A large-span complex steel structure assembling whole-process precision control method and system based on three-dimensional laser scanning

Abstract

The application belongs to the technical field of steel structure construction quality monitoring in civil engineering, and particularly relates to a large-span complex steel structure assembly whole-process precision control method and system based on three-dimensional laser scanning. The method comprises the following steps: based on the structure form, hoisting path and construction conditions of the steel structure to be spliced, a three-dimensional measurement control network is established; based on the control points of the three-dimensional measurement control network, a unified coordinate system is obtained, and a laser scanner station is arranged under the unified coordinate system; based on the laser scanner station, three-dimensional laser scanning is performed on the steel structure to be spliced in stages, and a spatial data set in stages is obtained; data analysis is performed on the point cloud data in the spatial data set, a correction feedback result is obtained, and the precision control of the whole process of the large-span complex steel structure assembly is realized. The application realizes real-time, continuous and closed-loop precision control in the whole process from factory prefabrication, on-site assembly to overall lifting, and solves the problems of dispersion, lag and lack of global control ability of the traditional measurement method.

Description

Technical Field

[0001] This invention belongs to the field of steel structure construction quality monitoring technology in civil engineering, specifically involving a method and system for precision control of the entire assembly process of large-span complex steel structures based on three-dimensional laser scanning. It is particularly suitable for quality control of the assembly and lifting process of large-span complex steel structures such as connecting corridors of super high-rise buildings. Background Technology

[0002] In recent years, with the continuous innovation and development of super high-rise building design, the application of large-span steel structures for connecting corridors in super high-rise buildings has become increasingly widespread. While this type of structural system enhances the functionality and spatial experience of buildings, its unique structural characteristics, such as complex shapes, large spans, and extremely high installation precision requirements, also pose severe challenges to construction quality control. For example, in a certain large-span connecting corridor project, the corridor structure exhibits an outward-expanding arc shape, with a maximum span of 78 meters. The assembly precision of such complex spatial structures directly affects the safety of the subsequent overall lifting process and the accuracy of the final placement.

[0003] Currently, in steel structure construction quality control, traditional methods mainly rely on single-point measurement tools such as total stations. While this method played an important role for a certain period, it has significant limitations. First, the measurement data is discretely distributed, making it difficult to comprehensively and continuously reflect the overall deformation and spatial shape of the components. Second, the measurement efficiency is low, failing to meet the rapid construction schedule requirements of modern building projects. Third, the lack of intuitive and visual data support hinders the timely detection and effective rectification of quality problems. Furthermore, from the perspective of quality control processes, existing technologies often focus on monitoring single stages of the construction process, lacking a systematic and precise control method covering the entire process from factory prefabrication and on-site assembly to overall lifting.

[0004] As building structures become increasingly complex and span large spaces, traditional single-point measurement methods, centered on total stations and theodolites, are no longer sufficient to meet the high spatial accuracy requirements of such complex steel structures during manufacturing, assembly, and overall lifting. Existing measurement methods suffer from problems such as scattered data acquisition and limited data quantity, making it difficult to continuously reflect the spatial variation patterns of the overall structural form. Furthermore, traditional inspection processes rely heavily on manual operation, lacking efficient data processing algorithms and automated feedback mechanisms. This results in insufficient real-time performance and accuracy in construction monitoring, making it prone to error accumulation and amplification of local deviations, thereby affecting the safety of the overall structural lifting and the final installation accuracy.

[0005] The steel structures of super high-rise buildings, such as long-span connecting corridors, are characterized by complex shapes, dense nodes, and limited installation space, placing higher demands on the stability, accuracy, and response speed of construction monitoring and control systems. However, existing technologies often only perform limited measurements at single construction stages (such as after hoisting or welding), lacking the ability to continuously track and monitor the structure across multiple stages and processes. This makes it impossible to achieve precise control throughout the entire process, from factory prefabrication and on-site assembly to overall lifting. Furthermore, existing systems have not yet formed an effective data fusion and information feedback system, failing to provide real-time error warnings and trend predictions, nor can they achieve construction correction and guidance through a visual interface, resulting in delayed response and low efficiency in the monitoring and control process.

[0006] Therefore, given the characteristics and challenges of constructing large-span connecting corridor steel structures in super high-rise buildings, there is an urgent need to develop an innovative technical method that can achieve rapid, comprehensive, and high-precision quality monitoring. This will ensure that the precision of each stage, from component prefabrication in the factory to on-site assembly, is controllable, thereby guaranteeing the overall quality and safety of the project. Summary of the Invention

[0007] To address the challenges of precision control, data dispersion, monitoring lag, and lack of systematic control mechanisms in the construction of large-span complex steel structures for super high-rise buildings, existing technologies offer a method and system for precision control throughout the assembly process of such structures based on 3D laser scanning. This method aims to establish a unified 3D spatial control network to achieve rapid acquisition and high-precision splicing of multi-station, high-density point cloud data; construct a spatial error analysis and automatic feedback model based on point cloud data to enable real-time monitoring and dynamic correction during construction; and integrate 3D visualization with a BIM system to achieve a closed-loop control system from data acquisition and error calculation to on-site adjustments. This comprehensively improves the precision control capabilities, information technology level, and construction safety of large-span complex steel structures in super high-rise buildings.

[0008] To achieve the above objectives, the present invention provides the following solution: A method for precision control throughout the assembly process of large-span complex steel structures based on three-dimensional laser scanning, the method comprising: A three-dimensional measurement and control network is established based on the structural form, hoisting path and construction conditions of the steel structure to be assembled. Based on the control points of the three-dimensional measurement and control network, a unified coordinate system is obtained, and laser scanner stations are set up under the unified coordinate system. Based on the laser scanner station, the steel structure to be spliced ​​is subjected to a phased three-dimensional laser scan to obtain a phased spatial dataset. Data analysis is performed on the point cloud data in the spatial dataset to obtain correction feedback results, thereby achieving precision control of the entire assembly process of large-span complex steel structures.

[0009] Preferably, the method for obtaining a phased spatial dataset by performing phased three-dimensional laser scanning of the steel structure to be assembled based on the laser scanner station includes: Phase 1: Pre-assembly scanning of the steel structure to be assembled in the factory to obtain geometric shape and node location data; The second stage: During the on-site splicing stage, the overall state of the first installation section of the connecting corridor after splicing is completed is scanned to obtain data on spatial location, installation posture and node coordination. Phase 3: After the second installation section of the connecting corridor is assembled and welded, a third scan is performed to obtain the overall assembly completion status. Phase 4: After the entire corridor structure is raised to the design elevation, a fourth scan is performed to obtain the data set after the raising. The first installation section of the connecting corridor is a preliminary installation section determined based on the segmentation of the connecting corridor structure, the hoisting sequence, the temporary support conditions, and the state in which it can independently form a spatially stable unit; the second installation section of the connecting corridor is a subsequent installation section that is adjacent to the first installation section of the connecting corridor and forms a complete connecting corridor system together after node connection and welding.

[0010] Preferably, the method for performing data analysis on the point cloud data in the spatial dataset to obtain correction feedback results and achieve precision control throughout the assembly process of large-span complex steel structures includes: The point cloud data in the spatial dataset are stitched together; Spatial registration and bias analysis are performed on the stitched point cloud to obtain the spatial bias distance; Based on the spatial deviation distance, a dynamic deviation judgment model is established to obtain correction feedback results, thereby achieving precision control of the entire assembly process of large-span complex steel structures.

[0011] Preferably, the method for establishing a dynamic deviation judgment model based on the spatial deviation distance and obtaining the correction feedback result includes: ; ; ; in, Spatial deviation distance This represents the average deviation of measuring points on the same component or at local nodes. For the standard deviation of the corresponding measuring point, For the first Local deviation statistics, For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point For design tolerances or construction acceptance tolerances, This is the corrective amount.

[0012] Preferably, the method for performing data analysis on the point cloud data in the spatial dataset to obtain correction feedback results and achieve precision control throughout the assembly process of large-span complex steel structures further includes: Establish a time series fitting model: ; ; in, For the first Component offset in each scan. These are the time series fitting coefficients. κ For curvature variation; Structural stability is determined by curvature changes; when | κ When the value is less than the set limit, the structural deformation is controlled; otherwise, a trend warning is issued.

[0013] The present invention also provides a precision control system for the entire assembly process of large-span complex steel structures based on three-dimensional laser scanning. The system is used to implement the aforementioned method and includes: a network establishment module, a station deployment module, a segmented scanning module, and a data analysis module. The network establishment module is used to establish a three-dimensional measurement and control network based on the structural form, hoisting path and construction conditions of the steel structure to be spliced. The station deployment module is used to obtain a unified coordinate system based on the control points of the three-dimensional measurement and control network, and to deploy laser scanner stations in the unified coordinate system. The segmented scanning module is used to perform a phased three-dimensional laser scan of the steel structure to be spliced ​​based on the laser scanner station, and obtain a phased spatial dataset. The data analysis module is used to perform data analysis on the point cloud data in the spatial dataset, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

[0014] Preferably, the segmented scanning module includes a first scanning unit, a second scanning unit, a third scanning unit, and a fourth scanning unit; The first scanning unit is used to perform pre-assembly scanning on the steel structure to be spliced ​​during the factory stage to obtain geometric shape and node position data; The second scanning unit is used to scan the overall state of the first installation section connecting corridor after the splicing is completed during the on-site splicing stage, and to obtain spatial position, installation posture and node coordination data. The third scanning unit is used to perform a third scan after the second installation section of the connecting corridor has been assembled and welded, in order to obtain the overall assembly completion status. The fourth scanning unit is used to perform a fourth scan after the entire corridor structure is raised to the design elevation, to obtain the data set after the raising. The first installation section of the connecting corridor is a preliminary installation section determined based on the segmentation of the connecting corridor structure, the hoisting sequence, the temporary support conditions, and the state in which it can independently form a spatially stable unit; the second installation section of the connecting corridor is a subsequent installation section that is adjacent to the first installation section of the connecting corridor and forms a complete connecting corridor system together after node connection and welding.

[0015] Preferably, the data analysis module includes: a splicing unit, a deviation analysis unit, and a correction feedback unit; The stitching unit is used to stitch together point cloud data in the spatial dataset; The deviation analysis unit is used to perform spatial registration and deviation analysis on the stitched point cloud to obtain the spatial deviation distance. The correction feedback unit is used to establish a dynamic deviation judgment model based on the spatial deviation distance, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

[0016] Preferably, the process by which the correction feedback unit establishes a dynamic deviation judgment model based on the spatial deviation distance and obtains the correction feedback result includes: ; ; ; in, Spatial deviation distance This represents the average deviation of measuring points on the same component or at local nodes. For the standard deviation of the corresponding measuring point, For the first Local deviation statistics, For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point For design tolerances or construction acceptance tolerances, This is the corrective amount.

[0017] Preferably, the data analysis module further includes: a deformation prediction module; The deformation prediction module is used to establish a time series fitting model, obtain curvature changes, and determine structural stability based on the curvature changes. The time series fitting model is as follows: ; ; in, For the first Component offset in each scan. These are the time series fitting coefficients. κ For curvature variation; Structural stability is determined by curvature changes; when | κ When the value is less than the set limit, the structural deformation is controlled; otherwise, a trend warning is issued.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Full-Process Precision Control and System Integration: This invention establishes for the first time a three-dimensional laser scanning precision control system covering the entire process from factory prefabrication and on-site assembly to overall lifting, breaking through the limitations of traditional measurement methods that can only perform single-point detection in local links or stage nodes. By continuously collecting point cloud data at each construction stage and maintaining a unified coordinate benchmark, continuous tracking and consistent control of component manufacturing precision, installation precision, and overall shape precision are achieved. This method forms a complete data chain and precision closed loop, providing a systematic and traceable quality assurance system for the construction of complex steel structures.

[0019] 2. Real-time Monitoring and Closed-Loop Error Feedback Mechanism: Traditional total station or theodolite measurement methods rely on manual data collection and discrete data, making real-time monitoring difficult. This invention utilizes multi-station 3D scanning technology, combined with spatial registration and statistical models, to achieve real-time detection and automatic analysis of component deviations. When the deviation of a local measuring point exceeds the limit, the system can automatically generate a deviation report and adjustment plan, which is presented in a 3D visualization within the BIM environment, enabling immediate feedback and correction of errors. Through time series analysis and trend prediction algorithms, this invention can also provide early warnings of potential cumulative deformation that may occur during the lifting or welding stages, shifting from "post-event detection" to "proactive control," significantly improving construction safety and accuracy stability.

[0020] 3. Information Integration and Intelligent Decision Support: This invention establishes a data format conversion, unified coordinate reference mapping, deviation result field mapping, and feedback instruction write-back mechanism to link the FARO SCENE, BuildIT Construction, and BIM platforms, achieving closed-loop management of data acquisition, analysis, and feedback. The technical challenge of linking these three platforms lies in the fact that FARO SCENE outputs multi-site point cloud stitching results, BuildIT Construction focuses on spatial deviation analysis between the measured point cloud and the design model, and the BIM system focuses on component coding, node location, and design semantics. These three platforms are inconsistent in data format, coordinate reference, component object numbering, and error expression methods. Existing technologies typically only allow point cloud stitching or deviation comparison within a single platform, or rely on manual import and export of results, failing to form a closed-loop feedback that corresponds one-to-one with construction component objects. This invention, through a unified coordinate chain and component coding mapping, enables continuous point cloud stitching, design model registration, deviation identification, risk classification, and correction instruction output from a single scan. The resulting technical effect is reduced secondary errors and feedback lag caused by manual conversion, transforming static detection into dynamic closed-loop control oriented towards the construction process. The system can automatically output deviation statistics charts, node error curves, and three-dimensional chromatographic models. Construction personnel can obtain spatial deviation information and correction instructions in real time, forming an intelligent closed loop of "data acquisition—analysis and judgment—visualized decision-making—on-site adjustment." Compared with the traditional mode that relies on manual comparison and experience correction, this invention significantly improves the automation level of data processing and the scientific nature of decision-making.

[0021] 4. Innovative Application Scope and Engineering Promotion Value: This invention was applied and verified in a large-span connecting corridor project, with measurement accuracy controlled within 8 mm and overall assembly error not exceeding 0.3%. This represents approximately twice the accuracy and 60% higher efficiency compared to traditional methods. This method is particularly suitable for construction scenarios involving large-span complex steel structures in super high-rise buildings, such as connecting corridors, trusses, and spatial roofs. It can be widely extended to large public buildings, bridges, and prefabricated structures, providing reliable technical support and engineering implementation paths for digital construction and intelligent construction monitoring in civil engineering. Attached Figure Description

[0022] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the process for controlling the accuracy of the entire assembly process of a large-span complex steel structure based on three-dimensional laser scanning, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the first installation section connecting corridor structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the layout of 17 scanning stations for the pre-assembled truss of the connecting corridor structure according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a multi-site cloud splicing model according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the point cloud stitching error and deviation analysis in an embodiment of the present invention; Figure 6 This is a schematic diagram of the spatial deviation of the steel structure components of the large-span connecting corridor according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Example 1 like Figure 1 As shown, this invention provides a method for precision control throughout the assembly process of large-span complex steel structures based on three-dimensional laser scanning, including: A three-dimensional measurement and control network is established based on the structural form, hoisting path and construction conditions of the steel structure to be assembled. Based on the control points of the three-dimensional measurement and control network, a unified coordinate system is obtained, and laser scanner stations are set up under the unified coordinate system. Based on the laser scanner station, the steel structure to be spliced ​​is subjected to a phased three-dimensional laser scan to obtain a phased spatial dataset. Data analysis is performed on the point cloud data in the spatial dataset to obtain correction feedback results, thereby achieving precision control of the entire assembly process of large-span complex steel structures.

[0027] The specific implementation process of this invention is as follows: The core technical challenges of this invention are: how to establish a unified three-dimensional spatial control network to achieve rapid acquisition and high-precision stitching of multi-station, high-density point cloud data; how to construct a spatial error analysis and automatic feedback model based on point cloud data to achieve real-time monitoring and dynamic correction of the construction process; and how to achieve a closed-loop control system from data acquisition and error calculation to on-site adjustment through the integration of three-dimensional visualization and BIM systems, thereby comprehensively improving the precision control capability, informatization level, and construction safety of large-span complex steel structures for super high-rise buildings.

[0028] This invention addresses the challenges of precision control, data dispersion, monitoring lag, and lack of systematic control mechanisms in the construction of large-span complex steel structures for super high-rise buildings. It proposes a full-process assembly precision control method based on three-dimensional laser scanning technology. By constructing a multi-station three-dimensional laser scanning system and a point cloud data processing and error feedback mechanism, real-time, continuous, and closed-loop precision control is achieved throughout the entire process from factory prefabrication and on-site assembly to overall lifting. This solves the problems of dispersion, lag, and lack of global control capability in traditional measurement methods.

[0029] The technical solution of this invention includes the following main steps: First, a three-dimensional measurement control network is established at the construction site based on the building structure and installation path. Control points are measured using a total station and adjusted to obtain a unified coordinate system, achieving coordinate consistency with the design BIM model. Second, laser scanning stations are deployed during the factory prefabrication stage, on-site assembly stage, and overall lifting stage to collect high-density point clouds of component geometry and installation positions, forming phased spatial datasets. Then, FARO SCENE software is used for multi-station point cloud stitching and registration. The least squares method is used to calculate the point cloud matching error, and the accuracy is evaluated in the form of root mean square error (RMS) to ensure that the overall stitching accuracy is controlled within the millimeter range.

[0030] During the data analysis phase, this invention spatially registers the stitched point cloud model with the BIM design model. Error analysis and visualization are then performed using BuildIT Construction software, automatically generating deviation distribution maps, node error tables, and truss deformation diagrams to quantitatively express structural installation errors. The system employs a dynamic deviation judgment model; when component deviations exceed limits, it automatically marks abnormal points, calculates adjustment amounts, generates correction schemes, and outputs them graphically in the 3D model, enabling real-time on-site guidance and precision adjustment. Simultaneously, by establishing a time-series fitting model and deformation trend prediction algorithm, it predicts and controls potential morphological changes during lifting and welding processes, ensuring the overall structure maintains morphological stability and spatial consistency at each construction stage.

[0031] This invention employs an integrated architecture combining FARO SCENE, BuildIT Construction, and BIM systems to achieve closed-loop linkage between data acquisition, error analysis, visualization, and construction feedback. Construction personnel can obtain real-time information on structural installation deviations and corrective suggestions through a 3D visualization interface, thereby realizing a precision control mode of "data-driven—intelligent analysis—on-site adjustment." Through full-process digitalization, visualization, and intelligentization, this invention significantly improves the construction accuracy, monitoring efficiency, and safety reliability of complex, large-span steel structures in super high-rise buildings, possessing broad engineering application and promotion value.

[0032] like Figures 2-6 As shown, taking a large-span connecting corridor project as an example, in the specific implementation process, a unified three-dimensional measurement control network is first established based on the structural form, hoisting path, and construction conditions of the steel structure to be assembled. The three-dimensional measurement control network adopts a layout combining closed traverse and free nets. Using the main tower as the coordinate reference, control points are set up at the two end nodes and the central support area of ​​the connecting corridor structure, with the point spacing controlled within the range of 10 to 20 meters. The coordinates of the control points are determined through multiple measurements with a total station and least-squares adjustment calculations, and then unified into a coordinate system consistent with the BIM design model to obtain a unified coordinate system. Subsequently, multiple laser scanner stations are deployed under this unified coordinate system, with the deployment principles being complementary viewpoints, sufficient overlap, and continuous spatial coverage. The viewpoint overlap rate between each station is maintained at 30% to 40%, and the formula for calculating the overall coverage C of the scanned area is as follows: A scan is considered complete when C is greater than or equal to 0.95.

[0033] In the formula, This represents the overall coverage of the scanned area; This represents the total number of laser scanner stations. For the first Effective scanning coverage area of ​​each station; For the first The first station and the first The overlapping coverage area between adjacent stations; This refers to the total area that needs to be scanned for the steel structure to be assembled, its connection nodes, and support areas. By limiting these parameters, the station layout, overlapping areas, and scan integrity can be directly correlated with the measurement and control requirements of the entire assembly process of large-span complex steel structures.

[0034] A large-span connecting corridor project actually deployed 17 scanning stations with a station spacing of about 5 meters. Each station was equipped with 6 high-precision reflective target spheres with a diameter of 145 mm as splicing control targets. The target spheres were distributed at the main nodes of the connecting corridor truss to construct a spatial redundancy constraint system, providing a globally unified coordinate reference for point cloud splicing.

[0035] The three-dimensional measurement and control network maintains consistent coordinate reference throughout the entire construction period, thereby ensuring the consistency of accuracy in subsequent point cloud analysis and deviation verification.

[0036] Based on a laser scanner station, a phased 3D laser scan is performed on the steel structure to be assembled, obtaining phased spatial datasets to cover the entire process from manufacturing to installation. First, in the factory stage, a pre-assembly scan is performed on the steel structure to be assembled (such as large truss components) to collect geometric shape and node location data of the components, establishing a reference point cloud model of the prefabricated components (Phase 1). Then, during the on-site assembly stage, the overall state of the first installation section of the connecting corridor is scanned after assembly, acquiring data on the spatial position, installation posture, and node fit of the components (Phase 2). A third scan is performed after the second installation section of the connecting corridor is assembled and welded to capture the overall assembled state (Phase 3). Finally, a fourth scan is performed after the connecting corridor structure is lifted to the design elevation, forming a post-lifting dataset (Phase 4).

[0037] The first and second installation section corridors are not fixed floor concepts, but are determined based on structural segmentation, hoisting sequence, temporary support conditions, welded closure nodes, and the state of being able to independently form stable spatial units. In specific implementation, they can be divided into "installation sections that first form stable spatial units" and "subsequent installation sections that connect with them and complete the overall closure." That is, the first installation section corridor is the first installation section determined based on the corridor's structural segmentation, hoisting sequence, temporary support conditions, and the state of being able to independently form stable spatial units; the second installation section corridor is the subsequent installation section adjacent to the first installation section corridor, which, after node connection and welding, together form a complete corridor system. Staged scanning can identify local cumulative deviations before node closure and overall lifting, avoiding errors only being discovered after a one-time overall scan. It can control welding deformation and overall deviations before lifting within a correctable stage, improving assembly closure accuracy and overall lifting safety.

[0038] A four-stage scanning process constitutes a morphological evolution database of the entire steel structure installation process. The laser scanner used has a point cloud resolution of 6 mm, and the scanning time for a single station is controlled within 15 minutes. The point cloud data from each station are stitched together using FARO SCENE software. The coordinates of the same common feature point in adjacent stations are set as follows: (…). )and( The splicing error is defined as: The overall root mean square error (RMS) of the stitching is expressed as: In the formula, For the first The splicing error of common target points or common feature points, ( )and( E represents the three-dimensional coordinates of the same common point in adjacent stations in their respective station coordinate systems or a unified coordinate system. RMS The overall splicing root mean square error. This represents the number of common points used in the splicing error statistics.

[0039] Calculations show that the root mean square error of splicing in a large-span connecting corridor project is controlled within 8 mm, with a horizontal error of 7.9 mm, a vertical error of 4.9 mm, and an overall average error of 1.2 mm, meeting the requirements of the "Code for Surveying and Mapping of Steel Structures" (GB 50205-2020). After splicing, a point cloud model in .e57 format is output uniformly to provide basic data for subsequent accuracy comparison and error analysis.

[0040] The stitched point cloud model and the design BIM model are imported into BuildIT Construction software for spatial registration and deviation analysis. The rotation matrix R and translation vector t are solved using the least squares fitting method to establish the registration matrix. In the formula, To register the transformation matrix, The rotation matrix is ​​used to transform the measured point cloud coordinates to the design BIM coordinate system. It is a translation vector. The first point cloud in the actual measurement Coordinates of each measuring point In designing BIM models and The corresponding theoretical point coordinates, The number of registration point pairs. Calculate the spatial deviation vector for each measuring point: In the formula, For the first Spatial deviation vector of each measuring point Here are the coordinates of the measured point, ( The coordinates of the corresponding design point are used to obtain the spatial deviation distance. for: in, , , This corresponds to the difference.

[0041] Overall structural average deviation for: In the formula, For the first Spatial deviation distance of each measuring point This represents the average deviation of the overall structure. This represents the total number of measurement points participating in the statistics.

[0042] Analysis revealed that the overall absolute deviation of the first installation section of the connecting corridor was 31.1 mm, with a maximum deviation of 28.7 mm for the main web members, resulting in a relative error of 0.30%. The maximum deviation of the second installation section of the connecting corridor was less than ±8 mm. Statistical analysis of the deviation distribution showed that over 50% of the measuring point deviations were located within... The range indicates that the structural assembly accuracy is excellent and the overall deformation is under control. The deviation distribution visualization function presents the error results in a color spectrum: green represents deviations within the allowable range, yellow indicates slight deviations, and red indicates an over-limit warning. On-site construction personnel can quickly identify and correct the location of abnormal components using the graphical interface.

[0043] To achieve dynamic and continuous construction monitoring, this invention establishes a real-time feedback and closed-loop control mechanism based on scanning data. A time series relationship is established between multi-stage scanning data to continuously track and analyze component deviations. The dynamic deviation judgment model is based on deviation limit control; when the measuring point meets the following conditions… At that time, outliers are automatically marked and local deviation statistics are generated. : like Exceeding design tolerances Then calculate the correction amount. : Based on the results, correction suggestions are generated, including deviation direction, adjustment amount, and construction control nodes, and the results are visualized and output through the BIM platform.

[0044] In this invention, and It is the mean and standard deviation obtained by statistically analyzing the deviations of measuring points within the same construction stage, the same component, or the same node area; Used to characterize the The average deviation level of each local area; Determined based on design documents, construction acceptance standards, or project control requirements; The direction of the adjustment that needs to be fed back to the site is determined by the spatial deviation vector. For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point.

[0045] Meanwhile, to predict the morphological evolution trend of the lifting process, this invention establishes a time series fitting model: in For the first Component offset in each scan, time series fitting coefficient It is obtained using the least squares method. From this, the curvature change can be calculated: With curvature change κ To determine structural stability, when | κ | Less than the set limit If structural deformation is considered under control, the system will assume it is; otherwise, it will issue an early trend warning. This predictive mechanism transforms error control from "post-event correction" to "real-time prediction," enabling proactive management of construction risks.

[0046] This invention establishes a data format conversion, unified coordinate reference mapping, deviation result field mapping, and feedback instruction write-back mechanism to link the FARO SCENE, BuildIT Construction, and BIM platforms, achieving closed-loop management of data acquisition, analysis, and feedback. The technical challenge of linking these three platforms lies in the fact that FARO SCENE outputs multi-site point cloud stitching results, BuildIT Construction focuses on spatial deviation analysis between the measured point cloud and the design model, and the BIM system centers on component coding, node location, and design semantics. These three platforms are inconsistent in data format, coordinate reference, component object numbering, and error expression methods. Existing technologies typically only allow point cloud stitching or deviation comparison within a single platform or rely on manual import / export of results, failing to form a closed-loop feedback mechanism that corresponds one-to-one with construction component objects. This invention, through a unified coordinate chain and component coding mapping, enables continuous point cloud stitching, design model registration, deviation identification, risk classification, and correction instruction output from a single scan. These operations reduce secondary errors and feedback lag caused by manual conversion, transforming static detection into dynamic closed-loop control oriented towards the construction process. Construction workers can monitor the structural status in real time through a 3D visualization interface. The system automatically identifies deviation categories based on the analysis results: green indicates normal, yellow is a warning, and red indicates exceeding limits. Correction amounts can also be used to determine the deviation level. Generate adjustment plans to achieve integrated "measurement-analysis-control" operations.

[0047] The application of this method in a large-span connecting corridor project demonstrates that the present invention achieves controllable precision and closed-loop information throughout the entire steel structure installation process. Through four-stage scanning and data analysis, the point cloud splicing error is no greater than 8 mm, the average surface deviation is no greater than 3 mm, the overall truss dimensional relative error is controlled within 0.3%, and over 95% of the measuring points show deviations within acceptable limits. Within the specified range, compared to traditional total station single-point measurement methods, the monitoring accuracy of this invention is improved by approximately 2 times, data acquisition efficiency is improved by approximately 60%, and it possesses spatial continuity and visualization characteristics, realizing the transformation from discrete detection to continuous monitoring, and from static measurement to dynamic control. The implementation of this method significantly improves the construction safety and installation accuracy of complex steel structures in super high-rise buildings, and verifies the applicability of three-dimensional laser scanning technology in the construction of large-span spatial structures.

[0048] In summary, this invention provides a method for precision control of the entire assembly process of complex, large-span steel structures for super high-rise buildings based on three-dimensional laser scanning. Addressing the challenge of real-time control over the installation precision of complex, large-span steel structures in super high-rise buildings, this invention establishes a complete three-dimensional scanning and control system. This system enables real-time, continuous, and closed-loop precision monitoring and error feedback control throughout the entire process, from component factory prefabrication and on-site assembly to overall lifting, ensuring the morphological consistency and precision reliability of the large-span steel structure during spatial installation.

[0049] This invention focuses on complex, large-span steel structures, particularly connecting corridors, in super high-rise buildings, constructing a digital precision control system covering the entire process from factory prefabrication and on-site assembly to overall lifting. Through real-time, continuous 3D scanning data acquisition and closed-loop error feedback, this system achieves dynamic precision control during the construction phase, ensuring a high degree of consistency between the structural spatial form and the design model. It demonstrates significant differentiation and innovation in systematic process management, real-time control capabilities, and intelligent applications, providing a scientific and scalable technical path for the construction quality control of future super high-rise buildings and complex, large-span structures.

[0050] Example 2 Based on the same inventive concept, the present invention also provides a precision control system for the entire assembly process of large-span complex steel structures based on three-dimensional laser scanning, used to implement the methods described in the foregoing embodiments. The system includes: a network establishment module, a station deployment module, a segmented scanning module, and a data analysis module. The network establishment module is used to establish a three-dimensional measurement and control network based on the structural form, hoisting path and construction conditions of the steel structure to be spliced. The station deployment module is used to obtain a unified coordinate system based on the control points of the three-dimensional measurement and control network, and to deploy laser scanner stations in the unified coordinate system. The segmented scanning module is used to perform a phased three-dimensional laser scan of the steel structure to be spliced ​​based on the laser scanner station, and obtain a phased spatial dataset. The data analysis module is used to perform data analysis on the point cloud data in the spatial dataset, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

[0051] Furthermore, in this embodiment, the segmented scanning module includes a first scanning unit, a second scanning unit, a third scanning unit, and a fourth scanning unit; The first scanning unit is used to perform pre-assembly scanning on the steel structure to be spliced ​​during the factory stage to obtain geometric shape and node position data; The second scanning unit is used to scan the overall state of the first installation section connecting corridor after the splicing is completed during the on-site splicing stage, and to obtain spatial position, installation posture and node coordination data. The third scanning unit is used to perform a third scan after the second installation section of the connecting corridor has been assembled and welded, in order to obtain the overall assembly completion status. The fourth scanning unit is used to perform a fourth scan after the entire corridor structure is raised to the design elevation, to obtain the data set after the raising. The first installation section of the connecting corridor is a preliminary installation section determined based on the segmentation of the connecting corridor structure, the hoisting sequence, the temporary support conditions, and the state in which it can independently form a spatially stable unit; the second installation section of the connecting corridor is a subsequent installation section that is adjacent to the first installation section of the connecting corridor and forms a complete connecting corridor system together after node connection and welding.

[0052] Furthermore, in this embodiment, the data analysis module includes: a splicing unit, a deviation analysis unit, and a correction feedback unit; The stitching unit is used to stitch together point cloud data in the spatial dataset; The deviation analysis unit is used to perform spatial registration and deviation analysis on the stitched point cloud to obtain the spatial deviation distance. The correction feedback unit is used to establish a dynamic deviation judgment model based on the spatial deviation distance, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

[0053] Furthermore, in this embodiment, the process by which the correction feedback unit establishes a dynamic deviation judgment model based on the spatial deviation distance and obtains the correction feedback result includes: ; ; ; in, Spatial deviation distance This represents the average deviation of measuring points on the same component or at local nodes. For the standard deviation of the corresponding measuring point, For the first Local deviation statistics, For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point For design tolerances or construction acceptance tolerances, This is the corrective amount.

[0054] Furthermore, in this embodiment, the data analysis module further includes: a deformation prediction module; The deformation prediction module is used to establish a time series fitting model, obtain curvature changes, and determine structural stability based on the curvature changes. The time series fitting model is as follows: ; ; in, For the first Component offset in each scan. These are the time series fitting coefficients. κ For curvature variation; Structural stability is determined by curvature changes; when | κ When the value is less than the set limit, the structural deformation is controlled; otherwise, a trend warning is issued.

[0055] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for precision control of the entire assembly process of large-span complex steel structures based on three-dimensional laser scanning, characterized in that, The method includes: A three-dimensional measurement and control network is established based on the structural form, hoisting path, and construction conditions of the steel structure to be assembled. Based on the control points of the three-dimensional measurement and control network, a unified coordinate system is obtained, and laser scanner stations are set up under the unified coordinate system. Based on the laser scanner station, the steel structure to be spliced ​​is subjected to a phased three-dimensional laser scan to obtain a phased spatial dataset. Data analysis is performed on the point cloud data in the spatial dataset to obtain correction feedback results, thereby achieving precision control of the entire assembly process of large-span complex steel structures.

2. The method according to claim 1, characterized in that, The method for obtaining a phased spatial dataset by performing a phased three-dimensional laser scan of the steel structure to be assembled based on the laser scanner station includes: Phase 1: Pre-assembly scanning of the steel structure to be assembled in the factory to obtain geometric shape and node location data; The second stage: During the on-site splicing stage, the overall state of the first installation section of the connecting corridor after splicing is completed is scanned to obtain data on spatial location, installation posture and node coordination. Phase 3: After the second installation section of the connecting corridor is assembled and welded, a third scan is performed to obtain the overall assembly completion status. Phase 4: After the entire corridor structure is raised to the design elevation, a fourth scan is performed to obtain the data set after the raising. The first installation section of the connecting corridor is a preliminary installation section determined based on the segmentation of the connecting corridor structure, the hoisting sequence, the temporary support conditions, and the state in which it can independently form a spatially stable unit; the second installation section of the connecting corridor is a subsequent installation section that is adjacent to the first installation section of the connecting corridor and forms a complete connecting corridor system together after node connection and welding.

3. The method according to claim 1, characterized in that, The method for performing data analysis on the point cloud data in the aforementioned spatial dataset to obtain correction feedback results and achieve precision control throughout the entire assembly process of large-span complex steel structures includes: The point cloud data in the spatial dataset are stitched together; Spatial registration and bias analysis are performed on the stitched point cloud to obtain the spatial bias distance; Based on the spatial deviation distance, a dynamic deviation judgment model is established to obtain correction feedback results, thereby achieving precision control of the entire assembly process of large-span complex steel structures.

4. The method according to claim 3, characterized in that, Based on the aforementioned spatial deviation distance, a dynamic deviation judgment model is established, and methods for obtaining correction feedback results include: ； ； ； in, Spatial deviation distance This represents the average deviation of measuring points on the same component or at local nodes. For the standard deviation of the corresponding measuring point, For the first Local deviation statistics, For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point For design tolerances or construction acceptance tolerances, This is the corrective amount.

5. The method according to claim 1, characterized in that, The method for performing data analysis on the point cloud data in the aforementioned spatial dataset to obtain correction feedback results and achieve precision control throughout the entire assembly process of large-span complex steel structures also includes: Establish a time series fitting model: ； ； in, For the first Component offset in each scan. These are the time series fitting coefficients. κ For curvature variation; Structural stability is determined by curvature changes; when | κ When the value is less than the set limit, the structural deformation is controlled; otherwise, a trend warning is issued.

6. A precision control system for the entire assembly process of large-span complex steel structures based on three-dimensional laser scanning, the system being used to implement the method described in any one of claims 1-5, characterized in that, The system includes: a network establishment module, a station deployment module, a segmented scanning module, and a data analysis module; The network establishment module is used to establish a three-dimensional measurement and control network based on the structural form, hoisting path and construction conditions of the steel structure to be spliced. The station deployment module is used to obtain a unified coordinate system based on the control points of the three-dimensional measurement and control network, and to deploy laser scanner stations in the unified coordinate system. The segmented scanning module is used to perform a phased three-dimensional laser scan of the steel structure to be spliced ​​based on the laser scanner station, and obtain a phased spatial dataset. The data analysis module is used to perform data analysis on the point cloud data in the spatial dataset, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

7. The system according to claim 6, characterized in that, The segmented scanning module includes a first scanning unit, a second scanning unit, a third scanning unit, and a fourth scanning unit; The first scanning unit is used to perform pre-assembly scanning on the steel structure to be spliced ​​during the factory stage to obtain geometric shape and node position data; The second scanning unit is used to scan the overall state of the first installation section connecting corridor after the splicing is completed during the on-site splicing stage, and to obtain spatial position, installation posture and node coordination data. The third scanning unit is used to perform a third scan after the second installation section of the connecting corridor has been assembled and welded, in order to obtain the overall assembly completion status. The fourth scanning unit is used to perform a fourth scan after the entire corridor structure is raised to the design elevation, to obtain the data set after the raising. The first installation section of the connecting corridor is a preliminary installation section determined based on the segmentation of the connecting corridor structure, the hoisting sequence, the temporary support conditions, and the state in which it can independently form a spatially stable unit; the second installation section of the connecting corridor is a subsequent installation section that is adjacent to the first installation section of the connecting corridor and forms a complete connecting corridor system together after node connection and welding.

8. The system according to claim 6, characterized in that, The data analysis module includes: a splicing unit, a deviation analysis unit, and a correction feedback unit; The stitching unit is used to stitch together point cloud data in the spatial dataset; The deviation analysis unit is used to perform spatial registration and deviation analysis on the stitched point cloud to obtain the spatial deviation distance. The correction feedback unit is used to establish a dynamic deviation judgment model based on the spatial deviation distance, obtain correction feedback results, and realize precision control of the entire assembly process of large-span complex steel structures.

9. The system according to claim 8, characterized in that, The process by which the correction feedback unit establishes a dynamic deviation judgment model based on the spatial deviation distance and obtains the correction feedback result includes: ； ； ； in, Spatial deviation distance This represents the average deviation of measuring points on the same component or at local nodes. For the standard deviation of the corresponding measuring point, For the first Local deviation statistics, For the first The number of measuring points within a local statistical region. d ij For the first Within the local statistical region, the first Spatial deviation distance of each measuring point For design tolerances or construction acceptance tolerances, This is the corrective amount.

10. The system according to claim 6, characterized in that, The data analysis module further includes: a deformation prediction module; The deformation prediction module is used to establish a time series fitting model, obtain curvature changes, and determine structural stability based on the curvature changes. The time series fitting model is as follows: ； ； in, For the first Component offset in each scan. These are the time series fitting coefficients. κ For curvature variation; Structural stability is determined by curvature changes; when | κ When the value is less than the set limit, the structural deformation is controlled; otherwise, a trend warning is issued.

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