A method, system, device and medium for quantitatively evaluating evolution of an interface transition zone of alkali-activated concrete

Abstract

This application relates to a method, system, equipment, and medium for quantitative evaluation of the evolution of the interface transition zone in alkali-activated concrete. The method includes: acquiring backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-activated concrete samples; removing aggregate regions from the backscattered electron images to obtain slurry region images and corresponding aggregate boundary mask data; performing phase segmentation on the slurry region images to obtain phase segmentation result data; dividing the aggregate boundary mask data to obtain mask data for each strip region; and extracting the microstructural features of each strip region mask data based on the phase segmentation result data and EDS elemental analysis data to obtain a partitioned feature dataset; quantifying the quality of the interface transition zone based on the partitioned feature dataset to obtain comprehensive quality evaluation parameters; and performing time-series analysis on the comprehensive quality evaluation parameters to obtain characteristic evolution data of the interface transition zone. This method can improve the accuracy of quantitative characterization of the evolution stages.

Description

Technical Field

[0001] This invention belongs to the field of building material quality evaluation, and in particular relates to a method, system, equipment and medium for quantitative evaluation of the evolution of the interface transition zone of alkali-activated concrete. Background Technology

[0002] With the rapid development of alkali-activated concrete as a green building material, the microstructure characteristics of its interface transition zone have attracted widespread attention. As a key link between aggregate and paste, the formation mechanism and evolution law of this region are fundamentally different from those of traditional silicate cement concrete, which leads to the existing methods for analyzing the microstructure of the interface transition zone.

[0003] Existing methods for analyzing the microstructure of the interface transition zone typically employ scanning electron microscopy combined with backscattered electron imaging to qualitatively observe the morphology of the interface transition zone. Phase segmentation of the image is performed by manually setting grayscale thresholds, or the existence range of the interface transition zone is indirectly inferred by using energy dispersive spectroscopy to perform line scanning along the direction perpendicular to the aggregate and observing changes in elemental content. These methods mainly rely on the researcher's experience to conduct static single-point or single-age-period analysis, making it difficult to systematically track the dynamic evolution of the interface transition zone over time.

[0004] However, current methods for analyzing the microstructure of the interface transition zone in alkali-activated concrete are limited by the low gray-scale contrast of the reaction products, the fine and dense pore structure, and the potential for densification of the interface transition zone. These limitations make it difficult to quantitatively characterize the complete evolution of the microstructure of the interface transition zone from its early formation to its later maturation, resulting in insufficient accuracy in quantitative characterization of the evolution stages. Summary of the Invention

[0005] Therefore, it is necessary to provide a quantitative evaluation method, system, equipment, and medium for the evolution of the transition zone of alkali-activated concrete interface that can improve the accuracy of quantitative characterization of the evolution stage, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a method for quantitatively evaluating the evolution of the transition zone at the interface of alkali-activated concrete, including:

[0007] Backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples were obtained; both backscattered electron images and EDS elemental analysis data were labeled with the sampling age.

[0008] The aggregate region in the backscattered electron image is removed to obtain the slurry region image and the corresponding aggregate boundary mask data;

[0009] Phase segmentation is performed on the slurry region image to obtain phase segmentation result data;

[0010] The aggregate boundary mask data is divided to obtain mask data for each strip region; and based on the phase segmentation results and energy dispersive spectroscopy elemental analysis data, the microstructural features of the mask data for each strip region are extracted to obtain the partition feature dataset.

[0011] Based on the partition feature dataset, the quality of the interfacial transition zone of alkali-activated concrete samples is quantified to obtain comprehensive quality evaluation parameters.

[0012] A time-series analysis was performed on the comprehensive quality evaluation parameters to obtain the characteristic evolution data of the interface transition zone.

[0013] Furthermore, by removing the aggregate region from the backscattered electron image, the slurry region image and the corresponding aggregate boundary mask data are obtained, including:

[0014] Based on the adaptive histogram equalization method, the backscattered electron image is contrast-enhanced to obtain an enhanced image;

[0015] Based on a preset filter kernel, the enhanced image is denoised to obtain a denoised image.

[0016] Edge detection is performed on the denoised image to extract the edges of the aggregate region, resulting in an aggregate edge image;

[0017] A morphological closure operation is performed on the aggregate edge image to connect the edge breakpoints and obtain the aggregate boundary mask data;

[0018] The aggregate boundary mask data and the denoised image are subjected to mask operation to remove the aggregate region and obtain the slurry region image.

[0019] Furthermore, both the aggregate boundary mask data and the phase segmentation results data are labeled with sampling age. The phase segmentation results data include porous phase mask data. The aggregate boundary mask data is divided to obtain mask data for each strip region. Based on the phase segmentation results data and energy dispersive spectroscopy (EDS) elemental analysis data, the microstructural features of the mask data for each strip region are extracted to obtain a partitioned feature dataset, including:

[0020] The spatial resolution of the backscattered electron image is obtained, and based on the spatial resolution, the preset strip physical width parameter is converted to obtain the strip pixel width parameter of the backscattered electron image.

[0021] Based on the aggregate boundary mask data, the boundaries of the aggregate region are identified to obtain aggregate boundary data; and starting from the aggregate boundary data, the strip pixel width parameter is used as the single expansion width to expand into the aggregate region one by one to obtain the strip region mask data of each strip region. The number of times the strip region is expanded into the aggregate region is the preset maximum transition zone strip parameter, and the strip region is marked with the sampling age.

[0022] Spatial registration was performed between the elemental analysis data from the energy dispersive spectrometer and the backscattered electron image to obtain the registered elemental distribution data.

[0023] For each strip region, based on the porous phase mask data, the number of pixels of the porous phase within the strip region mask data is counted to obtain the pore width parameter; and based on the pore width parameter and the strip region mask data, the porosity parameter is calculated.

[0024] For each strip region, the area of ​​the connected pore regions is statistically calculated based on the pore width parameter to obtain the pore size distribution parameter;

[0025] For each strip region, the average content of each element is extracted from the registered element distribution data to obtain the element content parameter.

[0026] For each sampling age identifier, the porosity parameters, pore size distribution parameters, and elemental content parameters of the strip regions with the same sampling age identifier are statistically integrated to obtain the partition feature dataset of the sampling age identifier.

[0027] Furthermore, based on the partitioned feature dataset, the quality of the interfacial transition zone of the alkali-activated concrete samples is quantified to obtain comprehensive quality evaluation parameters, including:

[0028] Based on the porosity and elemental content parameters of the strip region mask data with the same sampling age in the partition feature dataset, the comprehensive difference coefficient of the strip region is calculated.

[0029] Calculate the rate of change of the comprehensive difference coefficient of adjacent strip regions, and determine the strip region corresponding to the comprehensive difference coefficient whose rate of change meets the preset stable change threshold as the starting strip position. Divide the strip region according to the starting strip position to obtain the transition zone strip region set and the slurry matrix zone strip region set.

[0030] The pore size distribution parameters and element content parameters of each strip region in the transition zone strip region set are extracted from the partition feature dataset. The average pore size parameter of the interface transition zone is calculated based on the pore size distribution parameters, and the average element content value of the interface transition zone is calculated based on the element content parameters.

[0031] From the partition feature dataset, extract the pore size distribution parameters and element content parameters of each strip region in the slurry matrix region. Calculate the average pore size parameter of the matrix based on the pore size distribution parameters and the average element content value of the matrix based on the element content parameters.

[0032] For each sampling age, a comprehensive quality evaluation parameter is calculated based on the average pore size parameter of the interface transition zone, the average elemental content value of the interface transition zone, the average pore size parameter of the matrix, and the average elemental content value of the matrix. The expression for the comprehensive quality evaluation parameter is as follows:

[0033]

[0034] in, It is an index for any sampling age identifier. It is the first Comprehensive quality evaluation parameters for each sampling age identifier, It is the first Average pore size parameters of the interface transition zone for each sampling age. It is the first The average pore size parameter of the matrix at each sampling age. It is the first The average elemental content value of the interface transition zone of each sampling age identifier. It is the first Average elemental content of the matrix at each sampling age. It is the aperture weighting coefficient. It is the element weight coefficient.

[0035] Furthermore, a time-series analysis was performed on the comprehensive quality evaluation parameters to obtain the characteristic evolution data of the interface transition zone, including:

[0036] Using the sampling age identifier as the horizontal axis and the comprehensive quality evaluation parameters of the sampling age identifier as the vertical axis, the feature evolution curve of the interface transition zone is constructed.

[0037] The comprehensive quality evaluation parameters are subjected to first-order difference according to the order of the sampling age identifiers corresponding to the comprehensive quality evaluation parameters to obtain the quality change rate parameters of each sampling age identifier.

[0038] Based on a preset set of change cycle mapping rules, the quality change rate parameter is mapped to the change cycle level corresponding to the quality change rate parameter to obtain the change cycle level of the sampling age identifier. Based on the change cycle level of each sampling age identifier and the quality change rate parameter, the interface transition zone feature evolution evaluation data is obtained.

[0039] Based on the feature evolution curves of the interface transition region and the feature evolution evaluation data of the interface transition region, the feature evolution data of the interface transition region are obtained.

[0040] Secondly, this application also provides a quantitative evaluation system for the evolution of the interfacial transition zone in alkali-activated concrete, comprising:

[0041] The data acquisition module is used to acquire backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples; both the backscattered electron images and the EDS elemental analysis data are labeled with the sampling age.

[0042] The slurry segmentation module is used to remove aggregate regions from the backscattered electron image and obtain the slurry region image and the corresponding aggregate boundary mask data.

[0043] The image analysis module is used to perform phase segmentation on the slurry region image and obtain phase segmentation result data;

[0044] The strip analysis module is used to divide the aggregate boundary mask data to obtain the mask data of each strip region; and based on the phase segmentation results data and energy dispersive spectroscopy elemental analysis data, it extracts the microstructural features of the mask data of each strip region to obtain the partition feature dataset.

[0045] The quality evaluation module is used to quantify the quality of the interface transition zone of alkali-activated concrete samples based on the partition feature dataset, and obtain comprehensive quality evaluation parameters.

[0046] The curve construction module is used to perform time-series analysis on the comprehensive quality evaluation parameters to obtain interface transition zone feature evolution data; the interface transition zone feature evolution data includes interface transition zone feature evolution curves and interface transition zone feature evolution evaluation data.

[0047] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the quantitative evaluation methods for the evolution of the alkali-activated concrete interface transition zone described in the first aspect of this application.

[0048] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the quantitative evaluation methods for the evolution of the transition zone of alkali-activated concrete interfaces described in the first aspect of this application.

[0049] The aforementioned method, system, equipment, and medium for quantitative evaluation of the evolution of the interfacial transition zone in alkali-activated concrete acquire backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-activated concrete samples. Both backscattered electron images and EDS elemental analysis data include sampling age indicators. Aggregate regions are removed from the backscattered electron images to obtain slurry region images and corresponding aggregate boundary mask data. The slurry region images are then segmented to obtain phase segmentation results. The aggregate boundary mask data is further divided to obtain mask data for each strip region. Based on the phase segmentation results and EDS elemental analysis data, the microstructural features of each strip region mask data are extracted to obtain a partitioned feature dataset. Based on this partitioned feature dataset, the quality of the interfacial transition zone of the alkali-activated concrete sample is quantified to obtain comprehensive quality evaluation parameters. Time-series analysis of these comprehensive quality evaluation parameters yields the characteristic evolution data of the interfacial transition zone. This method achieves an objective and quantitative characterization of the evolution process of the interfacial transition zone in alkali-activated concrete, effectively overcoming the shortcomings of traditional methods that rely on subjective experience and static analysis, and improving the accuracy of quantitative characterization of the evolution stages. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a quantitative evaluation method for the evolution of the transition zone at the interface of alkali-activated concrete, provided as an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the structure of a quantitative evaluation system for the evolution of the transition zone at the interface of alkali-activated concrete, provided as an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] In one embodiment, such as Figure 1As shown, a quantitative evaluation method for the evolution of the transition zone at the interface of alkali-activated concrete is provided. This embodiment illustrates the application of this method to an evaluation terminal. It is understood that this method can also be applied to a server, and to a system including both an evaluation terminal and a server, and is implemented through the interaction between the evaluation terminal and the server. In this embodiment, the method includes S101-S106, wherein:

[0055] S101, acquire backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples; both backscattered electron images and EDS elemental analysis data are labeled with the sampling age.

[0056] Specifically, the evaluation terminal acquires backscattered electron images and energy-dispersive X-ray spectroscopy (EDS) elemental analysis data from alkali-activated concrete samples. The alkali-activated concrete samples are specimens prepared according to a preset mix proportion and cured under standard curing conditions to different ages (e.g., 3h, 12h, 24h, 3d, 7d, 28d). After hydration termination, resin impregnation, grinding and polishing, and carbon spraying, backscattered electron images are acquired using a field emission scanning electron microscope (FE-SEM), and elemental analysis data are simultaneously acquired using an energy-dispersive X-ray spectroscopy (EDS) instrument. The backscattered electron images are grayscale images, where the grayscale value of each pixel reflects the atomic number at that location, and its mathematical form can be expressed as: ,in and These represent the image height and width, respectively. The image resolution can be 1024×768 pixels, with a pixel size of 0.20~0.30μm. Energy dispersive spectroscopy (EDS) elemental analysis data includes area scan elemental distribution maps and point analysis data. The area scan elemental distribution map is a multi-channel image of the same region as the backscattered electron image, with each channel corresponding to the intensity value of one element (such as Na, Ca, Si, Al), mathematically represented as a three-dimensional array. , The number of elemental channels is used; point analysis data records the atomic percentage at each point perpendicular to the aggregate direction. Both backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data include sampling age indicators. ,in It is the first Each sampling age is identified. For example, within the same sampling age identifier, the evaluation terminal can acquire multiple backscattered electron images, which can be represented as follows: , … , It is the sampling age identification The total number of backscattered electron images collected by the evaluation terminal.

[0057] S102, remove the aggregate region from the backscattered electron image to obtain the slurry region image and the corresponding aggregate boundary mask data.

[0058] Specifically, the evaluation terminal identifies aggregate regions in the backscattered electron image using image processing algorithms (such as edge detection and morphological operations) and generates aggregate boundary mask data. The aggregate boundary mask data is a binary image with the same dimensions as the backscattered electron image, and its mathematical form can be... In this model, a pixel value of 1 indicates that the pixel belongs to the aggregate boundary contour, and 0 indicates that it does not. The aggregate boundary mask data and the backscattered electron image are subjected to masking operations. After removing the aggregate region, the slurry region image is obtained, which is mathematically represented as a matrix. , and It is the height and width (in pixels) of the slurry region image, each element This represents the grayscale value of a pixel (typically 0~255). Only the grayscale value of the slurry portion is retained, while the aggregate portion is set to invalid values. Both the slurry region image and the corresponding aggregate boundary mask data retain the original sampling age identifier.

[0059] S103, perform phase segmentation on the slurry region image to obtain phase segmentation result data.

[0060] Specifically, the evaluation terminal performs phase segmentation on the slurry region image to obtain phase segmentation result data. The purpose of phase segmentation is to divide the pixels in the slurry region image according to their corresponding phase categories. Specifically, an automatic segmentation model based on a gray-level histogram can be used to divide the image into porous phase, reaction product phase, and unreacted particle phase, resulting in phase segmentation result data. The phase segmentation result data is a set of label matrices of the same size as the slurry region image. ,in This is porous phase mask data, where a pixel value of 1 indicates that the pixel belongs to the porous phase, and 0 indicates that it does not belong to the porous phase. First, the gray-level distribution of all valid pixels in the slurry region image is statistically analyzed to obtain a gray-level histogram. ,in grayscale value The number of pixels appearing. Assuming the grayscale histogram is composed of three superimposed Gaussian peaks, corresponding to pores (low grayscale), reaction products (medium grayscale), and unreacted particles (high grayscale), a Gaussian mixture model is constructed. ,in , , The first The peak height, peak position, and standard deviation of each Gaussian peak are determined. The Expectation-Maximization (EM) algorithm is used to iteratively optimize the model parameters, making... Approximating the actual histogram After obtaining the optimal parameters, the intersection points of adjacent Gaussian curves are calculated as the segmentation threshold. (Porosity / Reaction Product Boundary) and (Boundary between reaction products and unreacted particles). Then, each pixel in the slurry region image is classified: those with gray values ​​less than... The pixels are labeled as porous phases, intermediate to... and The markings between them indicate the reaction product phase, which is larger than... The unreacted particulate phase is marked as such. The final phase segmentation result data is generated, and this data also includes a sampling age identifier. For example, the phase segmentation result data may also include reaction product phase mask data. Phase mask data of unreacted particles ,Right now Phase masking data of reaction products A pixel value of 1 indicates that the phase belongs to the reaction product phase, while the unreacted particle phase is represented by the mask data. A pixel value of 1 indicates that it belongs to the unreacted particle phase.

[0061] S104, the aggregate boundary mask data is divided to obtain mask data for each strip region; and based on the phase segmentation results data and energy dispersive spectroscopy elemental analysis data, the microstructural features of the mask data for each strip region are extracted to obtain the partition feature dataset.

[0062] Specifically, the evaluation terminal first generates a series of concentric strip region masks of equal width based on aggregate boundary mask data using a region partitioning method (e.g., based on distance transformation or morphological dilation). Each strip region mask data is a binary image. , ,in To maximize the number of strips, each strip is arranged sequentially away from the aggregate boundary, and the strip mask data includes a sampling age identifier. Then, based on the phase segmentation results, mask data for each strip region is applied. The number of pixels for each phase within a strip is counted from the phase segmentation results data. Pore regions are identified and their geometric features are statistically analyzed using image analysis techniques to obtain pore size distribution parameters (such as median pore size, average pore size, and maximum pore size). The average intensity or atomic percentage of each element within the strip is extracted from the energy dispersive spectroscopy (EDS) elemental analysis data to obtain elemental content parameters. These pore size distribution parameters and elemental content parameters together constitute the microstructural feature parameters of the strip region mask data. Finally, the microstructural feature parameters of all strip region mask data under the same sampling age are statistically integrated to form a partitioned feature dataset for that sampling age. The partitioned feature dataset, indexed by the sampling age, includes the distance of each strip from the aggregate boundary, porosity, pore size distribution parameters, elemental content parameters, etc., and its mathematical form can be represented as a series of vectors or tables.

[0063] S105, based on the partition feature dataset, quantifies the quality of the interfacial transition zone of alkali-activated concrete samples to obtain comprehensive quality evaluation parameters.

[0064] Specifically, the partitioned feature dataset contains the microstructural characteristic parameters of each strip region, and the strips are arranged in an orderly manner according to their distance from the aggregate boundary. The interface transition zone refers to the area where the microstructure near the aggregate changes. Its range can be determined by analyzing the changing trend of the characteristic parameters of each strip with distance. The evaluation terminal utilizes parameters such as porosity and elemental content in the partitioned feature dataset, and employs a difference analysis method to identify the locations where the characteristics tend to stabilize, thereby dividing the strips corresponding to the interface transition zone and the strips corresponding to the slurry matrix. Then, the characteristic parameters of the interface transition zone strips and matrix strips are statistically summarized, and their mean and other statistical quantities are calculated to obtain the average characteristic value of the interface transition zone and the average characteristic value of the matrix. Finally, by comparing the average characteristic values ​​of the interface transition zone and the matrix, a comprehensive quality evaluation parameter is constructed, resulting in the comprehensive quality evaluation parameter. The comprehensive quality evaluation parameter is a scalar value, with each sampling age corresponding to a parameter value, and its mathematical form can be expressed as: , is the The comprehensive quality evaluation parameter for each sampling age identifier can have its value range and physical meaning determined through pre-calibration.

[0065] S106. Time series analysis of the comprehensive quality evaluation parameters is performed to obtain the characteristic evolution data of the interface transition zone.

[0066] Specifically, the evaluation terminal evaluates the comprehensive quality assessment parameters for each sampling age marker. Time-series analysis was performed to examine the evolution of interface transition zone features. This included evaluating the comprehensive quality assessment parameters of the evaluation terminal based on the identifiers of each sampling age. The comprehensive quality evaluation parameter sequence was obtained. The comprehensive quality evaluation parameter sequence corresponds to different sampling ages. This reflects the dynamic changes in the quality of the interface transition zone over time. Specifically, the evaluation terminal performs time-series analysis on the comprehensive quality evaluation parameter sequence to extract the implicit evolutionary trends, change characteristics, and stage patterns, ultimately generating interface transition zone characteristic evolution data. This interface transition zone characteristic evolution data is a comprehensive dataset, including at least the interface transition zone characteristic evolution curve reflecting the quality change trend over time, quality change rate parameters characterizing the rate of change at different time periods, and evolutionary stage labels corresponding to the alkali-activated reaction mechanism. The interface transition zone characteristic evolution curve can be constructed with the sampling age as the x-axis and the comprehensive quality evaluation parameters as the y-axis, visually presenting the continuous change process of the interface transition zone quality from the early to the later stages. The evolutionary stage labels automatically identify different evolutionary stages by analyzing the change characteristics of the parameter sequence (such as increase / decrease trends, change rates, etc.), such as rapid change stage, stable change stage, and maturation stage, each stage corresponding to different mechanistic periods of the alkali-activated reaction. The evolutionary stage labels assign clear physical meanings to each stage, such as rapid reaction period, stable development period, and mature and dense period.

[0067] This embodiment provides a quantitative evaluation method for the evolution of the interface transition zone in alkali-activated concrete. By acquiring backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data from samples at multiple ages, microstructural features are extracted through steps such as aggregate removal, phase segmentation, and concentric strip division to construct a partitioned feature dataset. This data then quantifies the quality of the interface transition zone and generates comprehensive quality evaluation parameters. Finally, time-series analysis is used to identify the evolution stages, obtaining characteristic evolution data of the interface transition zone. This method achieves an objective and quantitative characterization of the evolution process of the interface transition zone in alkali-activated concrete, effectively overcoming the shortcomings of traditional methods that rely on subjective experience and static analysis, and improving the accuracy of quantitative characterization of the evolution stages.

[0068] In one embodiment, the aggregate region in the backscattered electron image is removed to obtain a slurry region image and corresponding aggregate boundary mask data, including:

[0069] S201, based on the adaptive histogram equalization method, performs contrast enhancement processing on the backscattered electron image to obtain an enhanced image.

[0070] Specifically, the evaluation terminal employs an adaptive histogram equalization (AHE) method to enhance the contrast of the backscattered electron image, resulting in an enhanced image. AHE is an image enhancement technique that divides the image into several non-overlapping sub-blocks (e.g., 8×8 pixel sub-blocks), performs independent histogram equalization within each sub-block, and smooths the boundaries between sub-blocks through interpolation, thus avoiding potential local over-enhancement caused by global equalization. This algorithm effectively stretches the grayscale distribution in local areas, making the previously blurred boundaries between aggregate and slurry, and the grayscale differences between pores and the solid phase, more apparent. The input is the original backscattered electron image. The output is the enhanced image. While the pixel values ​​remain grayscale, their distribution is more uniform, resulting in improved contrast. Key parameters involved in adaptive histogram equalization, such as sub-block size and contrast limit threshold, can be preset according to image resolution and actual effect. For example, the sub-block size can be set to 8×8, and the contrast limit threshold to 0.02.

[0071] S202, based on a preset filter kernel, performs noise reduction processing on the enhanced image to obtain a denoised image.

[0072] Specifically, the evaluation terminal uses a preset filter kernel to denoise the enhanced image. The preset filter kernel is a small two-dimensional matrix; its size and type can be preset based on the image noise level or determined experimentally. It can be a 3×3 Gaussian kernel or a median filter kernel. Gaussian filtering smooths the image through convolution operations; its kernel function is a Gaussian function, and the weights decrease as the distance from the center pixel increases, effectively suppressing Gaussian noise. Median filtering replaces the center pixel value with the median gray value of neighboring pixels, effectively suppressing salt-and-pepper noise. Specifically, the preset filter kernel can be set to Gaussian filtering, with a preset kernel size of 3×3 and a preset standard deviation of 1. The preset filter kernel is then used to denoise the enhanced image. Perform the calculation to obtain the denoised image. .

[0073] S203, perform edge detection on the denoised image, extract the edges of the aggregate region, and obtain the aggregate edge image.

[0074] Specifically, the evaluation terminal can use the Canny edge detection algorithm to perform edge detection on the denoised image, extracting the edges of the aggregate region to obtain the aggregate edge image. Specifically, the Canny edge detection algorithm includes several sub-steps: first, the image is denoised using Gaussian filtering to reduce noise; then, the gradient magnitude and direction of the denoised image are calculated (usually using the Sobel operator); next, non-maximum suppression is applied to the gradient magnitude, retaining the points with the largest local gradients as candidate edges; finally, weak edges are connected to form continuous edges through dual-threshold detection and hysteresis boundary tracking. The high and low thresholds in the Canny algorithm can be adaptively set according to the image gradient histogram; for example, the high threshold can be set to the top 70% quantile of the gradient magnitude, and the low threshold can be set to half of the high threshold. Finally, a binary aggregate edge image is obtained. The pixel value of 1 indicates that the point is an edge of the aggregate, and 0 indicates that it is not an edge.

[0075] S204 performs a morphological closure operation on the aggregate edge image, connecting edge breakpoints to obtain aggregate boundary mask data.

[0076] Specifically, the evaluation terminal evaluates the aggregate edge image. A morphological closure operation is performed to connect edge breakpoints, resulting in aggregate boundary mask data. Specifically, the morphological closure operation is a process of first dilation and then erosion. A preset structuring element (e.g., a circular structuring element with a radius of 3 pixels) is used to dilate the aggregate edge image, connecting adjacent edge points to form patches and filling small gaps. Then, erosion is performed to restore the approximate shape of the edges while maintaining connectivity. This operation effectively connects edge breakpoints and eliminates isolated noise points. The final aggregate boundary mask data is obtained. The pixel value of 1 indicates that the pixel belongs to the aggregate boundary contour, and 0 indicates that it does not. This mask data records the closed contour of the aggregate region.

[0077] S205, perform masking operations on the aggregate boundary mask data and the denoised image to remove the aggregate region and obtain the slurry region image.

[0078] Specifically, the evaluation terminal is based on closed aggregate boundary mask data. Aggregate region masks are generated using a region-filling algorithm. The pixel value of 1 indicates that the pixel belongs to the aggregate region. Then, the denoised image... With aggregate area mask Perform mask calculations: for each pixel position ,like If the value is zero, the grayscale value of that pixel is set to an invalid value (e.g., 0 or NaN); if it is 0, the original grayscale value is retained. The result of the calculation is the image of the slurry region. The image contains only the grayscale information of the slurry portion, while the aggregate portion is removed. The slurry region image retains the sampling age identifier of the original backscattered electron image.

[0079] This embodiment provides a quantitative evaluation method for the evolution of the interface transition zone in alkali-activated concrete. By sequentially performing adaptive histogram equalization enhancement, filtering and noise reduction, edge detection, morphological closure, and masking operations on backscattered electron images, it achieves precise removal of aggregate regions, obtaining clean images of the paste region and aggregate boundary mask data. This effectively eliminates the interference of aggregates on subsequent phase segmentation and strip analysis, improves the accuracy of microstructural feature extraction, and lays a reliable input data foundation for the quantitative evaluation of the interface transition zone.

[0080] In one embodiment, both the aggregate boundary mask data and the phase segmentation result data are labeled with sampling age. The phase segmentation result data includes porous phase mask data. The aggregate boundary mask data is divided to obtain mask data for each strip region. Based on the phase segmentation result data and energy dispersive spectroscopy (EDS) elemental analysis data, the microstructural features of each strip region mask data are extracted to obtain a partitioned feature dataset, including:

[0081] S301, obtain the spatial resolution of the backscattered electron image, and based on the spatial resolution, convert the preset strip physical width parameter to obtain the strip pixel width parameter of the backscattered electron image.

[0082] Specifically, the evaluation method assesses the spatial resolution of the backscattered electron image acquired by the evaluation terminal. Based on this spatial resolution, the preset stripe physical width parameters are converted to obtain the stripe pixel width parameters of the backscattered electron image. The spatial resolution of the backscattered electron image refers to the actual physical size represented by each pixel in the backscattered electron image. It can be calculated from the magnification of the scanning electron microscope and the image acquisition parameters, and its mathematical form is a scalar value. The unit is micrometers per pixel (μm / pixel). Preset strip physical width parameters. This is a pre-set constant based on research needs. It can be set according to the required precision of transition zone analysis in actual work; the default setting is 5μm. The evaluation terminal divides the image into strips in the image coordinate system based on the preset strip physical width parameter according to the spatial resolution. The specific conversion formula is as follows: ,in This indicates rounding down to the nearest integer, ensuring the pixel width is an integer. (Strip pixel width parameter) This is the basic unit for subsequent extended operations.

[0083] S302, based on the aggregate boundary mask data, identify the boundary of the aggregate region and obtain aggregate boundary data; starting from the aggregate boundary data, expand towards the aggregate region one by one with the strip pixel width parameter as the single expansion width to obtain strip region mask data of each strip region, wherein the number of expansions towards the aggregate region is the preset maximum transition zone strip parameter, and the strip region has a sampling age identifier.

[0084] Specifically, the aggregate boundary mask data is a binary image. A pixel value of 1 indicates that the point is located on the aggregate contour. To obtain continuous aggregate boundaries, the evaluation terminal can perform edge tracking or contour extraction on the aggregate boundary mask data to obtain aggregate boundary data, which is a set of pixel coordinates. Then, using this boundary as the starting contour, a morphological dilation operation is used to expand outwards successively. The dilation operation uses a preset structuring element (such as a radius of 1). (A circle), each expansion causes the current region to expand outwards. The pixel width is used to generate a new strip region. The first dilation is used to obtain the distance from the boundary. The stripes within the pixel range are dilated a second time to obtain the distance from the boundary. The number of expansions is determined by the preset maximum transition zone stripe parameter. The preset maximum transition zone strip parameter is determined. It can be configured according to the actual work requirements, for example... This indicates that the generation process is from near to far. There are concentric stripes. The binary image obtained from each dilation is the stripe region mask data for the corresponding stripe. A pixel value of 1 indicates that the pixel belongs to the first pixel. Each strip region is represented by a value of 0, indicating that it does not belong to any of the strip regions. All strip mask data inherit the sampling age identifier from the aggregate boundary mask data, ensuring that it is correlated with the corresponding sample age. Furthermore, each strip region mask data... The unique corresponding number Striped area.

[0085] S303 spatially registers the elemental analysis data from the energy dispersive spectrometer with the backscattered electron image to obtain the registered elemental distribution data.

[0086] Specifically, elemental analysis data from energy dispersive spectroscopy (EDS) is typically acquired simultaneously with backscattered electron (BSO) images. However, due to factors such as detector position or scan drift, slight spatial offsets may exist between the two. Spatial registration aims to ensure a one-to-one correspondence between the elemental distribution and image pixels, guaranteeing that the extracted elemental content matches the location of microstructural features. Registration methods can employ feature-point-based registration (e.g., manually selecting corresponding points in aggregates or pores) or automatic registration based on mutual information. Specifically, regions with prominent grayscale features in the BSO image (e.g., aggregate edges) can be selected as references. The EDS elemental distribution map is then translated or subjected to affine transformations to align the two, resulting in registered elemental distribution data. The registered elemental distribution data is still a multi-channel image. ,in The number of channels represents the element (e.g., Na, Ca, Si, Al). The value of each channel represents the relative intensity or atomic percentage of the element at that pixel location, and it has the same spatial coordinate system and pixel size as the backscattered electron image.

[0087] S304. For each strip region, based on the porous phase mask data, count the number of pixels of the porous phase within the strip region mask data to obtain the pore width parameter; and calculate the porosity parameter based on the pore width parameter and the strip region mask data.

[0088] Specifically, the pore phase mask data is a binary image extracted from the phase segmentation results data. A pixel value of 1 indicates that the pixel belongs to the porous phase. For each strip region, firstly, based on its strip region mask data... Calculate the total number of pixels within this strip region. Then, count the number of pixels within the strip region that simultaneously belong to the porous phase. This refers to the pore width parameter (number of pore pixels). The porosity parameter is defined as the proportion of pore pixels to the total number of pixels in the strip region. The result is a scalar between 0 and 1, representing the porosity within the strip region.

[0089] S305. For each strip region, the area of ​​the connected pore regions is statistically calculated based on the pore width parameter to obtain the pore size distribution parameter.

[0090] Specifically, the evaluation terminal further analyzes the geometric features of the pores in the strip region mask based on the pore width parameter. For each strip region mask, connected component analysis (typically using 8-neighborhood connectivity) is performed within the pore phase mask data, marking interconnected pore pixels as independent connected regions. For each connected region... Count the number of pixels it contains (i.e., area of ​​region, in pixels), and based on the spatial resolution of the backscattered electron image. Convert it to the area of ​​the connected pore region Then the equivalent circle diameter of the pore is calculated. For all connected regions equivalent circle diameter The average value is calculated to obtain the aperture distribution parameters of the strip region. .

[0091] S306. For each strip region, extract the average content of each element from the registered element distribution data to obtain the element content parameter.

[0092] Specifically, for each strip region, the evaluation terminal evaluates the registration element distribution data. The average element content of the stripe region is obtained by averaging the values ​​of all pixels in each element channel. For example, for element... Its average content is ,in This represents the total number of pixels within the strip region. The evaluation terminal integrates the average content of each element into a vector. That is, the element content parameter of the band region, which is used to characterize the chemical composition characteristics of the band region.

[0093] S307. For each sampling age identifier, the porosity parameters, pore size distribution parameters, and element content parameters of the strip regions with the same sampling age identifier are statistically integrated to obtain the partition feature dataset of the sampling age identifier.

[0094] Specifically, for the same sampling age identifier, the evaluation terminal will collect data synchronously. After processing in steps S301-S306, the backscattered electron images and corresponding energy dispersive spectroscopy (EDS) elemental analysis data generate a series of strip region mask data extending outward from the aggregate boundary, as well as porosity parameters (scalar values) for each strip region. , indicating the first Zhang Image No. Porosity of individual strip regions, pore size distribution parameters (average pore size) (i.e., the average diameter of the equivalent circle of all pores within the strip region) and elemental content parameters (vector). This represents the average content of each element within the strip region. The evaluation terminal statistically integrates the porosity parameters, pore size distribution parameters, and element content parameters of each strip region under the same sampling age identifier to obtain a partition feature dataset for that sampling age identifier. The purpose of statistical integration is to merge and organize the data of corresponding strip regions from multiple images under the same age to form a structured partition feature dataset. The mathematical form of the partition feature dataset can be represented by a strip region index. The first dimension of the data structure includes the physical distance of the strip region from the aggregate boundary under each strip region entry. (Derived from strip region index and strip pixel width parameter), a set of porosity parameters collected from multiple images. and a set of element content parameters for each photo. The integrated partition feature dataset, indexed by the sampling age, fully preserves the microstructural features of all original images within each strip region, for subsequent statistical analysis or feature extraction. For example, for the... Each sampling age identifier, and its partition feature dataset can be denoted as: .

[0095] This embodiment provides a quantitative evaluation method for the evolution of the interface transition zone in alkali-activated concrete. By dividing aggregate boundary mask data into concentric strips and combining the pore phase mask from the phase segmentation results with the registered elemental distribution data, microstructural features such as porosity, pore size distribution, and elemental content are extracted strip by strip. Finally, multiple image data are statistically integrated to form a partitioned feature dataset for each sampling age. This method achieves the transformation from raw images to structured microscopic feature data, organically integrating spatial distribution information with chemical composition information, and providing accurate and comprehensive input data for subsequent quantitative evaluation of the interface transition zone quality.

[0096] In one embodiment, based on a partitioned feature dataset, the quality of the interfacial transition zone of the alkali-activated concrete sample is quantified to obtain comprehensive quality evaluation parameters, including:

[0097] S401, based on the porosity parameters and element content parameters of the strip region mask data with the same sampling age identifier in the partition feature dataset, calculate the comprehensive difference coefficient of the strip region.

[0098] Specifically, for each sampling age marker Evaluation of the terminal from its partition feature dataset Extracting porosity parameters and element content parameters ,in For the first The first image Porosity parameters for each strip region. The evaluation terminal uses the porosity parameters of multiple images. The arithmetic mean is taken to obtain the average porosity of the strip region. Simultaneously, the elemental content parameters are averaged element by element to obtain the average elemental content vector of the strip region. Each element Then, take the first strip area closest to the aggregate ( Using this as a baseline, calculate the region for each strip. Comprehensive difference coefficient This coefficient is used to quantify the degree of difference in porosity and elemental composition between the current strip region and the reference strip region. Specifically, the porosity difference is first calculated. (like (Then a preset small value is used to avoid division by zero); then the difference between each element is calculated. The average of all elements is then used to obtain the comprehensive elemental difference. ,in This represents the number of element types. Finally, the overall difference coefficient is calculated. The weighted sum of porosity differences and overall elemental differences, i.e. ,in This is a preset balance coefficient (usually set to 0.5). The larger the value of this coefficient, the more significant the difference between this strip area and the near-aggregate area.

[0099] S402, calculate the rate of change of the comprehensive difference coefficient of adjacent strip regions, and determine the strip region corresponding to the comprehensive difference coefficient whose rate of change meets the preset stable change threshold as the starting strip position. Divide the strip region according to the starting strip position to obtain the transition zone strip region set and the slurry matrix zone strip region set.

[0100] Specifically, for each sampling age marker The evaluation terminal calculates the rate of change between the comprehensive difference coefficients of adjacent strip regions based on the strip pixel width parameter and the comprehensive difference coefficient. The specific formula is as follows: (like Then the absolute difference is used. The preset stable change threshold is... It is a small positive number (e.g., 0.05) used to determine whether the difference coefficient tends to stabilize, and can be set according to actual work. This rate of change reflects the degree of fluctuation of the overall difference coefficient as the strip region index increases. From the first strip region ( The scan begins backward, searching for the first instance where the rate of change of multiple consecutive strip regions (e.g., three consecutive regions) is all less than [a certain value]. The position of the stripe, denoted as That is, the starting position of the strip. The strip region preceding this position ( The interface transition zone is divided into a set of strip regions, denoted as . The microstructure within this region is significantly affected by the aggregate, and the characteristic parameters vary markedly with distance; the strip region at this location and thereafter ( The region is divided into strips representing the slurry matrix, denoted as […]. The microstructure in this region tends to be stable, representing the characteristics of the slurry matrix far from the aggregate.

[0101] S403: Extract the pore size distribution parameters and element content parameters of each strip region in the transition zone strip region set from the partition feature dataset, and calculate the average pore size parameter of the interface transition zone based on the pore size distribution parameters, and calculate the average element content value of the interface transition zone based on the element content parameters.

[0102] Specifically, for each sampling age marker The evaluation terminal first evaluates the transition zone strip region set determined by S402 (i.e., satisfying...). (a set of strip region indices), extracting aperture distribution parameters for all strip regions within the interface transition zone strip region set from the partition feature dataset identified by the sampling age. and element content parameters Then, for each strip region... The average aperture of the strip region is obtained by taking the arithmetic mean of the aperture distribution parameters of multiple images. Simultaneously, the elemental content vector is averaged element by element to obtain the average elemental content vector of the strip region. Each element Then, for all transition zone strip areas (total... (one) The arithmetic mean is taken to obtain the average pore size parameter of the interface transition zone. Regarding element content, first calculate the average element content vector for all transition zone strips. The average value of each element in the transition region is obtained by averaging each element. Then, the average value of these elements is combined into a scalar value, which is the average element content value of the interface transition zone. The synthesis method can employ an arithmetic mean. ,in The number of element types can be used, or a weighted average can be calculated based on the preset weights of element importance.

[0103] S404: Extract the pore size distribution parameters and element content parameters of each strip region from the partition feature dataset. Calculate the average pore size parameter of the matrix based on the pore size distribution parameters and the average element content value of the matrix based on the element content parameters.

[0104] Specifically, similar to S403, the evaluation terminal extracts the mask data aperture distribution parameters of all strip regions within the slurry matrix region strip region set from the partitioned feature dataset based on the slurry matrix region strip region set determined in step S402. and element content parameters Then, for each matrix region, the strip area... The average aperture of the strip region is obtained by averaging the aperture distribution parameters of multiple images. Simultaneously, the elemental content vector is averaged element by element to obtain the average elemental content vector of the strip region. Then, for all matrix strip regions (total... (one) The arithmetic mean was taken to obtain the average pore size parameter of the matrix. Regarding elemental content, first calculate the average elemental content vector for all matrix strip regions. The average value of each element in the matrix is ​​obtained by averaging each element. Then, the average values ​​of these elements are combined to obtain the average elemental content value of the matrix. The synthesis method is consistent with S403, and an arithmetic mean can be used. ,in This represents the number of element types.

[0105] S405, for each sampling age identifier, based on the average pore size parameter of the interface transition zone, the average elemental content value of the interface transition zone, the average pore size parameter of the matrix, and the average elemental content value of the matrix, the comprehensive quality evaluation parameter is calculated, wherein the expression of the comprehensive quality evaluation parameter is:

[0106]

[0107] in, It is an index for any sampling age identifier. It is the first Comprehensive quality evaluation parameters for each sampling age identifier, It is the first Average pore size parameters of the interface transition zone for each sampling age. It is the first The average pore size parameter of the matrix at each sampling age. It is the first The average elemental content value of the interface transition zone of each sampling age identifier. It is the first Average elemental content of the matrix at each sampling age. It is the aperture weighting coefficient. It is the element weight coefficient.

[0108] Specifically, for each sampling age identifier The evaluation terminal will calculate the average pore size parameters of the interface transition zone. and matrix average pore size parameters ratio and the average element content value of the interface transition zone Compared with the average elemental content value of the matrix ratio A weighted combination is performed. The aperture weight coefficient is among the factors considered. and element weight coefficients For a preset positive number, satisfying These coefficients are used to adjust the contribution of pore size and elemental content to the final quality evaluation. These two coefficients can be set empirically based on material properties or evaluation objectives (e.g., taking...). ), or can be determined through optimization using historical data (e.g., based on regression analysis). Comprehensive quality evaluation parameters It is a dimensionless scalar; the larger its value, the finer the pore size and the higher the element content (i.e., the higher the degree of reaction and the denser the structure) of the interface transition zone relative to the matrix, and the better the quality of the interface transition zone. The above process is repeated for all sampling age markers to obtain the comprehensive quality evaluation parameter sequence for each sampling age marker. Each comprehensive quality evaluation parameter corresponds to a curing time (sampling age marker), providing a quantitative basis for subsequent time-series evolution analysis.

[0109] This embodiment provides a quantitative evaluation method for the evolution of the interfacial transition zone in alkali-activated concrete. By calculating the comprehensive difference coefficient of each strip region and objectively determining the boundary of the interfacial transition zone based on the rate of change, the average pore size and elemental content of the interfacial transition zone and the matrix are extracted. Finally, a weighted comprehensive quality evaluation parameter is obtained. This method realizes the transformation from microstructural characteristics to a single quality index, eliminates the arbitrariness of subjective boundary delineation, and makes the quality of the interfacial transition zone of samples at different ages and with different mix proportions comparable. It provides a reliable quantitative tool for quantitatively studying the evolution law of the interfacial transition zone in alkali-activated concrete.

[0110] In one embodiment, a time-series analysis is performed on the comprehensive quality evaluation parameters to obtain interface transition zone feature evolution data, including:

[0111] S501, using the sampling age identifier as the horizontal axis and the comprehensive quality evaluation parameters of the sampling age identifier as the vertical axis, the characteristic evolution curve of the interface transition zone is constructed.

[0112] Specifically, each comprehensive quality evaluation parameter corresponds to a specific sampling age identifier. The sampling age identifier records the actual curing time of the sample from the completion of casting to the time of testing. Comprehensive quality evaluation parameters As a dimensionless scalar, its numerical value reflects the first... The evaluation terminal assesses the density and compositional uniformity of the interface transition zone relative to the slurry matrix at various curing ages. To visually represent the dynamic changes in the quality of the interface transition zone over curing time, the evaluation terminal uses the sampling age as the horizontal axis (either linear or logarithmic coordinates can be used; if using logarithmic coordinates, the actual time needs to be logarithmized to more clearly show the details of the early rapid change stage) and the corresponding comprehensive quality evaluation parameters as the vertical axis. Points are plotted sequentially in a two-dimensional coordinate system, and a smooth curve is used to connect the points, generating an interface transition zone characteristic evolution curve. This curve provides an intuitive visual representation of the quality evolution trend of the interface transition zone.

[0113] S502, perform first-order difference on the comprehensive quality evaluation parameters according to the order of the sampling age identifiers corresponding to the comprehensive quality evaluation parameters to obtain the quality change rate parameters of each sampling age identifier.

[0114] Specifically, first-order differencing is a commonly used method in time series analysis to calculate the rate of change between adjacent time points. The evaluation terminal groups the comprehensive quality evaluation parameters according to the order of their corresponding sampling age identifiers to obtain a comprehensive quality evaluation parameter sequence. ,in This represents the total number of sampling age identifiers, and the corresponding sampling age identifiers are as follows: Mass change rate parameter Defined as the first The sampling age was identified to the first The ratio of the change in the comprehensive quality evaluation parameter among the sampling age markers to the time interval, i.e. ,in This rate reflects the average rate of change in the quality of the interface transition zone within that time interval. Positive values ​​indicate quality improvement, negative values ​​indicate quality decline, and larger absolute values ​​indicate more drastic changes. The mathematical form of the rate of change parameter is a series of real values, each corresponding to a specific age interval.

[0115] S503, based on the preset change cycle mapping rule set, maps the quality change rate parameter to the change cycle level corresponding to the quality change rate parameter, obtains the change cycle level of the sampling age identifier, and obtains the interface transition zone feature evolution evaluation data based on the change cycle level of each sampling age identifier and the quality change rate parameter.

[0116] Specifically, the preset change cycle mapping rule set defines the conversion logic from the mass change rate parameter to the evolution stage level. The preset change cycle mapping rule set can take the form of... , It is a preset upper limit threshold for slow growth. The preset upper limit threshold for steady growth, the preset upper limit threshold for slow growth, and the preset upper limit threshold for steady growth are all less than 1, and satisfy the following conditions: The default setting is , Alternatively, the risk level can be set according to the actual work. The evaluation terminal maps the quality change rate parameter of each sampling age identifier to the change cycle level corresponding to the sampling age identifier through the preset change cycle mapping rule set, in the order of the sampling age identifier, to obtain the change cycle level of the sampling age identifier. The change cycle level and quality change rate parameter of each sampling age identifier are then integrated to obtain the interface transition zone feature evolution evaluation data.

[0117] S504. Based on the feature evolution curve of the interface transition zone and the feature evolution evaluation data of the interface transition zone, the feature evolution data of the interface transition zone is obtained.

[0118] Specifically, the evaluation terminal integrates the interface transition zone characteristic evolution curve constructed in step S501 with the interface transition zone characteristic evolution evaluation data obtained in step S503 to form complete interface transition zone characteristic evolution data. This data is a comprehensive set of results, including at least: an evolution curve of interface transition zone quality changing with curing time presented in graphical form; evolution stage division information recorded in text or tabular form (such as the start and end times and durations of the rapid reaction period, stable development period, and mature densification period); and quality change characteristic parameters for each stage (such as the quality change rate parameters of each sampling age within the stage). The interface transition zone characteristic evolution data can comprehensively and quantitatively describe the complete evolution process of the alkali-activated concrete interface transition zone from early formation to late maturity, providing a scientific basis for material performance prediction, mix proportion optimization, and engineering applications.

[0119] This embodiment provides a quantitative evaluation method for the evolution of the interface transition zone in alkali-activated concrete. Through a series of time-series analysis steps, such as constructing evolution curves, calculating change rates, comparing preset thresholds, and applying mapping rule sets, the comprehensive quality evaluation parameters of discrete ages are transformed into continuous evolution stage divisions and feature parameter extractions. This achieves an objective and quantitative characterization of the evolution law of the interface transition zone, effectively overcoming the shortcomings of traditional methods that rely on subjective experience to divide stages, and improving the accuracy and repeatability of evolution analysis.

[0120] The aforementioned quantitative evaluation method for the evolution of the interfacial transition zone in alkali-activated concrete involves acquiring backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-activated concrete samples. Both backscattered electron images and EDS elemental analysis data include sampling age indicators. Aggregate regions are removed from the backscattered electron images to obtain slurry region images and corresponding aggregate boundary mask data. The slurry region images are then segmented to obtain phase segmentation results. The aggregate boundary mask data is further divided to obtain mask data for each strip region. Based on the phase segmentation results and EDS elemental analysis data, the microstructural features of each strip region mask data are extracted to obtain a partitioned feature dataset. Based on this partitioned feature dataset, the quality of the interfacial transition zone of the alkali-activated concrete sample is quantified to obtain comprehensive quality evaluation parameters. Time-series analysis of these comprehensive quality evaluation parameters yields the characteristic evolution data of the interfacial transition zone. This method achieves an objective and quantitative characterization of the evolution process of the interfacial transition zone in alkali-activated concrete, effectively overcoming the shortcomings of traditional methods that rely on subjective experience and static analysis, and improving the accuracy of quantitative characterization of the evolution stages.

[0121] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0122] Based on the same inventive concept, this application also provides a quantitative evaluation system for the evolution of the transition zone of alkali-activated concrete interface, used to implement the aforementioned quantitative evaluation method for the evolution of the transition zone of alkali-activated concrete interface. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the quantitative evaluation system for the evolution of the transition zone of alkali-activated concrete interface provided below can be found in the limitations of the quantitative evaluation method for the evolution of the transition zone of alkali-activated concrete interface described above, and will not be repeated here.

[0123] In one exemplary embodiment, such as Figure 2 As shown, a quantitative evaluation system 200 for the evolution of the transition zone at the interface of alkali-activated concrete is provided, comprising:

[0124] The data acquisition module 201 is used to acquire backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples; both the backscattered electron images and the EDS elemental analysis data are labeled with the sampling age.

[0125] The slurry segmentation module 202 is used to remove the aggregate region in the backscattered electron image to obtain the slurry region image and the corresponding aggregate boundary mask data.

[0126] Image analysis module 203 is used to perform phase segmentation on the slurry region image to obtain phase segmentation result data;

[0127] The strip analysis module 204 is used to divide the aggregate boundary mask data to obtain the mask data of each strip region; and based on the phase segmentation result data and the energy dispersive spectroscopy elemental analysis data, it extracts the microstructural features of the mask data of each strip region to obtain the partition feature dataset.

[0128] The quality evaluation module 205 is used to quantify the quality of the interface transition zone of alkali-activated concrete samples based on the partition feature dataset, and obtain comprehensive quality evaluation parameters.

[0129] The curve construction module 206 is used to perform time-series analysis on the comprehensive quality evaluation parameters to obtain interface transition zone feature evolution data; the interface transition zone feature evolution data includes interface transition zone feature evolution curves and interface transition zone feature evolution evaluation data.

[0130] Furthermore, the slurry segmentation module can also be used for:

[0131] Based on the adaptive histogram equalization method, the backscattered electron image is contrast-enhanced to obtain an enhanced image;

[0132] Based on a preset filter kernel, the enhanced image is denoised to obtain a denoised image.

[0133] Edge detection is performed on the denoised image to extract the edges of the aggregate region, resulting in an aggregate edge image;

[0134] A morphological closure operation is performed on the aggregate edge image to connect the edge breakpoints and obtain the aggregate boundary mask data;

[0135] The aggregate boundary mask data and the denoised image are subjected to mask operation to remove the aggregate region and obtain the slurry region image.

[0136] Furthermore, both the aggregate boundary mask data and the phase segmentation results data include sampling age indicators. The phase segmentation results data include porous phase mask data. The strip analysis module can also be used for:

[0137] The spatial resolution of the backscattered electron image is obtained, and based on the spatial resolution, the preset strip physical width parameter is converted to obtain the strip pixel width parameter of the backscattered electron image.

[0138] Based on the aggregate boundary mask data, the boundaries of the aggregate region are identified to obtain aggregate boundary data; and starting from the aggregate boundary data, the strip pixel width parameter is used as the single expansion width to expand into the aggregate region one by one to obtain the strip region mask data of each strip region. The number of times the strip region is expanded into the aggregate region is the preset maximum transition zone strip parameter, and the strip region is marked with the sampling age.

[0139] Spatial registration was performed between the elemental analysis data from the energy dispersive spectrometer and the backscattered electron image to obtain the registered elemental distribution data.

[0140] For each strip region, based on the porous phase mask data, the number of pixels of the porous phase within the strip region mask data is counted to obtain the pore width parameter; and based on the pore width parameter and the strip region mask data, the porosity parameter is calculated.

[0141] For each strip region, the area of ​​the connected pore regions is statistically calculated based on the pore width parameter to obtain the pore size distribution parameter;

[0142] For each strip region, the average content of each element is extracted from the registered element distribution data to obtain the element content parameter.

[0143] For each sampling age identifier, the porosity parameters, pore size distribution parameters, and elemental content parameters of the strip regions with the same sampling age identifier are statistically integrated to obtain the partition feature dataset of the sampling age identifier.

[0144] Furthermore, the quality evaluation module can also be used for:

[0145] Based on the porosity and elemental content parameters of the strip region mask data with the same sampling age in the partition feature dataset, the comprehensive difference coefficient of the strip region is calculated.

[0146] Calculate the rate of change of the comprehensive difference coefficient of adjacent strip regions, and determine the strip region corresponding to the comprehensive difference coefficient whose rate of change meets the preset stable change threshold as the starting strip position. Divide the strip region according to the starting strip position to obtain the transition zone strip region set and the slurry matrix zone strip region set.

[0147] The pore size distribution parameters and element content parameters of each strip region in the transition zone strip region set are extracted from the partition feature dataset. The average pore size parameter of the interface transition zone is calculated based on the pore size distribution parameters, and the average element content value of the interface transition zone is calculated based on the element content parameters.

[0148] From the partition feature dataset, extract the pore size distribution parameters and element content parameters of each strip region in the slurry matrix region. Calculate the average pore size parameter of the matrix based on the pore size distribution parameters and the average element content value of the matrix based on the element content parameters.

[0149] For each sampling age, a comprehensive quality evaluation parameter is calculated based on the average pore size parameter of the interface transition zone, the average elemental content value of the interface transition zone, the average pore size parameter of the matrix, and the average elemental content value of the matrix. The expression for the comprehensive quality evaluation parameter is as follows:

[0150]

[0151] in, It is an index for any sampling age identifier. It is the first Comprehensive quality evaluation parameters for each sampling age identifier, It is the first Average pore size parameters of the interface transition zone for each sampling age. It is the first The average pore size parameter of the matrix at each sampling age. It is the first The average elemental content value of the interface transition zone of each sampling age identifier. It is the first Average elemental content of the matrix at each sampling age. It is the aperture weighting coefficient. It is the element weight coefficient.

[0152] Furthermore, the curve building module can also be used for:

[0153] Using the sampling age identifier as the horizontal axis and the comprehensive quality evaluation parameters of the sampling age identifier as the vertical axis, the feature evolution curve of the interface transition zone is constructed.

[0154] The comprehensive quality evaluation parameters are subjected to first-order difference according to the order of the sampling age identifiers corresponding to the comprehensive quality evaluation parameters to obtain the quality change rate parameters of each sampling age identifier.

[0155] Based on a preset set of change cycle mapping rules, the quality change rate parameter is mapped to the change cycle level corresponding to the quality change rate parameter to obtain the change cycle level of the sampling age identifier. Based on the change cycle level of each sampling age identifier and the quality change rate parameter, the interface transition zone feature evolution evaluation data is obtained.

[0156] Based on the feature evolution curves of the interface transition region and the feature evolution evaluation data of the interface transition region, the feature evolution data of the interface transition region are obtained.

[0157] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the quantitative evaluation method for the evolution of the alkali-activated concrete interface transition zone as described above.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0159] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0160] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A quantitative evaluation method for the evolution of the transition zone at the interface of alkali-activated concrete, characterized in that, The method includes: Backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples were acquired; both the backscattered electron images and the EDS elemental analysis data were labeled with the sampling age. The aggregate region in the backscattered electron image is removed to obtain the slurry region image and the aggregate boundary mask data corresponding to the slurry region image; Phase segmentation is performed on the image of the slurry region to obtain phase segmentation result data; The aggregate boundary mask data is divided to obtain mask data for each strip region; and based on the phase segmentation result data and the energy dispersive spectroscopy elemental analysis data, the microstructural features of the mask data for each strip region are extracted to obtain a partitioned feature dataset; Based on the partition feature dataset, the quality of the interface transition zone of the alkali-activated concrete sample is quantified to obtain comprehensive quality evaluation parameters. A time-series analysis was performed on the comprehensive quality evaluation parameters to obtain the feature evolution data of the interface transition zone.

2. The method according to claim 1, characterized in that, The process of removing aggregate regions from the backscattered electron image to obtain a slurry region image and corresponding aggregate boundary mask data includes: Based on the adaptive histogram equalization method, the backscattered electron image is subjected to contrast enhancement processing to obtain an enhanced image; Based on a preset filter kernel, the enhanced image is subjected to noise reduction processing to obtain a denoised image; Edge detection is performed on the denoised image to extract the edges of the aggregate region, resulting in an aggregate edge image; A morphological closure operation is performed on the aggregate edge image to connect the edge breakpoints and obtain the aggregate boundary mask data; The aggregate boundary mask data and the denoised image are subjected to mask operation to remove the aggregate region and obtain the slurry region image.

3. The method according to claim 1, characterized in that, Both the aggregate boundary mask data and the phase segmentation result data carry the sampling age identifier. The phase segmentation result data includes porous phase mask data. The aggregate boundary mask data is divided to obtain mask data for each strip region. Based on the phase segmentation results and the energy dispersive spectroscopy elemental analysis data, the microstructural features of the mask data for each strip region are extracted to obtain a partitioned feature dataset, including: The spatial resolution of the backscattered electron image is obtained, and based on the spatial resolution, the preset strip physical width parameter is converted to obtain the strip pixel width parameter of the backscattered electron image. Based on the aggregate boundary mask data, the boundary of the aggregate region is identified to obtain aggregate boundary data; and starting from the aggregate boundary data, the expansion is carried out towards the aggregate region step by step with the strip pixel width parameter as the single expansion width to obtain the strip region mask data of each strip region, wherein the number of expansions towards the aggregate region is a preset maximum transition zone strip parameter, and the strip region is marked with the sampling age identifier; Spatial registration is performed between the elemental analysis data from the energy dispersive spectrometer and the backscattered electron image to obtain registered elemental distribution data. For each of the strip regions, based on the porous phase mask data, the number of pixels of the porous phase within the strip region mask data is counted to obtain the pore width parameter; and based on the pore width parameter and the strip region mask data, the porosity parameter is calculated. For each of the strip regions, the area of ​​the connected pore regions is statistically calculated based on the pore width parameter to obtain the pore size distribution parameter; For each of the strip regions, the average content of each element is extracted from the registered element distribution data to obtain the element content parameter. For each sampling age identifier, the porosity parameter, pore size distribution parameter, and element content parameter of the strip region with the same sampling age identifier are statistically integrated to obtain the partition feature dataset of the sampling age identifier.

4. The method according to claim 3, characterized in that, Based on the partition feature dataset, the quality of the interface transition zone of the alkali-activated concrete sample is quantified to obtain comprehensive quality evaluation parameters, including: Based on the porosity parameter and the element content parameter of the strip region mask data with the same sampling age identifier in the partition feature dataset, the comprehensive difference coefficient of the strip region is calculated. Calculate the rate of change of the comprehensive difference coefficient of adjacent strip regions, and determine the strip region corresponding to the comprehensive difference coefficient whose rate of change satisfies the preset stable change threshold as the starting strip position. Divide the strip region according to the starting strip position to obtain the transition zone strip region set and the slurry matrix zone strip region set. Extract the pore size distribution parameters and element content parameters of each strip region in the transition zone strip region set from the partition feature dataset, and calculate the average pore size parameter of the interface transition zone based on the pore size distribution parameters, and calculate the average element content value of the interface transition zone based on the element content parameters. From the partition feature dataset, extract the pore size distribution parameters and element content parameters of each strip region in the slurry matrix region, calculate the average pore size parameter of the matrix based on the pore size distribution parameters, and calculate the average element content value of the matrix based on the element content parameters. For each sampling age identifier, the comprehensive quality evaluation parameter is calculated based on the average pore size parameter of the interface transition zone, the average elemental content value of the interface transition zone, the average pore size parameter of the matrix, and the average elemental content value of the matrix. The expression for the comprehensive quality evaluation parameter is: in, It is an index for any sampling age identifier. It is the first Comprehensive quality evaluation parameters for each sampling age identifier, It is the first Average pore size parameters of the interface transition zone for each sampling age. It is the first The average pore size parameter of the matrix at each sampling age. It is the first The average elemental content value of the interface transition zone of each sampling age identifier. It is the first Average elemental content of the matrix at each sampling age. It is the aperture weighting coefficient. It is the element weight coefficient.

5. The method according to claim 4, characterized in that, The time-series analysis of the comprehensive quality evaluation parameters yields interface transition zone feature evolution data, including: Using the sampling age identifier as the horizontal axis and the comprehensive quality evaluation parameter of the sampling age identifier as the vertical axis, an interface transition zone feature evolution curve is constructed. The comprehensive quality evaluation parameters are subjected to first-order difference according to the order of the sampling age identifiers corresponding to the comprehensive quality evaluation parameters to obtain the quality change rate parameters of each sampling age identifier; Based on a preset set of change cycle mapping rules, the quality change rate parameter is mapped to the change cycle level corresponding to the quality change rate parameter to obtain the change cycle level of the sampling age identifier, and based on the change cycle level of each sampling age identifier and the quality change rate parameter, interface transition zone feature evolution evaluation data is obtained. Based on the interface transition region feature evolution curve and the interface transition region feature evolution evaluation data, the interface transition region feature evolution data is obtained.

6. A quantitative evaluation system for the evolution of the transition zone at the interface of alkali-activated concrete, characterized in that, The system includes: The data acquisition module is used to acquire backscattered electron images and energy dispersive spectroscopy (EDS) elemental analysis data of alkali-excited concrete samples; both the backscattered electron images and the EDS elemental analysis data are marked with the sampling age. The slurry segmentation module is used to remove the aggregate region from the backscattered electron image to obtain the slurry region image and the aggregate boundary mask data corresponding to the slurry region image. The image analysis module is used to perform phase segmentation on the image of the slurry region to obtain phase segmentation result data; The strip analysis module is used to divide the aggregate boundary mask data to obtain mask data for each strip region; and based on the phase segmentation result data and the energy dispersive spectrometer elemental analysis data, to extract the microstructural features of the mask data for each strip region to obtain a partition feature dataset. The quality evaluation module is used to quantify the quality of the interface transition zone of the alkali-activated concrete sample based on the partition feature dataset, and obtain comprehensive quality evaluation parameters. The curve construction module is used to perform time-series analysis on the comprehensive quality evaluation parameters to obtain interface transition zone feature evolution data; the interface transition zone feature evolution data includes interface transition zone feature evolution curves and interface transition zone feature evolution evaluation data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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