Concrete strength compression resistance detection device and method
By analyzing the grayscale values, texture differences, and edge information of grayscale images of concrete surfaces, a smoothness factor is extracted. Combined with ultrasonic rebound and core drilling methods, the problem of the impact of concrete surface unevenness on detection accuracy is solved, and more accurate concrete strength and compressive strength testing is achieved.
Patent Information
- Application Number
- CN202511555533.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-29
- Publication Date
- 2025-12-02
AI Technical Summary
The unevenness of concrete surfaces affects the accuracy of ultrasonic rebound test results. Existing technology is difficult to accurately evaluate unevenness differences of different degrees and locations, resulting in inaccurate concrete strength and compressive strength test results.
By analyzing the grayscale values, texture differences, and edge information of the grayscale image of the concrete surface, the first, second, and third smoothness factors are extracted. Combined with the discrimination influence of the smoothness category, the smoothness category of the concrete to be tested is determined. The concrete strength is obtained by using the ultrasonic rebound method and the core drilling method, and the results are corrected.
It improves the accuracy of concrete strength and compressive strength testing, and enhances the precision of test results by evaluating the differences in concrete surface smoothness.
Smart Images
Figure CN121049385A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of concrete strength compressive strength testing technology, specifically to a concrete strength compressive strength testing device and method. Background Technology
[0002] The compressive strength of concrete directly determines the load-bearing capacity of buildings and structures. To ensure the load-bearing capacity of concrete, assess structural stability, and guarantee its compressive strength, compressive strength testing is necessary. The ultrasonic rebound method does not require destructive sampling of the concrete structure, therefore, it is generally used for compressive strength testing of concrete. However, differences in the smoothness of the concrete surface can affect the test results of the ultrasonic rebound method. Specifically, when the concrete surface is uneven, it affects the coupling of the ultrasonic transmitter, resulting in a lower sound velocity and a lower rebound value.
[0003] Generally, the difference in contrast between images of different concrete surfaces can be used to evaluate the smoothness difference of the concrete surface, and then the results of the compressive strength test can be adjusted accordingly. However, relying solely on the difference in image contrast is insufficient to fully evaluate the smoothness difference at different degrees and locations, and the obtained smoothness difference is often inaccurate, thus affecting the accuracy of the concrete strength compressive strength test results. Summary of the Invention
[0004] To address the aforementioned technical problems, this application provides a device and method for testing the compressive strength of concrete, the specific technical solution of which is as follows: In a first aspect, one embodiment of this application provides a method for testing the compressive strength of concrete, the method comprising the following steps: Select concrete specimens of different flatness categories, obtain the concrete strength of the concrete specimens and the concrete to be tested, collect concrete surface images of the concrete to be tested and each concrete specimen, and obtain the concrete surface grayscale image corresponding to the concrete surface image. Based on the differences in gray values between pixel blocks at different locations within the grayscale image of the concrete surface, as well as the differences in gray values between adjacent pixels, a first smoothness factor for the grayscale image of the concrete surface is determined. Based on the texture differences between adjacent pixels within the grayscale image of the concrete surface, a second smoothness factor for the grayscale image of the concrete surface is determined. Based on the gradient differences between pixels on the edge and adjacent pixels within the grayscale image of the concrete surface, a third smoothness factor for the grayscale image of the concrete surface is determined. The first smoothness factor, the second smoothness factor, and the third smoothness factor are all recorded as features of smoothness. Any feature of smoothness is denoted as the target smoothness factor. Based on the difference between the target smoothness factors of the grayscale images of the concrete surfaces of concrete specimens of the same smoothness category, the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor is determined. Combining the difference between the target smoothness factors of the grayscale images of the concrete surfaces of the concrete specimens and the concrete to be tested, the probability of the smoothness category of the concrete to be tested and the concrete specimens of each smoothness category is determined. Based on the probability of the smoothness category, the smoothness category of the concrete to be tested is determined. The results of the concrete strength compressive strength test are obtained based on the concrete strength of all concrete specimens of the same flatness category as the concrete to be tested, and the concrete strength of the concrete to be tested.
[0005] Furthermore, the method for obtaining the first smoothness factor is as follows: The grayscale image of the concrete surface is divided into pixel blocks with a side length of a third preset parameter. The variance of the grayscale values of all pixels in the pixel block is denoted as the first variance of the grayscale of the pixel block. The average value of the first variance of the grayscale of all pixel blocks in the same grayscale image of the concrete surface is denoted as the first average value of the same grayscale image of the concrete surface. Any pixel in the grayscale image of the concrete surface is designated as the target pixel. The variance of the grayscale values of all pixels in the eight neighborhoods of the target pixel and the target pixel is designated as the second variance of the grayscale of the pixel block. The average of the second variances of the grayscale of all pixels in the same grayscale image of the concrete surface is designated as the second average value of the same grayscale image of the concrete surface. The positive correlation between the first and second average values of the grayscale image of the concrete surface is denoted as the first smoothness factor of the grayscale image of the concrete surface.
[0006] Furthermore, the method for obtaining the second smoothness factor is as follows: The average of the absolute values of the differences between the target pixel and all pixels contained in its eight neighborhoods is denoted as the local texture difference of the target pixel. The normalized value of the mean of the local texture differences of all pixels in the same grayscale image of a concrete surface is denoted as the second smoothness factor of the same grayscale image of a concrete surface.
[0007] Furthermore, the method for obtaining the third smoothness factor is as follows: Obtain the edges and gradient of each pixel in a grayscale image of a concrete surface; For any pixel on any edge in a grayscale image of a concrete surface, the average of the absolute values of the differences between the pixel and all pixels in its eight neighborhoods is denoted as the local gradient difference of the pixel; the normalized value of the average of the local gradient differences of all pixels on all edges in the same grayscale image of a concrete surface is denoted as the third smoothness factor of the same grayscale image of a concrete surface.
[0008] Furthermore, the method for obtaining the discriminative influence degree of the same smoothness category corresponding to the target smoothness factor is as follows: The variance of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is denoted as the third variance of the same smoothness category. The average value of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is recorded as the first average value of the same smoothness category corresponding to the target smoothness factor. The sum of the first average values of all smoothness categories corresponding to the target smoothness factor is recorded as the first sum of the target smoothness factor. The ratio of the first cumulative sum of the target smoothness factors to the third difference of the same smoothness category is denoted as the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor.
[0009] Furthermore, the method for obtaining the probability of the smoothness category of the concrete to be tested and the concrete specimen for each smoothness category is as follows: The absolute value of the difference between the target smoothness factor of the grayscale images of the concrete surface of the concrete specimen and the concrete to be tested is denoted as the difference between the target smoothness factor of the concrete specimen and the concrete to be tested. The product of the difference between the target smoothness factor of the concrete specimen and the concrete to be tested and the discrimination influence degree of the smoothness category of the concrete specimen corresponding to the target smoothness factor is denoted as the first product of the difference between the target smoothness factor of the concrete to be tested and the concrete specimen. The sum of the first products of the differences in target smoothness factors of all concrete and concrete specimens under the same smoothness category is recorded as the second sum of concrete and concrete specimens under the same smoothness category. The negative correlation processing result of the sum of the second sums of the first, second, and third smoothness factors corresponding to the same smoothness factors of concrete and concrete specimens under the same smoothness category is recorded as the probability of the smoothness category of concrete and concrete specimens under the same smoothness category.
[0010] Furthermore, the specific method for determining the smoothness category of the concrete to be tested based on the probability of smoothness category includes: The flatness category corresponding to the maximum value among the possible flatness categories of the concrete to be tested and the concrete specimen is denoted as the flatness category of the concrete to be tested.
[0011] Furthermore, the specific method for obtaining the concrete strength compressive strength test result based on the concrete strength of all concrete specimens of the same smoothness category as the concrete to be tested, and the concrete strength of the concrete to be tested, includes: All concrete specimens with the same flatness category as the concrete to be tested are designated as flatness reference concrete specimens. The influence of the flatness reference concrete specimens on the concrete strength is determined based on the concrete strength of the flatness reference concrete specimens. The sum of the influence of the flatness reference concrete specimen on the concrete strength and the number 1 is recorded as the correction degree. The product of the correction degree and the concrete strength of the concrete to be tested is recorded as the corrected concrete strength of the concrete to be tested. The corrected concrete strength of the concrete to be tested is the result of the concrete strength compressive strength test of the concrete to be tested.
[0012] Furthermore, the method for obtaining the influence of the flatness reference concrete specimen on concrete strength is as follows: The absolute value of the difference in concrete strength between the reference concrete specimens for smoothness obtained by ultrasonic rebound and core drilling methods is denoted as the concrete strength difference of the reference concrete specimen for smoothness. The ratio of the concrete strength difference of the reference concrete specimen for smoothness to the concrete strength of the reference concrete specimen for smoothness obtained by ultrasonic rebound is denoted as the relative difference in concrete strength of the reference concrete specimen for smoothness. The mean of the relative differences in concrete strength among all flatness reference concrete specimens is denoted as the influence degree of concrete strength on flatness reference concrete specimens.
[0013] Secondly, another embodiment of this application provides a concrete strength compressive strength testing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described concrete strength compressive strength testing method.
[0014] The embodiments of this application have at least the following beneficial effects: This application extracts the smoothness features of the concrete to be tested and the concrete specimen based on the contrast difference, texture smoothness difference, and edge information of the grayscale images of the concrete surface. It obtains a first smoothness factor, a second smoothness factor, and a third smoothness factor from the grayscale images of the concrete surface. Then, based on the difference in target smoothness factors between the grayscale images of the concrete surfaces of concrete specimens of the same smoothness category, it evaluates the influence of the target smoothness factor on the smoothness category. Finally, combining the difference in target smoothness factors between the grayscale images of the concrete surfaces of the concrete specimen and the concrete to be tested, it determines the smoothness category for each type of concrete. The probability of flatness category of the concrete to be tested and the concrete specimen is considered. Based on the probability of flatness category, the flatness category of the concrete to be tested is determined, that is, by comparing the similarity of flatness between the concrete to be tested and the concrete specimen. Finally, based on the concrete strength of all concrete specimens with the same flatness category as the concrete to be tested, and the concrete strength of the concrete to be tested, the concrete strength compressive strength test results are obtained. This solves the problem of inaccurate evaluation of concrete surface flatness differences, which leads to insufficient accuracy of concrete strength compressive strength test results, and improves the accuracy of concrete strength compressive strength test. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.
[0016] Figure 1 A flowchart illustrating the steps of a concrete strength compressive strength testing method according to one embodiment of this application; Figure 2 This is a flowchart illustrating the process of obtaining the first flatness factor according to an embodiment of this application. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a concrete strength compressive strength testing device and method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the concrete strength compressive strength testing device and method provided in this application.
[0020] Please see Figure 1 The diagram illustrates a flowchart of a concrete strength compressive strength testing method according to an embodiment of this application. The method includes the following steps: Step S001: Select concrete specimens of different flatness categories, obtain the concrete strength of the concrete specimens and the concrete to be tested, collect concrete surface images of the concrete to be tested and each concrete specimen, and obtain the concrete surface grayscale image corresponding to the concrete surface image.
[0021] The ultrasonic rebound method is used to obtain the concrete strength of the concrete to be tested. The concrete to be tested is the concrete for which compressive strength testing is required.
[0022] Select Ten concrete specimens were artificially subjected to varying degrees of abrasion treatment, and the abrasion levels were categorized into ten different smoothness categories, arranged from least to most severe. Each smoothness category corresponds to... Concrete specimens subjected to different degrees of wear were tested. The concrete strength of each specimen was obtained using the ultrasonic rebound method and the core drilling method.
[0023] in, and These represent the first preset parameter and the second preset parameter, respectively. In this embodiment, the values of the first preset parameter and the second preset parameter are 200 and 20, respectively. The settings for the degree of wear treatment and the flatness category are set by experts in the field.
[0024] It is understandable that using the ultrasonic rebound method and the core drilling method to obtain two concrete strengths for each concrete specimen can result in differences between the two concrete strengths obtained by the ultrasonic rebound method and the core drilling method for the same concrete specimen.
[0025] Industrial cameras were used to capture images of the concrete to be tested and the concrete surface of each concrete specimen.
[0026] Each concrete surface image was denoised and converted to grayscale to obtain grayscale images of the concrete to be tested and each concrete specimen.
[0027] In this embodiment, Gaussian filtering is used to denoise each concrete surface image. Gaussian filtering is a well-known technique and will not be described in detail here. As other embodiments, based on achieving the goal of image denoising, implementers may use other methods in the prior art, such as median filtering, for image denoising. This application does not impose any special restrictions.
[0028] At this point, grayscale images of the concrete surface of the concrete to be tested and each concrete specimen have been obtained.
[0029] Step S002: Based on the differences in gray values between pixel blocks at different locations within the grayscale image of the concrete surface, and the differences in gray values between adjacent pixels, determine the first smoothness factor of the grayscale image of the concrete surface. Based on the texture differences between adjacent pixels within the grayscale image of the concrete surface, determine the second smoothness factor of the grayscale image of the concrete surface. Based on the gradient differences between pixels on the edge and adjacent pixels within the grayscale image of the concrete surface, determine the third smoothness factor of the grayscale image of the concrete surface. The first smoothness factor, the second smoothness factor, and the third smoothness factor are all recorded as features of smoothness.
[0030] Since the results of ultrasonic rebound testing are easily affected by the flatness of the concrete surface, in order to better avoid the influence of flatness, this application determines the flatness category of the concrete to be tested by comparing the similarity of flatness between the concrete to be tested and the concrete specimen. According to the flatness category to which the concrete specimen belongs, the influence of each flatness category on the concrete strength compressive strength test results is determined, and the influence of the surface flatness of the concrete to be tested on the concrete strength compressive strength test results is analyzed. Then, the concrete strength compressive strength test results are corrected based on the influence.
[0031] First, the characteristics of the flatness of the concrete and concrete specimens to be tested are extracted.
[0032] The first smoothness factor of the grayscale image of the concrete surface is determined based on the differences in grayscale values between pixel blocks at different locations within the grayscale image of the concrete surface, as well as the differences in grayscale values between adjacent pixels.
[0033] The grayscale image of the concrete surface is segmented into parts with side lengths of... For a pixel block, the variance of the gray values of all pixels within the pixel block is denoted as the first variance of the gray value of the pixel block. The average of the first variances of the gray values of all pixel blocks within the same grayscale image of the concrete surface is denoted as the first average value of the same grayscale image of the concrete surface.
[0034] in, This represents the third preset parameter, which is set to 20 in this embodiment.
[0035] Let any pixel in the grayscale image of the concrete surface be the target pixel. Let the variance of the grayscale values of all pixels in the eight neighborhoods of the target pixel and the target pixel be the second variance of the grayscale of the pixel block.
[0036] The same method can be used to obtain the second variance of the grayscale value of each pixel in the grayscale image of the concrete surface.
[0037] The average of the second variance of the gray levels of all pixels in the same grayscale image of the same concrete surface is denoted as the second average value of the same grayscale image of the same concrete surface.
[0038] The positive correlation between the first and second average values of the grayscale image of the concrete surface is denoted as the first smoothness factor of the grayscale image of the concrete surface.
[0039] The flowchart for obtaining the first flatness factor is as follows: Figure 2 As shown.
[0040] It is understood that a positive correlation is applied to the first and second average values of the grayscale image of the concrete surface, ensuring that the first and second average values of the grayscale image of the concrete surface are positively correlated with the first smoothness factor of the grayscale image of the concrete surface. It is understood that the positive correlation in this application refers to the relationship between the independent and dependent variables, where the independent variables are the first and second average values of the grayscale image of the concrete surface, and the dependent variable is the first smoothness factor of the grayscale image of the concrete surface. A positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive or multiplicative relationship.
[0041] Preferably, as an embodiment of this application, the normalized value of the product of the first average value and the second average value of the grayscale image of the concrete surface is denoted as the first smoothness factor of the grayscale image of the concrete surface.
[0042] It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In practical applications, implementers may use other methods of existing technology, such as the maximum-minimum normalization method or the sigmoid function, to calculate the normalized value, and no limitation is made here.
[0043] The smaller the difference in gray values between pixels at different locations within a grayscale image of a concrete surface, and the smaller the difference in gray values between adjacent pixels, the smaller the local contrast within the grayscale image of the concrete surface, and the smoother the surface of the concrete or concrete specimen corresponding to the grayscale image of the concrete surface. In this case, the smaller the first smoothness factor of the grayscale image of the concrete surface.
[0044] The smaller the difference in surface texture between the concrete to be tested and the concrete specimen, the smoother the surface of the concrete to be tested and the concrete specimen. Based on the smoothness of the texture in the grayscale image of the concrete surface of the concrete to be tested and the concrete specimen, the flatness features of the concrete to be tested and the concrete specimen are further extracted.
[0045] The second smoothness factor of the grayscale image of the concrete surface is determined based on the texture differences between adjacent pixels within the grayscale image of the concrete surface.
[0046] The LBP values of all pixels in the grayscale image of the concrete surface are obtained. The average of the absolute values of the differences between the target pixel and the LBP values of all pixels contained in the eight neighborhood of the target pixel is recorded as the local texture difference of the target pixel. The normalized value of the mean of the local texture differences of all pixels in the same grayscale image of the concrete surface is recorded as the second smoothness factor of the same grayscale image of the concrete surface.
[0047] The calculation of the LBP value of a pixel is a well-known technique and will not be elaborated further.
[0048] When the difference in LBP values between adjacent pixels in a grayscale image of a concrete surface is greater, the difference in surface texture of the concrete or concrete specimen to be tested corresponding to the grayscale image of the concrete surface is greater, and the surface of the concrete or concrete specimen to be tested corresponding to the grayscale image of the concrete surface is more uneven. At this time, the second smoothness factor of the grayscale image of the concrete surface is larger.
[0049] Unevenness on the concrete surface may be accompanied by fine cracks or other types of defects, which are generally represented as linear cracks in the image. Therefore, based on the edge information within the grayscale image of the concrete surface, the smoothness features of the concrete to be tested and the concrete specimen can be further extracted.
[0050] The Canny edge detection algorithm is used to process the grayscale image of the concrete surface to obtain the edges in the image, excluding those with a number of pixels less than or equal to [a certain value]. All edges are minimized to avoid excessively small edge texture information interfering with the analysis of fine cracks or other types of defects.
[0051] in, This represents the fourth preset parameter, and in this embodiment, the value of the fourth preset parameter is 30.
[0052] Calculate the gradient of each pixel in the grayscale image of the concrete surface. For any pixel on any edge in the grayscale image of the concrete surface, the average of the absolute values of the differences between the pixel and the gradients of all pixels in its eight neighborhoods is recorded as the local gradient difference of the pixel. The normalized value of the average of the local gradient differences of all pixels on all edges in the same grayscale image of the concrete surface is recorded as the third smoothness factor of the same grayscale image of the concrete surface.
[0053] When the gradient difference between the edge pixel and the eight neighbor pixels in the grayscale image of the concrete surface is greater, the curvature of the surface crack of the concrete or concrete specimen to be tested corresponding to the grayscale image of the concrete surface is greater, and the surface of the concrete or concrete specimen to be tested corresponding to the grayscale image of the concrete surface is more uneven. At this time, the third smoothness factor of the grayscale image of the concrete surface is larger.
[0054] It is understandable that the first, second, and third smoothness factors of the grayscale image of the concrete surface are the smoothness features extracted from the concrete to be tested and the concrete specimen.
[0055] At this point, the first flatness factor, second flatness factor, and third flatness factor of each concrete surface grayscale image are obtained.
[0056] Step S003: Record any smoothness feature as the target smoothness factor. Based on the difference between the target smoothness factors of the grayscale images of the concrete surfaces of concrete specimens of the same smoothness category, determine the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor. Combining the difference between the target smoothness factors of the grayscale images of the concrete surfaces of the concrete specimens and the concrete to be tested, determine the probability of the smoothness category of the concrete to be tested and the concrete specimens for each smoothness category. Based on the probability of the smoothness category, determine the smoothness category of the concrete to be tested.
[0057] The importance of the first, second, and third smoothness factors of the grayscale image of the concrete surface differs in the evaluation of the smoothness of the concrete surface. Therefore, it is necessary to evaluate the degree of influence of the first, second, and third smoothness factors when evaluating the smoothness of the concrete surface.
[0058] It is understandable that each grayscale image of a concrete surface corresponds to a first smoothness factor, a second smoothness factor, and a third smoothness factor. Therefore, each concrete specimen and the concrete to be tested corresponds to a first smoothness factor, a second smoothness factor, and a third smoothness factor, respectively.
[0059] The feature of any one of the smoothness factors among the first, second, and third smoothness factors of the grayscale image of the concrete surface is denoted as the target smoothness factor of the grayscale image of the concrete surface.
[0060] The variance of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is denoted as the third variance of the same smoothness category; the average value of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is denoted as the first average value of the same smoothness category corresponding to the target smoothness factor; the sum of the first average values of all smoothness categories corresponding to the target smoothness factor is denoted as the first sum of the target smoothness factor; the ratio of the first sum of the target smoothness factor to the third variance of the same smoothness category is denoted as the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor.
[0061] The discrimination influence is an evaluation value of the degree of influence of the target smoothness factor on the smoothness category. When the discrimination influence is greater, the degree of influence of the target smoothness factor in determining the category to which the smoothness of the concrete surface belongs is greater, and the degree of influence of the target smoothness factor on the smoothness category is greater.
[0062] The same method can be used to obtain the discrimination influence degree of each smoothness category corresponding to the first smoothness factor, the second smoothness factor and the third smoothness factor of the grayscale image of the concrete surface.
[0063] Based on the difference in target smoothness factors between the grayscale images of the concrete surfaces of the concrete specimens and the concrete to be tested, and the discriminative influence of each smoothness category corresponding to the target smoothness factor, the probability of the smoothness category of the concrete to be tested and the concrete specimens is determined.
[0064] The absolute value of the difference between the target smoothness factors of the grayscale images of the concrete surfaces of the concrete specimen and the concrete to be tested is denoted as the difference in target smoothness factors between the concrete specimen and the concrete to be tested. The product of the difference in target smoothness factors between the concrete specimen and the concrete to be tested and the discrimination influence of the smoothness category of the concrete specimen corresponding to the target smoothness factor is denoted as the first product of the difference in target smoothness factors between the concrete to be tested and the concrete specimen. The sum of the first products of the difference in target smoothness factors of all concrete to be tested and concrete specimens of the same smoothness category is denoted as the second sum of concrete to be tested and concrete specimens of the same smoothness category. The negative correlation processing result of the sum of the second sums of the first smoothness factor, the second smoothness factor, and the third smoothness factor corresponding to the second sum of concrete to be tested and concrete specimens of the same smoothness category is denoted as the probability of the smoothness category of concrete to be tested and concrete specimens of the same smoothness category.
[0065] It is understandable that a negative correlation processing is applied to the "sum of the second cumulative sums of the same smoothness category of concrete and concrete specimens corresponding to the first, second, and third smoothness factors," ensuring that the "sum of the second cumulative sums of the same smoothness category of concrete and concrete specimens corresponding to the first, second, and third smoothness factors" is negatively correlated with the probability of the smoothness category. It is also understood that the negative correlation in this application refers to the relationship between the independent and dependent variables. The independent variable is the "sum of the second cumulative sums of the same smoothness category of concrete and concrete specimens corresponding to the first, second, and third smoothness factors," and the dependent variable is the probability of the smoothness category. The negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases), and can be an inverse relationship, a subtraction relationship, etc.
[0066] Preferably, as an embodiment of this application, the normalized value of the sum of the second sums of the concrete and concrete specimens under test corresponding to the first flatness factor, the second flatness factor, and the third flatness factor of the same flatness category is recorded as the first normalized value of the concrete and concrete specimens under test of the same flatness category. The difference between the number 1 and the first normalized value of the concrete and concrete specimens under test of the same flatness category is recorded as the probability of the flatness category of the concrete and concrete specimens under test of the same flatness category.
[0067] The flatness category corresponding to the maximum value among the possible flatness categories of the concrete to be tested and the concrete specimen is denoted as the flatness category of the concrete to be tested.
[0068] At this point, the flatness category of the concrete to be tested is determined.
[0069] Step S004: Based on the concrete strength of all concrete specimens of the same flatness category as the concrete to be tested, and the concrete strength of the concrete to be tested, obtain the results of the concrete strength compressive strength test.
[0070] All concrete specimens with the same smoothness category as the concrete to be tested are designated as smoothness reference concrete specimens. The absolute value of the difference in concrete strength between the smoothness reference concrete specimens obtained using the ultrasonic rebound method and the core drilling method is designated as the concrete strength difference of the smoothness reference concrete specimens. The ratio of the concrete strength difference of the smoothness reference concrete specimens to the concrete strength of the smoothness reference concrete specimens obtained using the ultrasonic rebound method is designated as the relative difference in concrete strength of the smoothness reference concrete specimens. The mean of the relative differences in concrete strength of all smoothness reference concrete specimens is designated as the influence of concrete strength on the smoothness reference concrete specimens.
[0071] The sum of the influence of the flatness reference concrete specimen on the concrete strength and the number 1 is denoted as the correction degree. The product of the correction degree and the concrete strength of the concrete to be tested is denoted as the corrected concrete strength of the concrete to be tested.
[0072] The corrected concrete strength of the concrete to be tested is the value obtained after correcting the concrete strength of the concrete to be tested.
[0073] This completes the concrete strength and compressive strength test.
[0074] This application also proposes a concrete strength compressive strength testing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps described above. Since a concrete strength compressive strength testing method has been described in detail above, it will not be repeated here.
[0075] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0076] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0077] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for testing the compressive strength of concrete, characterized in that, The method includes the following steps: Select concrete specimens of different flatness categories, obtain the concrete strength of the concrete specimens and the concrete to be tested, collect concrete surface images of the concrete to be tested and each concrete specimen, and obtain the concrete surface grayscale image corresponding to the concrete surface image. Based on the differences in gray values between pixel blocks at different locations within the grayscale image of the concrete surface, as well as the differences in gray values between adjacent pixels, a first smoothness factor for the grayscale image of the concrete surface is determined. Based on the texture differences between adjacent pixels within the grayscale image of the concrete surface, a second smoothness factor for the grayscale image of the concrete surface is determined. Based on the gradient differences between pixels on the edge and adjacent pixels within the grayscale image of the concrete surface, a third smoothness factor for the grayscale image of the concrete surface is determined. The first smoothness factor, the second smoothness factor, and the third smoothness factor are all recorded as features of smoothness. Any feature of smoothness is denoted as the target smoothness factor. Based on the difference between the target smoothness factors of the grayscale images of the concrete surfaces of concrete specimens of the same smoothness category, the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor is determined. Combining the difference between the target smoothness factors of the grayscale images of the concrete surfaces of the concrete specimens and the concrete to be tested, the probability of the smoothness category of the concrete to be tested and the concrete specimens of each smoothness category is determined. Based on the probability of the smoothness category, the smoothness category of the concrete to be tested is determined. The results of the concrete strength compressive strength test are obtained based on the concrete strength of all concrete specimens of the same flatness category as the concrete to be tested, and the concrete strength of the concrete to be tested.
2. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The method for obtaining the first smoothness factor is as follows: The grayscale image of the concrete surface is divided into pixel blocks with a side length of a third preset parameter. The variance of the grayscale values of all pixels in the pixel block is denoted as the first variance of the grayscale of the pixel block. The average value of the first variance of the grayscale of all pixel blocks in the same grayscale image of the concrete surface is denoted as the first average value of the same grayscale image of the concrete surface. Let any pixel in the grayscale image of the concrete surface be the target pixel. Let the variance of the grayscale values of all pixels in the eight neighborhoods of the target pixel and the target pixel be the second variance of the grayscale of the pixel block. The average value of the second variance of gray levels of all pixels in the same grayscale image of the same concrete surface is denoted as the second average value of the same grayscale image of the same concrete surface. The positive correlation between the first and second average values of the grayscale image of the concrete surface is denoted as the first smoothness factor of the grayscale image of the concrete surface.
3. The method for testing the compressive strength of concrete according to claim 2, characterized in that, The method for obtaining the second smoothness factor is as follows: The average of the absolute values of the differences between the target pixel and all pixels contained in its eight neighborhoods is denoted as the local texture difference of the target pixel. The normalized value of the mean of the local texture differences of all pixels in the same grayscale image of a concrete surface is denoted as the second smoothness factor of the same grayscale image of a concrete surface.
4. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The method for obtaining the third smoothness factor is as follows: Obtain the edges and gradient of each pixel in a grayscale image of a concrete surface; For any pixel on any edge in a grayscale image of a concrete surface, the average of the absolute values of the differences between the pixel and all pixels in its eight neighborhoods is denoted as the local gradient difference of the pixel; the normalized value of the average of the local gradient differences of all pixels on all edges in the same grayscale image of a concrete surface is denoted as the third smoothness factor of the same grayscale image of a concrete surface.
5. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The method for obtaining the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor is as follows: The variance of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is denoted as the third variance of the same smoothness category. The average value of the target smoothness factor of the grayscale image of the concrete surface of concrete specimens of the same smoothness category is recorded as the first average value of the same smoothness category corresponding to the target smoothness factor. The sum of the first average values of all smoothness categories corresponding to the target smoothness factor is recorded as the first sum of the target smoothness factor. The ratio of the first cumulative sum of the target smoothness factors to the third difference of the same smoothness category is denoted as the discrimination influence degree of the same smoothness category corresponding to the target smoothness factor.
6. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The method for obtaining the probability of the smoothness category of the concrete to be tested and the concrete specimen for each smoothness category is as follows: The absolute value of the difference between the target smoothness factor of the grayscale images of the concrete surface of the concrete specimen and the concrete to be tested is denoted as the difference between the target smoothness factor of the concrete specimen and the concrete to be tested. The product of the difference between the target smoothness factor of the concrete specimen and the concrete to be tested and the discrimination influence degree of the smoothness category of the concrete specimen corresponding to the target smoothness factor is denoted as the first product of the difference between the target smoothness factor of the concrete to be tested and the concrete specimen. The sum of the first products of the differences in target smoothness factors of all concrete and concrete specimens under the same smoothness category is recorded as the second sum of concrete and concrete specimens under the same smoothness category. The negative correlation processing result of the sum of the second sums of the first, second, and third smoothness factors corresponding to the same smoothness factors of concrete and concrete specimens under the same smoothness category is recorded as the probability of the smoothness category of concrete and concrete specimens under the same smoothness category.
7. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The specific method for determining the smoothness category of the concrete to be tested based on the probability of smoothness category is as follows: The flatness category corresponding to the maximum value among the possible flatness categories of the concrete to be tested and the concrete specimen is denoted as the flatness category of the concrete to be tested.
8. The method for testing the compressive strength of concrete according to claim 1, characterized in that, The method for obtaining the concrete strength compressive strength test results based on the concrete strength of all concrete specimens of the same smoothness category as the concrete to be tested, and the concrete strength of the concrete to be tested, includes the following specific methods: All concrete specimens with the same flatness category as the concrete to be tested are designated as flatness reference concrete specimens. The influence of the flatness reference concrete specimens on the concrete strength is determined based on the concrete strength of the flatness reference concrete specimens. The sum of the influence of the flatness reference concrete specimen on the concrete strength and the number 1 is recorded as the correction degree. The product of the correction degree and the concrete strength of the concrete to be tested is recorded as the corrected concrete strength of the concrete to be tested. The corrected concrete strength of the concrete to be tested is the result of the concrete strength compressive strength test of the concrete to be tested.
9. The method for testing the compressive strength of concrete according to claim 8, characterized in that, The method for obtaining the influence of the flatness reference concrete specimen on concrete strength is as follows: The absolute value of the difference in concrete strength between the reference concrete specimens for smoothness obtained by ultrasonic rebound and core drilling methods is denoted as the concrete strength difference of the reference concrete specimen for smoothness. The ratio of the concrete strength difference of the reference concrete specimen for smoothness to the concrete strength of the reference concrete specimen for smoothness obtained by ultrasonic rebound is denoted as the relative difference in concrete strength of the reference concrete specimen for smoothness. The mean of the relative differences in concrete strength among all flatness reference concrete specimens is denoted as the influence degree of concrete strength on flatness reference concrete specimens.
10. A concrete strength compressive strength testing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the concrete strength compressive strength testing method as described in any one of claims 1 to 9.