A method for strengthening reinforced concrete column structures with quantifiable perfusion glue

Abstract

The present invention provides a method for strengthening a reinforced concrete column structure with quantifiable perfusion glue, belonging to the technical field of building reinforcement, including: removing the plaster on the surface of the column until the concrete surface of the column is exposed; grinding the concrete surface flat and repairing and leveling the damaged parts; installing angle steel on the column after the repair and leveling treatment, and welding gusset plates to the angle steel; wherein, the angle steel and the gusset plates together form a reinforcement frame; collecting the images of the reinforcement frame of the column in each direction; determining the dosage of the structural perfusion glue according to the images of the reinforcement frame; configuring the structural perfusion glue according to the dosage of the structural perfusion glue, and using the structural perfusion glue to bond the angle steel to the column. Relying on image processing technology, the present invention determines the dosage of the structural perfusion glue through the images of the angle steel, can achieve precise control of the dosage of the structural perfusion glue, and ensure the stability and reliability of the reinforcement effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of building reinforcement, and more specifically, relates to a method for reinforcing a reinforced concrete column structure with quantifiable perfusion glue. Background Art

[0002] Reinforced concrete column structures are widely used in construction engineering technology because of their high load-bearing capacity, large stiffness, good integrity, etc. However, when reinforced concrete column structures are applied in the natural environment, the concrete column structures are prone to induce phenomena such as steel bar corrosion and concrete column aging due to unfavorable factors such as overloading, improper design or construction, etc., and then the reinforced concrete columns cannot be used normally or even scrapped. Therefore, in order to extend the service life of reinforced concrete columns, the reinforcement, repair or transformation of concrete column structures has become a technical problem that needs to be solved urgently.

[0003] At present, when reinforcing a reinforced concrete column structure, a large amount of structural glue is usually required. During the glue mixing process, the dosage of the structural glue is usually determined by the construction worker based on work experience. However, determining the dosage of the structural glue based on work experience lacks clear standards and bases, and it is easy to cause unnecessary waste of the structural glue. Summary of the Invention

[0004] To solve the above problems, the purpose of the present invention is to provide a method for reinforcing a reinforced concrete column structure with quantifiable perfusion glue.

[0005] A method for reinforcing a reinforced concrete column structure with quantifiable perfusion glue includes:

[0006] Step 1: Remove the plaster on the surface of the column until the concrete surface of the column is exposed;

[0007] Step 2: Grind the concrete surface flat and repair and level the damaged parts;

[0008] Step 3: Install angle steel on the column after repair and leveling treatment, and weld the lacing plates to the angle steel; wherein, the angle steel and the lacing plates together form a reinforcement frame;

[0009] Step 4: Collect images of the reinforcement frame of the column in each direction;

[0010] Step 5: Determine the dosage of the structural perfusion glue according to the images of the reinforcement frame;

[0011] Step 6: Prepare the structural perfusion glue according to the dosage of the structural perfusion glue, inject it through the glue injection nozzle, and bond the reinforcement frame to the column.

[0012] Preferably, in step 5: determining the dosage of the structural perfusion glue according to the images of the reinforcement frame, it includes:

[0013] Step 5.1: Segment the angle steel image to obtain the segmented image;

[0014] Step 5.2: Binarize the segmented image to obtain the grayscale angle steel image;

[0015] Step 5.3: Perform edge detection on the grayscale angle steel image to obtain the angle steel contour image;

[0016] Step 5.4: Determine the dosage of the structural perfusion adhesive according to the area of the angle steel contour image.

[0017] Preferably, the step 5.1: Segment the angle steel image to obtain the segmented image, including:

[0018] Step 5.2.1: Obtain the characteristic coefficients of any two pixel points in the angle steel image according to the pixel values of the angle steel image in each channel;

[0019] Step 5.2.2: Obtain the color coefficient of any pixel point in the angle steel image according to the mean value of the pixel points on the angle steel image;

[0020] Step 5.2.3: Construct an image segmentation model according to the characteristic coefficients and the color coefficients;

[0021] Step 5.2.4: Segment the angle steel image according to the image segmentation model to obtain the segmented image.

[0022] Preferably, the step 5.2.1: Obtain the characteristic coefficients of any two pixel points in the angle steel image according to the pixel values of the angle steel image in each channel, including:

[0023] Adopt the formula:

[0024]

[0025] The characteristic coefficients of any pixel point in the angle steel image; where θ represents the characteristic coefficient between the i-th pixel point and the j-th pixel point, r i represents the value of the i-th pixel point in the red channel, r j represents the value of the j-th pixel point in the red channel, g i represents the value of the i-th pixel point in the green channel, g j represents the value of the j-th pixel point in the green channel, b i represents the value of the i-th pixel point in the blue channel, b j represents the value of the j-th pixel point in the blue channel.

[0026] Preferably, the step 5.2.2: Obtain the color coefficient of any pixel point in the angle steel image according to the mean value of the pixel points on the angle steel image, including:

[0027] Use the formula:

[0028]

[0029] Obtain the color coefficient of any pixel point in the angle steel image; where S r is the color coefficient of the red channel, S g is the color coefficient of the green channel, S b is the color coefficient of the blue channel.

[0030] Preferably, the image segmentation model is:

[0031]

[0032] where W r represents the preset weight of the red channel, W g represents the preset weight of the green channel, W b represents the preset weight of the blue channel, S ratio represents the adjustable coefficient, and W represents the pixel difference value.

[0033] Preferably, the step 5.2.4: Segment the angle steel image according to the image segmentation model to obtain the segmented image, including:

[0034] Step 5.2.4.1: Segment the angle steel image into a target image and a background image by using a preset pixel value;

[0035] Step 5.2.4.2: Calculate the total pixel difference value between all pixel points on the target image and the background image by using the segmentation evaluation model;

[0036] Step 5.2.4.3: Continuously adjust the preset pixel value until the total pixel difference value is the largest;

[0037] Step 5.2.4.4: Use the target image corresponding to the largest total pixel difference value as the segmented image.

[0038] Preferably, the step 5.2: Perform binarization processing on the segmented image to obtain a grayscale angle steel image, including:

[0039] Perform binarization processing on the angle steel image according to the pixel values of each point of the angle steel image to obtain a grayscale angle steel image; where the binarization processing formula is:

[0040] T(i,j) = 0.299 * R(i,j) + 0.587 * G(i,j) + 0.114 * B(i,j)

[0041] Wherein, T(i,j) represents the grayscale value after binarization of the pixel at the position (i,j), R(i,j) represents the pixel value of the red channel of the angle steel image at the position (i,j), G(i,j) represents the pixel value of the green channel of the angle steel image at the position (i,j), and B(i,j) represents the pixel value of the blue channel of the angle steel image at the position (i,j).

[0042] Preferably, in step 5.3: performing edge detection on the grayscale angle steel image to obtain an angle steel contour image, including:

[0043] Step 5.3.1: Taking a neighborhood window with an arbitrary point of the grayscale angle steel image as the center;

[0044] Step 5.3.2: Judging whether the pixel points on the neighborhood window satisfy the edge detection formula. If they satisfy, the corresponding pixel points are retained; if not, the corresponding pixel points are removed.

[0045] Preferably, the edge detection formula is:

[0046] |Vf(x,y) - Vf(s,t)| < T

[0047] arctan(Gy / Gx) - arctan(Gt / Gs) < A

[0048] Wherein, Vf(x,y) represents the gradient value of the grayscale angle steel image at (x,y), Vf(s,t) represents the gradient value of (s,t) in the neighborhood window, arctan(Gy / Gx) represents the gradient direction of the grayscale angle steel image at (x,y), arctan(Gt / Gs) represents the gradient direction of (s,t) in the neighborhood window, T represents the gradient threshold, and A represents the direction threshold.

[0049] The beneficial effects of a method for strengthening a reinforced concrete column structure with quantifiable perfusion glue provided by the present invention are as follows: Compared with the prior art, a method for strengthening a reinforced concrete column structure with quantifiable perfusion glue provided by the present invention includes: removing the plaster on the surface of the column until the concrete surface of the column is exposed; grinding the concrete surface flat and repairing and leveling the damaged parts; installing angle steel on the column after the repair and leveling treatment, and welding the batten plates to the angle steel; wherein, the angle steel and the batten plates together form a reinforcement frame; collecting the reinforcement frame images of the column in each direction; determining the dosage of the structural perfusion glue according to the reinforcement frame images; preparing the structural perfusion glue according to the dosage of the structural perfusion glue and injecting it through the glue injection nozzle to bond the reinforcement frame to the column. Relying on image processing technology, the present invention can determine the dosage of the structural perfusion glue through the angle steel image, can achieve precise control of the dosage of the structural perfusion glue, and ensure the stability and reliability of the reinforcement effect. Description of the Drawings

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 It is a flowchart of a method for strengthening a reinforced concrete column structure with quantifiable perfusion glue provided by an embodiment of the present invention.

[0052] Figure 2 It is a schematic diagram of angle steel image acquisition provided by an embodiment of the present invention.

[0053] 1. Column body; 2. Angle steel; 3. Lacing plate. Specific embodiments

[0054] In order to make the technical problems to be solved, technical solutions and beneficial effects of the present invention more clearly understood, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Please refer to Figure 1 , a method for strengthening a reinforced concrete column structure with quantifiable perfusion glue, including:

[0056] Step 1: Remove the plaster on the surface of the column body until the concrete surface of the column body is exposed;

[0057] Step 2: Grind the concrete surface flat and repair and level the damaged parts;

[0058] Step 3: Install angle steel on the column body after the repair and leveling treatment, and weld the lacing plate to the angle steel; wherein, the angle steel and the lacing plate together form a reinforcement frame;

[0059] In practical applications, the present invention needs to use the lacing plate 3 to fix the angle steel 2 on the column body 1.

[0060] Step 4: Collect the images of the reinforcement frame of the column body in each direction;

[0061] Please refer to Figure 2 , since the column body generally has 4 faces, it is necessary to use a camera to collect the images of the reinforcement frame of the column body in each direction.

[0062] Step 5: Determine the dosage of the structural perfusion glue according to the images of the reinforcement frame;

[0063] Further, step 5 includes:

[0064] Step 5.1: Segment the angle steel image to obtain the segmented image;

[0065] Among them, the specific steps of Step 5.1 are as follows:

[0066] Step 5.1.1: Obtain the characteristic coefficients of any two pixel points in the angle steel image according to the pixel values of the angle steel image in each channel; In the embodiment of the present invention, the calculation formula of the characteristic coefficient is:

[0067]

[0068] Among them, θ represents the characteristic coefficient between the i-th pixel point and the j-th pixel point, r i represents the value of the i-th pixel point in the red channel, r j represents the value of the j-th pixel point in the red channel, g i represents the value of the i-th pixel point in the green channel, g j represents the value of the j-th pixel point in the green channel, b i represents the value of the i-th pixel point in the blue channel, b j represents the value of the j-th pixel point in the blue channel.

[0069] Step 5.1.2: Obtain the color coefficients of any pixel point in the angle steel image according to the mean value of the pixel points on the angle steel image; In the embodiment of the present invention, the calculation formula of the color coefficient is:

[0070]

[0071] Among them, S r is the color coefficient of the red channel, S g is the color coefficient of the green channel, S b is the color coefficient of the blue channel

[0072] Step 5.1.3: Construct an image segmentation model according to the characteristic coefficient and the color coefficient; Further, the image segmentation model is:

[0073]

[0074] Among them, W r represents the preset weight of the red channel, W g represents the preset weight of the green channel, W b represents the preset weight of the blue channel, S ratio represents the adjustable coefficient, and W represents the pixel difference value.

[0075] Step 5.1.4: Segment the angle steel image according to the image segmentation model to obtain the segmented image.

[0076] Among them, Step 5.1.4 includes:

[0077] Step 5.1.4.1: Segment the angle steel image into a target image and a background image using a preset pixel value;

[0078] Step 5.1.4.2: Calculate the total pixel difference value between all pixel points on the target image and the background image using the segmentation evaluation model;

[0079] Step 5.1.4.3: Continuously adjust the preset pixel value until the total pixel difference value is maximized;

[0080] Step 5.1.4.4: Take the target image corresponding to the maximum total pixel difference value as the segmented image.

[0081] By segmenting the image using the total pixel difference value, the present invention can obtain the optimal image segmentation pixel value from the pixel value features between each pixel point according to the characteristics of the angle steel image, and at the same time, segment the image using the image segmentation pixel value can perfectly separate the background area and the target area of the angle steel image.

[0082] Step 5.2: Perform binarization processing on the segmented image to obtain a grayscale angle steel image;

[0083] It should be noted that the present invention can perform binarization processing on the angle steel image according to the pixel values of each point of the angle steel image to obtain a grayscale angle steel image; wherein, the binarization processing formula is:

[0084] T(i,j) = 0.299 * R(i,j) + 0.587 * G(i,j) + 0.114 * B(i,j)

[0085] In the formula, T(i,j) represents the grayscale value after binarization of the pixel at the (i,j) position, R(i,j) represents the pixel value of the red channel of the angle steel image at the (i,j) position, G(i,j) represents the pixel value of the green channel of the angle steel image at the (i,j) position, and B(i,j) represents the pixel value of the blue channel of the angle steel image at the (i,j) position.

[0086] Step 5.3: Perform edge detection on the grayscale angle steel image to obtain an angle steel contour image;

[0087] Among them, Step 5.3 includes:

[0088] Step 5.3.1: Take a neighborhood window with an arbitrary point of the grayscale angle steel image as the center;

[0089] Step 5.3.2: Determine whether the pixel points on the neighborhood window satisfy the edge detection formula. If they satisfy, retain the corresponding pixel points; if not, remove the corresponding pixel points. The edge detection formula is:

[0090] |Vf(x,y) - Vf(s,t)| < T

[0091] arctan(Gy / Gx) - arctan(Gt / Gs) < A

[0092] Wherein, Vf(x,y) represents the gradient value of the grayscale angle steel image at (x,y), Vf(s,t) represents the gradient value of the (s,t) at the neighborhood window, arctan(Gy / Gx) represents the gradient direction of the grayscale angle steel image at (x,y), arctan(Gt / Gs) represents the gradient direction of the (s,t) at the neighborhood window, T represents the gradient threshold, and A represents the direction threshold.

[0093] Edge detection can make the object edges in the image clearer and more prominent, thereby improving the visual quality and perception of the image.

[0094] Step 5.4: Determine the amount of structural perfusion adhesive according to the area of the angle steel contour image. In practical applications, the present invention can place a coin beside the angle steel for photographing, and then determine the area of the angle steel contour image through the ratio between the number of pixel points occupied by the coin contour image and the number of pixel points occupied by the angle steel contour image.

[0095] In practical applications, the present invention can use the area of the collected historical angle steel contour images and the corresponding amounts of structural perfusion adhesive as training data to train a neural network to obtain a calculation model for the amount of structural perfusion adhesive. Then, input the area of the currently collected angle steel contour image into the calculation model for the amount of structural perfusion adhesive to obtain the optimal amount of structural perfusion adhesive.

[0096] Step 6: Configure the structural perfusion adhesive according to the amount of the structural perfusion adhesive, inject it through the glue injection nozzle, and bond the reinforcement frame to the column body.

[0097] Relying on image processing technology, the present invention can determine the amount of structural perfusion adhesive through the angle steel image, which can achieve precise control of the amount of structural perfusion adhesive and ensure the stability and reliability of the reinforcement effect.

[0098] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for strengthening a reinforced concrete column structure with quantifiable perfusion glue, characterized in that, It includes the following steps: Step 1: Remove the plaster on the surface of the column until the concrete surface of the column is exposed; Step 2: Grind the concrete surface flat and repair and level the damaged parts; Step 3: Install angle steel on the column after repair and leveling treatment, and weld the batten plates to the angle steel; wherein, the angle steel and the batten plates together form a reinforcement frame; Step 4: Collect the images of the reinforcement frame of the column in each direction; Step 5: Determine the dosage of the structural perfusion adhesive according to the images of the reinforcement frame; Step 6: Prepare the structural perfusion adhesive according to the dosage of the structural perfusion adhesive, inject it through the glue injection nozzle, and bond the reinforcement frame to the column; The said Step 5: Determine the dosage of the structural perfusion adhesive according to the images of the reinforcement frame, including: Step 5.1: Segment the image of the angle steel to obtain the segmented image; Step 5.2: Perform binarization processing on the segmented image to obtain the grayscale image of the angle steel; Step 5.3: Perform edge detection on the grayscale image of the angle steel to obtain the contour image of the angle steel; Step 5.4: Determine the dosage of the structural perfusion adhesive according to the area of the contour image of the angle steel.

2. The method for strengthening a reinforced concrete column structure with a quantifiable perfusion glue as claimed in claim 1, wherein, The said Step 5.1: Segment the image of the angle steel to obtain the segmented image, including: Step 5.1.1: Obtain the feature coefficients of any two pixel points in the image of the angle steel according to the pixel values of the image of the angle steel in each channel; Step 5.1.2: Obtain the color coefficient of any pixel point in the image of the angle steel according to the mean value of the pixel points on the image of the angle steel; Step 5.1.3: Construct an image segmentation model according to the feature coefficients and the color coefficients; Step 5.1.4: Segment the image of the angle steel according to the image segmentation model to obtain the segmented image.

3. The method for strengthening a reinforced concrete column structure with a quantifiable perfusion adhesive according to claim 2, wherein, The said Step 5.1.1: Obtain the feature coefficients of any two pixel points in the image of the angle steel according to the pixel values of the image of the angle steel in each channel, including: Use the formula: Characteristic coefficient of any pixel point in the image of the angle steel; where θ represents the characteristic coefficient between the i-th pixel point and the j-th pixel point, r i represents the value of the i-th pixel point in the red channel, r j represents the value of the j-th pixel point in the red channel, g i represents the value of the i-th pixel point in the green channel, g j represents the value of the j-th pixel point in the green channel, b i represents the value of the i-th pixel point in the blue channel, b j represents the value of the j-th pixel point in the blue channel.

4. A method for strengthening a reinforced concrete column structure with quantifiable perfusion glue as described in claim 3, characterized in that, The said Step 5.1.2: Obtain the color coefficient of any pixel point in the image of the angle steel according to the mean value of the pixel points on the image of the angle steel, including: Use the formula: Obtain the color coefficient of any pixel point in the image of the angle steel; where S r is the color coefficient of the red channel, S g is the color coefficient of the green channel, S b is the color coefficient of the blue channel.

5. The method for reinforcing a reinforced concrete column structure with quantifiable perfusion glue as claimed in claim 4, characterized in that, The said image segmentation model is: Among them, W r represents the preset weight of the red channel, W g represents the preset weight of the green channel, W b represents the preset weight of the blue channel, S ratio represents the adjustable coefficient, and W represents the pixel difference value.

6. The method for strengthening a reinforced concrete column structure with a quantifiable perfusion adhesive according to claim 5, wherein, The said Step 5.1.4: Segment the image of the angle steel according to the image segmentation model to obtain the segmented image, including: Step 5.1.4.1: Segment the image of the angle steel into a target image and a background image by using a preset pixel value; Step 5.1.4.2: Calculate the total pixel difference value between all pixel points on the target image and the background image by using the image segmentation model; Step 5.1.4.3: Continuously adjust the preset pixel value until the total pixel difference value is the largest; Step 5.1.4.4: Take the target image corresponding to the largest total pixel difference value as the segmented image.

7. The method for strengthening a reinforced concrete column structure with quantifiable perfusion glue as described in claim 6, characterized in that, The said Step 5.2: Perform binarization processing on the segmented image to obtain the grayscale image of the angle steel, including: Perform binarization processing on the image of the angle steel according to the pixel values of each point of the image of the angle steel to obtain the grayscale image of the angle steel; wherein, the binarization processing formula is: T(i,j) = 0.299*R(i,j) + 0.587*G(i,j) + 0.114*B(i,j) Wherein, T(i, j) represents the grayscale value after binarization of the pixel at the position (i, j), R(i, j) represents the pixel value of the red channel of the angle steel image at the position (i, j), G(i, j) represents the pixel value of the green channel of the angle steel image at the position (i, j), and B(i, j) represents the pixel value of the blue channel of the angle steel image at the position (i, j).

8. The method for strengthening a reinforced concrete column structure with a quantifiable perfusion adhesive according to claim 7, characterized in that, The step 5.3: performing edge detection on the grayscale angle steel image to obtain an angle steel contour image, including: Step 5.3.1: taking a neighborhood window with an arbitrary point of the grayscale angle steel image as the center; Step 5.3.2: determining whether the pixel points on the neighborhood window satisfy the edge detection formula, if so, retaining the corresponding pixel points, and if not, removing the corresponding pixel points.

9. The method for reinforcing a reinforced concrete column structure with a quantifiable perfusion adhesive according to claim 8, characterized in that, The edge detection formula is: |Vf(x, y) - Vf(s, t)| < T arctan(Gy / Gx) - arctan(Gt / Gs) < A Wherein, Vf(x, y) represents the gradient value of the grayscale angle steel image at (x, y), Vf(s, t) represents the gradient value at (s, t) in the neighborhood window, arctan(Gy / Gx) represents the gradient direction of the grayscale angle steel image at (x, y), arctan(Gt / Gs) represents the gradient direction at (s, t) in the neighborhood window, T represents the gradient threshold, and A represents the direction threshold.

Images