porosity attenuation rate under different temperature environments is shown in the following table: Table 1 Relationship between the attenuation rate of various properties and the attenuation rate of void ratio In the table above, is the coefficient of determination for the fitting. As can be seen from the figure, the various road performance properties of PPUM are positively correlated with changes in its porosity, and there is a good correlation in freeze-thaw cycles, normal temperature immersion, and high temperature immersion environments. The coefficient of determination for the fitting is above 0.85, with the exception of a few that are 0.788. This reflects that changes in the porosity of PPUM in different water-temperature environments affect the attenuation of PPUM's road performance. The reason for this is that the tiny voids formed after water molecules penetrate PPUM continuously damage the PPU-mineral interface over time. In particular, changes in the volume of water in the freeze-thaw cycle affect the existing voids in the system, leading to the attenuation of PPUM's road performance. Using the table above, the attenuation rate of various properties can be quickly obtained.

[0051] The present invention enables non-destructive testing of internal voids in samples and analysis of various road performance characteristics of PPUM materials, facilitating rapid development and commissioning of PPUM materials and shortening R&D cycles. It can also be used to predict road surface lifespan.

[0052] It should be noted that in the present invention, the porosity attenuation rate refers to the change between the porosity before and after treatment in different water-temperature environments, and can also be called the damage rate. The calculation method is to divide the difference between the porosity before and after treatment by the original porosity.

[0053] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the scope of implementation of the present invention is not limited to what is shown in the drawings. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which do not exceed the spirit covered by the description and drawings, should be within the scope of protection of the present invention.

Claims

1. A method for determining the attenuation rate of cementitious material-mineral aggregate performance, characterized in that: The following steps are included: Obtaining samples; Perform CT scanning on the sample according to the set interval and obtain multiple two-dimensional scan images; Perform image processing on the two-dimensional scanned image to obtain the void extraction map and calculate the void ratio layer by layer; The three-dimensional model of the sample is reconstructed by performing three-dimensional model reconstruction on the void extraction map, and the equivalent diameter of each void is calculated; The void ratio attenuation rate is calculated along a direction perpendicular to the two-dimensional scanning image; and the attenuation rates of various properties are determined based on the void ratio attenuation rate.

2. The method for determining the binder-mineral aggregate performance attenuation rate according to claim 1, characterized in that: The sample is cylindrical; the set spacing is 40 to 60 μm, and the multiple two-dimensional scanning images are all plane images perpendicular to the center line of the sample.

3. The method for determining the performance attenuation rate of the binder-mineral aggregate according to claim 2, characterized in that: Methods for performing image processing on a two-dimensional scanned image include: Grayscale the two-dimensional scanned image; Perform image noise reduction on two-dimensional scanned images; Convert the original image grayscale histogram into a form that is evenly distributed across the entire grayscale range; Segment the image according to different grayscale values; Extract the gaps on each 2D scan image according to the set grayscale value.

4. The method for determining the binder-mineral aggregate performance attenuation rate according to claim 3, characterized in that: The calculation formula of layer-by-layer void ratio is: ； is the layer-by-layer void ratio; It is the area of ​​voxels contained in the gaps within a single CT scan slice; It is the number of voxels contained in the mineral material and glue in a single CT scan slice.

5. The method for determining the binder-mineral aggregate performance attenuation rate according to claim 1, characterized in that: The method for reconstructing the three-dimensional model of the void extraction map to obtain the three-dimensional model of the specimen is: Arrange all gap extraction images in sequence according to the set distance; Starting from any gap extraction map, all gaps on the gap extraction map are marked, and gaps on adjacent gap extraction maps are classified into first gaps and second gaps according to whether they can be merged with the previously marked gaps; the first gap is merged with the marked gap, and the second gap is marked; all gap extraction maps are processed in sequence according to the above method to obtain multiple marked three-dimensional gaps, the position of each three-dimensional gap, the gap extraction map corresponding to each three-dimensional gap, and the coordinates of the three-dimensional gap; The area outside the three-dimensional gap is filled to obtain a reconstructed three-dimensional model.

6. The method for determining the performance attenuation rate of a binder-mineral aggregate according to claim 1, characterized in that: The void equivalent diameter includes two-dimensional void equivalent diameter and three-dimensional void equivalent diameter; The calculation formula for the two-dimensional void equivalent diameter is: ； in, d is the equivalent diameter of the two-dimensional void; S is the two-dimensional void area, μm 2 ; N is the number of gaps; The calculation formula for the three-dimensional void equivalent diameter is: ； in, D is the three-dimensional void equivalent diameter; V is the three-dimensional void volume.

7. The method for determining the attenuation rate of the binder-mineral aggregate performance according to claim 1, characterized in that: The method for judging the attenuation rate of various properties according to the void ratio attenuation rate is: Substitute the void ratio into the equation of the relationship between each performance attenuation rate and void ratio attenuation rate for calculation; Among them, the performance attenuation rate includes: splitting strength attenuation rate, compressive strength attenuation rate, low-temperature bending failure strain attenuation rate, dynamic stability attenuation rate and dynamic modulus attenuation rate.

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