A water conservancy project concrete quality detection device and method

Abstract

The present invention relates to the technical field of concrete testing, and more specifically to a water conservancy project concrete quality testing device and method, comprising: obtaining basic information data and an ultrasonic image of a concrete sample; segmenting the ultrasonic image into regions based on the relative depth values ​​of pixels in the ultrasonic image to determine the crack rate of the initial region; analyzing and merging the initial regions based on the crack rate; obtaining the overall crack degree of the concrete sample based on the crack rate and average relative depth value of the new region and the morphological characteristics of the new region; obtaining the erosion coefficient of the concrete sample based on the composition of the water body in contact with the concrete sample; and constructing a concrete quality estimation model based on the basic information data of the concrete sample and the erosion coefficient to estimate the quality of the new concrete sample. The present invention effectively improves the accuracy and efficiency of concrete quality testing.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete detection, and in particular to a water conservancy project concrete quality detection device and method. Background Art

[0002] Detecting the extent of concrete cracks in water conservancy projects is crucial because it is directly related to the safety, durability, and long-term stability of the structure. Timely detection and accurate assessment of cracks can effectively prevent structural damage and ensure the safety of the project and personnel. At the same time, it can evaluate and improve the durability of concrete, guide maintenance and reinforcement measures, and thus improve the quality of the project and extend its service life. Currently, the widely used methods for detecting the extent of cracks include visual inspection, digital image processing, laser scanning, and ultrasonic testing. Visual inspection is simple and intuitive, but may miss small cracks; digital image processing identifies and quantifies cracks by analyzing high-resolution images; laser scanning provides high-precision three-dimensional crack data; and ultrasonic testing can detect the depth of cracks and their impact on the structure.

[0003] Although these methods can effectively detect the current degree of concrete cracks, they only consider the current degree of concrete cracks and cannot predict the change in the degree of cracks in combination with the mass loss of concrete during the change process. The subsequent change in the degree of cracks may be too large, causing the building to lose its function prematurely, resulting in greater losses. Summary of the Invention

[0004] The present invention provides a water conservancy project concrete quality detection device and method to solve the existing problems.

[0005] The present invention provides a water conservancy project concrete quality detection device and method using the following technical solutions:

[0006] An embodiment of the present invention provides a method for detecting the quality of concrete in a water conservancy project, the method comprising the following steps:

[0007] Obtain basic information data and ultrasonic images of concrete samples;

[0008] The ultrasonic image is segmented based on the relative depth values ​​of the pixels in the ultrasonic image to obtain several initial regions. The contours and depth information of the initial regions are analyzed to determine the crack rates of the initial regions. The initial regions are analyzed for merging ability based on the crack rates of the different initial regions and the initial regions are merged to obtain new regions. The overall crack degree of the concrete sample corresponding to the new ultrasonic image is obtained based on the crack rates corresponding to all new regions in the new ultrasonic image, the average relative depth values ​​within the new regions, and the morphological characteristics of the new regions.

[0009] The erosion coefficient of the concrete sample is obtained based on the composition of the water body that the concrete sample contacts, and a concrete quality estimation model is constructed by combining the basic information data of the concrete sample and the erosion coefficient;

[0010] The concrete quality estimation model is used to estimate the quality of new concrete samples.

[0011] Furthermore, the analysis of the profile and depth information of the initial area to determine the crack rate of the initial area includes the following specific methods:

[0012] First, the average value of the relative depth values ​​corresponding to all pixel points in any initial region is obtained, the aspect ratio of the minimum circumscribed rectangle of the arbitrary initial region is obtained, the minimum relative depth value and the maximum relative depth value in the ultrasound image are obtained, and the crack factor of the initial region is calculated based on the numerical level of the initial region in the ultrasound image relative to the minimum and maximum relative depth values ​​in the ultrasound image, as well as the shape contour of the initial region;

[0013] Then, the crack factor of the initial area is corrected using the average relative depth values ​​of other initial areas and the shortest distance between different initial areas to obtain the crack rate of the initial area.

[0014] Furthermore, the specific calculation method of the crack factor is:

[0015] For any initial region, obtain the absolute values ​​of the differences between the average relative depth value of all pixels in the initial region and the maximum relative depth value and the minimum relative depth value in the ultrasound image, and record them as the first difference and the second difference respectively;

[0016] A crack factor of the initial area is obtained according to the first difference, the second difference and the aspect ratio of the minimum circumscribed rectangle of the initial area, wherein the crack factor is positively correlated with the first difference, the second difference and the aspect ratio of the minimum circumscribed rectangle.

[0017] Furthermore, the specific calculation method of the crack rate is:

[0018] Record any initial area as a target initial area, and determine the average relative depth value of all pixels in each initial area except the target initial area as the analysis relative depth value;

[0019] Obtaining a difference between a maximum relative depth value and an analysis relative depth value in the ultrasound image, and recording the difference as a first relative difference of the initial area outside the target initial area;

[0020] Obtaining a product cumulative value of the minimum distance between the target initial area and each initial area outside the target initial area and the first relative difference, and recording the value as a relative rate of the target initial area;

[0021] According to the crack factor and the relative rate of the target initial area, the crack rate of the target initial area is obtained, and the crack rate is positively correlated with the crack factor and the relative rate.

[0022] Furthermore, the method of analyzing the merging properties of the initial regions by combining the crack rates of different initial regions and merging the initial regions to obtain new regions includes the following specific methods:

[0023] First, the merging properties of any two adjacent initial regions are calculated based on the crack rates corresponding to each of the two adjacent initial regions, the crack rates corresponding to the two adjacent initial regions when the two adjacent initial regions are considered as one region, and the number of pixels on the common boundary line between the two adjacent initial regions.

[0024] Then, two adjacent initial regions whose merging property is greater than or equal to a preset merging threshold are merged into one region. The image obtained by merging all initial regions in the ultrasound image that meet the merging threshold condition is recorded as a new ultrasound image, and the region in the new ultrasound image is recorded as a new region.

[0025] Furthermore, the merging properties of the two adjacent initial regions are calculated, including the following specific methods:

[0026] Get the adjacent initial area and the initial region , get the initial area and the initial region The corresponding crack rate as a region ; The initial area and the initial region The crack rates are recorded as and , get and 、 The minimum value of the corresponding differences between them is recorded as the combined similarity;

[0027] When the merged similarity is not 0, the merged similarity is combined with the adjacent initial area and the initial region The initial area is obtained by multiplying the number of pixels on the common boundary line between and the initial region The merging similarity between them; when the merging similarity is 0, the initial region and the initial region The mergeability between them is 0.

[0028] Furthermore, the method of obtaining the overall crack degree of the concrete sample corresponding to the new ultrasonic image based on the crack rates corresponding to all new areas in the new ultrasonic image and the average relative depth values ​​in the new areas, combined with the morphological characteristics of the new areas, includes the following specific methods:

[0029] The crack coefficient of the concrete sample is calculated by combining the crack rate of all new areas in the new ultrasonic image and the average relative depth value of all pixels in the new area;

[0030] Calculate the crack degree of any new area based on the distribution direction of the morphological skeleton, crack rate, and relative depth values ​​of the pixels in the area;

[0031] The overall crack extent of the concrete sample is calculated by combining the crack coefficient of the concrete sample and the crack extent of all new areas in the new ultrasonic image.

[0032] Furthermore, the specific method for obtaining the crack degree of the new area is:

[0033] For any new region, the skeleton of the new region is extracted using morphology, and then a line fitting is performed using the least squares method to obtain the angle between the fitted line of the connected domain skeleton of the new region and the horizontal line;

[0034] The crack degree of the new area is calculated based on the angle between the fitting straight line and the horizontal line of the new area, the crack rate and the average relative depth value of the pixels in the area. The angle is negatively correlated with the crack degree, and the crack rate and the average relative depth value are both positively correlated with the crack degree.

[0035] Furthermore, the calculating of the overall crack degree of the concrete sample further includes: the overall crack degree of the concrete sample is positively correlated with the crack coefficient of the concrete sample, and the overall crack degree of the concrete sample is positively correlated with the cumulative value of the crack degrees of all new areas in the new ultrasonic image.

[0036] A water conservancy project concrete quality detection device comprises 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 methods when executing the computer program.

[0037] The beneficial effects of the technical solution of the present invention are as follows: through automated analysis and merging of initial regions, crack distribution analysis can be performed over a larger area, making crack rate calculation more comprehensive and detailed. By merging and processing initial regions, errors in human operation are reduced, improving efficiency and accuracy. In addition, the erosion coefficient of the concrete sample is determined based on the composition of the water it contacts, which means that this method takes into account the impact of environmental factors on concrete quality, making quality assessment more realistic and targeted. By comprehensively analyzing basic information data, erosion coefficient, and crack rate, the constructed concrete quality estimation model can provide accurate quality assessments for new concrete samples, assist in effective water conservancy project maintenance and decision-making, improve detection accuracy and efficiency, and meet the needs of concrete quality testing in large-scale water conservancy projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is a flow chart of the steps of a method for detecting the quality of concrete in a water conservancy project according to the present invention;

[0040] Figure 2 This is an example of an ultrasound image of a normal concrete sample;

[0041] Figure 3 An example of an ultrasonic image of a cracked concrete sample. DETAILED DESCRIPTION

[0042] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a hydraulic engineering concrete quality testing device and method according to the present invention. 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.

[0043] 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 invention belongs.

[0044] The specific scheme of the water conservancy project concrete quality detection equipment and method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flow chart of a method for detecting the quality of concrete in a water conservancy project provided by one embodiment of the present invention, the method comprising the following steps:

[0046] Step S001: Obtain basic information data and ultrasonic images of concrete samples.

[0047] It should be noted that the concrete structures in water conservancy projects are subject to a variety of external environmental influences such as long-term water flow, load, temperature difference, chemical corrosion, etc. If the quality of concrete does not meet the standards, it may lead to problems such as concrete cracking, leakage, insufficient compressive strength, etc., thereby affecting the overall safety of the project. Therefore, regular and comprehensive concrete quality inspections can effectively discover hidden dangers and take timely maintenance or reinforcement measures to ensure the service life and safety of the project. When testing the quality of concrete, in order to accurately analyze the quality of concrete, the embodiment of the present invention chooses to analyze the basic information data and pouring information data of the concrete sample to improve the accuracy of the detection of concrete quality.

[0048] Specifically, in order to implement the water conservancy project concrete quality detection method proposed in this embodiment, it is first necessary to collect concrete samples and basic information data and ultrasonic images of the concrete samples. The specific process is as follows:

[0049] First, the concrete that needs to be quality tested is sampled to obtain several concrete samples, and the basic information data and pouring information data of each concrete sample are recorded in detail.

[0050] Then, the concrete sample is C-scanned by an ultrasonic device to obtain a corresponding ultrasonic image, the ultrasonic image is filtered and the relative depth value corresponding to the gray value of each pixel in the ultrasonic image is obtained.

[0051] like Figure 2 The figure shows an example of an ultrasonic image of a normal concrete sample, in which there are side wall echo areas formed by the reflection of ultrasound from the two concrete walls on both sides; Figure 3 The figure shows an example of an ultrasonic image of a cracked concrete sample. When a crack exists in the concrete sample, there is a corner echo area corresponding to the crack between the two side walls.

[0052] The basic information data of the concrete samples include the sampling location, date, concrete type and pouring time. It should be noted that in order to ensure that the concrete facilities of the water conservancy project can be comprehensively inspected for quality, the concrete samples obtained during sampling should include concrete of different concrete types, different usage times and different load conditions.

[0053] So far, the concrete sample and its basic information data and ultrasonic image are obtained through the above method.

[0054] Step S002: Based on the relative depth values ​​of the pixel points in the ultrasonic image, the ultrasonic image is segmented to obtain a number of initial regions, and the contours and depth information of the initial regions are analyzed to determine the crack rates of the initial regions; based on the crack rates of different initial regions, the merging properties between the initial regions are analyzed and the initial regions are merged to obtain new regions; based on the crack rates corresponding to all new regions in the new ultrasonic image, the average relative depth values ​​within the new regions, and the morphological characteristics of the new regions, the overall crack degree of the concrete sample corresponding to the new ultrasonic image is obtained.

[0055] It should be noted that in order to obtain the relationship between other concrete-related properties and the crack size obtained by ultrasonic testing, it is necessary to first confirm the internal crack size of the concrete sample through ultrasonic testing, and confirm the relationship between the crack size and the concrete crack size. In this embodiment, the crack size in the concrete sample is confirmed by ultrasonic C-scanning, and the degree of cracks in the sample is judged by the obtained C-scan image. A C-scan is performed on a concrete sample containing cracks. In the ultrasonic image of concrete, the shape of the crack area is generally narrow and long, and due to the depth of the crack, the corresponding relative depth value in the ultrasonic scanning image is different from that of the normal area. The normal area is represented by a smaller relative depth value, but the closer the area is to the contour structure in the sample, the more likely it is that the area represents a contour rather than a crack area, and the contour area is represented by a larger relative depth value.

[0056] Step S201 : performing region segmentation on the ultrasonic image based on relative depth values ​​of pixels in the ultrasonic image to obtain a number of initial regions, and analyzing contours and depth information of the initial regions to determine crack rates of the initial regions.

[0057] As an embodiment, the specific method of performing region segmentation on the ultrasound image to obtain a plurality of initial regions includes: processing the ultrasound image using a region growing algorithm to obtain a plurality of initial regions.

[0058] As an embodiment, a specific method for calculating the crack rate of the initial area includes:

[0059] First, the average value of the relative depth values ​​corresponding to all pixel points in any initial area is obtained, the aspect ratio of the minimum circumscribed rectangle of the arbitrary initial area is obtained, the minimum relative depth value and the maximum relative depth value in the ultrasound image are obtained, and the crack factor of the initial area is calculated according to the numerical level of the initial area in the ultrasound image relative to the minimum and maximum relative depth values ​​in the ultrasound image, as well as the shape contour of the initial area.

[0060] As a preferred embodiment, the method for obtaining the crack factor is: for any initial area, respectively obtain the absolute value of the difference between the average relative depth value of all pixel points in the initial area and the maximum relative depth value and the minimum relative depth value in the ultrasound image, and record them as the first difference and the second difference, respectively; according to the first difference, the second difference and the aspect ratio of the minimum circumscribed rectangle of the initial area, obtain the crack factor of the initial area, wherein the crack factor is positively correlated with the first difference, the second difference and the aspect ratio of the minimum circumscribed rectangle.

[0061] As a specific embodiment, the specific calculation method of the crack factor of the initial area is:

[0062]

[0063] in, For the The crack factor of the initial region, For the The average relative depth value of all pixels in the initial area, is the maximum relative depth value in the ultrasound image, is the minimum relative depth value in the ultrasound image, is the aspect ratio of the minimum bounding rectangle of the Dth initial area, is the absolute value symbol, is a linear normalization function.

[0064] It should be noted that As a whole, the more severe the average depth value of the region is biased towards one extreme, the lower the probability that the region is a crack region. The closer its aspect ratio is to 1, the lower the probability that it is a crack region. In addition, the closer the distance is to the region with a higher probability of belonging to the contour, the lower the probability that it is a crack, and the higher the probability that it is a contour edge.

[0065] Then, the crack factor of the initial area is corrected using the average relative depth values ​​of other initial areas and the shortest distance between different initial areas to obtain the crack rate of the initial area.

[0066] As a preferred embodiment, the method for obtaining the crack rate is: record any initial area as the target initial area, determine the average relative depth value of all pixel points in each initial area except the target initial area, and record it as the analysis relative depth value; obtain the difference between the maximum relative depth value and the analysis relative depth value in the ultrasound image, and record it as the first relative difference of the initial area outside the target initial area; obtain the product of the minimum distance between the target initial area and each initial area outside the target initial area and the first relative difference, and record it as the relative rate of the target initial area; obtain the crack rate of the target initial area based on the crack factor and the relative rate of the target initial area, and the crack rate is positively correlated with the crack factor and the relative rate.

[0067] As an optional embodiment, the specific calculation method of the crack rate of the initial area is:

[0068]

[0069] in, For the The crack rate of the initial region, For the The crack factor of the initial region, is the maximum relative depth value in the ultrasound image, To exclude outside the initial region The average relative depth value of all pixels in the initial area, For the The initial region and the The minimum distance of the initial region, is the number of initial regions in the ultrasound image.

[0070] It should be noted that because the same crack in concrete may be identified as multiple independent regions in the ultrasound image due to uneven relative depth distribution, this can affect the effectiveness of concrete quality assessment. Therefore, it is necessary to merge the initial regions based on their positional relationship characteristics. Specifically, for regions representing the same crack, if they are adjacent in spatial position and have similar probabilities of representing crack regions, the probability of the merged region representing a crack should be higher than that of any of the original regions. The greater the increase in probability, the more necessary it is to merge the two regions.

[0071] Step S202 : analyzing the merging properties of adjacent initial regions based on the crack rates of different initial regions and merging the adjacent initial regions based on the merging properties to obtain new regions.

[0072] As a preferred embodiment, the specific method for obtaining the new area includes: first, calculating the merging property of the two adjacent initial areas based on the crack rates corresponding to any two adjacent initial areas, the crack rates corresponding when the two adjacent initial areas are regarded as one area, and the number of pixels on the common boundary line between the two adjacent initial areas; then, merging the two adjacent initial areas whose merging property is greater than or equal to a preset merging threshold into one area, and recording the image obtained after merging all initial areas in the ultrasound image that meet the merging threshold condition as a new ultrasound image, and recording the area in the new ultrasound image as a new area.

[0073] It should be noted that the merging threshold is preset to 0.8 based on experience and can be adjusted according to actual conditions, and is not specifically limited in the embodiment of the present invention.

[0074] As a preferred embodiment, the merging property of the two adjacent initial regions is calculated, and the specific method includes: obtaining the adjacent initial regions and the initial region , get the initial area and the initial region The corresponding crack rate as a region ; The initial area and the initial region The crack rates are recorded as and , get and 、 The minimum value of the difference between the two corresponding values ​​is recorded as the merged similarity; when the merged similarity is not 0, the merged similarity is used to compare with the adjacent initial area. and the initial region The initial area is obtained by multiplying the number of pixels on the common boundary line between and the initial region The merging similarity between them; when the merging similarity is 0, the initial region and the initial region The mergeability between them is 0.

[0075] As an optional embodiment, a specific method for calculating the merging property between any two adjacent initial regions is as follows:

[0076]

[0077] in, For the adjacent initial area and the initial region The merging between The initial area The crack rate, The initial area The crack rate, To make the initial area and the initial region As a region, the corresponding crack rate is For the adjacent initial area and the initial region The number of pixels on the common boundary line between To obtain the minimum value, To obtain the maximum value, is a linear normalization function.

[0078] It should be noted that when it is necessary to calculate the crack size of the entire sample, the greater the possibility that a certain area represents a crack area, the greater the weight of the area when calculating the overall crack size, and the greater the relative depth value of the crack area, the higher the crack size of the overall sample.

[0079] Step S203 , obtaining the overall crack degree of the concrete sample corresponding to the new ultrasonic image based on the crack rates corresponding to all new areas in the new ultrasonic image, the average relative depth values ​​in the new areas, and the morphological characteristics of the new areas.

[0080] It should be noted that the calculation method of the crack rate corresponding to the new area is consistent with the crack rate of the initial area.

[0081] The crack coefficient of the concrete sample is calculated by combining the crack ratio of all new areas in the new ultrasonic image and the average relative depth value of all pixels in the new area.

[0082] As an embodiment, the specific calculation method of the crack coefficient of the concrete sample is:

[0083]

[0084] in, is the crack coefficient of the concrete sample, The new ultrasound image The crack rate of the new area, The new ultrasound image The average relative depth value of all pixels in the new area, is the number of new regions in the new ultrasound image, is a linear normalization function.

[0085] It should be noted that during long-term use, hydraulic engineering structures are subject to stress changes caused by changes in external loads, which may cause existing cracks to continue to expand. If cracks expand, they will affect the structure's bearing capacity and may even cause structural damage, especially in critical areas. Crack expansion may cause serious accidents. At the same time, further crack expansion may lead to water infiltration, which in turn accelerates steel corrosion, concrete spalling and other diseases, thereby shortening the service life of the structure. Therefore, it is necessary to determine the extension probability of cracks in concrete and further calculate the crack extent of the entire sample.

[0086] It should be noted that different types of cracks may extend in different ways. Generally, transverse cracks are easier to extend than longitudinal cracks. The depth and width of the crack will affect the possibility of its extension. Therefore, the greater the possibility that a certain area obtained in the above process represents a crack area, and the larger the crack size in the corresponding area, the greater the possibility of crack extension.

[0087] The crack degree of the new area is calculated based on the distribution direction of the morphological skeleton of the new area, the crack rate and the average relative depth value of the pixels in the area. The angle is negatively correlated with the crack degree, and the crack rate and the average relative depth value are both positively correlated with the crack degree.

[0088] As an example, the specific calculation method of the crack degree of the new area is:

[0089]

[0090] in, Indicates the the extent of cracks in the new area; Indicates the The angle between the fitted straight line of the connected domain skeleton of each new region and the horizontal line; Indicates the crack rate in each new area; Indicates the The average relative depth value in the new area.

[0091] The method for obtaining the fitting straight line of the connected domain skeleton of the new region is: using morphology to extract the skeleton of the new region, and then performing straight line fitting by the least square method.

[0092] The overall crack degree of the concrete sample is calculated by combining the crack coefficient of the concrete sample and the crack degree of the new area in the new ultrasonic image.

[0093] The overall crack degree of the concrete sample is positively correlated with the crack coefficient of the concrete sample, and the overall crack degree of the concrete sample is positively correlated with the cumulative value of the crack degrees of all new areas in the new ultrasonic image.

[0094] As an optional embodiment, the specific method for calculating the overall crack degree of the concrete sample is:

[0095]

[0096] in, Indicates the overall cracking extent of the concrete sample; is the crack coefficient of the concrete sample; Indicates the The degree of cracks in the new area, is the number of new regions in the new ultrasound image.

[0097] So far, the overall crack degree of the concrete sample has been obtained through the above method.

[0098] Step S003: Obtain the erosion coefficient of the concrete sample according to the composition of the water body that the concrete sample contacts, and construct a concrete estimation model by combining the basic information data of the concrete sample and the erosion coefficient.

[0099] It should be noted that during the use of concrete buildings in water conservancy projects, the development of building cracks is mainly related to the size of aggregates, the roughness of aggregates, the load bearing strength and the use time of the building during the concrete preparation process. The size of aggregates and the roughness of aggregates mainly determine the degree of bonding cracks in the building at the beginning of the concrete pouring. The degree of load borne and the use time mainly determine the degree of development of cracks compared to the initial state. At the same time, the chemical erosion of concrete by water bodies encountered in water conservancy projects should also be considered.

[0100] According to the basic information data corresponding to the collected samples, the preparation information of the concrete during design is obtained to confirm the size and roughness of the aggregate used, which are recorded as and At the same time, the load intensity of the structure to which the sample belongs is determined according to the water conservancy project building structure to which the sample belongs, and recorded as , the usage time of the building is recorded as At the same time, the degree of water erosion on concrete during use is recorded as ,These factors jointly affect the current degree of cracks in concrete buildings.

[0101] The load intensity is obtained by the method in the "Code for Load Design of Hydraulic Structures SL744-2016".

[0102] It should be noted that in the above process, in order to confirm the degree of water erosion on concrete during use, it is necessary to combine the water composition that can be exposed during use and the content of various components to confirm it, and confirm the degree of erosion of various components on concrete through relevant tests and inspections.

[0103] The components in the water that the concrete sample contacts that will corrode the concrete, the content of the components, and the degree of corrosion of the concrete by each component at different contents are obtained. The corrosion coefficient of the concrete sample is obtained through the content of each component in the water that the concrete sample contacts and the corresponding degree of corrosion.

[0104] As an optional embodiment, the specific calculation method of the erosion coefficient is:

[0105]

[0106] in, is the erosion coefficient of the concrete sample, The amount of components in the water that the concrete sample is exposed to that will corrode the concrete. The first water in the water that the concrete sample is exposed to will corrode the concrete. The content of each ingredient, The first water in the water that the concrete sample is exposed to will corrode the concrete. The degree of corrosion of concrete by each component at the corresponding content.

[0107] Among them, the method for obtaining the degree of corrosion of concrete by various components at different contents in the water to which the concrete sample is exposed is as follows:

[0108] First, the pH value (acidity) of the water sample is determined by laboratory testing. 、 、 、 Plasma concentration (unit: mg / L), temporary hardness (reflects content).

[0109] Then, the concrete samples were placed at different pH values ​​or The mass loss and strength change were regularly detected in the solution of the concentration; the specimens were observed to The expansion rate and cracking time under different concentrations can be tested; the actual environment can be simulated to detect the penetration depth of chloride ions and the degree of damage of magnesium salts to the gel structure.

[0110] Finally, based on the degree of mass change of concrete samples before and after treatment with various components at different contents during the experiment, the degree of erosion of concrete by various components at different contents in the water to which the concrete samples were exposed was quantified.

[0111] In order to fit the model, the data needs to be standardized or normalized to eliminate the influence of different dimensions. Taking into account the nonlinear relationship between various characteristics and the degree of concrete cracks, a nonlinear model is used to regress and predict this relationship and quantitatively analyze the relationship.

[0112] As an embodiment, a concrete quality estimation model is constructed by combining the erosion degree and the nonlinear function. The specific expression of the concrete quality prediction model is:

[0113]

[0114] in, is the concrete quality parameter, is the aggregate size in the concrete sample, is the roughness of the concrete sample, is the load strength of the structure where the concrete sample is located, is the service life of the structure where the concrete sample is located, is the erosion coefficient, parameter 、 、 、 as well as are all fitting parameters.

[0115] Finally, the actual concrete quality parameter data recorded was used to compare the parameters , , , , Perform fitting estimation to obtain the final concrete quality estimation model.

[0116] So far, the concrete quality estimation model is obtained through the above method.

[0117] Step S004: using the concrete quality estimation model to estimate the quality of the new concrete sample.

[0118] When a new water conservancy project design needs to verify whether the degree of concrete cracks used in various locations can always meet the requirements within the service life, the degree of concrete cracks at the longest service life is predicted based on the relationship between the degree of concrete cracks obtained by the above fitting and other data, and the qualified threshold value of the concrete crack degree of different structures is set in advance. If it is calculated that the concrete at a certain location does not meet the threshold requirements of the corresponding structure within the longest service life, the concrete needs to be adjusted during the design based on the difference between the predicted value and the expected value.

[0119] Through the above steps, the concrete quality inspection of water conservancy projects is completed.

[0120] An embodiment of the present invention also provides a water conservancy project concrete quality detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of a water conservancy project concrete quality detection method described in steps S001 to S004.

[0121] By automatically analyzing and merging initial regions, crack distribution analysis can be performed over a larger area, making crack rate calculations more comprehensive and detailed. By merging initial regions, human error is reduced, improving efficiency and accuracy. Furthermore, the erosion coefficient of a concrete sample is determined based on the composition of the water it contacts, meaning this method considers the impact of environmental factors on concrete quality, making quality assessments more realistic and targeted. By comprehensively analyzing basic information data, erosion coefficients, and crack rates, the constructed concrete quality estimation model can provide accurate quality assessments for new concrete samples, assisting in effective water conservancy project maintenance and decision-making, improving detection accuracy and efficiency, and meeting the needs of concrete quality testing in large-scale water conservancy projects.

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting the quality of concrete in a water conservancy project, characterized in that: The method comprises the following steps: Obtain basic information data and ultrasonic images of concrete samples; The ultrasonic image is segmented based on the relative depth values ​​of the pixels in the ultrasonic image to obtain several initial regions. The contours and depth information of the initial regions are analyzed to determine the crack rates of the initial regions. The initial regions are analyzed for merging ability based on the crack rates of the different initial regions and the initial regions are merged to obtain new regions. The overall crack degree of the concrete sample corresponding to the new ultrasonic image is obtained based on the crack rates corresponding to all new regions in the new ultrasonic image, the average relative depth values ​​within the new regions, and the morphological characteristics of the new regions. The erosion coefficient of the concrete sample is obtained based on the composition of the water body that the concrete sample contacts, and a concrete quality estimation model is constructed by combining the basic information data of the concrete sample and the erosion coefficient; Use the concrete quality estimation model to estimate the quality of new concrete samples; The specific method of analyzing the profile and depth information of the initial area to determine the crack rate of the initial area includes: First, the average value of the relative depth values ​​corresponding to all pixel points in any initial region is obtained, the aspect ratio of the minimum circumscribed rectangle of the arbitrary initial region is obtained, the minimum relative depth value and the maximum relative depth value in the ultrasound image are obtained, and the crack factor of the initial region is calculated based on the numerical level of the initial region in the ultrasound image relative to the minimum and maximum relative depth values ​​in the ultrasound image, as well as the shape contour of the initial region; Then, the crack factor of the initial area is corrected using the average relative depth values ​​of other initial areas and the shortest distance between different initial areas to obtain the crack rate of the initial area. The specific calculation method of the crack factor is: For any initial region, obtain the absolute values ​​of the differences between the average relative depth value of all pixels in the initial region and the maximum relative depth value and the minimum relative depth value in the ultrasound image, and record them as the first difference and the second difference respectively; Obtaining a crack factor of the initial region according to the first difference, the second difference, and the aspect ratio of the minimum circumscribed rectangle of the initial region, wherein the crack factor is positively correlated with the first difference, the second difference, and the aspect ratio of the minimum circumscribed rectangle; The specific calculation method of the crack rate is: Record any initial area as a target initial area, and determine the average relative depth value of all pixels in each initial area except the target initial area as the analysis relative depth value; Obtaining a difference between a maximum relative depth value and an analysis relative depth value in the ultrasound image, and recording the difference as a first relative difference of the initial area outside the target initial area; Obtaining a product cumulative value of the minimum distance between the target initial area and each initial area outside the target initial area and the first relative difference, and recording the value as a relative rate of the target initial area; Obtaining a crack rate of the target initial region according to the crack factor and the relative rate of the target initial region, wherein the crack rate is positively correlated with the crack factor and the relative rate; The method of analyzing the merging properties of the initial regions by combining the crack rates of different initial regions and merging the initial regions to obtain new regions includes: First, the merging properties of any two adjacent initial regions are calculated based on the crack rates corresponding to each of the two adjacent initial regions, the crack rates corresponding to the two adjacent initial regions when the two adjacent initial regions are considered as one region, and the number of pixels on the common boundary line between the two adjacent initial regions. Then, two adjacent initial regions whose merging property is greater than or equal to a preset merging threshold are merged into one region, and the image obtained by merging all initial regions in the ultrasound image that meet the merging threshold condition is recorded as a new ultrasound image, and the region in the new ultrasound image is recorded as a new region; The specific method for calculating the merging property of the two adjacent initial regions is as follows: Get the adjacent initial area and the initial region , get the initial area and the initial region The corresponding crack rate as a region ; The initial area and the initial region The crack rates are recorded as and , get and 、 The minimum value of the corresponding differences between them is recorded as the combined similarity; When the merged similarity is not 0, the merged similarity is combined with the adjacent initial area and the initial region The initial area is obtained by multiplying the number of pixels on the common boundary line between and the initial region The merging similarity between them; when the merging similarity is 0, the initial region and the initial region The mergeability between them is 0.

2. A method for detecting the quality of concrete in a water conservancy project according to claim 1, characterized in that: The method of obtaining the overall crack degree of the concrete sample corresponding to the new ultrasonic image based on the crack rate corresponding to all new areas in the new ultrasonic image and the average relative depth value in the new area, combined with the morphological characteristics of the new area, includes the following specific methods: The crack coefficient of the concrete sample is calculated by combining the crack rate of all new areas in the new ultrasonic image and the average relative depth value of all pixels in the new area; Calculate the crack degree of any new area based on the distribution direction of the morphological skeleton, crack rate, and relative depth values ​​of the pixels in the area; The overall crack extent of the concrete sample is calculated by combining the crack coefficient of the concrete sample and the crack extent of all new areas in the new ultrasonic image.

3. A method for detecting the quality of concrete in water conservancy projects according to claim 2, characterized in that: The specific method for obtaining the crack degree of the new area is: For any new region, the skeleton of the new region is extracted using morphology, and then a line fitting is performed using the least squares method to obtain the angle between the fitted line of the connected domain skeleton of the new region and the horizontal line; The crack degree of the new area is calculated based on the angle between the fitting straight line and the horizontal line of the new area, the crack rate and the average relative depth value of the pixels in the area. The angle is negatively correlated with the crack degree, and the crack rate and the average relative depth value are both positively correlated with the crack degree.

4. A method for detecting the quality of concrete in a water conservancy project according to claim 2, characterized in that: The calculating of the overall crack degree of the concrete sample also includes: the overall crack degree of the concrete sample is positively correlated with the crack coefficient of the concrete sample, and the overall crack degree of the concrete sample is positively correlated with the cumulative value of the crack degrees of all new areas in the new ultrasonic image.

5. A water conservancy project concrete quality 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 according to any one of claims 1 to 4 are implemented.

Images