A method for detecting bending resistance of steel-UHPC composite bridge deck based on visual inspection

Abstract

The present application provides a method for detecting the bending resistance of a steel-UHPC composite bridge deck based on visual inspection, comprising: acquiring an optical image of the bridge deck concrete surface, extracting the distribution characteristics of sparse texture areas based on grayscale gradients and generating a surface characteristic distribution map; generating phase-coded fringes according to the surface characteristic distribution map and projecting them onto the concrete surface, acquiring a distorted image and calculating calibration parameters; adjusting a projection device using the calibration parameters, projecting the image onto an undeformed surface and establishing a spatial coordinate system, and recording the coded image; acquiring a deformed image after applying a load, calculating a displacement vector and reconstructing a three-dimensional deformation field; extracting an interface displacement vector from the three-dimensional deformation field, calculating the bending stress distribution and generating a stress distribution map; meshing the stress distribution map, counting the stress characteristic values ​​of each unit, and evaluating the bending bearing capacity of the bridge deck based on the stress characteristic values ​​and a preset threshold.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for detecting the bending resistance of a steel-UHPC composite bridge deck based on visual detection. Background Art

[0002] With the widespread adoption of new materials such as ultra-high-performance concrete (UHPC) and steel composite bridge decks, accurately assessing their flexural properties has become a key issue in ensuring bridge service reliability. Traditional detection methods rely on contact sensors or simple optical measurements, but these methods often struggle with complex surface characteristics, particularly in areas with low texture. Existing solutions often struggle to adapt to the diverse nature of bridge deck surfaces, limiting the integrity and accuracy of deformation data. This is especially true in areas without distinct features, where reconstructing spatial deformation details becomes a technical bottleneck.

[0003] In this context, the limitations of current methods primarily lie in their lack of adaptability to low-texture surfaces and their inability to capture complex deformation features. While conventional optical measurement techniques are effective in highly textured areas, the discernibility of reflected signals decreases significantly when the surface lacks distinct features, leading to data loss. These deficiencies directly limit the comprehensiveness and reliability of flexural performance assessments. The technical challenge in addressing these issues lies in obtaining sufficient deformation information in low-texture areas and reconstructing spatial deformation details based on this information. Due to the lack of natural landmarks in low-texture areas, traditional encoding schemes struggle to effectively attach and identify them. Furthermore, misalignment between the projector and camera position calibration can amplify measurement errors. Furthermore, if the pre- and post-deformation image acquisitions are not precisely aligned, accurate feature extraction of the changes is impossible. These unresolved technical challenges collectively result in the loss of deformation data in featureless areas, which in turn affects the overall assessment of the bridge deck's flexural performance. Therefore, the key to resolving this technical challenge lies in designing an encoding scheme that matches the surface characteristics of low-texture concrete and, through precise calibration of the projection and imaging systems, efficiently acquiring encoded images before and after deformation to reconstruct spatial deformation details in featureless areas. Summary of the Invention

[0004] The present invention provides a method for detecting the bending resistance of a steel-UHPC composite bridge deck based on visual inspection, which mainly includes:

[0005] Acquire an optical image of the concrete surface of the bridge deck, extract the distribution characteristics of sparse texture areas based on grayscale gradients, and generate a surface characteristic distribution map; generate phase-coded fringes based on the surface characteristic distribution map and project them onto the concrete surface, collect distorted images, and calculate calibration parameters; use the calibration parameters to adjust the projection equipment, project them onto the undeformed surface, establish a spatial coordinate system, and record the coded image; after applying a load, collect the deformed image, calculate the displacement vector, and reconstruct the three-dimensional deformation field; extract the interface displacement vector from the three-dimensional deformation field, calculate the bending stress distribution, and generate a stress distribution map; mesh the stress distribution map, count the stress eigenvalues ​​of each unit, and evaluate the bending bearing capacity of the bridge deck based on the stress eigenvalues ​​and a preset threshold.

[0006] Furthermore, the method of obtaining an optical image of the bridge deck concrete surface, extracting surface texture features and generating a surface characteristic distribution map includes: obtaining an initial optical image of the concrete surface through an imaging device; calculating a grayscale gradient based on the initial optical image, and determining an area with a grayscale gradient value lower than a preset threshold as a texture sparse area; and generating a surface characteristic distribution map including boundary coordinates based on the texture sparse area.

[0007] Furthermore, the generating of phase-coded stripes according to the surface characteristic distribution map and projecting them onto the concrete surface, collecting the distorted image and calculating the calibration parameters includes: generating a phase-coded stripe pattern with dynamically adjusted density according to the area of ​​the sparse texture region in the surface characteristic distribution map, wherein the density of the phase-coded stripes is inversely proportional to the area of ​​the sparse texture region; projecting the phase-coded stripe pattern onto the concrete surface through a projection device to form a coded light field; collecting the coded light field image, extracting the distortion features of the stripe boundary, and calculating the inclination deviation and focal length deviation between the projection device and the concrete surface based on the distortion features to generate a calibration parameter set.

[0008] Furthermore, the method of using the calibration parameters to adjust the projection device, projecting onto the undeformed surface and establishing a spatial coordinate system, and recording the coded image includes: adjusting the projection parameters of the projection device according to the calibration parameter set; projecting phase-coded stripes onto the undeformed concrete surface through the adjusted projection device; generating the spatial coordinate system of the undeformed surface based on a structured light reconstruction algorithm, and recording the corresponding coded image.

[0009] Furthermore, the method of collecting deformation images after applying the load, calculating the displacement vector and reconstructing the three-dimensional deformation field includes: after applying the load on the concrete surface, collecting the deformed coded image through an imaging device; comparing the coded images before and after deformation through a sub-pixel registration algorithm, calculating the displacement vector of each pixel point, and generating preliminary deformation distribution data; and for the missing areas of the preliminary deformation distribution data, using an interpolation algorithm combined with displacement gradient and projection density parameters to reconstruct the complete three-dimensional deformation field.

[0010] Furthermore, the method of extracting the interface displacement vector from the three-dimensional deformation field, calculating the bending stress distribution and generating a stress distribution diagram includes: extracting the displacement vector of the interface between the steel section and the concrete from the three-dimensional deformation field; calculating the bending stress distribution of the concrete surface through an inverse solution algorithm based on preset material parameters and the interface displacement vector; and generating a corresponding stress distribution diagram based on the bending stress distribution.

[0011] Furthermore, the stress distribution diagram is gridded, the stress characteristic value of each unit is counted, and the bending bearing capacity of the bridge deck is evaluated based on the stress characteristic value and a preset threshold value, including: regional gridding of the stress distribution diagram; for each grid unit, calculating the mean and standard deviation of the stress value to generate a stress characteristic value set; and determining the bending bearing capacity of the bridge deck based on the ratio of the stress characteristic value set to the design allowable stress.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0013] 1. The present invention discloses a method for detecting the bending resistance of steel-UHPC composite bridge decks based on visual inspection. The method generates a surface characteristic distribution map for low-texture concrete surfaces, dynamically projects phase-coded stripes with variable density, and realizes real-time calibration of the projector by analyzing stripe distortion. After load deformation, the present invention performs sub-pixel displacement vector calculation on the surface, and uses the Kriging interpolation algorithm to compensate for missing data to construct a high-precision spatial deformation field. The displacement vector of the steel-UHPC interface is then extracted, and the bending stress distribution is inverted by combining the finite element inverse solution. Finally, the matching degree between the structural stress level and the design allowable stress is evaluated through gridding statistics.

[0014] 2. This invention analyzes the grayscale gradient distribution of the concrete surface and dynamically adjusts the density of the phase-encoded fringes, addressing the data loss problem caused by weak reflected signals in low-texture areas. The calibration parameters (inclination deviation Δθ and focal length deviation Δf) are calculated based on the mapping relationship between the fringe distortion characteristics and the geometric projection model, ensuring precise alignment between the projection device and the surface, thereby improving the reconstruction accuracy of the 3D deformation field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of a method for detecting bending resistance of a steel-UHPC composite bridge deck based on visual inspection according to the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] like Figure 1 In this embodiment, a method for detecting bending resistance of a steel-UHPC composite bridge deck based on visual inspection may specifically include:

[0018] S101, obtaining an optical image of the bridge deck concrete surface, extracting the distribution characteristics of the texture sparse area based on the grayscale gradient and generating a surface characteristic distribution map, including: obtaining an initial optical image of the concrete surface through an imaging device; calculating the grayscale gradient based on the initial optical image, and determining the area with a grayscale gradient value lower than a preset threshold as a texture sparse area; and generating a surface characteristic distribution map including boundary coordinates based on the texture sparse area.

[0019] When acquiring the initial optical image of the concrete surface using imaging equipment, typically a high-resolution digital camera or industrial camera, the imaging device is designed to capture the subtle texture features of the concrete surface. The camera's position and angle must be adjusted based on the actual conditions of the bridge deck to ensure overall image clarity and detail.

[0020] For example, the camera can be placed perpendicular to the bridge deck, about 1 to 2 meters away, with appropriate focus and aperture settings to ensure uniform lighting and avoid shadows or overexposure. The acquired images are usually saved at a high resolution, such as 4096 × 2160 pixels, so that subsequent processing can accurately extract surface features.

[0021] When calculating the grayscale gradient based on the initial optical image, the grayscale gradient is calculated by performing a differential operation on the grayscale value of each pixel in the image to obtain its gradient value. The grayscale gradient reflects the intensity of grayscale changes in the image. A larger gradient value indicates a denser texture, while a smaller gradient value indicates a sparser texture.

[0022] For example, the Sobel operator or the Prewitt operator can be used to perform convolution operations on the image to calculate the gradient amplitude of each pixel. The setting of the preset threshold needs to be determined according to the specific application scenario and experimental data. The specific method is: for UHPC specimens with different material properties, a standard load is applied and the grayscale gradient distribution on the surface is measured. The statistical gradient threshold range is 5-15 grayscale / mm. For example, for UHPC material with an elastic modulus of 45GPa, the gradient threshold is set to 10 grayscale / mm, and the area with a grayscale gradient value below this threshold is defined as a sparse texture area. The purpose of this step is to quantify the texture features in the image to facilitate subsequent analysis and processing.

[0023] When generating a surface property distribution map including boundary coordinates based on the sparsely textured regions, the sparsely textured regions must first be identified and marked. The boundary coordinates of these regions can be extracted using image processing algorithms (such as region growing or edge detection) and recorded in a data structure.

[0024] For example, using the contour detection function in the OpenCV library, you can extract a set of boundary points in a sparsely textured area and store their coordinate information as a two-dimensional array.

[0025] Then, a surface characteristic distribution map is generated based on the coordinate information, in which different colors or marks are used to represent the distribution of texture sparse areas.

[0026] For example, sparse texture areas can be marked in red, while dense texture areas can be marked in green, forming an intuitive distribution map. The purpose of this step is to provide a basis for subsequent phase-encoded fringe generation, ensuring that the fringe pattern can be dynamically adjusted according to regional characteristics.

[0027] Through these steps, the texture features of the bridge concrete surface can be accurately extracted and a surface property distribution map can be generated, providing basic data for subsequent structured light projection and 3D deformation field reconstruction. This method can effectively identify sparsely textured areas on the bridge surface and improve measurement accuracy and reliability by dynamically adjusting the phase-encoded fringes.

[0028] S102, generating phase-coded stripes according to the surface characteristic distribution map and projecting them onto the concrete surface, collecting distorted images and calculating calibration parameters, including: generating a phase-coded stripe pattern with dynamically adjusted density according to the area of ​​the sparse texture region in the surface characteristic distribution map; the density of the phase-coded stripes is inversely proportional to the area of ​​the sparse texture region; projecting the phase-coded stripe pattern onto the concrete surface through a projection device to form a coded light field; collecting the coded light field image, extracting stripe boundary distortion features, calculating the inclination angle deviation and focal length deviation between the projection device and the concrete surface based on the distortion features, and generating a calibration parameter set.

[0029] A phase-encoded fringe pattern with dynamically adjusted density is generated based on the area of ​​the sparsely textured region in the surface property distribution map. The density of the phase-encoded fringe is inversely proportional to the area of ​​the sparsely textured region. On concrete surfaces, the size of the sparsely textured region directly affects the optical image acquisition effect. For larger sparse regions, the deformation after bearing load is also larger. If a high-density fringe pattern is used, the fringe may be too dense and unable to accurately capture the distortion characteristics. For smaller sparse regions, the deformation after bearing load is small, and a low-density fringe pattern may not provide enough detailed information. Therefore, it is necessary to dynamically adjust the fringe density based on the area of ​​the region.

[0030] For example, for a sparse area of ​​10 square centimeters, a pattern with a density of 5 fringes per centimeter can be generated; while for an area of ​​50 square centimeters, the density can be adjusted to 2 fringes per centimeter. This dynamic adjustment ensures that the fringe pattern effectively reflects the surface characteristics in different areas, while avoiding measurement errors caused by excessively high or low density.

[0031] A phase-coded fringe pattern is projected onto the concrete surface using a projection device, creating a coded light field. The projection device must precisely project the generated fringe pattern to ensure uniformity and stability of the coded light field.

[0032] For example, a structured light projector with a resolution of 1920x1080 is used to project a generated fringe pattern onto a concrete surface at 30 frames per second. During projection, the projector's light intensity and projection angle must be precisely controlled to avoid uneven light fields or blurred fringe patterns. This creates a clear, encoded light field on the concrete surface, providing high-quality baseline data for subsequent image acquisition.

[0033] Collect coded light field images and extract fringe boundary distortion features. After the coded light field is formed, it is necessary to capture the image using a high-resolution camera.

[0034] For example, using a camera with a resolution of 4000x3000 and capturing images at 60 frames per second, the stripe boundaries in the captured image may be distorted due to uneven concrete surfaces or projector angle deviations. Image processing algorithms can be used to extract these distortion features.

[0035] For example, the Canny edge detection algorithm is used to identify stripe boundaries and calculate the curvature and offset of the boundary curve. These distortion features are key data for subsequently calculating the inclination and focal length deviations between the projection device and the concrete surface. Based on these distortion features, the inclination and focal length deviations between the projection device and the concrete surface are calculated to generate a calibration parameter set.

[0036] By analyzing the collected distortion characteristics, the inclination angle deviation and focal length deviation between the projection device and the concrete surface can be calculated. The specific calculation steps are as follows:

[0037] Extract the curvature change of the stripe boundary in the coded light field image and calculate it by the horizontal gradient of the curvature of the stripe boundary:

[0038] in, is the inclination deviation, is the calibration coefficient, is the horizontal gradient of the stripe boundary curvature.

[0039] Extract the curvature radius R of the distorted fringes and calculate it based on the geometric projection model:

[0040] in, is the focal length calibration coefficient, which is obtained by fitting multiple sets of experimental data under known Δf (for example, when Δf=5mm, R=10mm^{-1} is measured, then k_f=5 mm mm).

[0041] Among them, the calibration coefficient and The method for determining is:

[0042] In the experimental environment, by adjusting the inclination and focal length of the projection equipment, different and fringe distortion characteristics under Δf (curvature gradient , radius of curvature R); the least squares method is used to fit the linear relationship and obtain and .

[0043] For example, if the curvature of the fringe boundary is significantly greater on the left side of the image than on the right side, it indicates that the projection device has a tilt deviation; if the fringe boundary is blurred as a whole, it indicates a large focus deviation.

[0044] Based on these analysis results, a and the calibration parameter set of focal length deviation Δf.

[0045] For example, if the calculated tilt deviation is 2 degrees and the focal length deviation is 5 mm, the calibration parameter set can be recorded as Δθ = 2° and Δf = 5 mm. These calibration parameters are used to adjust the projection angle and focal length of the projection device to ensure that the subsequent projected fringe pattern is more accurate, thereby improving the accuracy of 3D deformation field reconstruction.

[0046] S103, using the calibration parameters to adjust the projection device, projecting onto the undeformed surface and establishing a spatial coordinate system, and recording a coded image, including: adjusting the projection parameters of the projection device according to the calibration parameter set; projecting phase-coded stripes onto the undeformed concrete surface through the adjusted projection device; generating the spatial coordinate system of the undeformed surface based on a structured light reconstruction algorithm, and recording the corresponding coded image.

[0047] The projection parameters of the projection device are adjusted according to the calibration parameter set. During adjustment, the projector lens is adjusted to a preset position based on the focal length deviation Δf in the calibration parameter set. For example, if Δf = 5mm in the standard parameter set, the focal length deviation is 5mm. If the focal length needs to be increased by 5mm in the direction opposite to the offset direction to ensure the focusing effect of the projected beam. At the same time, the projector installation angle is adjusted according to the tilt angle parameter Δθ in the calibration parameter set. For example, if Δθ = 2° in the standard parameter set, the tilt angle is adjusted by 2 degrees in the offset direction to eliminate projection deviation caused by improper device installation. This is done to ensure that the distribution of the projected phase-coded fringes on the undeformed surface is consistent with the expected distribution, providing high-precision basic data for subsequent 3D reconstruction.

[0048] Phase-coded stripes are projected onto the undeformed concrete surface using the adjusted projection equipment. The key to this step lies in the design and projection accuracy of the phase-coded stripes. The brightness distribution pattern of the phase-coded stripes is typically sinusoidal or square, and the stripe spacing and contrast are optimized based on the texture characteristics of the concrete surface.

[0049] For example, for a low-texture concrete surface, a stripe spacing of 5 mm and a contrast ratio of 80% can be designed to ensure the stripes are recognizable on the surface. During the projection process, the light source intensity and projection angle of the projection device need to be strictly controlled. For example, the light source intensity is set to 1000 lumens and the projection angle is 15 degrees with the surface normal to avoid overexposure or shadow interference. The purpose of this step is to form a high-contrast coded light field on the undeformed surface, providing clear image data for subsequent 3D reconstruction.

[0050] A structured light reconstruction algorithm generates the spatial coordinate system of the undeformed surface and records the corresponding encoded image. The core of this step is to infer the 3D geometry of the surface using the distortion information of the phase-encoded fringes. Structured light reconstruction algorithms typically include phase unwrapping and 3D coordinate inversion.

[0051] Specifically, the following steps are included:

[0052] The four-step phase shift method is used to demodulate the phase information of the phase-coded stripes. Four sets of sinusoidal stripes with a phase difference of π / 2 are projected, and the absolute phase is calculated using the following formula:

[0053] ;

[0054] in, I arrive is the light intensity value of the four phase-shifted images.

[0055] Phase ambiguity is eliminated by multi-frequency heterodyning. Two sets of sinusoidal fringes with different frequencies (such as frequency 、 ), generating the synthetic frequency , extending the phase measurement range to full field without ambiguity.

[0056] Based on the calibration parameters (intrinsic and extrinsic) of the projector and camera, the three-dimensional coordinates of the surface points are calculated through the triangulation model. The formula is:

[0057] ;

[0058] in, is the depth value, is the baseline distance, is the focal length of the projector, is the fringe period, is the phase offset.

[0059] For example, a high-resolution camera can capture images at 30 frames per second, ensuring clarity and contrast in every frame. The resulting spatial coordinate system accurately describes the geometry of the undeformed surface, providing benchmark data for subsequent deformation analysis. This approach offers the advantage of high-precision 3D reconstruction, providing a reliable foundation for subsequent displacement and stress analysis.

[0060] S104, collecting deformation images after applying the load, calculating displacement vectors and reconstructing the three-dimensional deformation field, including: after applying the load on the concrete surface, collecting the deformed coded image through an imaging device; comparing the coded images before and after deformation through a sub-pixel registration algorithm, calculating the displacement vector of each pixel point, and generating preliminary deformation distribution data; for the missing areas of the preliminary deformation distribution data, using an interpolation algorithm combined with displacement gradient and projection density parameters to reconstruct the complete three-dimensional deformation field.

[0061] After a load is applied to the concrete surface, an imaging device is used to capture coded images of the deformed surface. This imaging device typically uses a high-resolution camera to ensure that subtle deformation characteristics of the concrete surface can be captured.

[0062] For example, in bridge experiments, loads are applied to the bridge deck via hydraulics, and a camera captures coded images at a fixed angle and distance. To ensure image quality, the camera adjusts exposure time and aperture to accommodate varying lighting conditions. The captured coded images contain information about the deformation of the concrete surface under load, providing the foundational data for subsequent displacement vector calculations.

[0063] The coded images before and after deformation are compared using a sub-pixel registration algorithm, which calculates the displacement vector of each pixel and generates preliminary deformation distribution data. Sub-pixel registration is a high-precision image processing method that can improve image matching accuracy to the sub-pixel level.

[0064] In the experiment, the images before and after deformation were first preprocessed. This included Gaussian filtering to remove noise from the encoded images and histogram equalization to enhance contrast. Next, the cross-power spectrum of the two images was calculated using phase correlation, and the phase difference was extracted using Fourier transform to achieve feature point matching. Bicubic interpolation was performed on the matching results to increase the displacement resolution to 0.1 pixel. For example, the precise displacement value was calculated using the interpolated peak position. Finally, deformation distribution data containing the displacement vector of each pixel was generated.

[0065] For areas missing from the preliminary deformation distribution data, an interpolation algorithm is used to reconstruct the complete 3D deformation field using displacement gradients and projection density parameters. Missing areas are typically caused by incomplete coverage of the concrete surface by the phase-encoded fringes or uneven illumination. Kriging interpolation is a commonly used spatial interpolation method that can predict the displacement of missing areas based on the displacement vectors and spatial distribution patterns of known points.

[0066] For example, in the experiment, we first analyze the gradient changes of the known displacement vector and combine it with the density parameters of the structured light projection to determine the weight and range of the interpolation. Then, we use the Kriging algorithm to interpolate the missing areas to generate a complete displacement distribution map.

[0067] Through these steps, the displacement information of the concrete surface can be extracted from the coded image and a complete 3D deformation field can be reconstructed. This process not only intuitively demonstrates the deformation of concrete under load but also provides reliable data support for subsequent structural analysis and stress calculations.

[0068] For example, in bridge engineering, by analyzing the three-dimensional deformation field, the bending performance and durability of the bridge can be evaluated, providing a scientific basis for design optimization and maintenance.

[0069] S105, extracting the interface displacement vector from the three-dimensional deformation field, calculating the bending stress distribution and generating a stress distribution diagram, including: extracting the displacement vector of the interface between the steel section and the concrete from the three-dimensional deformation field; calculating the bending stress distribution of the concrete surface through an inverse solution algorithm based on preset material parameters and the interface displacement vector; and generating a corresponding stress distribution diagram based on the bending stress distribution.

[0070] Extracting the displacement vector at the interface between the steel section and concrete from the three-dimensional deformation field first requires accurate analysis of the three-dimensional deformation field data. The three-dimensional deformation field is obtained through structured light projection and image acquisition technology, which records the displacement changes of the concrete surface under load. Extracting the interface displacement vector requires accurately defining the interface between the steel section and concrete, typically achieved through image recognition or a preset spatial coordinate range.

[0071] For example, in a certain experiment, the interface between the steel section and the concrete was defined as an area with a width of 300 mm. By filtering and interpolating the displacement data in this area, the displacement vector perpendicular to the interface was extracted. The key to this step is to ensure the accuracy of the displacement vector, because the subsequent stress calculation depends on the accuracy of these vectors. According to the preset material parameters and the displacement vector of the interface, the bending stress distribution of the concrete surface is calculated by the inverse solution algorithm. The preset material parameters include the elastic modulus and Poisson's ratio of the concrete, which directly affect the results of the stress calculation. The inverse solution algorithm is a method of inferring stress distribution based on displacement data. Its core idea is to solve the unknown stress field through the known displacement field and material constitutive relationship. The inverse solution algorithm is based on the generalized Hooke's law under the plane stress assumption. The specific formula is:

[0072] in, is the bending stress, and are the displacement components along the load direction and the lateral direction respectively. In the bending stress calculation, Poisson's ratio Used to correct for changes in longitudinal stress due to lateral shrinkage. For example, when a concrete surface is bent, the lateral strain and longitudinal strain The relationship is , by introducing Poisson's ratio, the actual stress distribution can be reflected more accurately.

[0073] For example, assuming that the elastic modulus of concrete is 45GPa and the Poisson's ratio is 0.2, the extracted displacement vector can be converted into stress distribution data through the finite element inverse solution method. In a specific case, the maximum value of the interface displacement vector is 0.15 mm. After calculation by the inverse solution algorithm, the maximum bending stress obtained is 12MPa. The difficulty of this step lies in how to deal with the noise and uncertainty of the displacement data to ensure the reliability of the stress distribution. Generating the corresponding stress distribution map according to the bending stress distribution is a visual expression of the calculation results. The stress distribution map is usually presented in the form of a cloud map, and different colors represent different stress value ranges.

[0074] For example, in one experiment, red areas in the stress distribution graph indicate stress values ​​greater than 10 MPa, while blue areas indicate stress values ​​less than 5 MPa. This visualization allows for intuitive observation of stress concentration areas and stress distribution trends on the concrete surface. The technical benefit of this step is to provide engineers with an intuitive stress analysis tool, helping them quickly identify potential structural risks.

[0075] S106, gridding the stress distribution diagram, counting the stress characteristic values ​​of each unit, and evaluating the bending bearing capacity of the bridge deck based on the stress characteristic values ​​and a preset threshold, including: regional gridding the stress distribution diagram; calculating the mean and standard deviation of the stress value for each grid unit to generate a stress characteristic value set; and determining the bending bearing capacity of the bridge deck based on the ratio of the stress characteristic value set to the design allowable stress.

[0076] When performing regional meshing on a stress distribution map, the first step is to determine the mesh size and shape. Rectangular or triangular meshes are typically used. The mesh size should be determined based on the complexity of the stress distribution and the accuracy required. For example, smaller meshes can be used to improve resolution in areas with large stress variations, while larger meshes can be used to improve computational efficiency in areas with smaller stress variations.

[0077] After the meshing is completed, each mesh cell will correspond to a specific area for the subsequent calculation of stress eigenvalues. When calculating the mean and standard deviation of the stress value for each mesh cell, it is first necessary to obtain the data of all stress values ​​in the cell. When calculating the mean, all stress values ​​are added together and divided by the number of data points in the cell to obtain the average stress level of the cell. When calculating the standard deviation, first calculate the square of the difference between each stress value and the mean, and then take the square root of the average of these squared values ​​to obtain the degree of stress fluctuation of the cell. The mean reflects the overall stress level of the cell, while the standard deviation reflects the degree of dispersion of the stress values. After generating the set of stress eigenvalues, these eigenvalues ​​need to be compared with the design allowable stress.

[0078] The design allowable stress is the maximum allowable stress value determined during bridge design based on material properties and safety factors. By calculating the ratio of the mean stress value to the design allowable stress for each mesh element, it is possible to assess whether the stress level in that element is within the safe range. If the ratio for a particular element is greater than 1, it indicates that the stress level in that element exceeds the design allowable stress and may pose a safety hazard. If the ratio is less than 1, the stress level in that element is within the safe range. The ratio of the stress eigenvalue set to the design allowable stress can be used to further assess the bending capacity of the entire bridge deck. If the ratio for most mesh elements is less than 1, the overall bending capacity of the bridge deck is strong and can withstand the design loads. If the ratio for some elements is greater than 1, these areas require special attention and may require reinforcement or other measures to improve their bending capacity. This assessment method provides a comprehensive understanding of the stress distribution on the bridge deck, providing a scientific basis for bridge safety assessment and maintenance.

[0079] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection, characterized in that: include: Obtain an optical image of the bridge deck concrete surface, extract the distribution features of sparse texture areas based on grayscale gradients, and generate a surface property distribution map; Generating phase-coded fringes according to the surface characteristic distribution map and projecting them onto the concrete surface, collecting distorted images and calculating calibration parameters, including: generating a phase-encoding fringe pattern with dynamically adjusted density based on the area of ​​the sparse texture region in the surface property distribution map, wherein the density of the phase-encoding fringes is inversely proportional to the area of ​​the sparse texture region; Projecting the phase-coded fringe pattern onto the concrete surface through a projection device to form a coded light field; Acquiring the coded light field image, extracting fringe boundary distortion features, calculating the inclination angle deviation and focal length deviation between the projection device and the concrete surface based on the distortion features, and generating a calibration parameter set; Adjusting the projection device using the calibration parameters, projecting onto the undeformed surface and establishing a spatial coordinate system, and recording the encoded image; After applying the load, deformation images are collected, displacement vectors are calculated, and the three-dimensional deformation field is reconstructed; extracting an interface displacement vector from the three-dimensional deformation field, calculating the bending stress distribution and generating a stress distribution map; The stress distribution diagram is meshed, the stress characteristic value of each unit is counted, and the bending bearing capacity of the bridge deck is evaluated based on the stress characteristic value and the preset threshold.

2. The method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection according to claim 1, characterized in that: The method of acquiring an optical image of the bridge deck concrete surface, extracting the texture sparse area distribution characteristics based on grayscale gradient and generating a surface characteristic distribution map includes: Acquiring an initial optical image of the concrete surface by an imaging device; Calculating a grayscale gradient based on the initial optical image, and determining an area where the grayscale gradient value is lower than a preset threshold as a sparse texture area; A surface property distribution map including boundary coordinates is generated according to the texture sparse area.

3. The method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection according to claim 2, characterized in that: The method of adjusting the projection device using the calibration parameters, projecting onto the undeformed surface and establishing a spatial coordinate system, and recording the encoded image comprises: adjusting projection parameters of the projection device according to the calibration parameter set; Projecting phase-coded fringes onto the undeformed concrete surface by means of an adjusted projection device; A spatial coordinate system of the undeformed surface is generated based on a structured light reconstruction algorithm, and a corresponding coded image is recorded.

4. The method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection according to claim 3, characterized in that: After the load is applied, deformation images are collected, displacement vectors are calculated, and a three-dimensional deformation field is reconstructed, including: After a load is applied to the concrete surface, a coded image of the deformation is collected by an imaging device; The coded images before and after deformation are compared by using a sub-pixel registration algorithm, the displacement vector of each pixel is calculated, and preliminary deformation distribution data is generated; For the missing areas of the preliminary deformation distribution data, an interpolation algorithm is used in combination with displacement gradient and projection density parameters to reconstruct the complete three-dimensional deformation field.

5. The method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection according to claim 4, characterized in that: The extracting of the interface displacement vector from the three-dimensional deformation field, calculating the bending stress distribution and generating a stress distribution diagram includes: Extracting the displacement vector of the interface between the steel section and the concrete from the three-dimensional deformation field; Calculating the bending stress distribution on the concrete surface using an inverse solution algorithm based on preset material parameters and the interface displacement vector; A corresponding stress distribution diagram is generated according to the bending stress distribution.

6. The method for detecting bending resistance of steel-UHPC composite bridge decks based on visual inspection according to claim 5, characterized in that: The gridding of the stress distribution diagram, counting the stress characteristic values ​​of each unit, and evaluating the bending bearing capacity of the bridge deck based on the stress characteristic values ​​and a preset threshold value include: Performing regional grid division on the stress distribution map; For each grid cell, the mean and standard deviation of the stress value are calculated to generate a stress characteristic value set; The bending bearing capacity of the bridge deck is determined according to the ratio of the stress characteristic value set to the design allowable stress.

Images