A method for manufacturing and installing a steel bridge segment and adjusting errors thereof and a method for installing the same

Abstract

This invention discloses a method for adjusting manufacturing and installation errors of steel bridge segments and its installation method, solving the technical problem in existing technologies where the installation errors of already erected steel bridge segments are not considered during the assembly and construction of steel bridge segments, leading to increased errors in adjacent steel bridge segments to be erected and affecting the installation accuracy of the segments to be erected. This invention includes the following steps: Step 1, scanning the outline of the steel bridge segment; Step 2, processing the point cloud of the scanned outline of the steel bridge segment; Step 3, adjusting the manufacturing errors of the steel bridge segments to be erected; Step 4, comparative inspection of the formed steel bridge segments at the construction site; Step 5, adjusting the assembly errors of the steel bridge segments. This invention is scientifically and rationally designed. Under the premise that errors may exist in already erected steel bridge segments, it uses a three-dimensional model to predict the geometric morphological errors of the steel bridge segments to be erected, and adjusts the manufacturing and installation errors of the segments to be erected based on the prediction results.

Description

Technical Field

[0001] This invention belongs to the field of steel bridge structure construction technology, specifically relating to a method for adjusting manufacturing and installation errors of steel bridge segments and its installation method. Background Technology

[0002] Manufacturing errors are unavoidable during the production of components, causing the actual assembly to deviate from the theoretical design values. In the field of bridges, steel components are large in size, making the impact of manufacturing errors on assembly accuracy particularly pronounced. New technologies need to be explored to incorporate these manufacturing errors into the virtual assembly process, ensuring the accuracy of virtual assembly in verifying component assemblability.

[0003] The basic principle of virtual pre-assembly is to use the best approximation between the measured points and the design points, to fit the spatial position based on the points, lines, surfaces and various parameters of the measured points, to perform reverse 3D modeling of the components by scanning them with a 3D laser scanner, and to perform fitting analysis using model analysis software, thereby improving the accuracy and efficiency of pre-assembly.

[0004] For complex curved steel bridge structures, a 3D model was created using 3D scanning to virtually pre-assemble the bridge segments in order to aid in the analysis of the bridge's curve shape. However, since the modeling was performed in an ideal environment, the effects of factors such as self-weight, hoisting stress, weld shrinkage deformation, and temperature on the shape and position errors of the parts and the assembly accuracy were not considered. This would lead to certain differences between the virtual pre-assembly and the actual component assembly. Furthermore, the inelastic deformation of the mounting brackets would also affect the erection posture of the bridge segments. If the deviation of the already erected segments was not considered and the erection continued, the error might increase, failing to meet the design requirements.

[0005] Therefore, designing a method for adjusting manufacturing and installation errors of steel bridge segments and its installation method, in order to at least overcome some of the above-mentioned technical problems, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for adjusting the manufacturing and installation errors of steel bridge segments and its installation method, so as to at least solve some of the above-mentioned technical problems.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for adjusting manufacturing and installation errors of steel bridge segments includes the following steps:

[0009] Step 1: Scanning the outline of the steel bridge segment;

[0010] Step 2: Point cloud processing of steel bridge segment outline surface scanning;

[0011] Step 3: Adjustment of manufacturing errors in the steel bridge segments to be erected;

[0012] Step 4: On-site comparison and testing of steel bridge segment forming;

[0013] Step 5: Adjustment of assembly error of steel bridge segments.

[0014] Preferably, in step one, when scanning the outline of the steel bridge segment, the scanning stations are arranged around the steel bridge segment component, with the arrangement position facing the box surface of the steel bridge segment. No less than 4 spherical targets are arranged around the steel bridge segment, and each scanning station can scan no less than 3 spherical targets. A three-dimensional laser scanner is used to scan the 4 stations, the laser incident angle is kept within 65°, and the maximum measurement point spacing is limited to less than 10mm.

[0015] Preferably, in step two, when processing the point cloud of the steel bridge segment contour surface scanning, the redundancy of the multi-station laser point cloud data of the steel bridge segment is first removed, and then the point cloud data is registered.

[0016] Preferably, when removing redundancy from the multi-station laser point cloud data of the steel bridge segment, the noise points in the original laser point cloud are first deleted, then the curvature-considering adaptive downsampling method is used to reduce the amount of original laser point cloud data, and finally the multi-station point clouds are fused to form a complete point cloud of the steel bridge segment.

[0017] Preferably, point cloud data registration includes the following steps:

[0018] Step 1: Structure the cloud data of each measuring station, fit a sphere within each voxel using the random sampling consensus algorithm, and obtain the target sphere cloud data within the station by radius filtering;

[0019] Step 2: After obtaining the point cloud data of the target spheres within the station, extract the data of individual target spheres using a density clustering algorithm, and then extract the center and radius of the spheres using the least squares method.

[0020] Step 3: Using the center of the target sphere as the reference point, the data of each station is coarsely registered using the full permutation algorithm based on the reference point. Then, the coarsely registered data is finely registered using the iterative nearest neighbor algorithm to obtain the registered point cloud data of the tower bases in the plant and the tower bases on site.

[0021] Preferably, in step three, when adjusting the manufacturing error of the steel bridge segment to be erected, the Tekla model of the corresponding factory scan point cloud and the field scan point cloud is converted into a Revit model and then converted into a point cloud, respectively, to obtain design point cloud A and design point cloud B.

[0022] Based on design point cloud A and design point cloud B, the coordinate transformation of the scanned point cloud processed in step two is performed to unify it into the BIM coordinate system;

[0023] Design point cloud A and design point cloud B are divided into steel plate groups and converted into point cloud group A and point cloud group B composed of single steel plates respectively. Then, through nearest neighbor search, the single steel plates of the scanned point cloud after step two are initially segmented.

[0024] Based on the above single steel plate, a random sampling consensus algorithm and a region growing algorithm are used to extract a fine point cloud of the plate surface;

[0025] Based on the obtained point cloud of the plate, a virtual grid plane is divided in space. The coordinate values ​​in each grid are extracted to extract the edge of the connecting rib between the erected steel bridge segment and the steel bridge segment to be erected.

[0026] Based on the aforementioned rib edge, a nearest neighbor search is used to obtain the nearest point of the rib edge within the factory point cloud, and the manufacturing error of the steel bridge segment to be erected is calculated and initially adjusted.

[0027] Preferably, in step four, during the comparative testing of steel bridge segment formation at the construction site, the model of the steel bridge segment to be erected, established by scanning point cloud data, is aligned with the model of the already erected steel bridge segment using a surface matching method guided by the centroid and inertial principal axis graphics; the three-dimensional error of the steel bridge segment to be erected is obtained by comparing the distance deviations between the theoretical model and the measured model in the X, Y, and Z directions.

[0028] Preferably, when aligning the model of the steel bridge segment to be erected with the model of the already erected steel bridge segment, the centroid and principal axis of inertia of the theoretical model and the measured model are calculated respectively. Using the directions of the centroid and principal axis of inertia as a reference, the measured model is moved to the position of the theoretical model to complete the alignment of the two models. After obtaining the three-dimensional error of the steel bridge segment to be erected, the distribution of its contour error is observed through a chromatogram. By outputting the error data of all key points, an intuitive feedback on the processing error of the steel bridge segment to be erected is formed.

[0029] Preferably, in step five, when adjusting the assembly error of the steel bridge segments, the matching degree of the cross-sectional structural dimensions of adjacent steel bridge segments to be erected is detected based on the erected steel bridge segments to determine whether it is necessary to adjust their assembly positions.

[0030] The alignment and orientation of the steel bridge segments assembled in three-dimensional space are represented by the axial position of the steel bridge segments, the inclination angle of the assembly surface of the steel bridge segments, and the torsional angle of the assembly surface of the steel bridge segments. The torsional angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the edges of adjacent assembly surfaces. The axial position error of the steel bridge segments is evaluated by calculating the distance between the centroids of adjacent assembly surfaces. The inclination angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the normal vectors of adjacent assembly surfaces.

[0031] To address the tilt angle error of the steel bridge segment assembly surface, the angle of the steel bridge segment assembly surface is adjusted by cutting the assembly port of the steel bridge segment on the construction site. To address the torsional angle error and the axial position error of the steel bridge segment assembly surface, the positioning parameters are pre-adjusted during virtual assembly to optimize the positioning of the steel bridge segment. Simultaneously, multi-unit, multi-lifting-point construction simulation analysis is conducted to clarify the pre-control value of the aerial deformation of each unit of the steel bridge segment during construction. Based on the pre-control value, corresponding reverse deformation compensation is applied during the assembly of the steel bridge segment, and the results are inversely superimposed on the ideal coordinates of the control points of the steel bridge segment to obtain the actual coordinates of the control points of the steel bridge segment. The assembly error of the steel bridge segment is then adjusted based on these actual coordinates.

[0032] A method for installing steel bridge segments includes the following steps:

[0033] Step (1): Before the steel bridge segment is hoisted, a transverse limiting plate is welded to the joint of the previously hoisted steel bridge segment, and the longitudinal diaphragm of TDA-1-B is used as the longitudinal limiting plate; the longitudinal limiting plate is selected at the middle diaphragm, with 2 plates at each end and the middle, and 6 plates are arranged alternately on the left and right sides of the same transverse diaphragm.

[0034] The limiting plate is made of Q235 grade steel plate with a thickness of t=10mm. The longitudinal limiting plate has a plane size of 15cm×10cm and a chamfer of 2cm×10cm. The transverse limiting plate has a plane size of 21cm×6cm and a chamfer of 2cm×10cm.

[0035] The transverse limiting plate is based on the outer wall panel of the installed steel bridge segment, and the guide edge of the transverse limiting plate is welded tightly to the wall panel; the longitudinal limiting plate is determined by the surveyor's layout, and the longitudinal limiting plate is moved outward by 10mm based on the layout coordinates.

[0036] Step (2): The TDA-1-A is slowly lowered using the limiting plate and lands on the temporary support. The crawler crane is not under stress but does not unhook, in preparation for fine-tuning.

[0037] Three-way jacks and temporary blocks are placed at the adjustment brackets, and adjustments are made according to the instructions of the surveyors; the temporary blocks and adjustment brackets at the outer wall panels are placed at the intersection of the longitudinal and transverse partitions, and the inner side is set on the transverse partition.

[0038] Step (3): After the adjustment is completed, use thin steel plate pads to adjust the height of the temporary support piers, transfer the force of the steel bridge segment to the temporary support piers, remove the three-way jacks, and weld the steel bridge segment to the temporary support piers for fixation.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention is scientifically and rationally designed. Under the premise that there may be errors in the already erected steel bridge segments, it uses a three-dimensional model to predict the geometric errors of the steel bridge segments to be erected, and adjusts the manufacturing and installation errors of the steel bridge segments to be erected based on the prediction results.

[0041] This invention employs a virtual pre-assembly superimposed construction process simulation in the steel bridge segment erection process. It accurately simulates the positioning data and linear changes of the steel bridge segments, and dynamically adjusts the installation errors of the steel bridge segments to be erected and those already erected. This effectively overcomes the shortcomings of simply considering stress-free virtual pre-assembly, which cannot accurately simulate the erection posture of steel bridge segments on site, thus ensuring more precise steel bridge segment erection. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the feature extraction process for adjusting the manufacturing error of the steel bridge segment to be erected according to the present invention.

[0043] Figure 2 This is a schematic diagram of the laser point cloud data after redundancy removal and noise elimination according to the present invention.

[0044] Figure 3 This is a schematic diagram of the laser point cloud data after redundancy removal and curvature downsampling according to the present invention.

[0045] Figure 4 This is a schematic diagram of target sphere point cloud extraction according to the present invention.

[0046] Figure 5 This is a schematic diagram of target sphere feature extraction according to the present invention.

[0047] Figure 6 Point cloud data of steel bridge segments in the factory after registration for this invention.

[0048] Figure 7 This is the point cloud data of the steel bridge segment after registration for this invention.

[0049] Figure 8 This is a schematic diagram of the Tekla model of the present invention.

[0050] Figure 9 This is a schematic diagram of the stress and deformation during hoisting according to the present invention.

[0051] Figure 10 This is a schematic diagram of stress and deformation during the overturning and hoisting process of the present invention.

[0052] Figure 11 This is a schematic diagram of the arrangement of the limiting plate of the present invention.

[0053] Figure 12 This is a schematic cross-sectional view of the arrangement of the limiting plate of the present invention.

[0054] Figure 13 This is a schematic diagram of the transverse limiting plate structure of the present invention.

[0055] Figure 14 This is a schematic diagram of the longitudinal limiting plate structure of the present invention.

[0056] Figure 15 This is a detailed diagram of the lateral limiting plate arrangement of the present invention.

[0057] Figure 16 This is a detailed diagram of the longitudinal limiting plate arrangement of the present invention.

[0058] Figure 17 This is a schematic diagram of the arrangement of temporary support blocks and adjusting brackets according to the present invention.

[0059] Figure 18 This is a schematic cross-sectional view of the temporary support blocks and adjusting brackets of the present invention.

[0060] Figure 19 This is a schematic diagram of the three-way jack of the present invention.

[0061] Figure 20 This is a schematic diagram of the three-directional jack of the present invention.

[0062] Figure 21 This is a schematic diagram of the elevation of the three-way jack of the present invention.

[0063] Figure 22 This is a schematic diagram of the elevation of the adjusting bracket of the present invention.

[0064] Figure 23 This is a schematic diagram of the cross-section of the adjusting bracket of the present invention.

[0065] Figure 24 This is a schematic diagram of the plane of the adjusting bracket of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0067] like Figure 1-10 As shown, the present invention provides a method for adjusting manufacturing and installation errors of steel bridge segments, comprising the following steps:

[0068] Step 1: Scan the outline of the steel bridge segment.

[0069] When scanning the outline of a steel bridge segment, scanning stations are arranged around the steel bridge segment component, with the station positions facing the box surface of the steel bridge segment. No fewer than 4 spherical targets are arranged around the steel bridge segment, and each scanning station is required to scan at least 3 spherical targets. A 3D laser scanner is used to scan 4 stations, with the laser incident angle kept within 65° and the maximum spacing between measuring points limited to less than 10 mm.

[0070] Step 2: Processing the point cloud of the outline surface of the steel bridge segment.

[0071] When processing the point cloud of the outline surface of the steel bridge segment, the redundancy of the multi-station laser point cloud data of the steel bridge segment is first removed, and then the point cloud data is registered.

[0072] When removing redundancy from multi-station laser point cloud data of steel bridge segments, firstly, noisy points in the original laser point cloud are deleted, then an adaptive downsampling method that takes curvature into account is used to reduce the amount of original laser point cloud data, and finally, the multi-station point clouds are merged to form a complete point cloud of the steel bridge segment.

[0073] Point cloud data registration includes the following steps:

[0074] Step 1: Structure the cloud data of each measuring station, fit a sphere within each voxel using the random sampling consensus algorithm, and obtain the target sphere cloud data within the station by radius filtering;

[0075] Step 2: After obtaining the point cloud data of the target spheres within the station, extract the data of individual target spheres using a density clustering algorithm, and then extract the center and radius of the spheres using the least squares method.

[0076] Step 3: Using the center of the target sphere as the reference point, the data of each station is coarsely registered using the full permutation algorithm based on the reference point. Then, the coarsely registered data is finely registered using the iterative nearest neighbor algorithm to obtain the registered point cloud data of the tower bases in the plant and the tower bases on site.

[0077] Step 3: Adjustment of manufacturing errors in the steel bridge segments to be erected.

[0078] When adjusting the manufacturing error of the steel bridge segments to be erected, the Tekla models of the corresponding factory scan point clouds and field scan point clouds are converted into Revit models and then converted into point clouds to obtain design point cloud A and design point cloud B respectively.

[0079] Based on design point cloud A and design point cloud B, the coordinate transformation of the scanned point cloud processed in step two is performed to unify it into the BIM coordinate system;

[0080] Design point cloud A and design point cloud B are divided into steel plate groups and converted into point cloud group A and point cloud group B composed of single steel plates respectively. Then, through nearest neighbor search, the single steel plates of the scanned point cloud after step two are initially segmented.

[0081] Based on the above single steel plate, a random sampling consensus algorithm and a region growing algorithm are used to extract a fine point cloud of the plate surface;

[0082] Based on the obtained point cloud of the plate, a virtual grid plane is divided in space. The coordinate values ​​in each grid are extracted to extract the edge of the connecting rib between the erected steel bridge segment and the steel bridge segment to be erected.

[0083] Based on the aforementioned rib edge, a nearest neighbor search is used to obtain the nearest point of the rib edge within the factory point cloud, and the manufacturing error of the steel bridge segment to be erected is calculated and initially adjusted.

[0084] Step 4: On-site comparison and testing of steel bridge segment molding.

[0085] During the comparative testing of steel bridge segments at the construction site, the model of the steel bridge segment to be erected, established by scanning point cloud data, is aligned with the model of the already erected steel bridge segment using a surface matching method guided by the centroid and inertial principal axis graphics. The three-dimensional error of the steel bridge segment to be erected is obtained by comparing the distance deviations between the theoretical model and the measured model in the X, Y, and Z directions.

[0086] When the model of the steel bridge segment to be erected is aligned with the model of the steel bridge segment already erected, the centroid and principal axis of inertia of the theoretical model and the measured model are calculated respectively. Using the directions of the centroid and principal axis of inertia as a reference, the measured model is moved to the position of the theoretical model to complete the alignment of the two models. After obtaining the three-dimensional error of the steel bridge segment to be erected, the distribution of its contour error is observed through a chromatogram. By outputting the error data of all key points, a direct feedback on the processing error of the steel bridge segment to be erected is formed.

[0087] Step 5: Adjustment of assembly error of steel bridge segments.

[0088] When adjusting the assembly error of steel bridge segments, the matching degree of the cross-sectional structural dimensions of adjacent steel bridge segments to be erected is checked based on the already erected steel bridge segments to determine whether it is necessary to adjust their assembly positions.

[0089] The alignment and orientation of the steel bridge segments assembled in three-dimensional space are represented by the axial position of the steel bridge segments, the inclination angle of the assembly surface of the steel bridge segments, and the torsional angle of the assembly surface of the steel bridge segments. The torsional angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the edges of adjacent assembly surfaces. The axial position error of the steel bridge segments is evaluated by calculating the distance between the centroids of adjacent assembly surfaces. The inclination angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the normal vectors of adjacent assembly surfaces.

[0090] To address the tilt angle error of the steel bridge segment assembly surface, the angle of the steel bridge segment assembly surface is adjusted by cutting the assembly port of the steel bridge segment on the construction site. To address the torsional angle error and the axial position error of the steel bridge segment assembly surface, the positioning parameters are pre-adjusted during virtual assembly to optimize the positioning of the steel bridge segment. Simultaneously, multi-unit, multi-lifting-point construction simulation analysis is conducted to clarify the pre-control value of the aerial deformation of each unit of the steel bridge segment during construction. Based on the pre-control value, corresponding reverse deformation compensation is applied during the assembly of the steel bridge segment, and the results are inversely superimposed on the ideal coordinates of the control points of the steel bridge segment to obtain the actual coordinates of the control points of the steel bridge segment. The assembly error of the steel bridge segment is then adjusted based on these actual coordinates.

[0091] This invention is scientifically and rationally designed. Under the premise that there may be errors in the already erected steel bridge segments, it uses a three-dimensional model to predict the geometric errors of the steel bridge segments to be erected, and adjusts the manufacturing and installation errors of the steel bridge segments to be erected based on the prediction results.

[0092] This invention employs a virtual pre-assembly and superposition construction process simulation in the segmental steel bridge erection process. It accurately simulates the positioning data and linear changes of the steel bridge segments, while dynamically adjusting the installation errors between the segments to be erected and those already erected. This effectively overcomes the limitation of relying solely on stress-free virtual pre-assembly, which cannot accurately simulate the on-site erection posture of steel bridge segments, ensuring more precise segmental steel bridge erection.

[0093] This invention can solve the problem of insufficient space in the physical pre-assembly site and save the amount of temporary pre-assembly frames used, thus realizing green and intelligent construction.

[0094] like Figure 11-24 As shown, a method for installing steel bridge segments includes the following steps:

[0095] Step (1): Before the steel bridge segment is hoisted, a transverse limiting plate is welded to the joint of the previously hoisted steel bridge segment, and the longitudinal diaphragm of TDA-1-B is used as the longitudinal limiting plate; the longitudinal limiting plate is selected at the middle diaphragm, with 2 plates at each end and the middle, and 6 plates are arranged alternately on the left and right sides of the same transverse diaphragm.

[0096] The limiting plate is made of Q235 grade steel plate with a thickness of t=10mm. The longitudinal limiting plate has a plane size of 15cm×10cm and a chamfer of 2cm×10cm. The transverse limiting plate has a plane size of 21cm×6cm and a chamfer of 2cm×10cm.

[0097] The transverse limiting plate is based on the outer wall panel of the installed steel bridge segment, and the guide edge of the transverse limiting plate is welded tightly to the wall panel; the longitudinal limiting plate is determined by the surveyor's layout, and the longitudinal limiting plate is moved outward by 10mm based on the layout coordinates.

[0098] Step (2): The TDA-1-A is slowly lowered using the limiting plate and lands on the temporary support. The crawler crane is not under stress but does not unhook, in preparation for fine-tuning.

[0099] Three-way jacks and temporary blocks are placed at the adjustment brackets, and adjustments are made according to the instructions of the surveyors; the temporary blocks and adjustment brackets at the outer wall panels are placed at the intersection of the longitudinal and transverse partitions, and the inner side is set on the transverse partition.

[0100] Step (3): After the adjustment is completed, use thin steel plate pads to adjust the height of the temporary support piers, transfer the force of the steel bridge segment to the temporary support piers, remove the three-way jacks, and weld the steel bridge segment to the temporary support piers for fixation.

[0101] This invention provides a method for adjusting manufacturing errors and an installation error of steel bridge segments, specifically:

[0102] 1. Scanning the outline of the steel segment.

[0103] 1.1 Scanning parameters and station layout.

[0104] Considering the limitations of the bridge site environment, the laser incident angle for the optimal measurement range was kept within 65°. Measurement stations were deployed around the structural members, positioned near the box girder surface of the steel bridge segments. To achieve accurate stitching of multi-station cloud data, at least four spherical targets were placed around each segment, ensuring that each station could scan at least three spherical targets. A 3D laser scanner was used to scan all four stations. Simultaneously, the maximum spacing between measurement points was limited to less than 10mm to ensure the accuracy of reverse modeling of the steel bridge segments.

[0105] 2. Point cloud processing.

[0106] 2.1 Redundancy removal of segmental multi-station laser point cloud data.

[0107] The original laser point cloud contains a large number of noisy points, and the point density is too high, which will reduce the efficiency of post-processing. Therefore, it is necessary to reduce the amount of original laser point cloud data and then fuse the point clouds from multiple measurement sites to form a complete segmental point cloud. After removing noisy points, the point cloud needs to be downsampled to reduce the amount of point cloud data. Different parts of the steel bridge segment have different morphological characteristics, so a single standard cannot be used for downsampling. Therefore, an adaptive downsampling method that takes curvature into account is adopted. Redundancy removal from the laser point cloud image is as follows: Figure 2 and Figure 3 As shown.

[0108] 2.2 Point cloud registration.

[0109] When performing 3D laser scanning, the coordinate system used to acquire point cloud data is the coordinate system of each station when the instrument is set up. Since the coordinate system of each station is different, registration is required to unify the point cloud data to the same coordinate system. Because the registration of point cloud data from multiple stations can be broken down into registering each station in pairs sequentially.

[0110] Registration method: To facilitate the stitching and registration of cloud data from various stations, three target spheres at the same position are placed at adjacent stations during scanning. First, the cloud data from each station is structured. Within each voxel, a random sampling consistency algorithm is used to fit a sphere. After radius filtering, the point cloud of the target spheres within each station is obtained as follows: Figure 4 As shown.

[0111] Secondly, after obtaining the target sphere point cloud data, the data of individual target spheres is extracted using a density clustering algorithm, and then the center and radius of the spheres are extracted using the least squares method, such as... Figure 5 As shown.

[0112] Finally, using the center of the target sphere as the reference point, a full permutation algorithm based on the reference point was used to perform coarse registration of the data from each station. Then, an iterative nearest neighbor algorithm was used to refine the coarsely registered data, resulting in the registered point cloud data of the tower bases within the plant and on-site tower bases, as shown below. Figure 6 and Figure 7 As shown.

[0113] 3. Adjustment of manufacturing errors of the segments to be erected.

[0114] Because the irregularly shaped steel bridge is a spatially complex component, the fabrication process utilizes a Tekla model, such as... Figure 8 As shown, the Tekla models corresponding to the factory and site scan point clouds are first converted into Revit models and then converted into point clouds to obtain design point cloud 1 and design point cloud 2.

[0115] Based on design point cloud 1 and design point cloud 2, coordinate transformation is performed on the preprocessed scanned point cloud, that is, the scanned point clouds from different locations are unified into the BIM coordinate system through virtual means.

[0116] To segment individual components in complex structures, Revit models 1 and 2 were further divided into steel plate groups and converted into point cloud groups composed of individual steel plates by semantic content recognition of IFC files. Subsequently, individual steel plates of the scanned point cloud were initially segmented by nearest neighbor search.

[0117] Based on the initially segmented single steel plate, a random sampling consensus algorithm and a region growing algorithm are used to extract a fine point cloud of the plate surface.

[0118] Based on the obtained point cloud of the plate surface, a virtual grid plane is divided in space. The edge of the connecting rib plate of the two components can be extracted by extracting the coordinate values ​​within each grid.

[0119] Based on the extracted field rib edges, a nearest neighbor search is used to obtain the nearest point of the rib edge within the factory, and the manufacturing error of the segment to be erected is calculated for initial adjustment.

[0120] 4. On-site segmental forming comparison and testing.

[0121] The bridge segment to be erected, established using point cloud data, is aligned with the model of the already erected segment. Alignment is essentially a surface matching process, employing a surface matching method guided by the centroid and principal axes of inertia. This involves calculating the centroid and principal axes of inertia for both the theoretical and measured models, and using these directions as a reference, moving the measured model to the position of the theoretical model to complete the alignment. Finally, by comparing the distance deviations between the theoretical and measured models in the X, Y, and Z directions, the three-dimensional error of the steel bridge segment is obtained. A colorimetric chart provides a visual understanding of the component's contour error distribution, and error data for all key points can be output, providing direct feedback on the component's manufacturing errors.

[0122] 5. Adjustment of assembly error of steel bridge segments.

[0123] In actual construction, the assembly of steel bridge segments will be carried out in a certain construction sequence. After the assembly of the current adjacent segment is completed, the matching degree of the cross-sectional structural dimensions of the adjacent segments can be checked to determine whether the assembly position of the segments needs to be adjusted.

[0124] The alignment and orientation of a steel bridge segment in three-dimensional space can be represented by the axial position of the segment, the included angle of the assembly surface, and the torsional angle of the assembly surface.

[0125] Torsional error is assessed by calculating the included angle between the edges of adjacent assembly surfaces; centerline error is assessed by calculating the centroid distance between adjacent assembly surfaces; and surface tilt error is assessed by calculating the included angle between the normal vectors of adjacent assembly surfaces. All these error values ​​must meet the specifications. If significant errors occur, adjustments should be made to both optimize manufacturing values ​​and assembly positioning values ​​until the accuracy requirements are met.

[0126] For assembly surface tilt angle errors, the angle of the assembly surface can be changed by cutting the assembly port of the segment on the construction site; for torsional and axial errors, the segment positioning can be optimized by pre-adjusting the positioning parameters during virtual assembly. Simultaneously, simulation analysis based on multi-unit, multi-lifting-point construction of giant complex steel structures is conducted to clarify the pre-control values ​​of aerial deformation of each structural unit during construction. Based on the pre-control values, corresponding anti-deformation compensation is applied to the components during structural assembly. At the same time, the ideal coordinates of the segment control points are superimposed to obtain the actual coordinates of the segment control points, and the assembly key point coordinates are provided to the manufacturer for error adjustment. Lifting stress deformation, such as... Figure 9 As shown, the stress deformation during the turning and hoisting process is as follows: Figure 10 As shown.

[0127] This invention obtains point cloud data by performing three-dimensional laser scanning on both the existing and upcoming steel tower segments at the bridge site. This data is then imported into 3D software to create a model. The port status of the upcoming segment is compared with that of the existing segment. When torsional errors or axial deviations occur, the positioning parameters are pre-adjusted to optimize segment positioning. Simultaneously, a multi-unit, multi-lifting-point construction simulation analysis of a giant, complex steel structure is conducted to determine the pre-control values ​​for aerial deformation of each structural unit during construction. Based on these pre-control values, corresponding anti-deformation compensation is applied to the components during structural assembly. The ideal coordinates of the segment control points are then superimposed to obtain the actual coordinates of these control points. The coordinates of the key assembly points are provided to the manufacturer. Errors in the upcoming segment are considered during in-plant adjustments. On-site, longitudinal and transverse limiting plates and three-way jacks are used for precise positioning adjustments to ensure coordinated deformation at multiple points on the segment and seamless, synchronous docking of complex segments.

[0128] Steel bridge segment positioning and installation.

[0129] The repositioning of the base section of the tower will be explained using TDA-1-A as an example.

[0130] Step 1: Before hoisting the tower, weld transverse limiting plates to the joints of the previously hoisted tower segments, using the longitudinal diaphragm of TDA-1-B as the longitudinal limiting plate. The longitudinal limiting plates are selected at the middle diaphragm, with two plates at each end and two at the middle, alternating left and right sides of the same transverse diaphragm, for a total of six plates. The limiting plate arrangement is as follows: Figure 11 and 12 As shown (unit in the figure: cm).

[0131] The limiting plates are made of Q235 grade steel plate with a thickness of t=10mm. The longitudinal limiting plate has a planar dimension of 15cm×10cm and a chamfer of 2cm×10cm. The transverse limiting plate has a planar dimension of 21cm×6cm and a chamfer of 2cm×10cm. The transverse and longitudinal limiting plates are respectively as follows: Figure 13 and Figure 14 As shown (unit in the figure: mm).

[0132] The lateral limiting plate is based on the already installed outer wall panel of the tower segment. The guide edge of the limiting plate is welded tightly to the wall panel, such as... Figure 15 As shown (unit: mm in the figure); the longitudinal limiting plate is determined by the surveyor's layout. Based on the layout coordinates, the limiting plate moves outward by 10 mm, such as... Figure 16 As shown (unit in the figure: mm).

[0133] Step 2: The TDA-1-A is slowly lowered using the limit plate and lands on the temporary support. The crawler crane is not under load, but it is not allowed to unhook, in preparation for fine-tuning.

[0134] Three-way jacks and temporary supports are placed at the adjustment brackets, and adjustments are made according to the instructions of the surveyors. Temporary supports and adjustment brackets on the outer wall panels are placed near the intersection of the longitudinal and transverse partitions, while those on the inner side are placed on the transverse partitions.

[0135] The positioning jack is a 100t three-way jack, mainly used for precise positioning of steel beams. The base dimensions are 450×454mm. Figures 19-21 As shown (unit in the figure: mm).

[0136] The positioning bracket is made of double-section I-beams, welded to the web and diaphragms. Different steel combinations are used depending on the weight of the tower segment. The positioning bracket is as follows: Figures 22-24 As shown (unit in the figure: mm).

[0137] Step 3: After the repositioning is completed, use thin steel plates to shim and adjust the height of the temporary support piers to transfer the load on the tower to the temporary support piers. Remove the three-way jacks and weld the tower to the temporary support piers for fixation.

[0138] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention used to illustrate the technical solutions of the present invention, and are not intended to limit the invention, nor are they intended to limit the scope of the patent. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but which still solve the same technical problem as the present invention, should be included within the scope of protection of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention to other related technical fields are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for adjusting manufacturing and installation errors of steel bridge segments, characterized in that, Includes the following steps: Step 1: Scanning the outline of the steel bridge segments; Step 2: Point cloud processing of steel bridge segment outline surface scanning; Step 3: Adjustment of manufacturing errors in the steel bridge segments to be erected; Step 4: On-site comparison and testing of steel bridge segment forming; Step 5: Adjustment of assembly errors for steel bridge segments; In step three, when adjusting the manufacturing error of the steel bridge segment to be erected, the Tekla model of the corresponding factory scan point cloud and field scan point cloud is converted into a Revit model and then converted into a point cloud, respectively, to obtain design point cloud A and design point cloud B. Based on design point cloud A and design point cloud B, the coordinate transformation of the scanned point cloud processed in step two is performed to unify it into the BIM coordinate system; Design point cloud A and design point cloud B are divided into steel plate groups and converted into point cloud group A and point cloud group B composed of single steel plates respectively. Then, through nearest neighbor search, the single steel plates of the scanned point cloud after step two are initially segmented. Based on the above single steel plate, a random sampling consensus algorithm and a region growing algorithm are used to extract a fine point cloud of the plate surface; Based on the obtained point cloud of the plate, a virtual grid plane is divided in space. The coordinate values ​​in each grid are extracted to extract the edge of the connecting rib between the erected steel bridge segment and the steel bridge segment to be erected. Based on the aforementioned rib edge, a nearest neighbor search is used to obtain the nearest point of the rib edge within the factory point cloud, and the manufacturing error of the steel bridge segment to be erected is calculated and initially adjusted.

2. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 1, characterized in that, In step one, when scanning the outline of the steel bridge segment, scanning stations are arranged around the steel bridge segment component, with the arrangement position facing the box surface of the steel bridge segment. No less than 4 spherical targets are arranged around the steel bridge segment, and each scanning station can scan no less than 3 spherical targets. A three-dimensional laser scanner is used to scan the 4 stations, the laser incident angle is kept within 65°, and the maximum measurement point spacing is limited to less than 10mm.

3. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 1, characterized in that, In step two, when processing the point cloud of the steel bridge segment contour surface scanning, the redundancy of the multi-station laser point cloud data of the steel bridge segment is first removed, and then the point cloud data is registered.

4. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 3, characterized in that, When removing redundancy from multi-station laser point cloud data of steel bridge segments, firstly, noisy points in the original laser point cloud are deleted, then an adaptive downsampling method that takes curvature into account is used to reduce the amount of original laser point cloud data, and finally, the multi-station point clouds are merged to form a complete point cloud of the steel bridge segment.

5. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 3, characterized in that, Point cloud data registration includes the following steps: Step 1: Structure the cloud data of each measuring station, fit a sphere within each voxel using the random sampling consensus algorithm, and obtain the target sphere cloud data within the station by radius filtering; Step 2: After obtaining the point cloud data of the target spheres within the station, extract the data of individual target spheres using a density clustering algorithm, and then extract the center and radius of the spheres using the least squares method. Step 3: Using the center of the target sphere as the reference point, the data of each station is coarsely registered using the full permutation algorithm based on the reference point. Then, the coarsely registered data is finely registered using the iterative nearest neighbor algorithm to obtain the registered point cloud data of the tower bases in the plant and the tower bases on site.

6. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 1, characterized in that, In step four, during the comparative testing of steel bridge segment formation at the construction site, the model of the steel bridge segment to be erected, established by scanning point cloud data, is aligned with the model of the already erected steel bridge segment using a surface matching method guided by the centroid and inertial principal axis graphics. The three-dimensional error of the steel bridge segment to be erected is obtained by comparing the distance deviations between the theoretical model and the measured model in the X, Y, and Z directions.

7. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 6, characterized in that, When the steel bridge segment model to be erected is aligned with the steel bridge segment model that has already been erected, the center of mass and principal axis of inertia of the theoretical model and the measured model are calculated respectively. Using the directions of the center of mass and principal axis of inertia as a reference, the measured model is moved to the position of the theoretical model to complete the alignment of the two models. After obtaining the three-dimensional error of the steel bridge segment to be erected, the distribution of its contour error is observed through a chromatogram. By outputting the error data of all key points, a direct feedback on the processing error of the steel bridge segment to be erected is formed.

8. The method for adjusting manufacturing and installation errors of steel bridge segments according to claim 1, characterized in that, In step five, when adjusting the assembly error of the steel bridge segments, the matching degree of the cross-sectional structural dimensions of adjacent steel bridge segments to be erected is checked based on the erected steel bridge segments to determine whether it is necessary to adjust their assembly positions. The alignment and orientation of the steel bridge segments assembled in three-dimensional space are represented by the axial position of the steel bridge segments, the inclination angle of the assembly surface of the steel bridge segments, and the torsional angle of the assembly surface of the steel bridge segments. The torsional angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the edges of adjacent assembly surfaces. The axial position error of the steel bridge segments is evaluated by calculating the distance between the centroids of adjacent assembly surfaces. The inclination angle error of the assembly surface of the steel bridge segments is evaluated by calculating the included angle between the normal vectors of adjacent assembly surfaces. To address the tilt angle error of the steel bridge segment assembly surface, the angle of the steel bridge segment assembly surface is adjusted by cutting the assembly port of the steel bridge segment on the construction site. To address the torsional angle error and the axial position error of the steel bridge segment assembly surface, the positioning parameters are pre-adjusted during virtual assembly to optimize the positioning of the steel bridge segment. Simultaneously, multi-unit, multi-lifting-point construction simulation analysis is conducted to clarify the pre-control value of the aerial deformation of each unit of the steel bridge segment during construction. Based on the pre-control value, corresponding reverse deformation compensation is applied during the assembly of the steel bridge segment, and the results are inversely superimposed on the ideal coordinates of the control points of the steel bridge segment to obtain the actual coordinates of the control points of the steel bridge segment. The assembly error of the steel bridge segment is then adjusted based on these actual coordinates.

9. A method for installing steel bridge segments based on the method for adjusting manufacturing and installation errors of steel bridge segments according to any one of claims 1 to 8, characterized in that, Includes the following steps: Step (1): Before the steel bridge segment is hoisted, a transverse limiting plate is welded to the joint of the previously hoisted steel bridge segment, and the longitudinal diaphragm of TDA-1-B is used as the longitudinal limiting plate; the longitudinal limiting plate is selected at the middle diaphragm, with 2 plates at each end and the middle, and the same transverse diaphragm is arranged alternately on the left and right, for a total of 6 plates. The limiting plate is made of Q235 grade steel plate with a thickness of t=10mm. The longitudinal limiting plate has a plane size of 15cm×10cm and a chamfer of 2cm×10cm. The transverse limiting plate has a plane size of 21cm×6cm and a chamfer of 2cm×10cm. The transverse limiting plate is based on the outer wall panel of the installed steel bridge segment, and the guide edge of the transverse limiting plate is welded tightly to the wall panel; the longitudinal limiting plate is determined by the surveyor's layout, and the longitudinal limiting plate is moved outward by 10mm based on the layout coordinates. Step (2): TDA-1-A is slowly lowered using the limiting plate and lands on the temporary support. The crawler crane is not under stress but does not unhook, in preparation for fine-tuning. Three-way jacks and temporary blocks are placed at the adjustment brackets, and adjustments are made according to the instructions of the surveyors; the temporary blocks and adjustment brackets at the outer wall panels are placed at the intersection of the longitudinal and transverse partitions, and the inner side is set on the transverse partition. Step (3): After the adjustment is completed, use thin steel plate pads to adjust the height of the temporary support piers, transfer the force of the steel bridge segment to the temporary support piers, remove the three-way jacks, and weld the steel bridge segment to the temporary support piers for fixation.

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