Method for characterizing the homogeneity of different angle sections of manufactured sand concrete

Abstract

The application discloses a method for characterizing the homogeneity of different angle sections of machine-made sand concrete, and the method comprises the following steps: obtaining slice images of different angle sections of a machine-made sand concrete sample, equally dividing the slice images into two regions along the central axis, pre-processing the slice images by using an image pre-processing platform created by MATLAB software, further obtaining the maximum-to-minimum axial distance ratio of coarse aggregates, the number of coarse aggregate particles, the total area of the coarse aggregate region, and the total area of the fine aggregate and paste regions in each region of the slice images, and finally comprehensively characterizing the homogeneity of different angle sections of the machine-made sand concrete according to the above indexes. The method for characterizing the homogeneity of different angle sections of machine-made sand concrete provided by the application has high accuracy, comprehensively considers angles, is convenient to operate, and provides a reference for the application of material homogeneity characterization in the field of concrete.

Description

Technical Field

[0001] This invention relates to the field of concrete technology, and in particular to a method for characterizing the homogeneity of manufactured sand concrete at different angles. Background Technology

[0002] The strength and durability of concrete structures are largely influenced by the homogeneity of the concrete. In most engineering applications, workers generally assess the homogeneity of the concrete by observing whether the aggregate is roughly distributed throughout the concrete cross-section or the ratio of aggregate area to grout area. For the same batch of concrete samples, the shape, particle size, and distribution of the aggregate inside the sample can vary significantly under different vibration times and molding processes. This is especially true for different concrete samples. Regular aggregates and grout can evenly fill the gaps between each other, while irregular aggregates may leave gaps around which fine aggregates and grout cannot effectively fill. If some areas have a predominance of regular coarse aggregates, there will be more regular gaps allowing sufficient small particles and grout to fill evenly. However, if some areas have a predominance of irregular coarse aggregates, coupled with the influence of the mold shape, there will be more irregular gaps preventing other particles and grout from effectively filling. Even though visually, various aggregates may appear to be roughly distributed throughout the cross-section, and the aggregate and paste areas may be approximately the same, the material around irregular aggregates cannot be evenly filled, and the amount of coarse aggregate varies in different areas. This will affect the compactness, appearance, strength, and durability of the specimen. Especially when using manufactured sand to prepare concrete, the poor particle gradation of manufactured sand, with its low amount of medium-sized aggregates, makes uniform filling between aggregates difficult. Therefore, relying solely on observing the distribution and area ratio of aggregates to predict the homogeneity of manufactured sand concrete will result in significant errors.

[0003] Some techniques suggest cutting along the middle cross-section of the specimen to obtain slice images, but this approach is not comprehensive. During mold assembly, due to differences in vibration techniques and time, aggregate in some specimens may settle, resulting in a predominance of paste and fine aggregate in the upper area of ​​the specimen. Therefore, judging homogeneity solely based on the paste distribution in the middle cross-section is highly unreliable.

[0004] Based on the fact that traditional methods for characterizing the homogeneity of concrete are subject to randomness, have low accuracy, and lack a set of efficient calculation methods, this invention proposes a method for evaluating the homogeneity of manufactured sand concrete by comprehensively considering the differences between multiple angle sections, the maximum and minimum axial distance ratio of coarse aggregate, the regional coarse aggregate quantity ratio, and the regional aggregate area ratio. Summary of the Invention

[0005] The purpose of this invention is to address the limitations, low accuracy, and low computational efficiency of traditional methods for characterizing the homogeneity of manufactured sand concrete. To this end, based on MATLAB software, this invention provides a method for characterizing the homogeneity of manufactured sand concrete at different angles, which is comprehensive in its considerations, simple in its operation, and highly efficient in its computation.

[0006] The objective of this invention is achieved through the following technical steps:

[0007] Select manufactured sand concrete samples with the same mix proportions, consistent production conditions, and cross-sectional dimensions of Φ50×100mm. Divide them into three groups according to the angle of the cross-section to be cut and mark them. Using the horizontal axis of the sample as the starting axis, cut the samples at angles of 0 degrees, 45 degrees, and 90 degrees to the starting axis using mechanical cutting. Adjust the camera lens to an angle slightly parallel to the cut surface to take pictures and obtain the slice images to be processed. During the shooting process, ensure that the shooting angle, distance, and lighting of each cut surface remain approximately unchanged.

[0008] Cut the image into two regions along the central axis of the slice image and mark the regions accordingly.

[0009] The cut images were imported into an image preprocessing platform built using MATLAB for preprocessing. This platform allows for operations such as grayscale conversion, image binarization, filtering, hole filling, particle removal, and edge recognition and extraction. Each step allows for exporting and saving the corresponding image. During preprocessing, the original image is first converted to grayscale and subjected to multi-dimensional filtering. Next, the edges of the filtered image elements are identified, and hole filling within particles is performed as needed. Finally, negligible small particles are removed, and the outer edges of coarse aggregates are extracted.

[0010] Based on the preprocessed image, the coarse aggregates in the two regions are numbered and detection bounding boxes are drawn. The detection bounding box is the minimum bounding rectangle of each target aggregate. The aspect ratio of the minimum bounding rectangle is calculated as the maximum and minimum axial distance ratio of the coarse aggregate. The number of coarse aggregates in each region is obtained from the image. The total area of ​​the slice is obtained by extracting the outer edge of the cross section. The total area of ​​the region is obtained by extracting the edges of all coarse aggregates. The difference between the two is taken as the total area of ​​the fine aggregate and slurry region of the corresponding region.

[0011] Furthermore, the ratio of the maximum to minimum axial spacing of the coarse aggregate is D = d1 : d2, where d1 and d2 are the maximum and minimum axial spacings, respectively, which are also the length and width of the minimum bounding rectangle; the quantity of coarse aggregate in each region is denoted as m. i Where i is 1 or 2; the total area S of the fine aggregate and slurry regions i2 =S i -S i1 S i S refers to the total area of ​​slice region i. i1This refers to the total area occupied by coarse aggregate.

[0012] Furthermore, according to m i The ratio of coarse aggregate quantity between the two regions is obtained, i.e., n = m1 : m2.

[0013] Furthermore, according to S i1 S i2 The difference in the ratio of bone smegma area between the two regions is obtained, namely:

[0014] The homogeneity of a single cross section of manufactured sand concrete is comprehensively evaluated based on three parameters: the axis-to-spacing ratio D, the ratio of coarse aggregate quantity n in a region, and the difference ΔS between the ratios of aggregate area in a region and the area ratio of aggregate area in a region.

[0015] Furthermore, draw a wheelbase ratio chart, with the horizontal axis representing the maximum wheelbase interval and the vertical axis representing the minimum wheelbase interval. Calculate the difference between the wheelbase ratio and the ratio 1; calculate the difference between the coarse aggregate quantity ratio n and the ratio 1; and calculate the difference between the difference ΔS of the aggregate area ratio and the value 0.

[0016] The same parameter acquisition and analysis were performed on all three angle sections to comprehensively characterize the homogeneity of manufactured sand concrete at different angle sections.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] 1. This invention selects manufactured sand concrete samples prepared under the same conditions, reducing the influence of other variables. It also considers three different angles of cross-section cutting. The analysis of the results from the three angles can more comprehensively determine the homogeneity of the specimen and make it easier to consider what factors affect the homogeneity of the specimen.

[0019] 2. The software used in this invention is mainly MATLAB. The image preprocessing platform created based on MATLAB integrates the image processing techniques required for sliced ​​images, thereby improving image processing efficiency to a certain extent.

[0020] 3. Based on the consideration of the amount of coarse aggregate and the area ratio of aggregate, this invention analyzes the ratio of the maximum and minimum axial distance of particles to account for the influence of irregular particles on the homogeneity of the material. Combining the above parameters, a high-precision calculation model is proposed to characterize the homogeneity of the manufactured sand concrete at different angles. Attached Figure Description

[0021] Figure 1 This is a cross-sectional slice image with an angle of 0 degrees to the horizontal axis.

[0022] Figure 2 Images of regions 1-1 and 1-2.

[0023] Figure 3 This is the interface for the image preprocessing platform.

[0024] Figure 4 This is a diagram showing the axis distance ratio at 0 degrees. Detailed Implementation

[0025] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be described in more detail and completely below with reference to the following examples and accompanying drawings. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the claims.

[0026] Example 1: A method for characterizing the homogeneity of manufactured sand concrete at different angles, comprising the following steps:

[0027] 1. Select three manufactured sand concrete samples with a cross-sectional size of Φ50×100mm. The sample mix ratio and production conditions are consistent. The samples are grouped and numbered according to the angle to be cut. Fix the samples in the mechanical cutting machine and cut along the corresponding angle while spraying water. Select the horizontal axis slightly in the middle of sample one as the starting axis. First, cut sample one along this axis to obtain a 0-degree cross-section. Then, using this axis as a reference, cut the other two samples along the cross-sections at angles of 45 degrees and 90 degrees with the starting axis to obtain 45-degree and 90-degree cross-sections. Adjust the electronic camera to an angle parallel to each cross-section and take pictures to obtain three slice images to be processed. During the shooting process, ensure that the shooting angle, distance and light of each cross-section remain approximately unchanged.

[0028] 2. Using MATLAB software, three images were divided into equal parts. Finally, six images were imported into the preprocessing platform, which means there are six regions for analysis. The 0-degree sections are labeled as 1-1 and 1-2, the 45-degree sections as 2-1 and 2-2, and the 90-degree sections as 3-1 and 3-2.

[0029] 3. Import the original image into the preprocessing platform for preprocessing. First, identify the image and click "Grayscale Assignment" to adjust the grayscale of each pixel. Then, click "Binarization" to set the image to black and white. This step requires checking if the aggregate distribution can be preliminarily seen in the black and white image. If the image quality is unsatisfactory, a new slice image needs to be captured. Next, click "Filter and Smooth" to remove noise from the image. If there are gaps inside the aggregate in the image, click "Pore Filling" to eliminate the influence of small holes; this step is not necessary in this example. Finally, use 17.7mm... 2 The corresponding pixel values ​​are input into the data box after "Screen Particles" to screen out fine aggregate and retain coarse aggregate particles; finally, click "Edge Extraction" to extract the outer edge of the coarse aggregate needed for analysis. The preprocessing platform is shown in the attached figure. Figure 3 As shown.

[0030] 4. For the six regions, draw the minimum bounding rectangle for the outer edge of each extracted aggregate, and calculate the aspect ratio of each rectangle as the maximum and minimum axial distance ratio D of the coarse aggregate. Based on the axial distance ratio D, select the 0-degree section regions (1-1, 1-2) and draw the axial distance ratio diagram as shown in the attached figure. Figure 4 and Figure 4 Draw a y=x line graph to show the difference between each axis distance ratio and the ratio 1; obtain the quantity of coarse aggregate m1 and m2 for each region; obtain the total area S1 and S2 for each region, and the area of ​​coarse aggregate S. 11 S 21 Fine aggregate and slurry area S 12 S 22 Record the data in Table 1 below:

[0031] Table 1

[0032]

[0033]

[0034] 5. Based on the data in Table 1 above, the difference ΔS between the coarse aggregate quantity ratio n and the bone marrow area ratio in the two regions corresponding to each cross-sectional image is shown in Table 2 below:

[0035] Table 2

[0036] 0-degree cross-section image 45-degree cross-sectional image 90-degree cross-sectional image Quantity ratio n 1.1 0.9 0.8 The difference in area ratio ΔS 0.12 0.11 0.09

[0037] 6. Results Analysis: The axial spacing ratio diagram shows that most of the aggregate in the cross-section is irregular particles, with an average axial spacing ratio of 1.42. Although the aggregate quantity in the two regions of the 0-degree cross-section is similar, there is still a significant difference in the aggregate-to-paste area ratio. In the 45-degree cross-section, although region 2-2 has the most aggregate, its coarse aggregate area is smaller than that of region 2-1, indicating that small-diameter particles are mostly located in region 2-2. In the 90-degree cross-section, considering n and ΔS, small-particle aggregates are mostly located at the bottom of the sample, while fine aggregates and paste are more abundant at the top. Overall, the distribution of large-diameter and small-diameter aggregates is uneven from top to bottom, even within the same cross-section. Based on the analysis data, the homogeneity of the concrete should be optimized primarily by adjusting the aggregate gradation and the vibration compaction method.

Claims

1. A method for characterizing the homogeneity of manufactured sand concrete at different angles, characterized in that, The process includes the following steps: First, select three manufactured sand concrete samples and number them. Then, using the horizontal axis of the sample as the starting axis, mechanically cut the three samples at angles of 0 degrees, 45 degrees, and 90 degrees to the starting axis to obtain three sample cross-sections at different angles. Finally, use a camera to take parallel pictures of the three cross-sections under the same conditions to obtain the required slice images. Second, divide the slice image into two regions along the central axis. Use MATLAB software to build an image preprocessing platform and preprocess the equally divided slice images based on this platform. The third step involves using MATLAB to obtain the necessary parameters for the two regions of the preprocessed image, namely, the ratio of the maximum to minimum axial distance of the coarse aggregate, the number of coarse aggregate particles, the total area occupied by the coarse aggregate, and the total area occupied by the remaining fine aggregate and slurry. Specifically, in MATLAB, the coarse aggregate is numbered, and a detection block diagram is drawn to obtain the maximum axial distance d1 and minimum axial distance d2 of the coarse aggregate; the number of coarse aggregates m in the corresponding region i is obtained. i Where i takes the values ​​1 and 2, representing two regions after the slice image is cut; the outer edges of each region i are extracted, and the total area of ​​the region enclosed by the outer edges is calculated and denoted as S. i Calculate the area enclosed by the outer edge of the coarse aggregate, and consider it as the total area S occupied by the coarse aggregate. i1 Excluding the area enclosed by the outer edge of the coarse aggregate, the total area occupied by the remaining fine aggregate and slurry is denoted as S. i2 S i2 =S i -S i1 The fourth step involves performing the same image and data processing on all three angle sections. Combining the necessary parameters in different slice regions, the differences in the corresponding axial distance ratio, coarse aggregate quantity ratio, and slurry area ratio are further obtained. Specifically: combining the necessary parameters of the two regions, the maximum and minimum axial distance ratio D is obtained based on the maximum and minimum axial distance of the coarse aggregate, i.e., D = d1 / d2. The coarse aggregate quantity ratio n is obtained based on the quantities m1 and m2 of the coarse aggregate in the two regions, i.e., n = m1 / m2. The difference in slurry area ratio is denoted as ΔS, and let... Where S 11 S 21 S represents the total area occupied by coarse aggregate in the two regions. 12 S 22 The total area occupied by the remaining fine aggregate and paste in the two regions is represented; finally, the homogeneity of the manufactured sand concrete at different angle sections is characterized by the difference between the axial distance ratio D, the coarse aggregate quantity ratio n, and the aggregate-paste area ratio ΔS.

2. The method for characterizing the homogeneity of manufactured sand concrete at different angles according to claim 1, characterized in that: The selected manufactured sand concrete samples were Φ50×100mm in size, with consistent mix proportions and production conditions.

3. The method for characterizing the homogeneity of manufactured sand concrete at different angles according to claim 1, characterized in that: The sliced ​​image was cut into two regions along its central axis using MATLAB software.

4. The method for characterizing the homogeneity of manufactured sand concrete at different angles according to claim 1, characterized in that: The image preprocessing platform includes functions such as image display, grayscale conversion, binarization, filtering and smoothing adjustment, hole filling, particle removal, and edge recognition and extraction. During preprocessing, the original image is first converted into a grayscale image and multidimensional filtering is performed to remove noise. Then, edge recognition is performed on the filtered image elements. Finally, hole filling is performed according to the image conditions, and negligible small particles are removed and the outer edges of coarse aggregates are extracted.

Images