Concrete wall deformation degree analysis system

Abstract

The concrete wall deformation degree analysis system comprises a test point displacement analysis module, an image acquisition module, an image processing module, a deformation degree analysis system and a wall load applying module, the test point displacement analysis module is connected with the deformation degree analysis system, the image acquisition module is connected with the image processing module, and the wall load applying module is connected with the image processing module. The image processing module is connected with the deformation degree analysis system, the deformation degree analysis system is connected with the wall body load applying module, deformation of the concrete wall body is obtained by setting a plurality of test points, the deformation degree can be accurately obtained from different details, the image processing module is improved based on the prior art, images are enhanced, and the deformation degree of the concrete wall body can be accurately obtained. The problem of uneven brightness caused by reflection is solved.

Description

Technical Field

[0001] This invention relates to the field of testing, and more particularly to a system for analyzing the deformation of concrete walls. Background Technology

[0002] Concrete is the most widely used material in modern building structures. Its structures, especially walls, are subjected to various factors such as load, temperature changes, shrinkage and creep, foundation settlement and external environmental erosion during long-term service, resulting in deformation. The deformation of the walls, such as tilting, bending and cracking, is a key indicator for assessing the health status of the structure and diagnosing safety hazards. Excessive or uneven deformation not only affects the normal use and appearance of the building, but is also a precursor to structural instability, reduced load-bearing capacity and even collapse.

[0003] Therefore, accurate, efficient, and continuous monitoring and analysis of the deformation of concrete walls is of paramount importance for ensuring structural safety, extending service life, and achieving preventative maintenance. The traditional "periodic manual inspection and post-incident repair" model is no longer sufficient to meet the high demands of modern large-scale and complex structural engineering projects for real-time, accurate, and economical safety management. Developing an intelligent concrete wall deformation analysis system has become an important research direction in the field of civil engineering health monitoring.

[0004] Existing technologies can be mainly categorized as follows: First, measurements are taken by directly contacting the wall using mechanical instruments such as dial indicators, micrometers, and inclinometers. This method is simple, intuitive, and low-cost, but its accuracy is limited. It requires manual close-range readings, resulting in low efficiency and difficulty in achieving long-term automated monitoring and remote data transmission. Second, high-speed laser scanning can quickly acquire massive amounts of 3D point cloud data from the wall surface, thereby constructing a high-precision digital model for overall deformation analysis. However, its disadvantages include high equipment costs, complex data processing, and large computational load, making it difficult to meet the real-time requirements of monitoring scenarios. Third, in recent years, deformation monitoring methods based on digital image processing and computer vision have emerged. These methods utilize photogrammetry and computer vision technology, acquiring images or video sequences of the wall surface through one or more cameras. Image processing algorithms (such as feature point extraction, template matching, and digital image correlation (DIC)) are used to track the displacement of target points and calculate the deformation. However, the measurement accuracy is easily affected by factors such as changes in ambient lighting, camera shake, lens distortion, and atmospheric disturbances, requiring high robustness and computational efficiency from the algorithms. Summary of the Invention

[0005] The concrete wall deformation analysis system provided by this invention includes a test point displacement analysis module, an image acquisition module, an image processing module, a deformation analysis system, and a wall load application module. The test point displacement analysis module is connected to the deformation analysis system, the image acquisition module is connected to the image processing module, the image processing module is connected to the deformation analysis system, and the deformation analysis system is connected to the wall load application module.

[0006] Preferably, the deformation analysis system applies a preset load to the wall by controlling the wall load application module. Several test points are set at the edge of the wall. The test point displacement analysis module is used to calculate the displacement parameters of each test point and transmit the calculation results to the deformation analysis system. The deformation analysis system stores the displacement thresholds of each test point corresponding to the load on the wall. If the displacement of any test point exceeds the displacement threshold, the deformation analysis system sends an alarm message to the remote monitoring terminal. The image acquisition module is used to acquire image information of the wall, and the image processing module processes the acquired image and transmits it to the deformation analysis system.

[0007] Preferably, the deformation analysis system stores the displacement parameters and image information of each test point received under applied load, which facilitates subsequent processing of information such as cracks in the wall. Since the image processing module can extract clearer image information, it can better reflect information such as cracks in the wall.

[0008] Preferably, the test point displacement analysis module is used to calculate the displacement parameters of i test points, where i is the index of each test point, and the load of each test point is taken into account. Therefore,

[0009]

[0010] Where x0 represents the distance from the origin to the neutral axis in the x-direction, in mm. i p represents the x-axis coordinate of the test point with index i. i Let represent the load at test point i. The i-th test point has a hollow structure with a sensor inside. The load at the i-th test point is acquired through the built-in sensor. y0 represents the distance from the origin to the neutral axis in the y-direction, in mm. i Let y be the y-axis coordinate of the test point with serial number i. The x-direction is the horizontal direction of the wall, the y-direction is the vertical direction of the wall, and the origin is the lower left corner of the wall.

[0011] The relationships between the bending moment and rotation angle in the x and y directions are given by the following formulas.

[0012] M x =K x ×θ x

[0013] M y =K y ×θ y

[0014] M x =M y

[0015] Among them, M x The bending moment in the x-direction is expressed in N·mm, K. x The rotational stiffness is given by θ in the x-direction, expressed in N·mm / rad. x The angle of rotation in the x-direction is expressed in rad (m). y The bending moment in the y-direction is expressed in N·mm, K. y The rotational stiffness in the y-direction is expressed in N·mm / rad, θ y The angle is the rotation angle in the y-direction, measured in rad.

[0016] Calculating the deformation angle of the wall, we have:

[0017] R = θ x ×θ y

[0018] Where R is the deformation angle of the wall, and the unit is rad.

[0019] By weighting the secant stiffness at each test point, the displacement parameters at each test point are obtained, and then...

[0020] K x =∑K i ×(y i -y0) 2

[0021] K y =∑K i ×(x i -x0) 2

[0022] δ ix =(y i -y0)×θ x

[0023] δ iy =(x i -x0)×θ y

[0024]

[0025] Among them, K i Let δ be the secant stiffness of test point i. ix Let δ be the displacement of test point i in the x-direction. iyLet δ be the displacement of test point i in the y-direction. i The displacement of the test point with bit number i.

[0026] Preferably, the image processing module improves upon existing image processing algorithms.

[0027] Threshold T1 and threshold T2 are obtained through threshold segmentation. <T2)。

[0028] The image processing module is an algorithm for determining the segmentation threshold of a binary image. Based on the grayscale features of the image, it divides the image into two parts: foreground and background. When using the optimal threshold, the difference between these two parts should be maximized. The greater the inter-class variance between the foreground and background, the greater the difference between the two parts that make up the image.

[0029] The image is segmented into bright areas: (N) i >T2), Equilibrium region image: (T2>N) i >T1) and dark area images: (T2>N) i >T1), Ni is the grayscale value of the i-th pixel on the wall image acquired by the image acquisition module. The calculation process is as follows:

[0030] σ 2 =P1(E1-E) 2 +P2(E2-E) 2 +P3(E3-E) 2

[0031] Among them, P i Let P1, P2, and P3 be the pixel probabilities of region i in the image, where i = 0, 1, 2, ..., N, and satisfy the following:

[0032] P1 + P2 + P3 = 1

[0033] in:

[0034]

[0035] E i Let E be the average gray level of region i in the image, and let E be the average gray level of the entire image. E1, E2, and E3 satisfy the following:

[0036]

[0037] Maximize the inter-class variance σ to obtain thresholds T1 and T2.

[0038] By overlaying a bright area image onto a balanced area image to obtain a bright blended image, and then processing it using image processing algorithms to obtain a brightened image, the details in the bright areas of the image become clearer without overexposure or loss of detail. Similarly, by overlaying a dark area image onto a balanced area image to obtain a dark blended image, and then processing it using image processing algorithms to obtain a darkened image, the details and contrast in the dark areas of the image are enhanced, making the originally dark areas brighter and clearer.

[0039] Based on the ratio of thresholds T1 and T2, a composite image is obtained by fusing the bright and dark images. The fusion formula for the bright and dark images is as follows:

[0040]

[0041] Wherein, PicA is the composite image, PicB is the brightened image, PicC is the darkened image, and gamma is the correction coefficient, which is usually set to 0.001.

[0042] The concrete wall deformation analysis system provided by this invention obtains the deformation of the concrete wall by setting several test points. It can accurately determine the degree of deformation from different details. Since the concrete wall is bound to have uneven distribution, it can also obtain the internal concrete distribution of the concrete wall by measuring the displacement of each test point. In view of the problems of reflection and low contrast on the wall surface, the image processing module improves the existing technology to enhance the image, thereby improving the problem of uneven brightness caused by reflection and avoiding the problems of overall whitening and severe distortion of the image in the transmission algorithm. It effectively enhances the reflected image, saves time and cost in selecting the light source and image acquisition module during the measurement process, and reduces the adverse effects of ambient light on the reflection of the smooth surface of the workpiece. Attached Figure Description

[0043] Figure 1 This is a functional block diagram of the concrete wall deformation analysis system of the present invention;

[0044] Figure 2 This is a schematic diagram of the test point displacement analysis module of the present invention;

[0045] Figure 3 This is a schematic diagram of the workflow of the image processing module of the present invention.

[0046] Figure 2 The attached diagram is labeled as follows: 1-Load applied by the wall load application module; 2-Wall; 3-Test point. Detailed Implementation

[0047] The concrete wall deformation analysis system provided by the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0048] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in the block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0049] The terms "comprising," "including," and similar terms used should be understood as open-ended, meaning "including / including but not limited to," indicating that other contents may also be included. The same reference numerals used in the embodiments and accompanying drawings indicate the same or similar components or structural features. Furthermore, when describing a component as being disposed on another component, it means that a component is directly disposed on the other component, or a component is indirectly disposed on the other component, i.e., one or more components are disposed between the two components. Conversely, when describing a component as being directly disposed on another component, it means that there are no other components between the two components.

[0050] like Figure 1 As shown, the concrete wall deformation analysis system of the present invention includes a test point displacement analysis module, an image acquisition module, an image processing module, a deformation analysis system, and a wall load application module. The test point displacement analysis module is connected to the deformation analysis system, the image acquisition module is connected to the image processing module, the image processing module is connected to the deformation analysis system, and the deformation analysis system is connected to the wall load application module.

[0051] Specifically, the deformation analysis system applies a preset load 1 to the wall 2 by controlling the wall load application module. Several test points 3 are set at the edge of the wall 2. The test point displacement analysis module is used to calculate the displacement parameters of each test point 3 and transmit the calculation results to the deformation analysis system. The deformation analysis system stores the displacement thresholds of each test point corresponding to the load on the wall 2. If the displacement of any test point exceeds the displacement threshold, the deformation analysis system sends an alarm message to the remote monitoring terminal. The image acquisition module is used to acquire image information of the wall 2. The image processing module processes the acquired image and transmits it to the deformation analysis system.

[0052] Specifically, under the applied load 1, the deformation analysis system stores the displacement parameters and image information of each test point 3 received, which facilitates subsequent processing of information such as cracks in the wall 2. Since the image processing module can extract clearer image information, it can better reflect information such as cracks in the wall 2.

[0053] like Figure 2 As shown, the test point displacement analysis module is used to calculate the displacement parameters of i test points 3, where i is the index of each test point, and the load of each test point is considered. Therefore,

[0054]

[0055] Where x0 represents the distance from the origin to the neutral axis in the x-direction, in mm. i p represents the x-axis coordinate of the test point with index i. i Let represent the load at test point i. The i-th test point has a hollow structure with a sensor inside. The load at the i-th test point is acquired through the built-in sensor. y0 represents the distance from the origin to the neutral axis in the y-direction, in mm. i Let y be the y-axis coordinate of the test point with serial number i. The x-direction is the horizontal direction of wall 2, the y-direction is the vertical direction of wall 2, and the origin is the lower left corner of wall 2.

[0056] The relationships between the bending moment and rotation angle in the x and y directions are given by the following formulas.

[0057] M x =K x ×θ x

[0058] M y =K y ×θ y

[0059] M x =M y

[0060] Among them, M x The bending moment in the x-direction is expressed in N·mm, K. x The rotational stiffness is given by θ in the x-direction, expressed in N·mm / rad. x The angle of rotation in the x-direction is expressed in rad (m). y The bending moment in the y-direction is expressed in N·mm, K. y The rotational stiffness in the y-direction is expressed in N·mm / rad, θ y The angle is the rotation angle in the y-direction, measured in rad.

[0061] Calculate the deformation angle of wall 2, then we have:

[0062] R = θ x ×θ y

[0063] Where R is the deformation angle of wall 2, in rad.

[0064] By weighting the secant stiffness at each test point, the displacement parameters of each test point 3 are obtained, then...

[0065] K x =∑K i ×(y i -y0) 2

[0066] K y =∑K i ×(x i -x0) 2

[0067] δ ix =(y i -y0)×θ x

[0068] δ iy =(x i -x0)×θ y

[0069]

[0070] Among them, K i Let δ be the secant stiffness of test point i. ix Let δ be the displacement of test point i in the x-direction. iy Let δ be the displacement of test point i in the y-direction. i The displacement of the test point with bit number i.

[0071] Specifically, by setting up several test points 3 to obtain the deformation of the concrete wall 2, the degree of deformation can be accurately determined from different details. Since the concrete wall is bound to have uneven distribution, the internal concrete distribution of the concrete wall can also be obtained by measuring the displacement of each test point 3.

[0072] Specifically, the image acquisition module is used to acquire image information of the wall 2, and the image processing module performs image processing on the acquired images.

[0073] Wall 2 itself has no color. The color of Wall 2 is actually determined by the various frequency components of the light it reflects, and is not directly related to the intensity of the light. Furthermore, the reflective properties of Wall 2 itself are preserved as much as possible by reducing the influence of the incident image.

[0074] In the prior art, an image is considered to consist of a reflection image and a brightness image, and its expression is:

[0075] S(x,y)=R(x,y)*L(x,y)

[0076] Where S(x,y) is the image acquired by the image acquisition module, R(x,y) is the reflected image, which reflects the reflection characteristics of the object, and L(x,y) is the incident image, which directly determines the dynamic range that the pixels in the image can achieve. The image output of the image processing module is calculated as follows:

[0077]

[0078] Among them, R ′ (x,y) is the output image, F(x,y) is the Gaussian wrapping function, and F(x,y)*S(x,y) means convolution is performed on the original image using a Gaussian kernel.

[0079] The image processing module provided by this invention improves upon existing image processing algorithms. The workflow of the image processing module is as follows: Figure 3 As shown.

[0080] Threshold T1 and threshold T2 are obtained through threshold segmentation. <T2)。

[0081] The image processing module provided by this invention is an algorithm for determining the segmentation threshold of a binary image. Based on the grayscale features of the image, the image is divided into two parts: foreground and background. When the optimal threshold is used, the difference between these two parts should be maximized. The greater the inter-class variance between the foreground and background, the greater the difference between the two parts that make up the image.

[0082] The image is segmented into bright areas: (N) i >T2), Equilibrium region image: (T2>N) i >T1) and dark area images: (T2>N) i >T1), Ni is the grayscale value of the i-th pixel on the image of wall 2 acquired by the image acquisition module. The calculation process is as follows:

[0083] σ 2 =P1(E1-E) 2 +P2(E2-E) 2 +P3(E3-E) 2

[0084] Among them, P i Let P1, P2, and P3 be the pixel probabilities of region i in the image, where i = 0, 1, 2, ..., N, and satisfy the following:

[0085] P1 + P2 + P3 = 1

[0086] in:

[0087]

[0088] E i Let E be the average gray level of region i in the image, and let E be the average gray level of the entire image. E1, E2, and E3 satisfy the following:

[0089]

[0090] Maximize the inter-class variance σ to obtain thresholds T1 and T2.

[0091] By overlaying a bright area image onto a balanced area image to obtain a bright blended image, and then processing it using image processing algorithms to obtain a brightened image, the details in the bright areas of the image become clearer without overexposure or loss of detail. Similarly, by overlaying a dark area image onto a balanced area image to obtain a dark blended image, and then processing it using image processing algorithms to obtain a darkened image, the details and contrast in the dark areas of the image are enhanced, making the originally dark areas brighter and clearer.

[0092] Based on the ratio of thresholds T1 and T2, a composite image is obtained by fusing the bright and dark images. The fusion formula for the bright and dark images is as follows:

[0093]

[0094] Wherein, PicA is the composite image, PicB is the brightened image, PicC is the darkened image, and gamma is the correction coefficient, which is usually set to 0.001.

[0095] To address the issues of reflection and low contrast on the surface of wall 2, the image processing module proposed in this invention improves upon existing technologies by enhancing the image to mitigate the uneven brightness caused by reflection. This avoids the overall whitening and severe distortion issues that occur in traditional algorithms, effectively enhancing the reflected image. It saves time and costs associated with selecting the light source and image acquisition module during the measurement process and reduces the adverse effects of ambient light on the smooth surface reflection of the workpiece.

[0096] Finally, it should be reiterated that the constituent elements / components described in the foregoing embodiments of the present invention are merely illustrative examples and are not intended to limit the scope of the present invention. Substitutions or variations of other equivalent components should be within the scope of protection of the present invention.

Claims

1. A system for analyzing the deformation of concrete walls, characterized in that, The concrete wall deformation analysis system includes a test point displacement analysis module, an image acquisition module, an image processing module, a deformation analysis system, and a wall load application module. The test point displacement analysis module is connected to the deformation analysis system, the image acquisition module is connected to the image processing module, the image processing module is connected to the deformation analysis system, and the deformation analysis system is connected to the wall load application module. The deformation analysis system applies a preset load (1) to the wall (2) by controlling the wall load application module. Several test points (3) are set on the edge of the wall (2). The test point displacement analysis module is used to calculate the displacement parameters of each test point (3) and transmit the calculation results to the deformation analysis system. The deformation analysis system stores the displacement thresholds of each test point corresponding to the load on the wall (2). If the displacement of a test point exceeds the displacement threshold, the deformation analysis system sends an alarm message to the remote monitoring terminal. The image acquisition module is used to acquire image information of the wall (2). The image processing module processes the acquired image and transmits it to the deformation analysis system.

2. The concrete wall deformation analysis system according to claim 1, characterized by, The deformation analysis system stores the displacement parameters and image information of each test point (3) received under the applied load (1).

3. The concrete wall deformation analysis system according to claim 1, characterized by, The test point displacement analysis module is used to calculate the displacement parameters of i test points (3), where i is the index of each test point. Therefore, wherein x0 represents the distance from the coordinate origin to the neutral axis in the x direction, in mm, x i is the x-axis coordinate of the test point with serial number i, p i is the load of the test point with serial number i, the i-th test point is internally hollow, a sensor is arranged in the hollow structure, the load of the i-th test point is obtained through the built-in sensor, y0 is the distance from the coordinate origin to the neutral axis in the y direction, in mm, y i is the y-axis coordinate of the test point with serial number i, the x direction is the horizontal direction of the wall 2, the y direction is the vertical direction of the wall 2, and the origin is the lower left corner of the wall 2; The relationships between the bending moment and rotation angle in the x and y directions are given by the following formulas. M x = K x x θ x M y = K y x θ y M x = M y wherein M x is the bending moment in the x direction in N mm, K x is the rotational stiffness in the x direction in N mm / rad, θ x is the rotation angle in the x direction in rad, M y is the bending moment in the y direction in N mm, K y is the rotational stiffness in the y direction in N mm / rad, θ y is the rotation angle in the y direction in rad; Calculate the deformation angle of wall 2, then we have: R = θ x x θ y Where R is the deformation angle of wall 2, in rad; After obtaining the displacement parameters of each test point (3), we have: K x =∑K i ×(y i -y0) 2 K y =∑K i ×(x i -x0) 2 δ ix = (y i -y0) x θ x delta iy = (x i -x0) x theta y where K i is the secant stiffness of the test point numbered i, δ ix is the displacement of the test point numbered i in the x direction, δ iy is the displacement of the test point numbered i in the y direction, δ i is the displacement of the test point numbered i.

4. The concrete wall deflection analysis system of claim 1, wherein The image acquisition module is used to acquire the image information of the wall (2), and the image processing module performs image processing on the acquired image. Through threshold segmentation, threshold values T1 and T2 (T1 < T2) are obtained. The segmentation is divided into a bright area image: (N i > T2), a balanced area image: (T2 > N i > T1), and a dark area image: (T2 > N i > T1), where Ni is the gray value of the i-th pixel on the image of the wall (2) acquired by the image acquisition module. The calculation process is as follows: σ 2 = P1(E1-E) 2 + P2(E2-E) 2 + P3(E3-E) 2 where P i is the pixel probability of region i in the image, i = 0, 1, 2,... N, Pi, P2, P3 satisfy: P1 + P2 + P3 = 1 in: E i is the average gray level of the i region in the image, E is the average gray level of the entire image, and E1, E2, E3 satisfy: Maximize the inter-class variance σ to obtain thresholds T1 and T2. The bright region image is superimposed on the balanced region image to obtain a bright fusion image, and then processed by an image processing algorithm to obtain a bright processed image. The dark region image is superimposed on the balanced region image to obtain a dark fusion image, and then processed by an image processing algorithm to obtain a dark processed image. Based on the ratio of thresholds T1 and T2, a composite image is obtained by fusing the bright and dark images. The fusion formula for the bright and dark images is as follows: Wherein, PicA is the composite image, PicB is the brightened image, PicC is the darkened image, and gamma is the correction coefficient, which is usually set to 0.001.

Images