Structural damage assessment method fusing point cloud information and mean square curvature

Abstract

The invention discloses a structural damage assessment method fusing point cloud information and mean square curvature, and relates to the technical field of building structure monitoring. The method comprises the following steps: acquiring a lossless image before a structure is damaged and a lossy image with a damaged area after the structure is damaged; reconstructing the image data into three-dimensional point cloud data by using an SFM algorithm; applying an RANSAC algorithm to obtain an overall optimal fitting plane of the structure, and aligning three-dimensional point cloud data of the lossless image with an overall coordinate system of the fitting plane; registering the three-dimensional point cloud data of the lossy image with the aligned three-dimensional point cloud data of the lossy image; carrying out quadric surface fitting on the point cloud of the damaged area by adopting a local adaptive gradient method to obtain a local optimal fitting curved surface; calculating the mean square curvature of the local optimal fitting curved surface; the mapping relation between the mean square curvature and the structural rigidity is obtained through elastic mechanics, and the rigidity of the damage area is quantified. The technical problems that a traditional rigidity calculation method is low in efficiency and large in error are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of building structure monitoring, and particularly relates to a structural damage assessment method integrating point cloud information and mean square curvature. Background Technique

[0002] With the continuous development of engineering structures such as modern buildings and bridges, ensuring their safety and stability has become the core task of design and maintenance. Traditional structural stiffness calculation methods mainly rely on theoretical models and manual calculations. Although these methods can provide a certain degree of accuracy, in complex structures and large-scale projects, the methods relying on manual calculations and experience are often inefficient and have certain errors.

[0003] In recent years, with the progress of computer science and data acquisition technology, three-dimensional point cloud data of an object can be collected through photo or three-dimensional scanning technology, which can accurately describe the shape and details of the structure. However, such point cloud data itself contains a large amount of information. How to effectively extract the local deformation information on the object surface, establish a direct mapping relationship between the deformation information and the structural stiffness, and then evaluate the degree of structural damage is an urgent problem to be solved.

[0004] Aiming at the problems existing in the existing structural stiffness quantification methods and the characteristics of point cloud data, the present invention provides a structural damage assessment method integrating point cloud information and mean square curvature. This method uses the local point cloud data of the damaged area, fits the best surface in the neighborhood of the damaged point cloud through the local adaptive gradient method, calculates the mean square curvature of the best fitting surface in the damaged area, and combines with elasticity mechanics to obtain the mapping relationship between the mean square curvature and the stiffness in the damaged area, and then evaluates the degree of structural damage. It solves the technical problems of low efficiency and large error in the traditional stiffness calculation method mentioned above. Summary of the Invention

[0005] The purpose of the present invention is to provide a structural damage assessment method integrating point cloud information and mean square curvature to solve the problems of low efficiency and large error in the traditional stiffness calculation method mentioned in the above background technique.

[0006] To achieve the above purpose, the present invention is implemented by adopting the following technical solutions:

[0007] The present invention proposes a structural damage assessment method integrating point cloud information and mean square curvature, including the following steps:

[0008] S1. Collect image data: Obtain the non-destructive image before structural damage and the damaged image with the damaged area after structural damage;

[0009] S2. Three-dimensional point cloud reconstruction: Apply the SFM algorithm to reconstruct the image data into three-dimensional point cloud data;

[0010] S3. Coordinate alignment: Apply the RANSAC algorithm to obtain the best-fitting plane of the overall structure, and align the three-dimensional point cloud data of the non-damaged image with the overall coordinate system of the fitting plane;

[0011] S4. Point cloud registration: Apply the ICP algorithm based on the K-D tree to register the three-dimensional point cloud data of the damaged image with the three-dimensional point cloud data of the aligned non-damaged image;

[0012] S5. Damage surface fitting: Use the local adaptive gradient method to perform quadratic surface fitting on the point cloud in the damaged area to obtain the local best-fitting surface;

[0013] S6. Mean square curvature calculation: Calculate the mean square curvature of the local best-fitting surface;

[0014] S7. Structural stiffness quantification: Apply elasticity mechanics to obtain the mapping relationship between the mean square curvature and the structural stiffness, and quantify the stiffness of the damaged area.

[0015] Preferably, the S1 is specifically as follows:

[0016] When collecting the damaged image with the damaged area after the structure is damaged, calibrate the internal parameters of the camera and pre-plan the moving trajectory of the camera.

[0017] Preferably, the S2 specifically includes the following steps:

[0018] S201. Use the SFM algorithm to detect key feature points in the image, and match the same feature points between different images through the feature matching algorithm;

[0019] S202. Calculate the internal and external parameters of the camera through the matched feature points, and solve the pose of the camera through the essential matrix and epipolar geometry;

[0020] S203. Use the internal and external parameters of the camera and the matched feature points to calculate the three-dimensional coordinates of each feature point through triangulation, and generate sparse point cloud data;

[0021] S204. On the basis of the sparse point cloud, combine the camera pose and apply the multi-view stereo matching algorithm to obtain the dense point cloud data of the structure.

[0022] Furthermore, apply the SOR algorithm to denoise the point cloud data, and apply the downsampling algorithm to downsample the denoised point cloud data.

[0023] Preferably, the S3 is specifically as follows:

[0024] Apply the RANSAC algorithm to obtain the best-fitting plane of the overall structure, extract the coordinate transformation matrix of the fitting plane, and align the three-dimensional point cloud data of the non-damaged image with the overall coordinate system by using the inverse matrix of the coordinate transformation matrix.

[0025] Preferably, S4 specifically includes the following steps:

[0026] S401. Set an initial transformation matrix, and roughly align the damaged 3D point cloud data with the undamaged 3D point cloud data;

[0027] S402. For each point in the damaged 3D point cloud data, find the point in the undamaged 3D point cloud data that is closest to it to form a point pair;

[0028] S403. Solve the rigid transformation of each group of point pairs by the least squares method, and project the damaged 3D point cloud data to the corresponding position of the undamaged 3D point cloud data using the rigid transformation.

[0029] Furthermore, S403 is specifically as follows:

[0030] Use the ICP algorithm of the K-D tree for search, calculate the optimal rigid transformation according to the point pairs. The rigid transformation includes a rotation matrix and a translation vector, and the rigid transformation is achieved by minimizing the error between the source points and the target points;

[0031] Apply the rigid transformation to the damaged point cloud to obtain the transformed point cloud, and calculate the error between the damaged point cloud and the undamaged point cloud;

[0032] If the error is less than the preset threshold or the maximum number of iterations is reached, the registration ends; otherwise, repeat S402 - S403 until the error converges or the termination condition is reached.

[0033] Preferably, S5 is specifically as follows:

[0034] By setting the neighborhood range of each point in the damaged area point cloud, use the local adaptive gradient method to fit the surface within the neighborhood of the point.

[0035] Furthermore, for the neighborhood of each point in the damaged area, assume that the coordinates of each point in the neighborhood are p(x i , y i , z i ), i ∈ [1, n], where i represents the i-th point in the neighborhood;

[0036] Construct a local data matrix A and the corresponding b vector:

[0037]

[0038] b = (z 1 , z 2 , …, z n ) T

[0039] Use a quadratic surface to fit the surface of the damaged area. The fitting model form of the surface is as follows:

[0040] z = ax 2 + bxy + cy 2

[0041] where a, b, and c are the coefficients of the quadratic surface equation;

[0042] The adaptive gradient method is used to solve the parameters of the fitting model; the optimization objective of the adaptive gradient method is to minimize the squared error between the fitting surface and the point cloud data points:

[0043]

[0044] First, randomly initialize the parameters to constants:

[0045] a = c 1

[0046] b = c 2

[0047] c = c 3

[0048] Then, calculate the gradient of the objective function

[0049] By updating the first moment and the second moment of a, b, and c:

[0050] The first moment update is as follows:

[0051]

[0052] where β 1 is the decay factor, m t is the first moment at time t, and m t-1 is the first moment at time t - 1;

[0053] The second moment update is as follows:

[0054]

[0055] where β 2 is the decay factor, v t is the second moment at time t, and v t-1 is the second moment at time t - 1;

[0056] Bias correction:

[0057]

[0058] Update the parameters a, b, and c as follows:

[0059]

[0060] where ε is a small constant, usually taking values between 10 -8 and 10 -4 ;

[0061] Repeat the above steps until the gradient is less than the set value;

[0062] For the obtained quadratic surface, the fitting effect is measured by the root mean square error:

[0063]

[0064] If the error is large, reselect the neighborhood for fitting until the error is less than the specified value.

[0065] Preferably, the S6 is specifically as follows:

[0066] First, obtain the first fundamental form and the second fundamental form of the locally optimal fitting surface through differential geometry, and then use the ratio of the second fundamental form of the surface to the first fundamental form of the surface to obtain the normal curvature at any point on the surface. Calculate the maximum and minimum values of the normal curvature at this point, and the square average of them is the mean square curvature at this point.

[0067] Furthermore, write the locally optimal fitting surface in the form of a parametric equation:

[0068]

[0069] Obtain the first fundamental form and the second fundamental form of the surface through differential geometry:

[0070] The first fundamental form is as follows:

[0071] I = E(du) 2 + 2Fdudv + G(dv) 2

[0072] where E = r u ·r u ,F = r u ·r v ,G = r v ·r v ,where r u 、r v are the first-order partial derivatives of r;

[0073] The second fundamental form is as follows:

[0074] II = L(du) 2 + 2Mdudv + N(dv) 2

[0075] where L = r uu ·n,M = r uv ·n,G = rvv ·n, where r uu 、r uv 、r vv is the second - order partial derivative of r, and n is the unit normal vector of the surface;

[0076] The normal curvature at any point on the surface is obtained by using the ratio of the second fundamental form of the surface to the first fundamental form of the surface:

[0077]

[0078] where k n is the normal curvature at any point;

[0079] Calculate the maximum and minimum values of the normal curvature at this point, and the root - mean - square average of them is the mean - square curvature at this point:

[0080]

[0081] where k is the mean - square curvature at any point.

[0082] Preferably, the S7 is specifically as follows:

[0083] According to the principle of elasticity, the negative correlation between the mean - square curvature and the structural stiffness is obtained. The high - curvature region corresponds to low stiffness, and the low - curvature region corresponds to high stiffness.

[0084] Compared with the prior art, the beneficial effects of the present invention are:

[0085] In the method of the present invention, photos are collected by a camera, and the three - dimensional point clouds before and after structural damage are generated by applying the structure - from - motion recovery technology. The damaged point cloud is projected onto the undamaged point cloud by applying the ICP algorithm, realizing the comparative evaluation before and after structural damage. At the same time, by calculating the mean - square curvature of the point cloud in the damaged area and combining the principle of elasticity, the mapping relationship between the mean - square curvature and the stiffness in the damaged area is obtained, realizing the stiffness quantification of the structural damaged area, and providing a strong basis for efficiently evaluating the current bearing capacity of the structure. Brief Description of the Drawings

[0086] Figure 1 is the flow chart of the structural damage assessment method that fuses point cloud information and mean - square curvature in the present invention.

[0087] Figure 2 is the point cloud map without damage in Embodiment 1 of the present invention;

[0088] Figure 3 is the point cloud map with damage in Embodiment 1 of the present invention;

[0089] Figure 4 is the registered point cloud map in Embodiment 1 of the present invention;

[0090] Figure 5 This is the damage assessment diagram in Embodiment 1 of the present invention. Specific implementation

[0091] Next, in conjunction with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0092] Embodiment 1:

[0093] In this embodiment, a square thin plate structure is taken as an example to illustrate a structural damage assessment method that fuses point cloud information and mean square curvature proposed by the present invention. As Figure 1 shown, in order to quantify the stiffness of the damaged area of the square thin plate, the present invention mainly includes the following steps:

[0094] Step 1: Take photos of the structure before and after damage;

[0095] Specifically, in Step 1, the undamaged photo refers to a photo of the target structure surface without any damage, while the damaged photo is a photo of the structure surface with damages such as cracks, depressions, and defects. The photos meet the following conditions:

[0096] The photos should cover different perspectives of the object, ensuring that there is an overlapping area of at least 60% or more for the same scene or object in at least two photos.

[0097] The feature points (such as corner points, textures, etc.) in the photos are evenly distributed, avoiding areas that are too concentrated or lack features.

[0098] During the shooting process, try to use the same focal length. Providing more details by using a camera with a higher resolution helps to extract more feature points, thereby improving the accuracy of the reconstruction result.

[0099] Avoid overexposure or low light conditions. Try to shoot in natural light as much as possible, and use appropriate artificial light sources when necessary to ensure the clarity and contrast of the image.

[0100] Step 2: Perform 3D reconstruction;

[0101] First, according to the undamaged and damaged photos obtained in Step 1, use the SFM algorithm to detect key feature points in the images.

[0102] Then, through the feature matching algorithm, match the same feature points between different images.

[0103] Subsequently, the internal and external parameters of the camera are calculated through the matched feature points, namely the position, orientation, and focal length of the camera. The pose of the camera is solved through the essential matrix and epipolar geometry. Using the internal and external parameters of the camera and the matched feature points, the three-dimensional coordinates of each feature point are calculated through triangulation to generate a sparse point cloud.

[0104] Finally, bundle adjustment is used to globally optimize the three-dimensional point cloud and the camera pose to reduce errors and improve accuracy. Based on the sparse point cloud, dense point cloud reconstruction is further performed through multi-view stereo vision technology.

[0105] The reconstructed undamaged point cloud is as Figure 2 shown, and the reconstructed damaged point cloud is as Figure 3 shown.

[0106] Step 3: Coordinate alignment;

[0107] Specifically, the RANSAC algorithm is applied to fit the best plane of the structure, the coordinate transformation matrix of the fitted plane is extracted, and the inverse matrix of the coordinate transformation matrix is used to align the three-dimensional point cloud data before damage with the global coordinate system.

[0108] Specifically, first select a group of reference planes in the point cloud generated in step 2, use the RANSAC algorithm to fit the plane, obtain the coordinate transformation matrix of the plane, find the inverse matrix of the obtained coordinate transformation matrix, and then apply the obtained inverse matrix to the point cloud data before damage to align the point cloud data before damage with the global coordinate axes.

[0109] Step 4: Registration of damaged point cloud and undamaged point cloud;

[0110] Specifically, in step 4, the undamaged point cloud and the damaged point cloud are registered to align them in the same coordinate system. Specifically, the damaged point cloud is aligned with the undamaged point cloud through transformation.

[0111] First, the undamaged point cloud and the damaged point cloud are coarsely registered.

[0112] Then, for each point in the damaged point cloud, find the point in the undamaged point cloud that is closest to it, and use the K-D tree to accelerate the search for the spatial data structure. According to the corresponding points between the damaged point cloud and the undamaged point cloud, calculate the optimal rigid transformation, including the rotation matrix and the translation vector, by minimizing the error between the source points and the target points. Apply this transformation matrix to the damaged point cloud to obtain the transformed point cloud, and calculate the error between the damaged point cloud and the undamaged point cloud.

[0113] Finally, if the error is less than the preset threshold or the maximum number of iterations is reached, the registration ends. Otherwise, repeat the above steps until the error converges or the termination condition is reached.

[0114] The registered point cloud is as shown in Figure 4 the figure below.

[0115] Step 5: Local surface fitting;

[0116] Specifically, in Step 5, local surface fitting is performed on the registered damaged point cloud data. For the neighborhood of each point in the damaged area, assume that the coordinates of each point in the neighborhood are p(x i , y i , z i ), where i ∈ [1, n], and i represents the i-th point in the neighborhood.

[0117] Construct a local data matrix A and the corresponding b vector:

[0118]

[0119] b = (z 1 , z 2 , …, z n ) T

[0120] Use a quadratic surface to fit the surface of the damaged area. The form of the quadratic surface is as follows:

[0121] z = ax 2 + bxy + cy 2

[0122] In the formula, a, b, and c are the coefficients of the quadratic surface equation;

[0123] Use the adaptive gradient method to solve the parameters of the fitting model. The optimization objective of the adaptive gradient method is to minimize the squared error between the fitting surface and the point cloud data points:

[0124]

[0125] First, randomly initialize the parameters as constants:

[0126] a = c 1

[0127] b = c 2

[0128] c = c 3

[0129] Calculate the gradient of the objective function

[0130] Update a, b, and c through the first moment and the second moment:

[0131] The first moment update is as follows:

[0132]

[0133] where β 1 is the attenuation factor, m t is the first moment at time t, and m t-1 is the first moment at time t - 1;

[0134] The second moment is updated as follows:

[0135]

[0136] where β 2 is the attenuation factor, v t is the second moment at time t, and v t-1 is the second moment at time t - 1;

[0137] Bias correction:

[0138]

[0139] Update the parameters a, b, and c as follows:

[0140]

[0141] where ε is a small constant, usually taking values between 10 -8 and 10 -4 ;

[0142] Repeat the above steps until the gradient is less than the set value.

[0143] For the obtained quadratic surface, the fitting effect is measured by the root mean square error (RMSE):

[0144]

[0145] If the error is large, reselect the neighborhood for fitting until the error is less than the specified value.

[0146] Step 6: Calculate the mean square curvature;

[0147] Specifically, in Step 6, write the surface in Step 5 in the form of a parametric equation:

[0148]

[0149] Obtain the first fundamental form and the second fundamental form of the surface in Step 5 through differential geometry:

[0150] The first fundamental form is as follows:

[0151] I = E(du) 2 + 2Fdudv + G(dv) 2

[0152] where \(E = r\) u · \(r\) u and \(F = r\) u · \(r\) v and \(G = r\) v · \(r\) v where \(r\) u and \(r\) v are the first - order partial derivatives of \(r\);

[0153] The second fundamental form is as follows:

[0154] II = L(du) 2 + 2Mdudv + N(dv) 2

[0155] where \(L = r\) uu · \(n\), \(M = r\) uv · \(n\), \(G = r\) vv · \(n\), where \(r\) uu , \(r\) uv , \(r\) vv are the second - order partial derivatives of \(r\), and \(n\) is the unit normal vector of the surface;

[0156] The normal curvature at any point on the surface is obtained by using the ratio of the second fundamental form of the surface to the first fundamental form of the surface:

[0157]

[0158] Then, the maximum and minimum values of the normal curvature at this point are calculated, and the root - mean - square average of them is the mean - square curvature at this point:

[0159]

[0160] Step 7: Stiffness quantification;

[0161] Specifically, in Step 7, based on the mean - square curvature calculated in Step 6, the stiffness of the object surface is further quantified.

[0162] According to the principle of elasticity mechanics, a negative correlation between curvature and stiffness is obtained. The high - curvature region corresponds to lower stiffness, while the low - curvature region corresponds to higher stiffness. By analyzing the distribution of the mean - square curvature in the damaged area, the stiffness characteristics of the damaged area can be evaluated, and then the overall load - bearing capacity of the structure can be obtained. Finally, the structural damage obtained by applying the method of the present invention is as Figure 5 shown.

[0163] The above is only used to help understand the method of the present invention and its core concept. However, the protection scope of the present invention is not limited thereto. For those of ordinary skill in the art within the technical scope disclosed by the present invention, any equivalent substitution or change made according to the technical solution and inventive concept of the present invention should be covered within the protection scope of the present invention. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A structural damage assessment method integrating point cloud information and mean square curvature, characterized in that: The steps include: S1. Collecting image data: obtaining a lossless image before structural damage and a lossy image with a damaged area after structural damage; S2, 3D point cloud reconstruction: Apply the SFM algorithm to reconstruct the image data into 3D point cloud data; S3, coordinate alignment: Apply the RANSAC algorithm to obtain the best fitting plane of the structure as a whole, and align the three-dimensional point cloud data of the lossless image with the overall coordinate system of the fitting plane; S4, point cloud registration: Apply the ICP algorithm based on KD tree to register the 3D point cloud data of the lossy image with the 3D point cloud data of the aligned lossless image; S5, damage surface fitting: the local adaptive gradient method is used to perform quadratic surface fitting on the point cloud of the damage area to obtain the local best fitting surface; S6, mean square curvature calculation: calculate the mean square curvature of the local best fitting surface; S7. Quantification of structural stiffness: Apply elastic mechanics to obtain the mapping relationship between mean square curvature and structural stiffness, and quantify the stiffness of the damaged area.

2. A structural damage assessment method integrating point cloud information and mean square curvature according to claim 1, characterized in that: The S1 is specifically as follows: When collecting lossy images with damaged areas after structural damage, the internal parameters of the camera are calibrated and the movement trajectory of the camera is planned in advance.

3. The structural damage assessment method of integrating point cloud information and mean square curvature according to claim 2 is characterized in that: The S2 specifically includes the following steps: S201, using the SFM algorithm to detect key feature points in the image, and matching the same feature points between different images through a feature matching algorithm; S202, calculating the internal and external parameters of the camera through the matched feature points, and solving the camera posture through the essential matrix and epipolar geometry; S203, using the internal and external parameters of the camera and the matched feature points, calculating the three-dimensional coordinates of each feature point through triangulation to generate sparse point cloud data; S204. Based on the sparse point cloud, a multi-view stereo matching algorithm is applied in combination with the camera pose to obtain structured dense point cloud data.

4. The structural damage assessment method of integrating point cloud information and mean square curvature according to claim 3 is characterized in that: The S3 is as follows: The RANSAC algorithm is applied to obtain the best fitting plane of the structure as a whole, and the coordinate transformation matrix of the fitting plane is extracted. The inverse matrix of the coordinate transformation matrix is ​​used to align the three-dimensional point cloud data of the lossless image with the overall coordinate system.

5. The structural damage assessment method of integrating point cloud information and mean square curvature according to claim 4 is characterized in that: The S4 specifically includes the following steps: S401, setting an initial transformation matrix, and roughly aligning the three-dimensional point cloud data after damage with the three-dimensional point cloud data before damage; S402, for each point in the three-dimensional point cloud data after damage, find the point closest to it in the three-dimensional point cloud data before damage to form a point pair; S403, solving the rigid transformation of each group of point pairs by the least square method, and using the rigid transformation to project the three-dimensional point cloud data after damage to the corresponding position of the three-dimensional point cloud data before damage.

6. A structural damage assessment method integrating point cloud information and mean square curvature according to claim 5, characterized in that: The S403 is specifically as follows: The ICP algorithm of the KD tree is used to search and calculate the optimal rigid transformation based on the point pair. The rigid transformation includes the rotation matrix and the translation vector. The rigid transformation is achieved by minimizing the error between the source point and the target point. Applying rigid transformation to the damaged point cloud to obtain the transformed point cloud, and calculating the error between the damaged point cloud and the intact point cloud; If the error is less than the preset threshold or the maximum number of iterations is reached, the registration ends; Otherwise, repeat S402-S403 until the error converges or the termination condition is reached.

7. A structural damage assessment method integrating point cloud information and mean square curvature according to claim 1 or 6, characterized in that: The S5 is specifically as follows: By setting the neighborhood range of each point in the point cloud of the damage area, the surface in the neighborhood of the point is fitted using the local adaptive gradient method.

8. The structural damage assessment method of integrating point cloud information and mean square curvature according to claim 7, characterized in that: For the neighborhood of each point in the damaged area, assume that the coordinates of each point in the neighborhood are p(x i ,y i ,z i ),i∈[1,n], where i represents the i-th point in the neighborhood; Construct a local data matrix A and the corresponding b vector: b=(z1,z2,…,z n ) T The quadratic surface is used to fit the surface of the damaged area. The fitting model of the surface is as follows: z=ax 2 +bxy+cy 2 In the formula, a, b, and c are the coefficients of the quadratic surface equation; The adaptive gradient method is used to solve the parameters of the fitting model; the optimization goal of the adaptive gradient method is to minimize the square error between the fitting surface and the point cloud data points: First, randomly initialize the parameters to constants: a=c1 b=c2 c=c3 Then find the gradient of the objective function By updating the first-order moment and second-order moment of a, b, and c: The first-order moment update is as follows: Where β1 is the attenuation factor, m t is the first-order moment at time t, m t-1 is the first-order moment at time t-1; The second-order moment is updated as follows: Where β2 is the attenuation factor, v t is the second-order moment at time t, v t-1 is the second-order moment at time t-1; Bias correction: Update parameters a, b, and c as follows: In the formula, ε is a small constant with a value between 10 -8 to 10 -4 between; Repeat the above steps until the gradient is less than the set value; For the solved quadratic surface, the fitting effect is measured by the square root error: If the error is large, reselect the neighborhood for fitting until the error is less than the specified value.

9. The structural damage assessment method of integrating point cloud information and mean square curvature according to claim 8, characterized in that: The S6 is specifically as follows: First, the first basic form and the second basic form of the local best fitting surface are obtained through differential geometry, and then the normal curvature of any point on the surface is obtained by using the ratio of the second basic form of the surface to the first basic form of the surface. The maximum and minimum values ​​of the normal curvature of the point are calculated, and the square average is the mean square curvature of the point.

10. A structural damage assessment method integrating point cloud information and mean square curvature according to claim 9, characterized in that: The local best fit surface is written as a parametric equation: The first and second fundamental forms of the surface are obtained through differential geometry: The first basic form is as follows: I=E(you) 2 +2Fdudv+G(dv) 2 Where, E = r u ·r u , F = r u ·r v , G = r v ·r v , where r u 、r v is the first-order partial derivative of r; The second basic form is as follows: II=L(black) 2 +2Mdudv+N(dv) 2 Where L = r uu n, M = r uv n, G = r vv n, where r uu 、r uv 、r vv is the second-order partial derivative of r, and n is the unit normal vector of the surface; The normal curvature of any point on the surface can be obtained by using the ratio of the second fundamental form of the surface to the first fundamental form of the surface: Among them, k n is the normal curvature at any point; Calculate the maximum and minimum values ​​of the normal curvature at the point, and their square average is the mean square curvature of the point: Where k is the mean square curvature at any point.

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