A method and device for detecting strength of concrete used in construction

Abstract

The present application relates to the technical field of concrete strength testing, and specifically to a method and device for testing the strength of concrete for construction. The method comprises: collecting surface moisture information, rebound strength, core drill strength, and grayscale images of each concrete specimen; obtaining the rebound core drill strength difference of each concrete specimen; obtaining the humidity-affected strength coefficient; obtaining the average distribution density of pits in the grayscale image of the concrete surface of each concrete specimen; obtaining the pit distribution uniformity coefficient of each concrete specimen; and further obtaining the degree of concrete surface roughness; obtaining the concrete strength influence coefficient of all concrete specimens; and modifying the rebound strength of all concrete specimens by improving the distance formula of the clustering algorithm based on the concrete strength influence coefficient and the humidity-affected strength coefficient. The present application improves the detection accuracy of concrete strength.

Description

Technical Field

[0001] The present application relates to the technical field of concrete strength detection, and in particular to a method and device for detecting the strength of concrete used in construction. Background Art

[0002] As the foundational material of modern construction, concrete plays a key role in ensuring structural integrity and stability. Concrete strength determines the loads and stresses a building or component can withstand, affecting its performance against external environmental pressures, natural disasters, or everyday use. Its strength directly impacts project quality and building safety. Effective concrete strength testing ensures that material properties meet design requirements, thereby improving overall project quality and safety. Scientific testing methods can identify and resolve potential problems early in construction, avoiding quality issues caused by insufficient concrete strength and reducing unnecessary rework and repair costs. With the continuous advancement of construction technology and improvements in construction processes, the requirements for concrete strength testing technology are also increasing. When using existing testing methods, the accuracy of measurement results may be reduced due to the influence of the measurement environment, necessitating a more reliable concrete strength testing method.

[0003] This application uses the rebound method to measure concrete strength. However, when using the rebound method to measure concrete strength, due to different measurement times, the concrete surface smoothness and concrete surface humidity are also different, which may lead to different deviations in the corresponding measurement results. Therefore, it is necessary to analyze interference factors such as historical measurement results and real-time humidity changes to correct the measured strength and obtain a more accurate concrete strength value. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and device for testing the strength of concrete for construction. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for detecting the strength of construction concrete, the method comprising the following steps:

[0006] Collect surface moisture information, rebound strength, core drilling strength and concrete surface grayscale image of each concrete specimen;

[0007] Based on the difference between the rebound strength and the drill core strength, the rebound drill core strength difference of each concrete specimen is obtained;

[0008] All concrete specimens are clustered based on surface moisture information, and the moisture-affected strength coefficient is obtained according to the average difference in rebound drill core strength between clusters in the clustering results.

[0009] Based on the distribution difference of the number of corner points between each window in the grayscale image of the concrete surface, the average distribution density of the pitting in the grayscale image of the concrete surface of each concrete specimen is obtained;

[0010] The pitting uniformity coefficient of each concrete specimen is obtained based on the difference in the number of corner points, the degree of dispersion and the distance;

[0011] The ratio of the average distribution density of pits to the uniformity coefficient of pit distribution is taken as the surface roughness of each concrete specimen.

[0012] Based on the differences in concrete surface roughness and rebound drill core strength, the concrete strength influence coefficient of all concrete specimens was obtained;

[0013] Improve the distance formula of clustering algorithm based on concrete strength influence coefficient and humidity influence strength coefficient;

[0014] The rebound strength of all concrete specimens is clustered based on the distance formula of the improved clustering algorithm, and the rebound strength of all concrete specimens is corrected according to the clustering results.

[0015] Furthermore, the method for obtaining the rebound core drill strength difference is: for each concrete specimen, calculating the absolute value of the difference between the rebound strength and the core drill strength of the concrete specimen as the rebound core drill strength difference of each concrete specimen.

[0016] Furthermore, the method for obtaining the humidity influence intensity coefficient is:

[0017] Clustering algorithm is used to cluster the surface moisture information of each concrete specimen to obtain each moisture cluster;

[0018] The calculation formula of the humidity influence intensity coefficient is: Where Tc is the humidity influence intensity coefficient; norm() is the normalization function, Sc (i，j) is the absolute value of the difference between the mean of the rebound drill core strength differences of all concrete specimens in the i-th humidity cluster and the mean of the rebound drill core strength differences of all concrete specimens in the j-th humidity cluster, and F is the number of humidity clusters.

[0019] Furthermore, the average distribution density of the pits includes:

[0020] For the concrete surface grayscale image of each concrete specimen, a corner detection algorithm is used to obtain the corner points in the concrete surface grayscale image; the concrete surface grayscale image of each concrete specimen is divided into rectangular windows with sides of a preset length; for each rectangular window in the concrete surface grayscale image of each concrete specimen, the number of corner points in the rectangular window is used as the pitting density of each rectangular window, and the average of the pitting densities of all rectangular windows in the concrete surface grayscale image is used as the average distribution density of pitting in the concrete surface grayscale image.

[0021] Furthermore, the pitting distribution uniformity coefficient includes:

[0022] Based on the difference, dispersion and distance between the pitting densities, the mean value of the pitting difference, the variance of the pitting density and the mean value of the pitting distance of the concrete surface grayscale image of each concrete specimen were obtained. The product of the mean value of the pitting difference, the variance of the pitting density and the mean value of the pitting distance was taken as the pitting distribution uniformity coefficient of each concrete specimen.

[0023] Furthermore, the pit difference mean, pit density variance and pit distance mean include:

[0024] For each rectangular window in each concrete surface grayscale image, the average of the absolute values of the differences in pitting density between the rectangular window and all other rectangular windows in its eight neighborhoods is calculated as the average difference in pitting density of each rectangular window, and the average of the average differences in pitting density of all rectangular windows in the concrete surface grayscale image is used as the mean pitting difference of the concrete surface grayscale image of each concrete specimen; the variance of the pitting density of all rectangular windows in the concrete surface grayscale image is calculated as the variance of the pitting density of the concrete surface grayscale image of each concrete specimen; the clustering algorithm is used to cluster the pitting density of each rectangular window to obtain each pitting cluster; for each element in each pitting cluster, the minimum Euclidean distance between it and the elements in all other pitting clusters is calculated, and the average of the Euclidean distances of all elements in all pitting clusters is used as the mean pitting distance of the concrete surface grayscale image of each concrete specimen.

[0025] Furthermore, the concrete strength influence coefficient includes:

[0026] Clustering algorithm is used to cluster the surface roughness of each concrete specimen to obtain each rough cluster;

[0027] The calculation formula of the concrete strength influence coefficient is: Where, Yx is the concrete strength influence coefficient of all concrete specimens; norm() is the normalization function, Ec (u，v)It represents the absolute value of the difference between the mean of the rebound drill core strength difference of all elements in the rough cluster u and the mean of the rebound drill core strength difference of all elements in the rough cluster v, and C is the number of rough clusters.

[0028] Furthermore, the distance formula of the clustering algorithm improved based on the concrete strength influence coefficient and the humidity influence strength coefficient includes:

[0029] Based on the concrete strength influence coefficient and humidity influence strength coefficient, the surface humidity data of each concrete specimen and the weight of the concrete surface roughness are obtained. The calculation formula is: Tc′=1-Yx′; where Yx′ is the weight of the concrete surface roughness of each concrete specimen, Tc′ is the weight of the surface humidity data of each concrete specimen, Yx is the concrete strength influence coefficient of all concrete specimens, and Tc is the humidity influence strength coefficient;

[0030] According to the surface humidity data of each concrete specimen and the weight of the concrete surface roughness, the distance between each element in the DBSCAN algorithm is improved. The calculation formula is: Where Yx′ is the weight of the concrete surface roughness of each concrete specimen, Tc′ is the weight of the surface humidity data of each concrete specimen, S1 and S2 represent the surface humidity information of any two concrete specimens, and H1 and H2 represent the concrete surface roughness of any two concrete specimens.

[0031] Furthermore, the distance formula based on the improved clustering algorithm is used to correct the rebound strength of all concrete specimens, including:

[0032] According to the distance formula of the improved clustering algorithm, the rebound strength of all concrete specimens is clustered to obtain the clusters of each rebound strength;

[0033] The rebound strength of all concrete specimens was corrected using the following formula: Where ZX′ is the modified rebound strength of each concrete specimen; HT′ is the rebound strength of each concrete specimen, Zx p is the core strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, HT p is the rebound strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, and M is the number of elements in the rebound strength cluster of the pth concrete specimen.

[0034] In a second aspect, an embodiment of the present application further provides a construction concrete strength detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0035] This application has at least the following beneficial effects:

[0036] This application analyzes the parameters that affect the measurement results using the rebound method, calculates their influence on the rebound measurement results under different conditions, and then clusters them according to the different influence levels to obtain information about concrete specimens with similar parameters of the concrete to be tested. Based on this information, the strength obtained by the rebound method of the concrete to be tested is corrected. Ultimately, the impact of the testing environment is reduced, the test accuracy is improved, and when there is sufficient test data, reliable measurement results can be obtained by referring to the test data for non-destructive testing, thereby improving the detection accuracy of concrete strength. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 A flowchart of a method for testing the strength of concrete for construction provided in one embodiment of the present application;

[0039] Figure 2 A flowchart for obtaining the concrete strength influence coefficient is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0040] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and device for testing the strength of concrete for construction, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0041] Unless defined otherwise, 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 belongs.

[0042] The following describes in detail a method and device for detecting the strength of concrete for construction provided by the present application with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flowchart of a method for detecting the strength of construction concrete provided by an embodiment of the present application, the method comprising the following steps:

[0044] Step S1: collecting surface moisture information, rebound strength, core drilling strength and concrete surface grayscale image of each concrete specimen.

[0045] For each concrete specimen, a temperature sensor is used to collect surface moisture information of each concrete specimen, a rebound method is used to measure the strength of each concrete specimen as the rebound strength of each concrete specimen, and a core drilling method is used to measure the strength of each concrete specimen as the core drilling strength of each concrete specimen.

[0046] A CMOS camera is used to obtain the concrete surface image of each concrete specimen, a Gaussian filter is used to denoise the concrete surface image, and the denoised concrete surface image is grayscaled to obtain the concrete surface grayscale image of each concrete specimen.

[0047] Step S2: based on the difference between the rebound strength and the drill core strength, obtain the rebound drill core strength difference of each concrete specimen; cluster all concrete specimens based on the surface humidity information, and obtain the humidity influence strength coefficient according to the difference in the average rebound drill core strength difference between the clusters in the clustering results; based on the distribution difference of the number of corner points between each window in the concrete surface grayscale image, obtain the average distribution density of pits in the concrete surface grayscale image of each concrete specimen; based on the difference in the number of corner points, the degree of dispersion and the distance, obtain the pit distribution uniformity coefficient of each concrete specimen; take the ratio of the average distribution density of pits to the pit distribution uniformity coefficient as the concrete surface roughness of each concrete specimen; based on the difference in the concrete surface roughness and the rebound drill core strength difference, obtain the concrete strength influence coefficient of all concrete specimens.

[0048] For each concrete specimen, there is an error between the rebound strength of the concrete specimen and the actual strength of the concrete. When the core drilling method is used to measure the concrete strength, the concrete specimen is drilled. Therefore, for each concrete specimen, the rebound strength of each concrete specimen is corrected according to the drilling strength and rebound strength of other concrete specimens before the tested concrete specimen, as well as the surface humidity information, so as to test the concrete strength.

[0049] Furthermore, for each concrete specimen, the absolute value of the difference between the rebound strength and the core drilling strength of the concrete specimen is calculated as the rebound core drilling strength difference of each concrete specimen.

[0050] The greater the difference in rebound core strength between concrete specimens, the greater the deviation in the rebound strength measurement results. Specifically, the current temperature of each concrete specimen has a greater impact on the rebound strength measurement results. Based on the above analysis, a clustering algorithm is used to cluster the surface moisture information of each concrete specimen to obtain moisture clusters. The DBSCAN clustering algorithm is used in this embodiment. The implementer may select another clustering algorithm based on actual circumstances. The DBSCAN clustering algorithm is a well-known technique and is not described in detail in this embodiment.

[0051] Furthermore, in order to reflect the strength change of concrete specimens affected by humidity, the humidity-affected strength coefficient was obtained based on the difference between the average rebound core strength differences of each humidity cluster. The calculation formula is: Where Tc is the humidity influence intensity coefficient; norm() is the normalization function, Sc (i，j) is the absolute value of the difference between the mean of the rebound drill core strength differences of all concrete specimens in the i-th humidity cluster and the mean of the rebound drill core strength differences of all concrete specimens in the j-th humidity cluster, and F is the number of humidity clusters.

[0052] It should be noted that the surface moisture information of each concrete specimen is different, and the impact on the rebound strength is different. When the influence of the surface moisture information on the rebound strength is greater, the difference between the mean values of the rebound drill core strength differences of all concrete specimens in any two humidity clusters is greater, and the obtained humidity influence strength coefficient is greater; conversely, the obtained humidity influence strength coefficient is smaller.

[0053] Since the roughness of the concrete surface will affect the rebound value measured by the rebound method during measurement, it is necessary to analyze the roughness of the concrete surface for each strength measurement and make corrections based on the results.

[0054] For the grayscale image of the concrete surface of each concrete specimen, since the concrete surface itself is not smooth, many dot-like pitting patterns can be observed on the surface of the concrete specimen. Therefore, the concrete surface image can be analyzed based on this feature.

[0055] According to the above analysis, for the grayscale image of the concrete surface of each concrete specimen, a corner detection algorithm is used to obtain the corner points in the grayscale image of the concrete surface. The corner detection algorithm selected in this embodiment is the Harris corner detection algorithm. The implementer can select other corner detection algorithms according to actual conditions. Among them, the corner detection algorithm is a well-known technology and will not be described in detail in this embodiment. Further, the grayscale image of the concrete surface of each concrete specimen is divided into rectangular windows with a side length of N. In this embodiment, the value of N is 10, and the implementer can select other values according to actual conditions.

[0056] For each rectangular window in the grayscale image of the concrete surface of each concrete specimen, the number of corner points in the rectangular window is taken as the pitting density of each rectangular window, and the mean of the pitting density of all rectangular windows in the grayscale image of the concrete surface is taken as the average distribution density of the pitting of the grayscale image of the concrete surface.

[0057] Furthermore, for each rectangular window in each concrete surface grayscale image, the mean of the absolute values of the differences in pit density between the rectangular window and all other rectangular windows in its eight-neighborhood region is calculated as the average pit density difference of each rectangular window, and the mean of the average pit density differences of all rectangular windows in the concrete surface grayscale image is used as the mean pitting difference of the concrete surface grayscale image of each concrete specimen; the variance of the pitting density of all rectangular windows in the concrete surface grayscale image is calculated as the variance of the pitting density of the concrete surface grayscale image of each concrete specimen; a clustering algorithm is used to cluster the pitting density of each rectangular window to obtain each pitting cluster, and for each element in each pitting cluster, the minimum Euclidean distance between it and the elements in all other pitting clusters is calculated, and the mean of the Euclidean distances of all elements in all pitting clusters is used as the mean pitting distance of the concrete surface grayscale image of each concrete specimen. It should be noted that the greater the coordinate distance between each element in each cluster and the nearest element in another category, the greater the degree of uneven distribution of the concrete surface. The clustering algorithm used in this embodiment is the DBSCAN clustering algorithm. The implementer may select other clustering algorithms according to actual conditions. The DBSCAN clustering algorithm is a well-known technology and will not be described in detail in this embodiment. The eight neighborhoods are eight rectangular windows of the surrounding neighborhood centered on each rectangular window.

[0058] According to the above analysis, the pitting uniformity coefficient of each concrete specimen is obtained based on the mean value of pitting difference, the variance of pitting density and the mean value of pitting distance. The calculation formula is: Jc = G × W × E; where Jc is the pitting uniformity coefficient of each concrete specimen, G is the mean value of pitting difference in the grayscale image of the concrete surface of each concrete specimen, W is the variance of pitting density in the grayscale image of the concrete surface of each concrete specimen, and E is the mean value of pitting distance in the grayscale image of the concrete surface of each concrete specimen.

[0059] It should be noted that Jc reflects the uniformity of pitting on the concrete specimen surface. The more uniform the distribution of pitting on the concrete specimen surface, the larger the mean pitting difference, the variance of pitting density, and the mean pitting distance, and the larger the obtained pitting distribution uniformity coefficient. Conversely, the obtained pitting distribution uniformity coefficient is smaller. Parameter G reflects the local uniformity of the pitting window density within the window, while parameters W and E reflect the uniformity of the overall pitting window distribution.

[0060] Furthermore, the surface roughness of each concrete specimen is obtained based on the pitting uniformity coefficient and the average distribution density of pitting. The calculation formula is: Where H is the surface roughness of each concrete specimen; Md is the average distribution density of pits in the grayscale image of the concrete surface of each concrete specimen; and Jc is the uniformity coefficient of pit distribution of each concrete specimen.

[0061] It should be noted that, that is, the more the number of pits obtained per unit area of the concrete surface is, and the more uneven the distribution of the pits on the concrete surface is, the rougher the concrete surface is, and the greater the value of the concrete surface roughness of each concrete specimen obtained at this time; conversely, the smaller the value of the concrete surface roughness obtained is.

[0062] Furthermore, a clustering algorithm is used to cluster the surface roughness of each concrete specimen to obtain rough clusters. The clustering algorithm selected in this embodiment is the DBSCAN clustering algorithm. The implementer can select other clustering algorithms according to actual conditions. The DBSCAN clustering algorithm is a well-known technology and will not be described in detail in this embodiment.

[0063] Furthermore, the concrete strength influence coefficient of all concrete specimens was calculated using the following formula: Where, Yx is the concrete strength influence coefficient of all concrete specimens; norm() is the normalization function, Ec (u，v) It represents the absolute value of the difference between the mean of the rebound drill core strength difference of all elements in the rough cluster u and the mean of the rebound drill core strength difference of all elements in the rough cluster v, and C is the number of rough clusters. The flowchart for obtaining the influence coefficient of concrete strength is as follows: Figure 2 shown.

[0064] It should be noted that the greater the difference in concrete strength between the rebound method and the core drilling method under different concrete surface roughness categories, the greater the influence of the concrete surface roughness on the accuracy of concrete strength measurement by the rebound method. At this time, the obtained concrete strength influence coefficient is greater, and vice versa.

[0065] Step S3: improving the distance formula of the clustering algorithm based on the concrete strength influence coefficient and the humidity influence strength coefficient; clustering the rebound strength of all concrete specimens based on the distance formula of the improved clustering algorithm, and correcting the rebound strength of all concrete specimens according to the clustering results.

[0066] According to the above analysis results, the surface humidity data of each concrete specimen and the weight of the concrete surface roughness are obtained based on the concrete strength influence coefficient and the humidity influence strength coefficient. The calculation formula is: Tc′=1-Yx′; where Yx′ is the weight of the concrete surface roughness of each concrete specimen, Tc′ is the weight of the surface humidity data of each concrete specimen, Yx is the concrete strength influence coefficient of all concrete specimens, and Tc is the humidity influence strength coefficient.

[0067] Furthermore, the distance between each element in the DBSCAN algorithm is improved according to the surface moisture data of each concrete specimen and the weight of the concrete surface roughness. The calculation formula is: Where Yx′ is the weight of the concrete surface roughness of each concrete specimen, Tc′ is the weight of the surface humidity data of each concrete specimen, S1 and S2 represent the surface humidity information of any two concrete specimens, and H1 and H2 represent the concrete surface roughness of any two concrete specimens.

[0068] According to the improved distance formula, the rebound strength of all concrete specimens is clustered to obtain the rebound strength clusters, and the rebound strength of all concrete specimens is corrected. The correction formula is: Where ZX′ is the modified rebound strength of each concrete specimen; HT′ is the rebound strength of each concrete specimen, Zx p is the core strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, HT p is the rebound strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, and M is the number of elements in the rebound strength cluster of the pth concrete specimen.

[0069] This completes the correction of the concrete strength measurement results using the rebound method.

[0070] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a construction concrete strength detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned construction concrete strength detection methods are implemented.

[0071] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0073] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for testing the strength of concrete for construction, characterized in that: The method comprises the following steps: Collect surface moisture information, rebound strength, core drilling strength and concrete surface grayscale image of each concrete specimen; Based on the difference between the rebound strength and the drill core strength, the rebound drill core strength difference of each concrete specimen is obtained; All concrete specimens are clustered based on surface humidity information to obtain humidity clusters. The humidity-affected strength coefficient is obtained based on the average difference in rebound core strength between clusters in the clustering results. The calculation formula for the humidity-affected strength coefficient is: Where, is the humidity effect intensity coefficient; is the normalization function, is the absolute value of the difference between the mean of the rebound drill core strength differences of all concrete specimens in the i-th humidity cluster and the mean of the rebound drill core strength differences of all concrete specimens in the j-th humidity cluster, and F is the number of humidity clusters; For the concrete surface grayscale image of each concrete specimen, a corner detection algorithm is used to obtain each corner point in the concrete surface grayscale image; the concrete surface grayscale image of each concrete specimen is divided into rectangular windows with sides of a preset length; for each rectangular window in the concrete surface grayscale image of each concrete specimen, the number of corner points in the rectangular window is used as the pitting density of each rectangular window; and the average of the pitting densities of all rectangular windows in the concrete surface grayscale image is used as the average distribution density of pitting in the concrete surface grayscale image; Based on the difference, dispersion and distance between the pitting density, the mean value of the pitting difference, the variance of the pitting density and the mean value of the pitting distance of the concrete surface grayscale image of each concrete specimen are obtained, and the product of the mean value of the pitting difference, the variance of the pitting density and the mean value of the pitting distance is used as the pitting distribution uniformity coefficient of each concrete specimen. The ratio of the average distribution density of pits to the uniformity coefficient of pit distribution is taken as the surface roughness of each concrete specimen. The clustering algorithm is used to cluster the surface roughness of each concrete specimen to obtain each rough cluster; the calculation formula of the concrete strength influence coefficient is: Where, is the concrete strength influence coefficient of all concrete specimens; is the normalization function, It represents the absolute value of the difference between the mean of the rebound drill core strength difference of all elements in the rough cluster u and the mean of the rebound drill core strength difference of all elements in the rough cluster v, is the number of rough clustering clusters; The distance formula of the clustering algorithm is improved based on the concrete strength influence coefficient and the humidity influence strength coefficient, including: obtaining the surface humidity data of each concrete specimen and the weight of the concrete surface roughness based on the concrete strength influence coefficient and the humidity influence strength coefficient. The calculation formula is: ; Where, is the weight of the concrete surface roughness of each concrete specimen, is the weight of the surface moisture data of each concrete specimen, is the concrete strength influence coefficient of all concrete specimens, is the humidity-affected strength coefficient; the distance of each element in the DBSCAN algorithm is improved according to the surface humidity data of each concrete specimen and the weight of the concrete surface roughness. The calculation formula is: Where, is the weight of the concrete surface roughness of each concrete specimen, is the weight of the surface moisture data of each concrete specimen, 、 Respectively represent the surface moisture information of any two concrete specimens, 、 Respectively represent the surface roughness of any two concrete specimens; The rebound strength of all concrete specimens is clustered based on the distance formula of the improved clustering algorithm. The rebound strength of all concrete specimens is corrected according to the clustering results, including: According to the distance formula of the improved clustering algorithm, the rebound strength of all concrete specimens is clustered to obtain the clusters of each rebound strength; The rebound strength of all concrete specimens was corrected using the following formula: Where, is the modified rebound strength of each concrete specimen; is the rebound strength of each concrete specimen, is the core drilling strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, is the rebound strength of the pth concrete specimen in the rebound strength cluster of each concrete specimen, and M is the number of elements in the rebound strength cluster of the pth concrete specimen.

2. A construction concrete strength testing method as claimed in claim 1, characterized in that: The method for obtaining the rebound core drill strength difference is: for each concrete specimen, calculating the absolute value of the difference between the rebound strength and the drill core strength of the concrete specimen as the rebound core drill strength difference of each concrete specimen.

3. A construction concrete strength testing method as claimed in claim 1, characterized in that: The pit difference mean, pit density variance and pit distance mean include: For each rectangular window in each concrete surface grayscale image, the average of the absolute values of the differences in pitting density between the rectangular window and all other rectangular windows in its eight neighborhoods is calculated as the average difference in pitting density of each rectangular window, and the average of the average differences in pitting density of all rectangular windows in the concrete surface grayscale image is used as the mean pitting difference of the concrete surface grayscale image of each concrete specimen; the variance of the pitting density of all rectangular windows in the concrete surface grayscale image is calculated as the variance of the pitting density of the concrete surface grayscale image of each concrete specimen; the clustering algorithm is used to cluster the pitting density of each rectangular window to obtain each pitting cluster; for each element in each pitting cluster, the minimum Euclidean distance between it and the elements in all other pitting clusters is calculated, and the average of the Euclidean distances of all elements in all pitting clusters is used as the mean pitting distance of the concrete surface grayscale image of each concrete specimen.

4. A construction concrete strength detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for detecting the strength of construction concrete as described in any one of claims 1 to 3 are implemented.

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