Method and device for detecting strength of concrete for construction

Abstract

The invention relates to the technical field of concrete strength detection, in particular to a concrete strength detection method and device for construction, and the method comprises the following steps: collecting surface humidity information, rebound strength, core drilling strength and concrete surface gray level images of each concrete test piece; obtaining the rebound drill core strength difference of each concrete test piece; acquiring a humidity influence strength coefficient; obtaining the average distribution density of pits of the concrete surface gray level image of each concrete test piece; obtaining a pit distribution uniformity coefficient of each concrete test piece; the surface roughness of the concrete is obtained; obtaining concrete strength influence coefficients of all the concrete test pieces; and improving a distance formula of a clustering algorithm based on the concrete strength influence coefficient and the humidity influence strength coefficient, and correcting the rebound strength of all the concrete test pieces. The detection precision of the concrete strength is improved.

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 basic material of modern buildings, concrete plays a key role in ensuring structural integrity and stability. Concrete strength determines the load and stress that a building or component can withstand, affecting its performance against external environmental pressure, natural disasters or daily use. Its strength directly affects the quality of the project and the safety of the building. Effective concrete strength testing can ensure that the material performance meets the design requirements, thereby improving the overall project quality and safety. Through scientific testing methods, potential problems can be discovered and solved in the early stages of construction, avoiding quality problems caused by insufficient concrete strength, thereby reducing unnecessary rework and repair costs. With the continuous advancement of construction technology and the improvement of construction technology, the requirements for concrete strength testing technology are also constantly increasing. When using existing methods for testing, the accuracy of the measurement results may be reduced due to the influence of the measurement environment, so a more reliable concrete strength testing method is needed.

[0003] This application uses the rebound method to measure the concrete strength. However, when the rebound method is used to measure the concrete strength, the surface smoothness and humidity of the concrete are different due to different measurement times, which may lead to different deviations in the corresponding measurement results. Therefore, it is necessary to analyze the interference factors such as historical measurement results and real-time humidity changes, so as 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 concrete for construction, 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 core drilling strength, the rebound core drilling 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 difference in the average rebound core strength difference 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 pits in the grayscale image of the concrete surface of each concrete specimen is obtained;

[0010] The uniformity coefficient of the pitting distribution 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 the concrete surface roughness and the rebound drill core strength, the concrete strength influence coefficient of all concrete specimens was obtained;

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

[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 drilling strength difference is: 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.

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

[0017] Clustering algorithms are used to cluster the surface humidity information of each concrete specimen to obtain humidity clusters;

[0018] The calculation formula of the humidity influence intensity coefficient is: Where Tc is the humidity intensity coefficient; norm() is the normalization function, Sc (i，j) is the absolute value of the difference between the mean of the rebound core strength difference of all concrete specimens in the i-th humidity cluster and the mean of the rebound core strength difference 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 grayscale image of the concrete surface of each concrete specimen, a corner detection algorithm is used to obtain each corner point in the grayscale image of the concrete surface; the grayscale image of the concrete surface of each concrete specimen is divided into rectangular windows with sides of a preset length, and 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 used as the pockmark density of each rectangular window, and the mean of the pockmark density of all rectangular windows in the grayscale image of the concrete surface is used as the average distribution density of pockmarks in the grayscale image of the concrete surface.

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

[0022] Based on the difference, discreteness 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 taken as the uniformity coefficient of the pitting distribution of each concrete specimen.

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

[0024] For each rectangular window in each concrete surface grayscale image, the average of the absolute values ​​of the differences in the pitting density between the rectangular window and all other rectangular windows in its eight neighborhoods is calculated as the average difference in the pitting density of each rectangular window, and the average of the average difference in the pitting density of all rectangular windows in the concrete surface grayscale image is used as the mean of the pitting difference in 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 clustering cluster, and for each element in each pitting clustering cluster, the minimum Euclidean distance between it and the elements in all other pitting clustering clusters is calculated, and the average of the Euclidean distances of all elements in all the pitting clustering clusters is used as the mean of the pitting distances in 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 clustering 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 core strength difference of all elements in the rough cluster u and the mean of the rebound 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 of each element in the DBSCAN algorithm is improved, and 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 corrects 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 correction 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 drilling 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 method measurement results under different conditions, and then clusters them according to different influence levels to obtain concrete specimen information with similar parameters of the concrete to be tested, and corrects the strength of the concrete to be tested using the rebound method based on this information. Ultimately, the impact of the testing environment is reduced, the detection accuracy is improved, and it is ensured that when there is sufficient specimen experimental data, reliable measurement results can be obtained by referring to the specimen 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 in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0038] Figure 1 A flowchart of a method for detecting 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] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a method and device for detecting the strength of concrete for construction proposed in accordance with the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[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 specific scheme of a method and device for detecting the strength of concrete for construction provided by the present application is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flowchart of a method for detecting the strength of concrete for construction 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 core drilling strength, obtain the rebound core drilling 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 core drilling strength difference between the clustering clusters in the clustering result; 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 discreteness and the distance, obtain the uniformity coefficient of the distribution of pits of each concrete specimen; take the ratio of the average distribution density of pits to the uniformity coefficient of pit distribution as the concrete surface roughness of each concrete specimen; based on the difference in the concrete surface roughness and the rebound core drilling 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, and when the core drilling method is used to measure the concrete strength, the concrete specimen will be drilled. Therefore, for each concrete specimen, the rebound strength of each concrete specimen to be tested 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 the rebound core strength of each concrete specimen, the greater the degree of deviation of the strength measurement result of the rebound method, that is, the greater the influence of the current temperature condition of each concrete specimen on the strength measurement result measured by the rebound method; according to the above analysis, a clustering algorithm is used to cluster the surface humidity information of each concrete specimen to obtain each humidity clustering cluster. 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.

[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 intensity coefficient; norm() is the normalization function, Sc (i，j) is the absolute value of the difference between the mean of the rebound core strength difference of all concrete specimens in the i-th humidity cluster and the mean of the rebound core strength difference 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 humidity information of each concrete specimen is different, and the influence on the rebound strength is different. When the influence of the surface humidity information on the rebound strength is greater, the difference between the means of the rebound core strength differences of all concrete specimens in any two humidity clusters is greater, and the obtained humidity influence strength coefficient is greater at this time; 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 each concrete surface for 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 pockmarks 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] Further, for each rectangular window in each concrete surface grayscale image, the mean of the absolute value of the difference between the pit density of the rectangular window and all other rectangular windows in its eight neighborhoods is calculated as the average difference of the pit density of each rectangular window, and the mean of the average difference of the pit density of all rectangular windows in the concrete surface grayscale image is used as the mean of the pit difference of the concrete surface grayscale image of each concrete specimen; the variance of the pit density of all rectangular windows in the concrete surface grayscale image is calculated as the variance of the pit density of the concrete surface grayscale image of each concrete specimen; the pit density of each rectangular window is clustered using a clustering algorithm to obtain each pit clustering cluster, and for each element in each pit clustering cluster, the minimum Euclidean distance between it and the elements in all other pit clustering clusters is calculated, and the mean of the Euclidean distance of all elements in all pit clustering clusters is used as the mean of the pit distance of the concrete surface grayscale image of each concrete specimen. It should be noted that when the coordinate distance between each element in each clustering cluster and the nearest element in other categories is larger, it means that the uneven distribution of the concrete surface is greater. 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 neighborhoods centered on each rectangular window.

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

[0059] It should be noted that Jc reflects the uniformity of the pitting on the surface of the concrete specimen. When the distribution of the pitting on the surface of the concrete specimen is more uniform, the mean value of the pitting difference, the variance of the pitting density and the mean value of the pitting distance are larger, and the value of the obtained pitting distribution uniformity coefficient is larger; conversely, the value of the obtained pitting distribution uniformity coefficient is smaller. Among them, the local uniformity of the density of the pitting window in the window is reflected by the parameter G, and the 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, when the number of pits obtained per unit area of ​​the concrete surface is greater and the distribution of the pits on the concrete surface is more uneven, 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 each rough clustering cluster. 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 core strength difference of all elements in the rough cluster u and the mean of the rebound 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 the rebound method in measuring concrete strength. At this time, the greater the influence coefficient of concrete strength obtained, 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 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 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 clusters of each rebound strength, 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 drilling 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 method, an embodiment of the present application also provides a construction concrete strength detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and 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 above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and 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 substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope 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 core drilling strength, the rebound core drilling strength difference of each concrete specimen is obtained; All concrete specimens are clustered based on surface moisture information, and the moisture-affected strength coefficient is obtained according to the difference in the average rebound core strength difference between clusters in the clustering results. 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 pits in the grayscale image of the concrete surface of each concrete specimen is obtained; The uniformity coefficient of the pitting distribution of each concrete specimen is obtained based on the difference in the number of corner points, the degree of dispersion and the distance; 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; Based on the differences in the concrete surface roughness and the rebound drill core strength, the concrete strength influence coefficient of all concrete specimens was obtained; The distance formula of clustering algorithm is improved based on the influence coefficient of concrete strength and the influence coefficient of humidity on strength; 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.

2. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The method for obtaining the rebound core drilling strength difference is: 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.

3. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The method for obtaining the humidity influence intensity coefficient is: Clustering algorithms are used to cluster the surface humidity information of each concrete specimen to obtain humidity clusters; The calculation formula of the humidity influence intensity coefficient is: Where Tc is the humidity intensity coefficient; norm() is the normalization function, Sc (i,j) is the absolute value of the difference between the mean of the rebound core strength difference of all concrete specimens in the i-th humidity cluster and the mean of the rebound core strength difference of all concrete specimens in the j-th humidity cluster, and F is the number of humidity clusters.

4. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The average distribution density of pits includes: For the grayscale image of the concrete surface of each concrete specimen, a corner detection algorithm is used to obtain each corner point in the grayscale image of the concrete surface; the grayscale image of the concrete surface of each concrete specimen is divided into rectangular windows with sides of a preset length, and 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 used as the pockmark density of each rectangular window, and the mean of the pockmark density of all rectangular windows in the grayscale image of the concrete surface is used as the average distribution density of pockmarks in the grayscale image of the concrete surface.

5. A method for detecting strength of construction concrete as claimed in claim 4, characterized in that: The pitting distribution uniformity coefficient includes: Based on the difference, discreteness 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 taken as the uniformity coefficient of the pitting distribution of each concrete specimen.

6. A method for detecting strength of construction concrete as claimed in claim 5, 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 the pitting density between the rectangular window and all other rectangular windows in its eight neighborhoods is calculated as the average difference in the pitting density of each rectangular window, and the average of the average difference in the pitting density of all rectangular windows in the concrete surface grayscale image is used as the mean of the pitting difference in 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 clustering cluster, and for each element in each pitting clustering cluster, the minimum Euclidean distance between it and the elements in all other pitting clustering clusters is calculated, and the average of the Euclidean distances of all elements in all the pitting clustering clusters is used as the mean of the pitting distances in the concrete surface grayscale image of each concrete specimen.

7. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The concrete strength influence coefficient includes: Clustering algorithm is used to cluster the surface roughness of each concrete specimen to obtain each rough clustering cluster; 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 core strength difference of all elements in the rough cluster u and the mean of the rebound core strength difference of all elements in the rough cluster v, and C is the number of rough clusters.

8. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The distance formula of the clustering algorithm improved based on the concrete strength influence coefficient and the humidity influence strength coefficient includes: 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 ′ ; In the formula, 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; According to the surface humidity data of each concrete specimen and the weight of the concrete surface roughness, the distance of each element in the DBSCAN algorithm is improved, and the calculation formula is: In the formula, Yx ′ is the weight of the concrete surface roughness of each concrete specimen, Tc ′ is the weight of the surface moisture data of each concrete specimen, S1 and S2 represent the surface moisture information of any two concrete specimens, and H1 and H2 represent the surface roughness of any two concrete specimens.

9. A method for detecting strength of construction concrete as claimed in claim 1, characterized in that: The distance formula based on the improved clustering algorithm is used to correct the rebound strength of all concrete specimens, 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 correction formula: In the formula, ZX ′ is the modified rebound strength of each concrete specimen; HT ′ is the rebound strength of each concrete specimen, Zx p is the core drilling 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.

10. 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 a method for detecting strength of construction concrete as described in any one of claims 1 to 9 are implemented.

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