Analysis method and device for pre-assembly of truss piece of bolted steel truss girder bridge and storage medium

Abstract

This invention belongs to the technical field of pre-assembly analysis of bolted steel truss bridge segments, and relates to an analysis method, device, and storage medium for the pre-assembly of bolted steel truss bridge segments. The method includes the following steps: setting pre-assembly preparation conditions, acquiring theoretical and test data of individual members, and forming a single-member model; extracting the corner coordinates of the single-member model, and using the least squares method to solve the transformation matrix to achieve switching between the local and global coordinate systems of the single member; analyzing the member length parameters of the single member and calculating the cumulative error; performing matching calculations on the web members based on the single-member model; performing matching calculations on the splice plates based on the single-member model; and outputting the pre-assembly analysis data of the web members and splice plates. This invention provides an analysis method for the pre-assembly of bolted steel truss bridge segments, aiming to achieve accurate analysis and quality control of the pre-assembly process of steel truss bridge segments through systematic coordinate transformation, error calculation, and parameter matching, thereby improving assembly accuracy and efficiency.

Description

Technical Field

[0001] This invention relates to the field of analysis technology for pre-assembly of bolted steel truss bridge segments, and particularly to analysis methods, apparatus and storage media for the pre-assembly of bolted steel truss bridge segments. Background Technology

[0002] As a crucial structural form for long-span bridges, bolted steel truss bridges rely heavily on pre-assembly processes, which directly impact construction accuracy, efficiency, and structural safety. With the continuous increase in bridge spans, pre-assembly technology has evolved from traditional on-site assembly and manual measurement and welding to factory-based, standardized, and modular approaches. In terms of testing technology, the application of 3D laser scanning and digital simulation assembly has improved pre-assembly accuracy from centimeter-level measurements using traditional total stations to millimeter-level measurements. Through fitting analysis between actual point cloud models and BIM models, real-time deviation correction and "N+1" rounds of accuracy control have been achieved, significantly reducing on-site engineering workload.

[0003] While existing pre-assembly methods have made some progress in digitalization and precision control, they still have significant limitations: First, they lack environmental adaptability; for example, in high-altitude and cold regions, temperature differences causing steel structure deformation require passive control measures such as additional stiffeners. Second, their intelligent integration is low; the combination of digital simulation with the Internet of Things and predictive adjustments is not yet mature. Third, data collaboration is lacking; individual component inspection data, truss assembly parameters, and node plate optimization are not systematically linked throughout the entire process. Furthermore, traditional 3D laser scanning requires the acquisition of full point cloud data, resulting in low model processing efficiency and a lack of temperature compensation capabilities, making it difficult to meet the high-precision measurement requirements of long structural members.

[0004] The key technical bottlenecks currently faced in the pre-assembly process mainly include: slow response time in measurement data processing, and low model matching efficiency due to full point cloud data; complex switching between local and global coordinate systems, lacking a unified coordinate transformation standard; a single method for controlling cumulative errors, without establishing a dynamic adjustment mechanism based on statistical theory; reliance on manual experience for matching web members and nodes, resulting in insufficient matching accuracy and efficiency for complex nodes (such as V-shaped nodes); and the lack of standardized reports for splice plate optimization data and overall truss parameter output, making it difficult to effectively guide on-site construction. These problems collectively result in pre-assembly accuracy, efficiency, and process guidance failing to meet the engineering requirements of large-span bolted steel truss bridges. Summary of the Invention

[0005] The purpose of this invention is to provide an analysis method, device, and storage medium for the pre-assembly of bolted steel truss bridge segments. The aim is to achieve precise analysis and quality control of the pre-assembly process of steel truss bridge segments through systematic coordinate transformation, error calculation, and parameter matching, thereby improving assembly accuracy and efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides an analysis method for the pre-assembly of bolted steel truss bridge truss segments, comprising the following steps: Set up pre-assembly preparation conditions, obtain theoretical and test data of single members, and form a single member model; The corner coordinates of a single member model are extracted, and the transformation matrix is ​​solved by the least squares method to achieve the switching between the local coordinate system and the global coordinate system of the single member. Analyze the length parameters of a single member and calculate the cumulative error; Based on the single-member model, matching calculations are performed on the web members; Matching calculations are performed on the splicing panels based on the single-member model; Output pre-assembly analysis data for the web members and splice plates.

[0007] In some examples, the theoretical data is a set of three-dimensional spatial points derived from the design BIM model, which includes the three-dimensional coordinates of the corner points or feature points of the members; the detection data is a set of three-dimensional spatial points obtained by measuring with a laser tracker equipped with temperature compensation. The steps to form a single-member model include: An error analysis framework for a single member is established, defining the error vector as the difference between the coordinates of the detection point and the coordinates of the theoretical point, and constraining the square root of the sum of the squares of the magnitudes of the error vector to be less than a set threshold. Define a local coordinate system for a single member, with its origin located at the center of the main box section of the single member. Calculate the covariance matrix of the theoretical point set using principal component analysis. The covariance matrix is ​​obtained by averaging the products of the deviation vectors between each theoretical point and the center of the main box section. Define the local coordinate axes using the eigenvectors of the covariance matrix. The least squares method is used to optimize the detection data, and the rigid transformation parameters are solved by singular value decomposition to obtain the optimized detection point set, so as to form a single rod model.

[0008] In some examples, the algorithmic flow for optimizing detection data includes: Input the theoretical point set and the detection point set; Calculate the principal box center of the theoretical point set and the principal box center of the detection point set; The theoretical point set and the detection point set are respectively centered by subtracting their respective main box centers; Calculate the covariance matrix between the centered theoretical point set and the detection point set; Singular value decomposition of the covariance matrix yields three matrices. The rotation matrix and translation vector are then solved. The rotation matrix is ​​the product of the two orthogonal matrices obtained from the decomposition, and the translation vector is the center of the detection point set minus the product of the rotation matrix and the center of the theoretical point set. The optimized detection point set is obtained by transforming the theoretical point set using a rotation matrix and a translation vector.

[0009] In some examples, the steps for extracting the corner coordinates of a single-member model include: The corner points of the main box-shaped single bar are extracted as feature points. The coordinate system transformation is described based on the special Euclidean group. The coordinate transformation is achieved by the rotation matrix and the translation vector. The transformation method is that the global coordinates are equal to the product of the rotation matrix and the local coordinates plus the translation vector. By solving the rotation matrix and translation vector through least squares matching, the sum of squared errors between the detection point and the theoretical point after coordinate transformation is minimized. The root mean square error between the theoretical point set and the detection point set in the global coordinate system is calculated. If the error is less than the tolerance, the matching is considered good.

[0010] In some examples, the algorithm flow for coordinate system transformation includes: Input the local corner point set and the origin of the global coordinate system; Extract the corner indexes of the local corner set and calculate its main box center; After centering the local corner point set, the covariance matrix of the detected corner point set is calculated. The rotation matrix and translation vector are solved by singular value decomposition, and the theoretical point set in the global coordinate system is obtained by applying transformation. Calculate the root mean square error of the theoretical point set and the detection point set in the global coordinate system.

[0011] In some examples, the steps for analyzing the member length parameters of a single member and calculating the cumulative error include: The length of the kth member in the assembly sequence is defined as the straight-line distance between the geometric center of the main box-shaped component and the previous member in the global coordinate system. The cumulative error is the sum of the assembly errors of individual members from the first member to the kth member; Two thresholds, τ1 and τ2, are set. When the cumulative error is less than τ1, the assembly is performed according to the theoretical position. When the cumulative error is greater than or equal to τ1 and less than τ2, a translation vector is introduced. The translation vector is the cumulative error of the previous member. The assembly position is adjusted to the theoretical position plus the translation vector. When the cumulative error is greater than or equal to τ2, assemble according to the position of the 3D model and reset the cumulative error to zero.

[0012] In some examples, the algorithm for cumulative error adjustment includes: Initialize the cumulative error to zero; Traverse the sequence of links from the first to the mth link: The cumulative error plus the current assembly error of the member; If the cumulative error is less than τ1, the assembly position is the theoretical position; If the cumulative error is greater than or equal to τ1 and less than τ2, the assembly position is the theoretical position plus the cumulative error of the previous member; If the cumulative error is greater than or equal to τ2, the assembly position is the model position, and the cumulative error is reset to zero.

[0013] In some examples, the steps for analyzing the member length parameters of a single member and calculating the cumulative error include: Define a set of node templates, where each template is a set of points or a parametric model; extract the set of points at the ends of the web members and the center points of the node openings; calculate the distance between the ends of the web members and each node template, and select the node template with the smallest distance for assembly.

[0014] In some examples, the steps for performing matching calculations on the web members include: Input the set of web member points and the set of node templates; Traverse each node template and calculate the distance between the set of web member points and the node template; Select the node template with the smallest distance and assemble the web member to the corresponding node position.

[0015] In some examples, the steps for performing matching calculations on the splicing panels include: Extract the coordinates of adjacent nodes and calculate the intersection of the convex hulls of adjacent nodes for the splicing plate region; The height, length, and diagonal length of the truss segment are calculated using extreme points. The height is the difference between the maximum and minimum values ​​of the Y-coordinate, the length is the difference between the maximum and minimum values ​​of the X-coordinate, and the diagonal length is the straight-line distance between the maximum and minimum coordinate points. An assembly accuracy report including root mean square error and maximum deviation is output. The algorithm flow for outputting an assembly accuracy report that includes the root mean square error and the maximum deviation includes: Input the assembled set; The splicing plate region is calculated as the intersection of the convex hulls of adjacent nodes; The height, length, and diagonal length of the truss segment are calculated using the coordinate difference of the extreme points. The output includes a data table containing geometric parameters and assembly accuracy indicators.

[0016] The present invention also provides an analytical apparatus, the analytical apparatus comprising: The acquisition module is used to acquire theoretical and test data of a single member. The theoretical data is a set of three-dimensional spatial points from the design BIM model, including the three-dimensional coordinates of the member's corner points or feature points. The test data is a set of three-dimensional spatial points obtained by measuring with a laser tracker with temperature compensation function. The coordinate transformation module is used to extract the corner coordinates of a single member model and to switch between the local coordinate system and the global coordinate system of the single member by solving the transformation matrix using the least squares method. The error analysis module is used to analyze the length parameters of a single member and calculate the cumulative error. The matching calculation module is used to perform matching calculations on web members and splice plates based on the single-member model; The output module is used to output pre-assembly analysis data for the web members and splice plates.

[0017] The present invention also provides a storage medium storing a program or instructions, which, when executed by a processor, implement the steps of the analysis method for pre-assembly of bolted steel truss bridge segments as described in any one of claims 1-10.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a pre-assembly quality control scheme based on multi-dimensional parameter collaborative analysis. Through precise fusion of BIM models and measured data, it achieves full-process digital management from single-member error analysis to overall assembly accuracy assessment. Compared to the traditional pre-assembly mode relying on manual experience, this invention seamlessly connects the local coordinate system with the global coordinate system through a coordinate transformation algorithm, improving the accuracy of cumulative error calculation by more than 30%. It innovatively introduces a dynamic optimization mechanism for web member matching, solving the node misalignment problem caused by the accumulation of member tolerances in traditional assembly, increasing assembly efficiency by 40%. Simultaneously, through intelligent calculation of the splice plate area and visualized output of accuracy indicators (RMSE, maximum deviation), it provides a quantitative basis for project acceptance, effectively reducing rework rates. This invention is not only applicable to the factory prefabrication stage of long-span steel truss bridges but can also be extended to the inspection and maintenance of existing bridges, demonstrating significant engineering practical value and economic benefits. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the analysis method for the pre-assembly of bolted steel truss bridge segments in this invention. Detailed Implementation

[0020] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0021] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] In this invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one of a, b, or c" can represent: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0023] This invention presents an innovative technical solution for the pre-assembly analysis of bolted steel truss bridge segments in the field of bridge engineering. Specifically designed for the factory prefabrication stage of long-span steel truss bridges, it achieves digital quality control of the entire pre-assembly process of the segments through multi-dimensional parameter collaborative analysis and deep integration with a BIM model. This method integrates coordinate transformation algorithms, dynamic error compensation, and intelligent web member matching technology, enabling adaptive adjustment of analysis parameters based on the structural characteristics of the steel truss bridge. Furthermore, it provides visualized output of accuracy indicators, balancing engineering practicality and data reliability.

[0024] In bridge prefabrication in factories or on-site assembly, this analysis method can be applied to achieve full-process quality control of truss pre-assembly. Such technical solutions need to be adaptable to multiple scenarios, including batch analysis modes for standardized factory production, detailed analysis modes for complex nodes, and dynamic monitoring modes for the construction process, to meet the needs of different construction stages. For professional-grade pre-assembly analysis methods, coordinate transformation accuracy, error accumulation control, algorithm iteration efficiency, and data compatibility are core elements affecting the value of engineering applications. Existing traditional analysis methods generally suffer from large coordinate system matching deviations and distortions in cumulative error calculations, which can easily lead to node misalignment during on-site assembly. Therefore, a novel analysis method for the pre-assembly of bolted steel truss bridge trusses is needed.

[0025] This invention provides an analysis method for the pre-assembly of bolted steel truss bridge segments. The method employs a dual data-driven architecture of "theory-experimental," with the main process handling foundation coordinate transformation and error calculation, and the auxiliary process achieving dynamic optimization of web member matching. Combined with high-precision detection data from a laser tracker, it ensures accurate mapping between the theoretical model and the physical components. The method incorporates a parametric analysis engine capable of storing pre-assembly parameter libraries for different bridge types and supports seamless data integration with BIM platforms.

[0026] This invention provides an analysis method for the pre-assembly of bolted steel truss bridge segments. The method employs a dual data-driven architecture of "theory-experimental," with the main process handling basic coordinate transformation and error calculation, and the auxiliary process achieving dynamic optimization of web member matching. Combined with high-precision detection data from a laser tracker, it ensures accurate mapping between the theoretical model and the physical components. The method incorporates a parametric analysis engine, constructing a dual closed-loop control system through point set registration units and error calculation units. It can store pre-assembly parameter libraries for different bridge types, supports seamless data integration with BIM platforms, and enables visualization and intelligent adjustment suggestions for assembly accuracy indicators (RMSE, maximum deviation).

[0027] To facilitate the description of the analysis method for the pre-assembly of bolted steel truss bridge segments, unless otherwise specified, this manual describes the analysis process with the segments positioned on a horizontal pre-assembly jig. The "working end" refers to the core components directly involved in the assembly analysis, including the coordinate acquisition module, error calculation module, and matching optimization module. Furthermore, the coordinate system defined in this manual is based on the general coordinate system for bridge design, with the X-axis representing the longitudinal direction, the Y-axis representing the transverse direction, and the Z-axis representing the vertical direction.

[0028] As an example, Figure 1 A flowchart illustrating the pre-assembly analysis method for bolted steel truss bridge segments provided in an embodiment of the present invention is shown.

[0029] Reference Figure 1 This invention provides an analysis method for the pre-assembly of bolted steel truss bridge truss segments, comprising the following steps: Step S100: Set pre-assembly preparation conditions, obtain theoretical and test data of single members, and form a single member model.

[0030] In the above steps, theoretical and test data of individual members can be obtained by setting pre-assembly preparation conditions, which can provide basic data support for subsequent assembly analysis. The resulting single member model is the basic unit for the analysis of the entire assembly process, so that the subsequent analysis of various parameters of the member has a specific object, ensuring the pertinence and accuracy of the analysis.

[0031] Specifically, single-member inspection data can be obtained through a laser tracker and matched and optimized with theoretical BIM model data to ensure high accuracy and efficient processing of measurement data.

[0032] The laser tracker features temperature compensation, reducing the impact of ambient temperature differences on steel structure deformation and improving the measurement accuracy of long members. It can optimize detection data using the least squares method combined with singular value decomposition (SVD), achieving a rigid transformation between the theoretical point set and the detection point set. The L2 norm of the error vector is controlled within the threshold δ, and the data processing response is fast with a simple interface.

[0033] Step S200: Extract the corner coordinates of the single-member model, and solve the transformation matrix using the least squares method to switch between the local coordinate system and the global coordinate system of the single-member.

[0034] The above steps extract the corner coordinates of a single member model, providing crucial data for switching between the local and global coordinate systems of individual members. By switching coordinate systems, the position and orientation of a single member in the local coordinate system can be transformed to the global coordinate system, thereby achieving precise positioning of each member within the overall assembly environment. This lays the spatial foundation for subsequent overall assembly analysis and error calculation.

[0035] The above steps enable coordinate system transformation, specifically switching between local and global coordinates. By establishing a link between the 3D model and the measurement model, free switching between the local and global coordinate systems can be achieved, providing a unified coordinate reference for the assembly of multiple components.

[0036] Specifically, the corner points of the main box can be extracted as feature points. Based on the special Euclidean group SE(3) theory, coordinate transformation is achieved through rotation matrix and translation vector. The matching error (RMSE) is less than the tolerance ε, ensuring accurate comparison between the model and the actual measurement data.

[0037] Step S300: Analyze the length parameters of a single member and calculate the cumulative error; The above steps analyze the length parameters of the members and calculate the cumulative error, providing a clear understanding of the deviation between the actual and theoretical lengths of the members. Calculating the cumulative error helps assess the impact of errors generated during processing and transportation on the overall assembly accuracy, providing crucial quantitative data for subsequent assembly adjustments and quality control, ensuring that the assembled structure meets design requirements.

[0038] The above steps can achieve cumulative error control (length parameter analysis and dynamic adjustment). Specifically, by defining the length of the member as the Euclidean distance between the geometric centers of adjacent main box sections, a cumulative error model is established based on the assumption of independent and identically distributed errors. By setting two thresholds (τ1=2σ, τ2=3σ), a three-level adjustment strategy is implemented, which includes direct assembly for small errors, translation compensation for medium errors, and resetting to zero for large errors. Statistical theory is used to dynamically control the cumulative assembly error, avoid the impact of error accumulation on overall accuracy, and effectively reduce the assembly error of long members.

[0039] Step S400: Based on the single-member model, perform matching calculations on the web members; The above steps allow for matching calculations of the web members, accurately determining their connection relationships and relative positions with other members. These matching calculations verify whether the web members are correctly installed during assembly, ensuring the rationality and stability of their stress distribution within the structure and preventing safety hazards caused by improper web member matching.

[0040] In other words, the above steps can realize the matching calculation of the web members (template-based matching of nodes). Specifically, a set of node type templates is constructed by parameterization, and the distance between the web member end point set and the template is calculated by using the minimum distance search algorithm. The node corresponding to the minimum distance is selected first. Combined with the average value of the center point of the box opening, the automatic and accurate matching of web members and nodes is realized, which improves the assembly efficiency and process guidance of complex nodes (such as V-shaped nodes).

[0041] Step S500: Based on the single-member model, perform matching calculations on the splicing plate.

[0042] The above steps can perform calculations and output data for the splice panels, providing precise parameters for their design and fabrication. Specifically, it can output complete assembly data, providing quantitative guidance for on-site construction. As a component connecting various members, the calculation results of the splice panel directly affect the strength and reliability of the connection. The output data can guide the production and manufacturing of the splice panels, ensuring that they meet actual assembly requirements and guaranteeing the connection quality and stability of the overall structure.

[0043] Specifically, the splicing plate area (intersection of convex hulls of adjacent nodes) and the overall parameters of the truss (height, length, diagonal length) are calculated to generate an accuracy report including RMSE and maximum deviation, realizing full-process data integration of single-piece data, truss assembly, and splicing plate optimization.

[0044] Step S600: Output the pre-assembly analysis data of the web members and splicing plates.

[0045] The pre-assembly analysis data output above can provide the bridge construction team with comprehensive and accurate information, helping them to better carry out subsequent assembly work. Construction personnel can use this data to identify potential problems in advance, such as deviations in member dimensions or mismatches in splicing, and thus take timely and appropriate measures for adjustment.

[0046] For the pre-assembly analysis data of the web members, construction personnel can determine whether fine-tuning or replacement of the web members is necessary based on their compatibility with other members, ensuring that the web members can accurately transfer loads after assembly and guaranteeing structural stability. Meanwhile, the pre-assembly analysis data of the splice panels helps determine the optimal installation position and method for the splice panels, improving the quality and efficiency of splicing.

[0047] In practical applications, this pre-assembly analysis data can also be integrated with the construction progress management system to achieve real-time monitoring and dynamic adjustment of the entire bridge assembly process. By comparing the analysis data with preset quality standards, potential quality issues during assembly can be identified in a timely manner, preventing large-scale rework and delays in the construction schedule.

[0048] Furthermore, the bolted steel truss bridge pre-assembly analysis method provided by this invention also possesses excellent scalability and compatibility. With the continuous development and changes in bridge engineering technology, this method can adapt to changes in different bridge types and construction requirements by updating the pre-assembly parameter library in the parametric analysis engine. Simultaneously, it can be combined with other advanced engineering technologies, such as the Internet of Things and big data analysis, to further enhance the intelligence level and quality control capabilities of bridge pre-assembly.

[0049] In future bridge construction, this pre-assembly quality control scheme based on multi-dimensional parameter collaborative analysis is expected to be more widely applied and promoted, making a greater contribution to improving the quality and safety of bridge engineering.

[0050] It should be noted that the single member in this invention includes members such as the upper chord, lower chord, and web member, which refer to the basic load-bearing components that exist independently in building structures (such as trusses, space frames, etc.). The upper chord, lower chord, and web member are specific examples of such members.

[0051] The upper chord is a horizontal or inclined member located at the top of a truss or similar structure. It mainly bears axial pressure and is the main load-bearing component in the upper part of the structure.

[0052] The lower chord is a horizontal or inclined member located at the bottom of a truss or similar structure. It mainly bears axial tensile force and together with the upper chord, forms the upper and lower edges of the structure, creating an overall load-bearing frame.

[0053] Web members are members that connect the upper chord and the lower chord. They can be divided into diagonal web members and vertical web members according to the direction of force. Their main function is to transfer loads, maintain the geometric stability of the structure, and make the upper chord and the lower chord form a cooperative force-bearing system.

[0054] The splicing plate in this invention refers to a conceptual or physical structure used to connect adjacent nodes, its function being to realize the connection relationship between nodes. Primarily used to connect adjacent nodes, it is a key medium for establishing associations between nodes. Mathematically, the splicing plate can be quantitatively described by the intersection volume or contact surface of adjacent point sets, thereby reflecting characteristics such as the tightness or range of the connection between nodes.

[0055] The splice plate in the pre-assembly of truss segments of a bolted steel truss bridge is a key connecting component used in the pre-assembly stage of the truss segments during construction. Its main function is to temporarily or permanently connect the various components of the truss segments, ensuring pre-assembly accuracy and structural stability. Its core function is to temporarily fix the chords, web members, and other components of the steel truss beam through bolt connections during truss segment pre-assembly, forming an integral truss segment structure, so as to check the geometric dimensions, hole alignment, and the tightness of the component connections.

[0056] In terms of materials, they are usually made of high-strength steel (such as Q345 steel), which has high tensile and compressive strength. The surface may be treated with anti-corrosion treatment (such as galvanizing) to improve durability. The connection method is to bolt to the truss components. The bolts must meet the design torque requirements to ensure the rigidity and force transmission performance of the splice. After pre-assembly, the bolts may be retained or replaced with permanent connection bolts according to design requirements. Its construction significance is great. The pre-assembly process achieved by splicing plates can detect problems such as component processing errors and hole position deviations in advance, reduce the difficulty of on-site installation, and ensure the overall construction quality and efficiency of steel truss bridges.

[0057] In some examples, the theoretical data is a three-dimensional spatial point set {Pi} derived from the design BIM model, where Pi = {pi1, pi2, ..., pin}, and pij ∈ R³ represents the coordinates of the corner points or feature points of the members; The detection data is a three-dimensional spatial point set {Qi} obtained by measuring with a laser tracker with temperature compensation function, where Qi = {qi1, qi2, ..., qin}; The steps to form a single-member model include: Establish an error analysis framework, define the error vector eij=qij-pij, and constrain the L2 norm of eij to be less than the threshold δ; Define a local coordinate system for a single member, with its origin located at the center position ci of the main box-shaped section of the single member. Calculate the covariance matrix Σ_ of the theoretical point set using PCA. The local coordinate axes are defined by the eigenvectors of the covariance matrix; The least squares method is used to optimize the detection data, and the rigid transformation parameters are solved by singular value decomposition (SVD) to obtain the optimized detection point set Q_optimized, which is used to form a single-bar model.

[0058] After the single-member model is constructed, it can be further used for multi-dimensional engineering analysis and evaluation. First, based on the optimized detection point set Q_optimized and the theoretical point set {Pi}, the overall deformation parameters of the single-member can be calculated, including translation error and rotation error. The translation error can be obtained by calculating the Euclidean distance between the origin ci of the two coordinate systems and the origin c'_i of the optimized detection coordinate system, i.e., Δc = ||c'_i - ci||; the rotation error is calculated by comparing the direction cosine matrices of the theoretical local coordinate system and the optimized detection coordinate system, and using the Rodrigues formula to convert the rotation matrix into a rotation vector, the magnitude of which can characterize the overall rotation deviation.

[0059] For critical stress areas of single members (such as connection nodes and variable cross-section locations), the error vectors eij of corresponding feature points can be extracted for in-depth analysis. By statistically analyzing the distribution characteristics of the errors at each feature point, such as the mean, standard deviation, and maximum deviation, areas of abnormal local deformation can be identified. For example, if the error vectors of multiple feature points at a certain node are all along the same direction and have large values, it may indicate that there is welding stress concentration or installation deviation in that area, requiring verification in conjunction with construction records.

[0060] Individual member models can be integrated with the overall structural BIM model, feeding back actual deformation data of the members to structural analysis software for structural safety verification during the construction phase. By comparing the differences between theoretically designed internal forces and those considering actual deformation, the load-bearing capacity requirements of the members can be assessed. Simultaneously, a construction error database can be established based on the error data of the individual member models, providing a reference for controlling construction accuracy in subsequent similar projects and optimizing construction techniques and measurement schemes.

[0061] To achieve dynamic monitoring of the construction quality of individual members, the individual member model can be linked with the on-site real-time measurement system. By periodically collecting and updating the model with test data, the trend of error changes can be tracked. If the error growth rate exceeds a preset threshold, an early warning mechanism is triggered, and construction parameters are adjusted in a timely manner to ensure that the installation accuracy of the member remains within a controllable range.

[0062] The above steps, by combining theoretical data from the BIM model with detection data from the laser tracker, constructed an error analysis framework, effectively controlling the error range. By defining a local coordinate system for a single member and using the PCA method to calculate the covariance matrix and its eigenvectors, the direction of the local coordinate axes was clarified, providing a foundation for subsequent coordinate transformations. Simultaneously, the detection data was optimized using the least squares method combined with singular value decomposition (SVD) to solve for the rigidity transformation parameters, resulting in an optimized set of detection points. This further improved the accuracy and reliability of the data, providing high-quality data support for subsequent pre-assembly analysis.

[0063] In practice, the analysis methods for the pre-assembly of bolted steel truss bridge segments can also be supplemented by a professional data analysis team. This team can conduct in-depth analysis of the output pre-assembly data, combining it with experience from similar past bridge projects to provide the construction team with more forward-looking suggestions. For example, based on data predictions of potential assembly difficulties, contingency plans can be developed in advance to avoid unforeseen circumstances that could affect the construction schedule.

[0064] During construction, an intelligent monitoring system can be introduced. This system can collect various data from the construction site in real time, such as environmental factors like temperature, humidity, and wind force, as well as structural data such as the stress and displacement changes of the components. By combining this real-time data with pre-assembly analysis data, dynamic monitoring and intelligent adjustment of the entire assembly process can be achieved.

[0065] In some examples, the algorithmic flow for optimizing detection data described above includes: Input the theoretical point set P and the detection point set Q; Calculate the main box center c_P of P and the main box center c_Q of Q; The centralized point set P_centered = P - c_P, Q_centered = Q - c_Q; Calculate the covariance matrix C = P_centered^T·Q_centered; Perform SVD decomposition on C to obtain U, S, and V, and solve for the rotation matrix R = V·U^T and the translation vector t = c_Q - R·c_P; The optimized point set Q_optimized = R·P + t is obtained by applying the transformation.

[0066] The above steps detail how to optimize the detection data using Singular Value Decomposition (SVD). First, the theoretical point set P and the detection point set Q are input as initial data. Next, the principal box centers c_P and c_Q of these two point sets are calculated to determine their center positions. Then, the point sets are centered to obtain the centered point sets P_centered and Q_centered; this step is to eliminate the influence of positional deviations on subsequent calculations. Subsequently, the covariance matrix C is calculated, reflecting the correlation between the theoretical and detection point sets. By performing SVD decomposition on the covariance matrix C, the eigenvector matrix U, the singular value matrix S, and the eigenvector matrix V can be obtained. Using these matrices, the rotation matrix R and the translation vector t can be solved, describing the rotation and translation transformations of the detection point set relative to the theoretical point set. Finally, these transformation parameters are applied to rotate and translate the theoretical point set P, resulting in the optimized detection point set Q_optimized. This series of steps ensures the high accuracy and reliability of the detection data, providing a solid foundation for subsequent pre-assembly analysis.

[0067] In some examples, step 200 includes: The corner points of the main box of a single bar are extracted as feature points. The coordinate system transformation is described based on the special Euclidean group SE(3). The coordinate transformation is achieved by the rotation matrix R∈SO(3) and the translation vector t∈R³. The transformation formula is p'=Rp+t, where p is the local coordinate and p' is the global coordinate. R and t are solved by least squares matching, i.e., minimizing the objective function minΣ||q_ Calculate the matching error If E is less than the tolerance ε, then the match is considered good.

[0068] The above steps describe in detail how to extract the corner points of the main box-shaped single-member structure as feature points and use the special Euclidean group SE(3) to realize coordinate system transformation. Specifically, by using the rotation matrix R and the translation vector t, the point p in the local coordinate system can be transformed into the point p' in the global coordinate system. The transformation formula is p'=Rp+t. In this process, the least squares matching method is used to solve for the optimal rotation matrix R and translation vector t, that is, to find the best match by minimizing the objective function minΣ||qi−(Rpi+t)||². The calculated matching error E is evaluated by RMSE (root mean square error). If E is less than the preset tolerance ε, it is determined that the local coordinate system and the global coordinate system are well matched, providing accurate spatial position information for subsequent assembly analysis.

[0069] In some examples, the algorithm flow for coordinate system transformation in step 200 includes: Input the local corner point set P_local and the global coordinate system origin O_global; Extract the corner indices of P_local and calculate its main box center c_local; After centering the point set, calculate the covariance matrix between P_local and the detected corner point set Q; By solving for R and t using SVD, and applying the transformation, we obtain P_global = R·P_local + t; Calculate matching error .

[0070] The above steps detail the algorithm flow for coordinate system transformation. First, the local corner point set P_local and the global coordinate system origin O_global are input as initial parameters. Next, the corner point indices of P_local are extracted, and its main box center c_local is calculated; this step is to determine the center position of the local coordinate system. Then, the point set is centered, and the covariance matrix between P_local and the detected corner point set Q is calculated. This matrix reflects the correlation between the two. By decomposing the covariance matrix using SVD, the rotation matrix R and translation vector t can be solved. These two parameters describe the transformation relationship from the local coordinate system to the global coordinate system. Applying these transformation parameters, the local corner point set P_local is rotated and translated to obtain the point set P_global in the global coordinate system. Finally, the matching error E is calculated, and the accuracy of the transformation is evaluated using RMSE (Root Mean Square Error). If E is within an acceptable range, the coordinate system transformation is successful, providing an accurate spatial positioning basis for subsequent assembly analysis.

[0071] In some examples, step 300 includes: Define the length l_k of the k-th member in the assembly sequence as l_k = ||c_k - c_{k-1}||, where c_k is the geometric center of the main box-shaped member k in the global coordinate system; The cumulative error is E_k=Σ_{i=1}^k ei, where ei is the single-member assembly error; Set thresholds τ1 and τ2, and assemble according to the theoretical position when E_k < τ1; When τ1≤E_k<τ2, a translation vector d=E_prev is introduced to adjust the assembly position to the theoretical position +d, where E_prev is the cumulative error of the previous member; When E_k≥τ2, assemble according to the position of the three-dimensional model and reset the cumulative error E=0.

[0072] The above steps detail how to analyze the member length parameters and calculate the cumulative error. First, the length l_k of the k-th member in the assembly sequence is defined. This length is obtained by calculating the Euclidean distance between the main box-shaped geometric center c_k of member k in the global coordinate system and the geometric center c_{k-1} of the previous member. Next, the cumulative error E_k is defined as the sum of the single-member assembly errors ei from the first member to the k-th member. To effectively control error accumulation during assembly, two thresholds τ1 and τ2 are set. When the cumulative error E_k is less than threshold τ1, it indicates that the assembly error of the current member is within an acceptable range, and assembly can proceed according to the theoretical position. When the cumulative error E_k is between thresholds τ1 and τ2, it indicates that the error has accumulated to a certain extent. At this point, a translation vector d needs to be introduced. This vector is equal to the cumulative error E_prev of the previous member. By adjusting the assembly position to the theoretical position plus the translation vector d, the impact of error accumulation is compensated. When the cumulative error E_k is greater than or equal to the threshold τ2, it indicates that the error accumulation has become quite serious. At this point, assembly needs to be performed according to the positions of the 3D model, and the cumulative error E should be reset to 0 to prevent further error accumulation and its greater impact on the overall assembly accuracy. This series of steps effectively controls the cumulative error during the assembly process by dynamically adjusting the assembly strategy, ensuring that the overall assembly accuracy meets the design requirements.

[0073] In some examples, the algorithm flow for cumulative error adjustment in step 300 includes: Initialize the cumulative error E=0; Traverse the sequence of rods from k=1 to m: E += e(k); If E < τ1, the assembly position = the theoretical position; If τ1≤E<τ2, the assembly position = theoretical position + E_prev, where E_prev is the E of the previous member; If E≥τ2, the assembly position = the model position, and E=0 is reset.

[0074] The above steps detail the algorithm flow for adjusting the cumulative error. First, the cumulative error E is initialized to 0, serving as the starting point for error accumulation. Next, the sequence of links is traversed, processing each link from the first to the m-th link sequentially. During this traversal, for each link processed, its corresponding single-link assembly error e(k) is added to the cumulative error E. Subsequently, different assembly strategies are adopted based on the magnitude of the cumulative error E: if E is less than the threshold τ1, the current cumulative error is small, and assembly can proceed according to the theoretical position; if E is between the thresholds τ1 and τ2, the error has accumulated to a certain extent, requiring the assembly position to be adjusted to the theoretical position plus the cumulative error E_prev of the previous link to compensate for the impact of error accumulation; if E is greater than or equal to the threshold τ2, the error accumulation is quite severe, requiring assembly according to the 3D model position, and resetting the cumulative error E to 0 to prevent further error accumulation and its greater impact on overall assembly accuracy. This series of steps, through dynamic adjustment of the assembly strategy, effectively controls the cumulative error during assembly, ensuring that the overall assembly accuracy meets design requirements.

[0075] In some examples, step 400 includes: Define a node template set T = {Ti, T_V, ...}, where each template is a point set or a parametric model; extract the point set P_ab at the end of the web member and the center point of the node opening, and calculate the distance between the end points of the web member and each node template. Select the node template corresponding to the minimum distance for assembly.

[0076] Specifically, a comprehensive set of node templates, T, is first constructed, encompassing various types such as Ti-type and T_V-type. Each template is precisely described in the form of a point set or a parametric model. During pre-assembly, the system extracts the point set P_ab at the end of the web member and the center point of the node opening as key matching features. Subsequently, by calculating the distance d(p, Tk) between the end point of the web member and each template in the node template set, the system can accurately evaluate the matching degree between each template and the current web member. Among all calculated distances, the node template corresponding to the minimum distance is selected as the best match, thereby guiding the precise assembly of the web member and the node. This process not only improves the automation level of assembly but also significantly enhances the assembly efficiency and process guidance of complex nodes (such as V-shaped nodes), ensuring the stability and safety of the overall structure.

[0077] In some examples, the algorithm flow for web member matching in the above steps includes: Input the set of web member points P_ab and the set of node templates T; Iterate through each template T_k∈T and calculate the distance d_k = d(P_ab, T_k); Select the node template corresponding to the smallest d_k and assemble the web member to the corresponding node position.

[0078] The above steps detail the algorithm flow for brace matching. First, the initial data consists of a brace point set P_ab and a node template set T. P_ab contains key point information at the ends of the braces, while T covers various possible node types and their geometric features. Next, each template T_k in the node template set T is traversed, and the matching degree between the brace and the current template T_k is evaluated by calculating the distance d_k between P_ab and T_k. This distance calculation method accurately reflects the geometric differences between the brace ends and the node templates. After calculating the distances for all templates, the template with the smallest distance is selected as the best match, meaning that this template is geometrically closest to the brace ends. Finally, based on the geometric information of the best-matching template, the braces are precisely assembled to the corresponding node positions, thus completing the brace-node matching process. This algorithm, through precise distance calculation and template matching, achieves automated and high-precision assembly of braces and nodes, providing strong assurance for the stability and safety of the overall structure.

[0079] In some examples, step 500 includes: Extract the coordinates of adjacent nodes and calculate the intersection of the convex hulls of adjacent nodes for the splicing plate region; The height, length, and diagonal length of the truss segment are calculated using extreme points, and an assembly accuracy report including RMSE and maximum deviation is output. Height = max y - min y, Length = max x - min x, Diagonal length = |p_max - p_min|. The algorithm flow that outputs an assembly accuracy report including RMSE and maximum deviation includes: Input the assembled point set S; The splicing plate region is calculated as the intersection of the convex hulls of adjacent nodes; The height, length, and diagonal length of the truss segment are calculated using extreme point differences. The output includes a data table containing geometric parameters and assembly accuracy indicators (RMSE, maximum deviation).

[0080] The above steps detail how to generate an assembly accuracy report including RMSE and maximum deviation. First, the assembled point set S is input, containing the spatial coordinates of all key points on the truss panel. Next, by extracting the coordinates of adjacent nodes, the splicing plate area is calculated. This area is defined as the intersection of the convex hulls of adjacent nodes, reflecting the actual geometry of the truss panel at the splice. Then, using the extreme point difference method, the height, length, and diagonal length of the truss panel are calculated. The height is obtained by the difference between the maximum and minimum y-coordinates, the length by the difference between the maximum and minimum x-coordinates, and the diagonal length by the distance between the two farthest points in the point set. After calculating the geometric parameters, assembly accuracy indicators, including RMSE (Root Mean Square Error) and maximum deviation, are further calculated. These indicators quantify the magnitude of errors generated during assembly. Finally, the calculated geometric parameters and assembly accuracy indicators are compiled into a data table and output, providing intuitive and accurate data support for evaluating the assembly quality of the truss panels. This series of steps, through systematic calculation and analysis, ensures the comprehensiveness and accuracy of the assembly precision report, providing an important basis for subsequent project acceptance and quality assessment.

[0081] The present invention also provides an analytical apparatus, the analytical apparatus comprising: The acquisition module is used to acquire theoretical and test data of a single member. The theoretical data is a set of three-dimensional spatial points from the design BIM model, including the three-dimensional coordinates of the member's corner points or feature points. The test data is a set of three-dimensional spatial points obtained by measuring with a laser tracker with temperature compensation function. The coordinate transformation module is used to extract the corner coordinates of a single member model and to switch between the local coordinate system and the global coordinate system of the single member by solving the transformation matrix using the least squares method. The error analysis module is used to analyze the length parameters of a single member and calculate the cumulative error. The matching calculation module is used to perform matching calculations on web members and splice plates based on the single-member model; The output module is used to output pre-assembly analysis data for the web members and splice plates.

[0082] In the actual operation of the aforementioned analysis device, the various modules collaborated closely to complete the analysis task of pre-assembling the bolted steel truss bridge segments. The acquisition module, serving as the information entry point for the entire device, provided theoretical and test data that formed the basis for subsequent analyses. The theoretical data originated from the design BIM model, accurately providing the three-dimensional coordinates of member corner points or feature points, while the test data was obtained through measurements using a laser tracker with temperature compensation capabilities, ensuring the accuracy and reliability of the data.

[0083] After receiving the data from the acquisition module, the coordinate transformation module quickly extracts the corner coordinates of the single-member model. It then uses the least squares method to solve for the transformation matrix, switching the local coordinate system of the single member to the global coordinate system. This process is crucial for subsequent error analysis and matching calculations, enabling unified analysis of different members within the same coordinate system and providing accurate spatial location information for subsequent steps.

[0084] Based on the coordinate transformation results, the error analysis module performs a detailed analysis of the length parameters of individual members and calculates the cumulative error. By setting reasonable thresholds and dynamically adjusting the assembly strategy according to the magnitude of the cumulative error, the module effectively controls the accumulation of errors during the assembly process, ensuring that the overall assembly accuracy meets the design requirements.

[0085] The matching calculation module performs matching calculations on the web members and splicing plates based on the single-member model. By inputting the web member point set and node template set, it iterates through the node template set to calculate the distance, selects the node template corresponding to the minimum distance for assembly, and realizes automated and high-precision matching between web members and nodes.

[0086] Finally, the output module clearly and intuitively displays the pre-assembly analysis data of the web members and splice plates. This data includes not only geometric parameters such as the height, length, and diagonal length of the truss segments, but also assembly accuracy indicators such as RMSE and maximum deviation. This data provides engineers with comprehensive and accurate information, helping them evaluate and optimize the pre-assembly process, ensuring the pre-assembly quality of bolted steel truss bridge segments, and providing strong support for subsequent construction and project acceptance.

[0087] Furthermore, this analytical device can be further optimized and expanded. For example, a data storage module can be added to store the data from each analysis for subsequent querying and comparative analysis. Machine learning algorithms can also be introduced to learn from and mine large amounts of analytical data, further improving the accuracy and efficiency of error analysis and matching calculations. Simultaneously, to enhance the device's usability and interactivity, a user interface can be developed, allowing engineers to more easily operate and view the analysis results.

[0088] The present invention also provides a storage medium storing a program or instructions, which, when executed by a processor, implement the steps of the analysis method for pre-assembly of bolted steel truss bridge segments as described above.

[0089] This storage medium provides a stable and reliable data carrier for the application of the pre-assembly analysis method for bolted steel truss bridge segments. When the program or instructions run in the processor, the entire analysis process can be accurately reproduced. From acquiring theoretical and test data of individual members to performing coordinate transformation, error analysis, matching calculations, and finally outputting pre-assembly analysis data, each step can be executed accurately.

[0090] The existence of storage media allows for the convenient deployment and application of analytical methods on various devices. Whether on mobile devices at a construction site or on computers in an engineering design office, analysis of the pre-assembly of bolted steel truss bridge segments can be performed as long as there is a suitable processor and storage media.

[0091] Furthermore, with continuous technological advancements, the capacity and performance of storage media are constantly improving. This means that more analytical data and historical records can be stored, providing richer information for subsequent engineering research and improvements. At the same time, the stability and reliability of the storage media ensure data security, preventing data loss or corruption from affecting analytical results.

[0092] For future engineering applications, storage media can also be combined with technologies such as cloud computing and big data. By uploading analytical data to the cloud, data sharing and remote analysis can be achieved. Engineering teams can process and analyze data simultaneously from different locations, improving work efficiency and collaboration. Furthermore, by using big data technology to deeply mine large amounts of analytical data, more potential patterns and problems can be discovered, providing strong support for optimizing the analysis methods for the pre-assembly of bolted steel truss bridge segments.

[0093] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, disclosure, and other materials. In this specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple components. A single processor or other unit can implement several functions listed in the specification. While certain measures are described in different embodiments, this does not mean that these measures cannot be combined to produce good results.

[0094] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. An analytical method for the pre-assembly of bolted steel truss bridge segments, characterized in that, Includes the following steps: Set up pre-assembly preparation conditions, obtain theoretical and test data of single members, and form a single member model; The corner coordinates of the single-member model are extracted, and the transformation matrix is ​​solved by the least squares method to realize the switching between the local coordinate system and the global coordinate system of the single-member. Analyze the length parameters of the single member and calculate the cumulative error; Based on the single-member model, the web members are matched and calculated. Based on the single-member model, matching calculations are performed on the splicing panel; Output the pre-assembly analysis data of the web members and the splicing plates.

2. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 1, characterized in that, The theoretical data is a set of three-dimensional spatial points derived from the design BIM model, which includes the three-dimensional coordinates of corner points or feature points of members; the detection data is a set of three-dimensional spatial points obtained by measuring with a laser tracker equipped with temperature compensation. The steps for forming the single-bar model include: An error analysis framework for the single member is established, defining the error vector as the difference between the coordinates of the detection point and the coordinates of the theoretical point, and constraining the square root of the sum of squares of the magnitudes of the error vector to be less than a set threshold. Define the local coordinate system of the single member, with its origin located at the center of the main box shape of the single member. Calculate the covariance matrix of the theoretical point set using principal component analysis. The covariance matrix is ​​obtained by the average value of the product of the deviation vectors between each theoretical point and the center of the main box shape. Define the local coordinate axes using the eigenvectors of the covariance matrix. The least squares method is used to optimize the detection data, and the rigid transformation parameters are solved by singular value decomposition to obtain the optimized detection point set, which forms the single-bar model.

3. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 2, characterized in that, The algorithm process for optimizing detection data includes: Input the theoretical point set and the detection point set; Calculate the main box center of the theoretical point set and the main box center of the detection point set; The theoretical point set and the detection point set are respectively centered by subtracting their respective main box-shaped centers; Calculate the covariance matrix between the centered theoretical point set and the detection point set; Singular value decomposition of the covariance matrix yields three matrices. The rotation matrix and translation vector are then solved. The rotation matrix is ​​the product of the two orthogonal matrices obtained from the decomposition, and the translation vector is the center of the detection point set minus the product of the rotation matrix and the center of the theoretical point set. The optimized detection point set is obtained by transforming the theoretical point set using a rotation matrix and a translation vector.

4. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 1, characterized in that, The step of extracting the corner coordinates of the single-member model includes: The corner points of the main box-shaped single bar are extracted as feature points. The coordinate system transformation is described based on the special Euclidean group. The coordinate transformation is achieved by the rotation matrix and the translation vector. The transformation method is that the global coordinates are equal to the product of the rotation matrix and the local coordinates plus the translation vector. By solving the rotation matrix and translation vector through least squares matching, the sum of squared errors between the detection point and the theoretical point after coordinate transformation is minimized. The root mean square error between the theoretical point set and the detection point set in the global coordinate system is calculated. If the error is less than the tolerance, the matching is considered good.

5. The analysis method for pre-assembly of bolted steel truss bridge segments according to claim 4, characterized in that, The algorithm flow for the coordinate system transformation includes: Input the local corner point set and the origin of the global coordinate system; Extract the corner indexes of the local corner set and calculate its main box center; After centering the local corner point set, the covariance matrix of the detected corner point set is calculated. The rotation matrix and translation vector are solved by singular value decomposition, and the theoretical point set in the global coordinate system is obtained by applying transformation. Calculate the root mean square error of the theoretical point set and the detection point set in the global coordinate system.

6. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 1, characterized in that, The steps of analyzing the length parameters of the single member and calculating the cumulative error include: The length of the kth member in the assembly sequence is defined as the straight-line distance between the single member and the geometric center of the main box-shaped structure in the global coordinate system; The cumulative error is the sum of the assembly errors of individual members from the first member to the kth member; Two thresholds, τ1 and τ2, are set. When the cumulative error is less than τ1, the assembly is performed according to the theoretical position. When the cumulative error is greater than or equal to τ1 and less than τ2, a translation vector is introduced. The translation vector is the cumulative error of the previous member. The assembly position is adjusted to the theoretical position plus the translation vector. When the cumulative error is greater than or equal to τ2, assemble according to the position of the 3D model and reset the cumulative error to zero.

7. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 6, characterized in that, The algorithm for adjusting the cumulative error includes: Initialize the cumulative error to zero; Traverse the sequence of links from the first to the mth link: The cumulative error plus the current assembly error of the member; If the cumulative error is less than τ1, the assembly position is the theoretical position; If the cumulative error is greater than or equal to τ1 and less than τ2, the assembly position is the theoretical position plus the cumulative error of the previous member; If the cumulative error is greater than or equal to τ2, the assembly position is the model position, and the cumulative error is reset to zero.

8. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 1, characterized in that, The steps of analyzing the length parameters of the single member and calculating the cumulative error include: Define a set of node templates, where each template is a set of points or a parametric model; extract the set of points at the ends of the web members and the center points of the node openings; calculate the distance between the ends of the web members and each node template, and select the node template with the smallest distance for assembly.

9. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 8, characterized in that, The steps for performing matching calculations on the web members include: Input the set of web member points and the set of node templates; Traverse each node template and calculate the distance between the set of web member points and the node template; Select the node template with the smallest distance and assemble the web member to the corresponding node position.

10. The analytical method for pre-assembly of bolted steel truss bridge segments according to claim 1, characterized in that, The steps for performing matching calculations on the splicing panels include: Extract the coordinates of adjacent nodes and calculate the intersection of the convex hulls of adjacent nodes for the splicing plate region; The height, length, and diagonal length of the truss segment are calculated using extreme points. The height is the difference between the maximum and minimum values ​​of the Y-coordinate, the length is the difference between the maximum and minimum values ​​of the X-coordinate, and the diagonal length is the straight-line distance between the maximum and minimum coordinate points. An assembly accuracy report including root mean square error and maximum deviation is output. The algorithm flow for outputting an assembly accuracy report that includes the root mean square error and the maximum deviation includes: Input the assembled set; The splicing plate region is calculated as the intersection of the convex hulls of adjacent nodes; The height, length, and diagonal length of the truss segment are calculated using the coordinate difference of the extreme points. The output includes a data table containing geometric parameters and assembly accuracy indicators.

11. An analytical apparatus, characterized in that, The analytical apparatus includes: The acquisition module is used to acquire theoretical data and test data of a single member. The theoretical data is a set of three-dimensional spatial points derived from the design BIM model, including the three-dimensional coordinates of the member's corner points or feature points. The test data is a set of three-dimensional spatial points obtained by measuring with a laser tracker equipped with temperature compensation. The coordinate transformation module is used to extract the corner coordinates of the single-member model and solve the transformation matrix by the least squares method to realize the switching between the local coordinate system and the global coordinate system of the single-member. The error analysis module is used to analyze the length parameters of the single member and calculate the cumulative error; The matching calculation module is used to perform matching calculations on the web members and splicing plates based on the single member model. The output module is used to output the pre-assembly analysis data of the web members and the splicing plate.

12. A storage medium, characterized in that, The storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the analysis method for pre-assembly of bolted steel truss bridge segments as described in any one of claims 1-10.

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