A method for structural component parameter extraction and deformation measurement based on laser point cloud

Abstract

A kind of structural member parameter extraction and deformation measurement method based on laser point cloud, comprising: laser point cloud data acquisition;Laser point cloud data preprocessing;Laser point cloud data and BIM model overall calibration;According to floor axis net information, laser point cloud data is segmented, and the point cloud data after layering is registered with floor axis net;From the calibrated point cloud model of each floor, structural member with rectangular and H-shaped section is extracted;Structural member section geometric parameter extraction and optimization;Structural member inclination and bending degree calculation.Can check the section size of the actual built structure system, check whether there is no archived design change;The present application can find the structural member with abnormal inclination and bending rate, and the BIM model can be modified based on the above comparison results, so that BIM model can reflect the current real situation of structure system.

Description

Technical Field

[0001] This invention relates to the field of structural engineering measurement, and in particular to a method for extracting structural component parameters and measuring deformation based on laser point clouds. Background Technology

[0002] Large-scale structural projects that have already been built often differ from the design drawings and have undergone multiple modifications and repairs during their long service life. When major structural modifications are required, the design drawings and technical modification data usually cannot fully reflect the current condition of the structure, which brings difficulties to the modification design.

[0003] Traditional surveying methods, such as theodolites and total stations, are time-consuming, and their quality is limited by the observation angle and location. For complex structural engineering projects with densely packed components, when time is tight and the workload is heavy, it is impossible to capture and record all aspects. Therefore, the surveying results have little impact on improving the quality of the renovation design. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method for extracting structural component parameters and measuring deformation based on laser point clouds to overcome or at least partially solve the above problems.

[0005] To address the aforementioned technical problems, the embodiments of this application disclose the following technical solutions:

[0006] A method for extracting structural component parameters and measuring deformation based on laser point clouds includes:

[0007] S100. Use laser scanning equipment to perform a panoramic coverage scan of the target structure system to obtain point cloud data of the target structure system;

[0008] S200. Preprocess the laser point cloud data to remove noise and redundant data unrelated to structural components, and thin and simplify the point cloud data to ensure uniform spacing between sampled point clouds;

[0009] S300. Perform overall registration between the preprocessed laser point cloud data and the BIM 3D model to unify the origin positioning and coordinates;

[0010] S400. After overall registration, the point cloud model is divided into floors and registered with the floor grid, and the coordinates of the point cloud model of each floor are adjusted.

[0011] S500. Extract point cloud data belonging to structural components from the calibrated point cloud model of each layer. The cross-sectional shapes of the structural components include rectangular and I-shaped.

[0012] S600. Fit the three-dimensional outer contour plane and the cross-sectional geometric boundary of the structural component to obtain the cross-sectional geometric parameters of the component;

[0013] S700. Calculate the inclination and curvature of the structural member based on the obtained geometric parameters of the member section.

[0014] Furthermore, in S100, the laser scanning equipment adopts a ground-based laser scanning system with a scanning accuracy of not less than 1mm; the scanning range covers all main structural components and connection nodes of the target structural system.

[0015] Furthermore, in S200, the point cloud data is denoised by using the Euclidean distance outlier method to remove discrete interference noise points; redundant point cloud data, including human figures and clutter, is removed by manual intervention; and the point cloud data is thinned and simplified by the uniform grid method to ensure that the sampling point cloud spacing is uniform and the point cloud spacing is no more than 1mm.

[0016] Furthermore, in S300, the preprocessed laser point cloud data is registered with the BIM 3D model as a whole, including: adjusting the global origin and 3D coordinate direction of the measured laser point cloud data according to the global origin and 3D coordinate direction of the BIM model, calculating the overlap between the measured laser point cloud data and the BIM model after each adjustment, and assigning new 3D coordinate values ​​to the measured laser point cloud according to the coordinate change parameters when the overlap between the two models is at its maximum.

[0017] Furthermore, in S400, the point cloud model after overall registration is segmented by floor and registered with the floor grid. The coordinates of the point cloud model of each floor are adjusted. Specifically, this includes: segmenting the point cloud data in the direction of floor height according to the elevation information of each floor of the building; picking the centroid of the cross section of the main frame column for the segmented floor point cloud data, connecting the centroids of the cross sections of each frame column, and calibrating with the floor grid through the coordinates of the centroids of the cross sections of the frame columns and the direction of the connecting lines.

[0018] Furthermore, in S500, point cloud data of target structural components are extracted from the calibrated point cloud models of each layer, including: for structural components existing in the BIM model, point cloud data of the spatial occupancy of the structural component and its surrounding area are extracted based on the spatial coordinates and cross-sectional geometric design parameters of the structural component in the BIM model; by comparing the BIM model and the measured point cloud model, newly added structural components that do not exist in the BIM model are identified. For such newly added structural components, the point cloud data of the newly added structural components are obtained by directly selecting them in the point cloud model.

[0019] Furthermore, in S600, the three-dimensional outer contour plane of the structural member is fitted and the cross-sectional geometric boundary is fitted to obtain the cross-sectional geometric parameters of the member. Specifically, this includes: fitting the three-dimensional outer contour plane of the structural member using the RANSAC method to determine the accurate spatial position of the structural member, and then filtering out point cloud data that does not belong to the structural member; dividing the structural member into multiple segments according to its axial length, cutting each segment, fitting the boundary lines of the member's cross-section using the RANSAC method, measuring the cross-sectional geometric design parameters of the member, and calculating the average value and root mean square error of the cross-sectional geometric design parameters based on the measurement results of multiple segments; where, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an I-shaped cross-section, the geometric design parameters include the length and thickness of the flange and web.

[0020] Furthermore, the specific steps for fitting a single plane include:

[0021] Three non-collinear seed points are randomly selected from the point cloud dataset of the components to establish an initial plane L0: α0x+β0y+γ0z+δ0=0;

[0022] Calculate the distance from each point in the point cloud dataset of the component, excluding the three seed points, to the plane L0; when the distance is less than a given distance threshold d... ε If the point is counted, then the point will be included in the set of points in the plane L0.

[0023] Repeat the above steps n times, each time comparing the number of points in the current plane with the number of points in the previous plane, and keeping the plane with the larger number of points;

[0024] After n iterations, the optimal plane parameters α, β, γ, and δ are recalculated based on the point cloud data of the point set with the largest number of points in the plane.

[0025] Furthermore, the specific steps for fitting the boundary lines on a single component cross-section include:

[0026] From the point cloud dataset of the cross section, randomly select two non-collinear seed points to establish an initial straight line l0: A0x+B0y+C0=0;

[0027] The perpendicular distance from the point cloud dataset of the calculated section, excluding the two seed points, to the line l0; when the distance is less than a given distance threshold d ∈ If the point is counted, then the point will be included in the set of points inside the line l0.

[0028] Repeat the above steps m times, each time comparing the number of points in the current line set with the number of points in the previous line set, and keeping the line with the larger number of points;

[0029] After m iterations, the optimal line parameters A0, B0, and C0 are recalculated based on the point cloud data of the point set with the largest number of points within the line.

[0030] Furthermore, the inclination and curvature of the extracted structural members are calculated, including: calculating the inclination of the structural members in the two directions of the centroidal axis of the cross section based on the centroidal coordinates of the two end faces of the structural members and the length of the members; and calculating the curvature of the members in the two directions of the centroidal axis of the cross section based on the centroidal coordinates of one end cross section and the mid-span cross section of the structural members.

[0031] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0032] This invention discloses a method for extracting structural component parameters and measuring deformation based on laser point clouds, including: laser point cloud data acquisition; laser point cloud data preprocessing; overall calibration of laser point cloud data and BIM model; segmentation of laser point cloud data according to floor grid information, and registration of the segmented point cloud data with the floor grid; extraction of structural components with rectangular and I-shaped cross-sections from the calibrated point cloud models of each floor; extraction and optimization of the geometric parameters of the structural component cross-sections; and calculation of the inclination and curvature of the structural components. This method can verify the cross-sectional dimensions of structural components in an actual constructed structural system, checking for any unarchived design changes. It can also identify structural components with abnormal inclination and curvature, allowing for modifications to the BIM model based on these comparison results, ensuring the BIM model accurately reflects the current state of the structural system.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0035] Figure 1 This is a flowchart of a method for extracting structural component parameters and measuring deformation based on laser point clouds, as described in Embodiment 1 of the present invention. Detailed Implementation

[0036] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0037] To address the problems existing in the prior art, embodiments of the present invention provide a method for extracting structural component parameters and measuring deformation based on laser point clouds.

[0038] Example 1

[0039] This embodiment discloses a method for extracting structural component parameters and measuring deformation based on laser point clouds, such as... Figure 1 ,include:

[0040] S100. A laser scanning device is used to perform a panoramic coverage scan of the target structural system to obtain point cloud data of the target structural system. Specifically, in this embodiment S100, the laser scanning device is a ground-based laser scanning system with a scanning accuracy of not less than 1 mm. The scanning range covers all main structural components and connection nodes of the target structural system.

[0041] S200. Preprocess the laser point cloud data to remove noise and redundant data unrelated to structural components, and thin and simplify the point cloud data to ensure uniform spacing between sampled point clouds;

[0042] In S200 of this embodiment, the laser point cloud data is preprocessed, including: denoising the point cloud data by using the Euclidean distance outlier method to remove discrete interference noise points; removing redundant point cloud data, including human figures and debris, by manual intervention; and thinning and simplifying the point cloud data by using the uniform grid method to ensure that the sampling point cloud spacing is uniform and the point cloud spacing is no greater than 1mm.

[0043] S300. Perform overall registration between the preprocessed laser point cloud data and the BIM 3D model to unify the origin positioning and coordinates;

[0044] In S300 of this embodiment, the preprocessed laser point cloud data is registered with the BIM 3D model as a whole, including: adjusting the global origin and 3D coordinate direction of the measured laser point cloud data according to the global origin and 3D coordinate direction of the BIM model, calculating the overlap between the measured laser point cloud data and the BIM model after each adjustment, and assigning new 3D coordinate values ​​to the measured laser point cloud according to the coordinate change parameters when the overlap between the two models is at its maximum.

[0045] Specifically, in this embodiment, it is necessary to calculate the overlap between the denoised measured laser point cloud data and the BIM model. The specific method is as follows: Based on the global origin and three-dimensional coordinate direction of the BIM model, adjust the global origin and three-dimensional coordinate direction of the measured laser point cloud data to maximize the overlap between the two models. The registration objective function is...

[0046] m is the total number of point clouds; q i For the measured laser point cloud p i The projection point on the nearest plane or curved surface of the BIM model; R is the coordinate rotation transformation matrix of the measured laser point cloud, and t is the coordinate translation vector.

[0047] The measured laser point cloud is assigned new 3D coordinate values ​​based on the origin and 3D coordinates at the point of maximum overlap, thus completing model registration.

[0048] The accuracy of the BIM model should ensure that spatial coordinate information and cross-sectional geometric parameters of structural members can be extracted from it, including but not limited to: the x, y, and z coordinates of the centroids of the cross-sections at both ends of the member; the length and width of rectangular cross-sections; and the length and width of the web and flanges of I-shaped cross-sections. The data accuracy of the BIM model should be no less than 0.1 mm.

[0049] S400. After overall registration, the point cloud model is divided into floors and registered with the floor grid, and the coordinates of the point cloud model of each floor are adjusted.

[0050] In S400 of this embodiment, the point cloud model after overall registration is divided by floor and registered with the floor grid. The coordinates of the point cloud model of each floor are adjusted. Specifically, the point cloud data is divided in the floor height direction according to the elevation information of each floor of the building. The centroids of the cross sections of the main frame columns are picked out from the divided floor point cloud data, and the centroids of the cross sections of each frame column are connected. The coordinates of the centroids of the cross sections of the frame columns and the direction of the connecting lines are used to calibrate with the floor grid.

[0051] S500. Extract point cloud data belonging to structural components from the calibrated point cloud model of each layer. The cross-sectional shapes of the structural components include rectangular and I-shaped.

[0052] In S500 of this embodiment, the point cloud data of the target structural component is extracted from the point cloud model after calibration of each layer, including: for structural components existing in the BIM model, the point cloud data of the spatial occupancy of the structural component and a certain range around it are extracted according to the spatial coordinates and cross-sectional geometric design parameters of the structural component in the BIM model; by comparing the BIM model and the measured point cloud model, new structural components that do not exist in the BIM model are found. For such new structural components, the point cloud data of the new structural components are obtained by directly selecting them in the point cloud model.

[0053] Specifically, in this embodiment, the extracted component cross-sectional shapes include rectangular and I-shaped. Two approaches are used: First, through the BIM model, select the structural component of interest, and extract the point cloud data of the component's spatial location and its surrounding area based on the component's spatial coordinates and cross-sectional geometric parameters in the BIM model; second, for newly added structural components not present in the BIM model after comparing the BIM model and the measured point cloud data model, directly select them within the point cloud data model. The selected point cloud data is then smoothed and denoised using a bilateral filtering method.

[0054] S600. Fit the three-dimensional outer contour plane and the cross-sectional geometric boundary of the structural component to obtain the cross-sectional geometric parameters of the component;

[0055] In S600 of this embodiment, fitting the three-dimensional outer contour plane and the cross-sectional geometric boundary of the structural member to obtain the cross-sectional geometric parameters of the member specifically includes: fitting the three-dimensional outer contour plane of the structural member using the RANSAC method to determine the accurate spatial position of the structural member, and then filtering out point cloud data that does not belong to the structural member; dividing the structural member into multiple segments according to the axial length, cutting each segment cross-section, fitting the boundary lines of the member cross-section using the RANSAC method, measuring the cross-sectional geometric design parameters of the member, and calculating the average value and root mean square error of the cross-sectional geometric design parameters based on the measurement results of multiple segments; wherein, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an I-shaped cross-section, the geometric design parameters include the length and thickness of the flange and the web.

[0056] In some preferred embodiments, the specific steps of single-plane fitting include:

[0057] Three non-collinear seed points are randomly selected from the point cloud dataset of the components to establish an initial plane L0: α0x+β0y+γ0z+δ0=0;

[0058] Calculate the distance from each point in the point cloud dataset of the component, excluding the three seed points, to the plane L0; when the distance is less than a given distance threshold d... ε If the point is counted, then the point will be included in the set of points in the plane L0.

[0059] Repeat the above steps n times, each time comparing the number of points in the current plane with the number of points in the previous plane, and keeping the plane with the larger number of points;

[0060] After n iterations, the optimal plane parameters α, β, γ, and δ are recalculated based on the point cloud data of the point set with the largest number of points in the plane.

[0061] For structural members with rectangular cross-sections, a total of 4 plane fitting operations were performed; for structural members with I-shaped cross-sections, a total of 12 plane fitting operations were performed. After fitting each plane, the point cloud data within that plane was removed before fitting the next plane. ε The value of depends on the mapping accuracy and sampling density, and should not exceed 5cm; the value of n depends on the number of points in the point cloud dataset of the component, and should not be less than 30. The two end faces of the component are determined by fitting plane and point cloud data along the axial length direction of the component.

[0062] In some preferred embodiments, the specific steps for fitting the boundary lines on a single component cross-section include:

[0063] From the point cloud dataset of the cross section, randomly select two non-collinear seed points to establish an initial straight line l0: A0x+B0y+C0=0;

[0064] The perpendicular distance from the point cloud dataset of the calculated section, excluding the two seed points, to the line l0; when the distance is less than a given distance threshold d ∈ If the point is counted, then the point will be included in the set of points inside the line l0.

[0065] Repeat the above steps m times, each time comparing the number of points in the current line set with the number of points in the previous line set, and keeping the line with the larger number of points;

[0066] After m iterations, the optimal line parameters A0, B0, and C0 are recalculated based on the point cloud data of the point set with the largest number of points within the line.

[0067] For rectangular cross-section structural members, boundary line fitting was performed 4 times for each cross-section; for I-shaped cross-section structural members, boundary line fitting was performed 12 times; after fitting each straight line, the point cloud data within that line was removed before fitting the next straight line; d ∈ The value of is adjusted according to the surveying accuracy and sampling density, and should not be greater than 5cm; the value of m is determined according to the number of points in the point cloud dataset of the component, and should not be less than 30.

[0068] For a single member, divide it into w segments based on its axial length, perform boundary fitting on w+1 sections, measure the boundary dimensions of each section, and calculate the average and standard deviation of the member's cross-sectional geometric design parameters. The value of w should be adjusted according to the member's length, and it is recommended to use an integer between 8 and 20. For a rectangular cross-section, the geometric design parameters include the length and width of the rectangle; for a rectangular cross-section, the geometric design parameters include the length and thickness of the flanges and web.

[0069] S700. Calculate the inclination and curvature of the structural member based on the obtained geometric parameters of the member section.

[0070] In S700 of this embodiment, the calculation of the inclination rate and curvature of the extracted structural member includes: calculating the inclination rate of the structural member in two directions of the centroidal axis of the cross section based on the centroidal coordinates of the two end faces of the structural member and the member length; and calculating the curvature of the member in two directions of the centroidal axis of the cross section based on the centroidal coordinates of one end cross section and the mid-span cross section of the structural member.

[0071] Specifically, by using the centroid coordinates of the two end faces of the component, the inclination rate of the component in the two directions of the principal centroidal axes of the cross section is calculated. The formula for calculating the inclination rate of the component is as follows:

[0072]

[0073] δx and δy represent the inclination rates of the component along the x-axis and y-axis directions of the centroidal axes of the cross-section, respectively. Δx1 and Δy1 are the coordinate differences of the centroidal coordinates of the two ends of the component in the x and y directions. X1 and Y1 are the centroidal coordinates of end face 1 of the component, and X2 and Y2 are the centroidal coordinates of end face 2 of the component. L is the length of the component, which is determined by the absolute value of the coordinate difference |Z1-Z2| in the direction of the normal phase of the centroids of the two ends of the component.

[0074] Specifically, by using the centroid coordinates of one end face and the mid-span section of the member, the curvature of the member in the two directions of the principal centroidal axes of the section is calculated. The formula for calculating the curvature of the member is:

[0075]

[0076] εx and εy represent the curvature of the component along the x-axis and y-axis directions of the centroidal axis of the cross section, respectively. Δx2 and Δy2 are the coordinate differences between the centroidal coordinates of the end face and the mid-span cross section in the x, y, and z directions. X3 and Y3 are the centroidal coordinates of the mid-span cross section 3 of the component.

[0077] This embodiment discloses a method for extracting structural component parameters and measuring deformation based on laser point clouds, including: performing a panoramic scanning of the target structural system using a laser scanning device to obtain point cloud data of the target structural system; preprocessing the laser point cloud data to remove noise and redundant data unrelated to the structural components, and thinning and simplifying the point cloud data to ensure uniform spacing between sampled point clouds; performing overall registration of the preprocessed laser point cloud data with a BIM 3D model to unify the origin positioning and coordinates; dividing the overall registered point cloud model by floor and registering it with the floor grid, adjusting the coordinates of the point cloud model of each floor; extracting point cloud data belonging to structural components from the calibrated point cloud model of each floor, wherein the cross-sectional forms of the structural components include rectangular and I-shaped; fitting the 3D outer contour plane of the structural component and fitting the cross-sectional geometric boundary to obtain the cross-sectional geometric parameters of the component; and calculating the inclination and curvature of the structural component based on the obtained cross-sectional geometric parameters of the component.

[0078] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0079] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0080] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0081] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0082] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0083] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for extracting structural component parameters and measuring deformation based on laser point clouds, characterized in that, include: S100. Use laser scanning equipment to perform a panoramic coverage scan of the target structure system to obtain point cloud data of the target structure system; S200. Preprocess the laser point cloud data to remove noise and redundant data unrelated to structural components, and thin and simplify the point cloud data to ensure uniform spacing between sampled point clouds; S300. Perform overall registration between the preprocessed laser point cloud data and the BIM 3D model to unify the origin positioning and coordinates; S400. After overall registration, the point cloud model is segmented by floor and registered with the floor grid, and the coordinates of each floor point cloud model are adjusted. Specifically, this includes: segmenting the point cloud data along the floor height direction based on the elevation information of each floor of the building; picking the centroid of the cross-section of the main frame column for the segmented floor point cloud data, connecting the centroids of the cross-sections of each frame column, and calibrating with the floor grid through the coordinates of the centroids of the cross-sections of the frame columns and the direction of the connecting lines. S500. Extract point cloud data belonging to structural components from the calibrated point cloud model of each layer. The cross-sectional shapes of the structural components include rectangular and I-shaped. S600. Fit the three-dimensional outer contour plane and cross-sectional geometric boundaries of the structural member to obtain the cross-sectional geometric parameters of the member; specifically, this includes: fitting the three-dimensional outer contour plane of the structural member using the RANSAC method to determine the accurate spatial position of the structural member, and then filtering out point cloud data that does not belong to the structural member; dividing the structural member into multiple segments according to its axial length, cutting each segment, fitting the boundary lines of the member's cross-section using the RANSAC method, measuring the cross-sectional geometric design parameters of the member, and calculating the average value and root mean square error of the cross-sectional geometric design parameters based on the measurement results of multiple segments; where, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an I-shaped cross-section, the geometric design parameters include the length and thickness of the flange and web; For structural members with rectangular cross-sections, a total of 4 plane fitting operations were performed; for structural members with I-shaped cross-sections, a total of 12 plane fitting operations were performed. After fitting each plane, the point cloud data within that plane was removed before fitting the next plane. For structural members with rectangular cross-sections, a total of 4 boundary line fitting operations were performed for each cross-section; for structural members with I-shaped cross-sections, a total of 12 boundary line fitting operations were performed. After fitting each straight line, the point cloud data within that line was removed before fitting the next straight line. S700. Based on the obtained geometric parameters of the structural member cross-section, calculate the inclination rate and curvature of the structural member, including: calculating the inclination rate of the structural member in two directions along the centroidal axis of the cross-section based on the centroidal coordinates of the two end faces of the structural member and the member length; and calculating the curvature rate of the member in two directions along the centroidal axis of the cross-section based on the centroidal coordinates of one end cross-section and the mid-span cross-section of the structural member.

2. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, In S100, the laser scanning equipment adopts a ground-based laser scanning system with a scanning accuracy of no less than 1mm; the scanning range covers all main structural components and connection nodes of the target structural system.

3. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, In S200, point cloud data is denoised by Euclidean distance outlier method to remove discrete interference noise points; redundant point cloud data, including human figures and objects, are removed by manual selection; and point cloud data is thinned and simplified by uniform grid method to ensure uniform spacing between sampled point clouds, with a point cloud spacing of no more than 1mm.

4. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, In S300, the preprocessed laser point cloud data is registered with the BIM 3D model as a whole. This includes: adjusting the global origin and 3D coordinate direction of the measured laser point cloud data according to the global origin and 3D coordinate direction of the BIM model; calculating the overlap between the measured laser point cloud data and the BIM model after each adjustment; and assigning new 3D coordinate values ​​to the measured laser point cloud data according to the coordinate change parameters when the overlap between the two models is at its maximum.

5. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, In S500, point cloud data of target structural components are extracted from the calibrated point cloud models of each layer. This includes: for structural components existing in the BIM model, point cloud data of the spatial occupancy of the structural component and its surrounding area are extracted based on the spatial coordinates and cross-sectional geometric design parameters of the structural component in the BIM model; by comparing the BIM model and the measured point cloud model, newly added structural components that do not exist in the BIM model are identified. For such newly added structural components, the point cloud data of the newly added structural components are obtained by directly selecting them in the point cloud model.

6. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, The specific steps for fitting a single plane include: Three non-collinear seed points are randomly selected from the point cloud dataset of the components to establish an initial plane. ; The calculation involves extracting all points from the point cloud dataset of the component, excluding the three seed points, and mapping them to the plane. The distance; when the distance is less than a given distance threshold Then the point will be included in the statistics of the plane. A set of points in the plane; Repeat the above steps Each time, the number of points in the current set of points in the plane is compared with the number of points in the previous set of points in the plane, and the plane with the larger number of points is retained; After each iteration, the optimal plane parameters are recalculated based on the point cloud data with the largest number of points in the plane. and .

7. The method for extracting structural component parameters and measuring deformation based on laser point clouds as described in claim 1, characterized in that, The specific steps for fitting boundary lines on a single component cross-section include: From the point cloud dataset of the cross section, randomly select two non-collinear seed points to establish an initial straight line. ; The calculation involves taking the point cloud dataset of the cross section, excluding the two seed points, and considering all points from the remaining points to the straight line. Vertical distance; when the distance is less than a given distance threshold Then the point will be included in the statistics of the line. The set of points inside the line; Repeat the above steps Each time, compare the number of points in the current set of points on the line with the number of points in the previous set of points on the line, and keep the line with the larger number of points; After each iteration, the optimal line parameters are recalculated based on the point cloud data with the largest number of points within the line. , and .

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