A cold excitation based infrared detection and quantification method for CFST arch bridge voids

Abstract

The present invention discloses a method for infrared detection and quantification of hollowing of CFST arch bridges based on cold excitation, and belongs to the technical field of steel tube concrete bridge detection. The method comprises: shooting infrared temperature images of steel tube concrete test areas before and after controllable cooling excitation; regularizing the images and calculating the real area of ​​a single pixel after regularization, performing a first cropping and a second cropping on the regularized images, extracting the temperature matrix before and after cooling excitation through the cropped infrared images, and obtaining a temperature difference matrix. The average value and standard deviation of the temperature difference matrix are then calculated, and a hollowing threshold is obtained through the calculation of the average value and the standard deviation. The hollowing threshold is used to determine hollowing of the infrared image and to count the number of hollowing pixels. The hollowing area is finally calculated according to the scale. The present invention can simply and accurately perform quantitative infrared detection of hollowing of CFST arch bridges, and realize quantitative detection of hollowing of steel tube concrete.

Description

Technical Field

[0001] The invention relates to the technical field of steel tube concrete bridge detection, and more specifically to a cold excitation-based CFST arch bridge hollow infrared detection and quantification method. Background Art

[0002] At present, the detection methods for defects such as hollowing and debonding of CFST arch bridges include percussion method, ultrasonic method and other technical means. However, the above methods are limited by the small effective detection distance (usually close detection is required), and they are inefficient and require manual high-altitude work when facing the detection tasks of long and large CFST structures (such as large-span CFST arch bridges). Infrared detection method is an existing non-destructive testing technology. Infrared detection method has the characteristics of long-distance non-contact detection, so it can effectively solve the above problems.

[0003] However, the current infrared thermal imaging method cannot achieve quantitative detection of the void area well. There are methods such as principal component analysis and two-dimensional Fourier analysis for void detection. However, the above methods are relatively complex, with large amount of calculation, slow calculation speed, and low detection accuracy, making them difficult to be widely used in actual detection. In recent years, there are also methods proposed to use cooling excitation to enhance the contrast of infrared void areas. However, although cooling excitation can quickly improve the contrast, it is still impossible to quantitatively detect the area of ​​the void area.

[0004] Therefore, how to propose a computationally simple and high-precision infrared detection and quantification method for CFST arch bridge voids based on cold excitation is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a CFST arch bridge void infrared detection and quantification method based on cold excitation. On the basis of enhancing the void contrast by cooling excitation, the void of CFST arch bridge is simply and highly accurately quantitatively detected by infrared, thereby realizing the quantitative detection of voids in steel tube concrete.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A cold excitation-based infrared detection and quantification method for CFST arch bridge voids, comprising:

[0008] S100: judging whether the tested area of ​​the steel tube concrete meets the void detection condition, and if so, proceeding to S200 to carry out void quantitative detection;

[0009] S200: Setting up an infrared device and a water spraying device, spraying water to cool down the steel tube concrete to be tested through the water spraying device, and at the same time, collecting an infrared image of the area to be tested through the infrared device;

[0010] S300: performing regularization processing on the infrared image by using an image regularization algorithm, calculating the scale of the infrared image, obtaining a real area corresponding to a single pixel, and shearing the infrared image to obtain a sheared infrared image;

[0011] S400: extracting the temperature field matrix before and after the water spray cooling excitation based on the cut infrared image, and obtaining the temperature field difference matrix;

[0012] S500: Calculate the average value of the temperature field difference matrix and calculate the standard deviation of the temperature field difference matrix;

[0013] S600: Calculating the empty threshold;

[0014] S700: performing a hollow determination on the area to be tested, counting the number of hollow pixel points in the infrared image, and calculating the hollow area;

[0015] S800: Determine whether the detection of all the areas to be tested is completed, and if so, output the detection results;

[0016] If not, the measurement area is switched and S100 - S700 are repeated until all the areas to be measured are detected and the detection results are output.

[0017] Preferably, the step S100: judging whether the tested area of ​​the tested steel tube concrete meets the void detection condition is:

[0018] Determine whether the temperature of the area to be tested is higher than the external environment temperature.

[0019] Preferably, the water spraying device controls the atomization degree to be small when spraying water to cool the steel tube concrete to be tested, the temperature of the cooling water is lower than the ambient temperature, and the excitation time for each 0.25 cubic meter area to be tested is 125 seconds.

[0020] Preferably, the acquisition frequency of the infrared device is selected as 1H Z .

[0021] Preferably, the step of shearing the infrared image to obtain the sheared infrared image comprises:

[0022] Primary cropping: cropping the infrared image to obtain an infrared image of only the area to be measured, wherein the area to be measured does not contain any other uneven structures except the main structure of the concrete-filled steel tube;

[0023] Secondary cropping: only the area in the infrared image of the area to be measured where the angle between the surface normal direction and the direction of the infrared device is less than 60° is retained to obtain a cropped infrared image.

[0024] Preferably, the expression of the temperature field difference matrix is:

[0025] ΔT=T before -T after ;

[0026] Where, T before is the temperature field matrix before water spray cooling excitation, T after is the temperature field matrix after water spray cooling excitation, and ΔT is the temperature field difference matrix.

[0027] Preferably, the S500 includes:

[0028] Calculate the average value T of the temperature field difference matrix mean , the expression is:

[0029]

[0030] Where n is the total pixels of the cropped infrared image, ΔT i is the temperature value of the i-th pixel in the cut infrared image;

[0031] Calculate the standard deviation T of the temperature field difference matrix st , the expression is:

[0032]

[0033] Where n is the total pixels of the cropped infrared image, T mean is the average value of the temperature field difference matrix, ΔT i is the temperature value of the i-th pixel in the cropped infrared image.

[0034] Preferably, the S600 includes:

[0035] Calculate the empty threshold δ, the expression is:

[0036] δ=T mean +mT st ;

[0037] In the formula, δ is the empty threshold, T mean is the average value of the temperature field difference matrix, T st is the standard deviation of the temperature field difference matrix, and m is the correlation coefficient.

[0038] Preferably, the process of determining whether the area to be detected is empty includes:

[0039] Traverse the temperature field difference matrix to determine each pixel in the cut infrared image:

[0040] When ΔTi>δ, the i-th pixel in the sheared infrared image is missing;

[0041] When ΔTi≤δ, the i-th pixel in the sheared infrared image is not missing.

[0042] Preferably, the number of pixels with gaps in the infrared image is counted, and the gap area is calculated, and the expression is:

[0043] A d =A r ×D;

[0044] In the formula, A r is the actual area corresponding to a single pixel, D is the number of pixels missing in the cropped infrared image, and A d The empty area.

[0045] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a cold-excitation-based infrared detection and quantification method for CFST arch bridge voids, which realizes the effective detection and quantitative evaluation of void areas in steel tube concrete structures. The specific beneficial effects are as follows:

[0046] (1) Using infrared thermal imaging technology, subtle changes in temperature distribution can be quickly captured without contacting the object to be tested, thus providing a non-invasive and efficient solution for detection.

[0047] (2) By regularizing and precisely cropping the infrared image, the accuracy and reliability of the data are ensured, and only the key information related to the surface to be tested is retained, thereby improving the specificity of the detection.

[0048] (3) The accuracy of air gap detection is improved by using the water spray cooling method.

[0049] (4) By calculating the mean and standard deviation of the temperature difference matrix and setting the void threshold accordingly, accurate judgment and quantification of the void area can be achieved.

[0050] (5) By counting the number of void pixels and combining it with scale calculation, the actual area of ​​the void area can be accurately calculated, providing important data support for bridge maintenance and repair.

[0051] In summary, the present invention provides a CFST arch bridge void infrared detection and quantification method based on cold excitation. On the basis of enhancing the void contrast by cooling excitation, the void of the CFST arch bridge can be simply and highly accurately quantitatively detected by infrared, thereby realizing quantitative detection of voids in steel tube concrete. At the same time, it can greatly improve the accuracy and efficiency of void detection of CFST arch bridge, which has important practical value for ensuring the safety of bridge structures and extending their service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0053] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0054] Figure 2 Schematic diagram of the regularization process;

[0055] Figure 3 This is a schematic diagram of the scale calculation process;

[0056] Figure 4 This is a schematic diagram of a cutting process;

[0057] Figure 5 Schematic diagram of the secondary cutting process;

[0058] Figure 6 This is a schematic diagram of the temperature field interpolation matrix calculation process;

[0059] Figure 7 This is a schematic diagram of the temperature field difference calculation results;

[0060] Figure 8 It is a schematic diagram of the process of determining the air escape;

[0061] Fig. 9 This is a schematic diagram of the results of the air escape determination. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1 As shown, the embodiment of the present invention discloses a cold excitation-based CFST arch bridge hollow infrared detection and quantification method, including:

[0064] S100: judging whether the tested area of ​​the steel tube concrete meets the void detection condition, and if so, proceeding to S200 to carry out void quantitative detection;

[0065] S200: Setting up an infrared device and a water spraying device, spraying water to cool down the steel tube concrete to be tested through the water spraying device, and at the same time, collecting an infrared image of the test area of ​​the steel tube concrete to be tested through the infrared device;

[0066] S300: performing regularization processing on the infrared image by using an image regularization algorithm, calculating the scale of the infrared image, obtaining a real area corresponding to a single pixel, and shearing the infrared image to obtain a sheared infrared image;

[0067] S400: extracting the temperature field matrix before and after the water spray cooling excitation based on the cut infrared image, and obtaining the temperature field difference matrix;

[0068] S500: Calculate the average value of the temperature field difference matrix and calculate the standard deviation of the temperature field difference matrix;

[0069] S600: Calculating the empty threshold;

[0070] S700: performing a hollow determination on the area to be tested, counting the number of hollow pixel points in the infrared image, and calculating the hollow area;

[0071] S800: Determine whether the detection of all the areas to be tested is completed, and if so, output the detection results;

[0072] If not, the measurement area is switched and S100 - S700 are repeated until all the areas to be measured are detected and the detection results are output.

[0073] In a specific embodiment, the specific steps of the present invention are shown by taking infrared hollow detection of a steel pipe segment as an example:

[0074] Step 1: Environmental determination, determine whether the temperature of the concrete-steel plate interface of the steel tube concrete is higher than the external environment. If it meets the requirements, the quantitative detection of voids can be carried out. The steel tube concrete is in the hydration heat stage, and the temperature of the concrete-steel plate interface is significantly higher than the external environment temperature. The detection can be started.

[0075] Step 2: Set up the infrared camera. Set up the infrared camera so that the center of the camera screen coincides with the area to be tested as much as possible. Adjust the camera focal length to make the image clear. The camera should remain relatively still with the test area during the subsequent process.

[0076] Step 3: Infrared image acquisition, start infrared temperature field acquisition, and select the acquisition frequency as 1H Z .

[0077] Step 4: Water spray cooling and excitation. Use water spray equipment to spray water to cool the specimen. When spraying water, control the atomization degree to a small atomization degree. The temperature of the cooling water should be slightly lower than (2°C) the ambient temperature. The excitation time for each 0.25 square meter test area is 125 seconds.

[0078] Step 5: Infrared images are continuously acquired. After the cooling excitation is completed, the infrared device continues to collect temperature fields for 10 seconds and then stops collecting temperature fields.

[0079] Step 6: Image regularization, regularize the image through a straight rectangular area in the image. Regularize each frame of infrared image (regularization algorithms include but are not limited to: perspective transformation method, PNP algorithm, etc.), refer to the process Figure 2 .

[0080] Step 7: Scale conversion. Calculate the infrared image scale through the regularized image to obtain the actual area A corresponding to a single pixel. r (The infrared image scale, i.e. the real area Ar corresponding to a single pixel, can be determined by the ratio of the actual size of the object being measured to the size of the infrared image.) Figure 3 .

[0081] Step 8: Crop all infrared images at once so that only the area to be tested is included in the image, and the area to be tested does not contain any other uneven structures (web members, flanges, welds, etc.) except the main structure of the steel tube concrete. The process is shown in Figure 4 .

[0082] Step 9: Secondary cropping: perform secondary cropping on the infrared image after the first cropping, and only keep the area where the angle between the surface normal direction and the camera direction is less than 60° in the test area. For the process, refer to Figure 5 .

[0083] Step 10: Extract the temperature matrix before and after the excitation. Extract the secondary cutting temperature field matrix of the frame before the cooling excitation as T before , extract the secondary cutting temperature field matrix of one frame after the water spraying is T after .

[0084] Step 11: Calculate the temperature field difference matrix through T before Subtract T after Get the temperature field difference matrix ΔT, the process is as follows Figure 6 ;

[0085] See also Figure 7 As shown in the figure, it is a schematic diagram of temperature field difference calculation. In the figure, the color card from blue to red indicates that the temperature difference gradually increases, and each pixel in the image represents the temperature difference of that point.

[0086] Step 12: Calculate the average value of the temperature field difference matrix and calculate the average value T of the ΔT matrixmean ,Right now:

[0087]

[0088] Where n is the total pixels of the image after secondary cropping, ΔT i is the temperature value of the “i”th pixel in the cropped image;

[0089] Step 13: Calculate the standard deviation of the temperature field difference matrix and calculate the standard deviation T of the ΔT matrix st ,Right now:

[0090]

[0091] Where n is the total pixels of the image after secondary cropping, ΔT i is the temperature value of the “i”th pixel in the cropped image;

[0092] Step 14: Calculate the empty threshold value, calculate the empty threshold value (δ), that is:

[0093] δ=T mean +mT st ;

[0094] In the formula, δ is the empty threshold, T mean is the average value of the temperature field difference matrix, T st is the standard deviation of the temperature field difference matrix, and m is the correlation coefficient, which can be determined through experiments. The experimental measurement of m = 1, and the calculation results show that δ = 1.166, see Fig. 9 shown.

[0095] Step 15: Empty area determination, determine the empty area, traverse the ΔT matrix to determine each pixel, when ΔT i >δ, it is considered that the point is empty. i When ≤δ, it is considered that the point has not been de-aired. For the process, refer to Figure 8 .

[0096] Step 16: Counting the missing points, counting the number of pixels with missing points in the image (D);

[0097] Step 17: Calculate the void area using formula A d =A r ×D to calculate the empty area, where "A d " is the detected empty area. The calculated empty area is 15625mm 2 ;

[0098] Step 18: Switch the measurement area, adjust the camera position to the next area and repeat all the above steps until all the areas to be measured are inspected.

[0099] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0100] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cold excitation based infrared detection and quantification method for CFST arch bridge voids, characterized in that: include: S100: judging whether the tested area of ​​the steel tube concrete meets the void detection condition, and if so, proceeding to S200 to carry out void quantitative detection; S200: Setting up an infrared device and a water spraying device, spraying water to cool down the steel tube concrete to be tested through the water spraying device, and at the same time, collecting an infrared image of the area to be tested through the infrared device; S300: performing regularization processing on the infrared image by using an image regularization algorithm, calculating the scale of the infrared image, obtaining a real area corresponding to a single pixel, and shearing the infrared image to obtain a sheared infrared image; S400: Based on the cut infrared image, extract the temperature field matrix before and after the water spray cooling excitation, and obtain the temperature field difference matrix; wherein the expression of the temperature field difference matrix is: ΔT=T before -T after ; Where, T before is the temperature field matrix before water spray cooling excitation, T after is the temperature field matrix after water spray cooling excitation, ΔT is the temperature field difference matrix; S500: Calculate the average value of the temperature field difference matrix and calculate the standard deviation of the temperature field difference matrix; wherein the average value T of the temperature field difference matrix is ​​calculated. mean , the expression is: Where n is the total pixels of the cropped infrared image, ΔT i is the temperature value of the i-th pixel in the cut infrared image; Calculate the standard deviation T of the temperature field difference matrix st , the expression is: Where n is the total pixels of the cropped infrared image, T mean is the average value of the temperature field difference matrix, ΔT i is the temperature value of the i-th pixel in the cut infrared image; S600: Calculate the empty threshold; wherein the empty threshold δ is calculated, and the expression is: δ=T mean +mT st ; In the formula, δ is the empty threshold, T mean is the average value of the temperature field difference matrix, T st is the standard deviation of the temperature field difference matrix, and m is the correlation coefficient; S700: performing a hollow determination on the area to be tested, counting the number of hollow pixel points in the infrared image, and calculating the hollow area; S800: Determine whether the detection of all the areas to be tested is completed, and if so, output the detection results; If not, the measurement area is switched and S100 - S700 are repeated until all the areas to be measured are detected and the detection results are output.

2. The infrared detection and quantification method of CFST arch bridge void based on cold excitation according to claim 1 is characterized in that: S100: judging whether the tested area of ​​the steel tube concrete meets the void detection condition: Determine whether the temperature of the area to be tested is higher than the external environment temperature.

3. The cold excitation-based infrared detection and quantification method for CFST arch bridge voids according to claim 1 is characterized in that: When the water spraying device sprays water to cool the steel tube concrete to be tested, the atomization degree is controlled to be small, the temperature of the cooling water is lower than the ambient temperature, and the excitation time for each 0.25 square meter of the tested area is 125 seconds.

4. The cold excitation-based infrared detection and quantification method for CFST arch bridge voids according to claim 1 is characterized in that: The acquisition frequency of the infrared device is selected to be 1 Hz.

5. The cold excitation-based infrared detection and quantification method for CFST arch bridge voids according to claim 1 is characterized in that: The shearing of the infrared image to obtain the sheared infrared image comprises: Primary cropping: cropping the infrared image to obtain an infrared image of only the area to be measured; Secondary cropping: only the area in the infrared image of the area to be measured where the angle between the surface normal direction and the direction of the infrared device is less than 60° is retained to obtain a cropped infrared image.

6. The cold excitation-based infrared detection and quantification method for CFST arch bridge voids according to claim 1 is characterized in that: The method of determining the area to be tested is empty includes: Traverse the temperature field difference matrix to determine each pixel in the cut infrared image: When ΔTi>δ, the i-th pixel in the sheared infrared image is missing; When ΔTi≤δ, the i-th pixel in the sheared infrared image is not missing.

7. The cold excitation based infrared detection and quantification method of CFST arch bridge void according to claim 1 is characterized in that: The number of pixels with gaps in the infrared image is counted, and the gap area is calculated. The expression is: A d =A r ×D; In the formula, A r is the actual area corresponding to a single pixel, D is the number of pixels missing in the cropped infrared image, and A d The empty area.

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