Railway bridge construction positioning method and system

Abstract

The invention provides a construction positioning method and system for a railroad bridge, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining first information, second information, third information and fourth information of a construction site of a target railroad bridge; performing dynamic geometric reconstruction according to the first information and the second information to obtain a dynamic geometric model; performing thermal deformation simulation according to the dynamic geometric model and the third information to obtain a geometric state under the temperature influence; performing construction load simulation according to the geometric state under the temperature influence and the fourth information to obtain a predicted deformation field; performing positioning inversion according to the predicted deformation field, solving a correction vector of an initial geometric position, and obtaining adjustment parameters of construction positioning; and carrying out optimization verification according to the adjustment parameters, carrying out real-time simulation by taking the correction vector as input, and judging whether the deviation between an output geometric state and a design form is converged within a preset tolerance or not to obtain final construction positioning data.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a construction positioning method and system for railway bridges. Background Technology

[0002] In railway bridge construction, precise construction positioning is the core foundation for ensuring that the final bridge alignment and internal force state meet design requirements, guaranteeing structural safety and long-term service performance. As bridge structures increasingly develop towards large spans, high precision, and rapid construction, construction in complex wind fields and with significant diurnal temperature variations, such as in mountainous canyons and sea crossings, faces severe challenges. The unfinished bridge structure under construction is subjected to time-varying environmental loads and dynamic construction machinery loads, causing its shape to continuously change dynamically. This leads to significant and unpredictable deviations in theoretical positioning parameters calculated based on static or quasi-static assumptions. Currently, existing technologies mainly rely on post-construction measurements, manual comparisons, and experience-based adjustments to correct these deviations. These methods lag behind the construction process and are highly dependent on human experience, failing to anticipate and compensate for the comprehensive deformation of the structure under complex time-varying conditions before construction, severely restricting the improvement of construction accuracy and efficiency.

[0003] Based on the shortcomings of the existing technology, there is an urgent need for a construction positioning method and system for railway bridges. Summary of the Invention

[0004] The purpose of this invention is to provide a construction positioning method and system for railway bridges to improve the aforementioned problems. To achieve this objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a construction positioning method for railway bridges, comprising:

[0006] The system acquires first, second, third, and fourth information about the construction site of the target railway bridge. The first information is laser point cloud data of the incomplete geometric shape of the current bridge structure. The second information is real-time wind speed and direction time series data. The third information is ambient temperature time series data. The fourth information is real-time hydraulic pressure data of the main beam support legs of the bridge erecting machine and the traveling mechanism of the overhead crane.

[0007] Based on the first and second information, dynamic geometric reconstruction is performed. By inputting the point cloud data sequence into the fluid dynamics model, the geometric deformation of the point cloud driven by the unsteady wind pressure distribution on the structural surface is calculated, and a dynamic geometric model is obtained.

[0008] Thermal deformation simulation is performed based on the dynamic geometric model and the third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, the displacement field generated by the constraint thermal stress inside the structure is calculated, and the geometric state under the influence of temperature is obtained.

[0009] Based on the geometric state under the influence of temperature and the fourth information, the construction load simulation is carried out. By mapping the real-time hydraulic data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, the time-varying deformation response of the structure under the dynamic load is solved to obtain the predicted deformation field.

[0010] Based on the predicted deformation field, the positioning inversion is performed. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning.

[0011] The optimization and verification are carried out based on the adjustment parameters. By using the correction vector as input for real-time simulation, it is determined whether the deviation between the output geometric state and the design form converges within the preset tolerance, and the final construction positioning data is obtained.

[0012] Secondly, this application also provides a construction positioning system for railway bridges, comprising:

[0013] The acquisition module is used to acquire first, second, third and fourth information of the construction site of the target railway bridge. The first information is laser point cloud data of the incomplete structural geometry of the current bridge. The second information is real-time wind speed and wind direction time series data. The third information is ambient temperature time series data. The fourth information is real-time hydraulic pressure data of the main beam support legs of the bridge erecting machine and the traveling mechanism of the overhead crane.

[0014] The reconstruction module is used to perform dynamic geometric reconstruction based on the first information and the second information. It calculates the geometric deformation of the point cloud by inputting the point cloud data sequence into the fluid dynamics model to drive the unsteady wind pressure distribution on the structural surface, thereby obtaining the dynamic geometric model.

[0015] The simulation module is used to perform thermal deformation simulation based on the dynamic geometric model and the third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, the displacement field generated by the constraint thermal stress inside the structure is calculated, and the geometric state under the influence of temperature is obtained.

[0016] The prediction module is used to simulate construction loads based on the geometric state under the influence of temperature and the fourth information. By mapping real-time hydraulic data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, the time-varying deformation response of the structure under dynamic load is solved to obtain the predicted deformation field.

[0017] The inversion module is used to perform positioning inversion based on the predicted deformation field. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning.

[0018] The verification module is used to perform optimization verification based on the adjustment parameters. By taking the correction vector as input for real-time simulation, it determines whether the deviation between the output geometric state and the design form converges within the preset tolerance, and obtains the final construction positioning data.

[0019] The beneficial effects of this invention are as follows:

[0020] By acquiring multi-source real-time data from railway bridge construction sites, the aerodynamic response of the structure under unsteady wind fields, constrained thermal deformation in non-uniform temperature fields, and time-varying behavior under dynamic construction machinery loads are simulated sequentially. This allows for the reverse deduction of pre-correction values ​​for the construction configuration, and self-calibration through closed-loop iteration. Ultimately, before component installation, the precise spatial attitude correction values ​​under the combined influence of complex time-varying environments and construction disturbances can be pre-calculated. This fundamentally transforms construction positioning from a passive correction mode relying on lag measurement and experience-based judgment to a precise control mode based on real-time prediction and active compensation, significantly improving the one-time forming accuracy, alignment smoothness, and operational efficiency of railway bridge construction in harsh environments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart illustrating a construction positioning method for a railway bridge as described in an embodiment of the present invention.

[0023] Figure 2 This is a structural schematic diagram of a construction positioning system for a railway bridge as described in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a construction positioning method for a railway bridge as described in an embodiment of the present invention.

[0025] The markings in the figure are as follows: 800, a device for positioning during the construction of a railway bridge; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component; 901, an acquisition module; 902, a reconstruction module; 903, a simulation module; 904, a prediction module; 905, an inversion module; and 906, a verification module. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] In actual railway bridge construction, especially in mountainous valleys or sea-crossing areas with complex terrain, geological conditions, and variable weather, high-precision construction positioning is crucial to ensuring smooth bridge alignment, internal force conditions that meet design expectations, and the long-term safe and stable operation of trains. Traditional construction positioning methods rely on static measurement control networks established using total stations and levels, combined with theoretical coordinates from design drawings for layout. During construction, large equipment such as bridge erecting machines hoist and erect beam segments according to this theoretical configuration. However, this method, based on the assumption of ideal rigid bodies and static measurement benchmarks, reveals a series of insurmountable bottlenecks when faced with the actual behavior of large-span flexible bridge structures during construction.

[0029] The unfinished bridge structure under construction is a time-varying system. On the one hand, it is exposed to a complex natural environment. The rapidly changing canyon winds in mountainous areas, the strong winds in coastal areas, and the significant temperature differences caused by the alternation of day and night will impose continuously changing loads on the structure. Taking wind load as an example, the wind pressure acting on the flexible unfinished bridge structure is not uniform and static, but has strong non-steady-state characteristics, which can cause small vibrations and cumulative deformation of the structure. On the other hand, the construction activities themselves, especially the movement of the bridge erecting machine, the support and lifting of the outriggers, and the travel and hoisting of the overhead crane, will impose dynamic and position-changing construction machinery loads on the unfinished bridge structure. More importantly, environmental loads and construction loads do not act independently, but are coupled and jointly affect the instantaneous stiffness and deformation state of the structure. For example, sunlight causes a temperature difference between the top and bottom plates of the steel beams, causing structural bending; the movement of the bridge erecting machine and overhead crane to the mid-span will generate additional downward deflection. The superposition of these two effects results in actual deformation far exceeding the calculated values ​​based on a single load or static model.

[0030] Existing technologies are ill-equipped to handle the time-varying effects of multi-physics coupling. Their approach is essentially a reactive, "post-construction measurement and correction" model. That is, after a construction phase is completed, measurements reveal deviations between the completed bridge section's alignment and the design goals. Then, relying on engineers' experience, adjustments are made to the installation positions of subsequent beam sections based on empirical judgment. This method has several fundamental flaws: First, the correction lags behind the occurrence of deformation, failing to prevent problems before they arise and leading to error accumulation. Second, it heavily relies on engineers' personal experience, lacking unified, quantifiable scientific decision-making criteria, making it difficult to guarantee accuracy and consistency.

[0031] Example 1:

[0032] This embodiment provides a method for positioning railway bridges during construction.

[0033] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0034] Step S100: Obtain the first, second, third and fourth information of the target railway bridge construction site. The first information is the laser point cloud data of the current bridge's incomplete structural geometry. The second information is the real-time wind speed and wind direction time series data. The third information is the ambient temperature time series data. The fourth information is the real-time hydraulic pressure data of the bridge erecting machine's main beam legs and the overhead crane's traveling mechanism.

[0035] Understandably, data acquisition in step S100 is fundamental to the entire method, its core being the simultaneous collection of multi-source heterogeneous data that comprehensively reflects the real-time states of the environment, structure, and machinery. The laser point cloud data representing the incomplete geometric shape of the bridge structure is a set of three-dimensional spatial coordinates obtained by periodically scanning the exposed surface of the constructed section using multiple 3D laser scanners deployed on the bridge deck, piers, and nearby high points. This data accurately depicts the true spatial shape of the structure at any given time. Real-time wind speed and direction time-series data are continuously measured by ultrasonic anemometers installed at key locations such as the ends of the main beams and the tops of the bridge towers, capturing the characteristics of unsteady, pulsating winds acting on the flexible structure. Ambient temperature time-series data originates from a distributed temperature sensor network deployed on the structural surface, interior, and around the construction area. The real-time hydraulic pressure data of the main girder legs and the traveling mechanism of the bridge erecting machine are directly acquired through pressure sensors on the main pipeline of the bridge erecting machine's hydraulic system and on the cylinders of each actuator. These data are essentially indirect, real-time measurements of the dynamic loads applied to the bridge structure by all actions of the bridge erecting machine, such as support, travel, and hoisting. These four types of data are correlated in time and space, and together constitute the original input for subsequent numerical simulations.

[0036] Step S200: Perform dynamic geometric reconstruction based on the first and second information. By inputting the point cloud data sequence into the fluid dynamics model, calculate the geometric deformation of the point cloud driven by the unsteady wind pressure distribution on the structural surface, and obtain the dynamic geometric model.

[0037] It should be noted that the dynamic geometric reconstruction in step S200 directly addresses the practical challenge of long-span flexible bridge structures being extremely sensitive to wind loads during construction. Traditional methods often treat the structure as an undeformable rigid body for wind load estimation, which differs significantly from reality. This step directly uses the structural geometry, represented by the first information and already exhibiting initial deformation, as the boundary for computational fluid dynamics analysis. By using the time-varying wind field described by the second information as a condition, the fluid control equations are solved to obtain an unsteady aerodynamic load distribution that closely matches the instantaneous true shape of the structure. Subsequently, the dynamic wind pressure field is mapped back onto each laser point cloud constituting the structural shape, and the spatial distribution differences drive the point clouds to shift, thereby generating a physically more reasonable dynamic structural model that changes in real time with the wind field.

[0038] Step S300: Perform thermal deformation simulation based on the dynamic geometric model and third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, calculate the displacement field generated by the constrained thermal stress inside the structure and obtain the geometric state under the influence of temperature.

[0039] Understandably, the difference in thermal expansion coefficients between steel and concrete, along with the internal constraints of large structures, means that even uniform temperature changes can lead to inconsistent deformation and secondary stresses. The processing logic in this step is to use the dynamic geometric model output from step S200 as the physical carrier of the current structure, embedding and transmitting the spatiotemporal temperature data provided by the third-party information to the entire structural space, generating a refined transient three-dimensional temperature field. This temperature field, combined with the predefined non-uniform thermal expansion characteristics of the materials in the model, first calculates the theoretical free thermal strain at each point of the structure under unconstrained conditions due to temperature changes. However, as a continuous whole, the mutual constraints between its parts prevent the complete release of this free deformation, thus generating internal constraint stress. By inputting the calculated free thermal strain as a load into a mechanical model that considers the actual connection and support conditions of the structure, and solving for its equilibrium state, the actual structural displacement and stress redistribution caused by temperature effects can be accurately obtained, i.e., the true geometric state under the influence of temperature.

[0040] Step S400: Based on the geometric state under the influence of temperature and the fourth information, perform construction load simulation. By mapping real-time hydraulic data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, solve the time-varying deformation response of the structure under dynamic load and obtain the predicted deformation field.

[0041] It should be noted that the construction load simulation in step S400 addresses the sophisticated simulation problem of the time-varying influence of heavy dynamic construction equipment on the mechanical behavior of the unfinished bridge structure. Traditional static equivalent loads cannot reflect the dynamic operation process. The core transformation in this step lies in converting the hydraulic system pressure—the internal state signal of the equipment—recorded in the fourth information, into a true time-varying force sequence acting on the bridge structure at the contact points, such as the time histories of the support reaction forces of each leg and the time histories of the concentrated loads from the crane's movement, through the specific mechanical structure and hydraulic transmission principle of the bridge erecting machine. These time-varying force sequences are dynamically applied to the structural mechanics model that has already borne the temperature effect. By solving the equations of motion of the structure under the coupled action of initial temperature stress and dynamic construction loads, the complete time-varying response of the structure from microscopic elastic deformation to the macroscopic geometric configuration changes can be calculated, thus obtaining a comprehensive predicted deformation field that integrates all major load effects of the environment and construction activities.

[0042] Step S500: Perform positioning inversion based on the predicted deformation field. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, solve for the correction vector of the initial geometric position and obtain the adjustment parameters for construction positioning.

[0043] Understandably, the positioning inversion in step S500 is a mathematical process that transitions from "passive adaptation to deformation" to "active pre-compensation for deformation." Essentially, it solves an inverse geometric and mechanical problem: given the structure's target design shape and its predicted deformation response under multiple loads, it needs to work backwards to determine the initial configuration the structure should be placed in before load application so that its final position, after undergoing these load deformations, precisely matches the design target. This step establishes a system of mathematical equations to mathematically describe the geometric compatibility between the target design geometry, the predicted combined deformation field, and the initial construction configuration. The goal of solving this system of equations is to find an optimal initial configuration correction vector that minimizes the overall deviation from the design shape after predicted deformation under this initial configuration. This correction vector is the theoretically validating construction adjustment parameter that can offset or compensate for the predicted deformation.

[0044] Step S600: Optimize and verify according to the adjusted parameters. Perform real-time simulation by taking the correction vector as input, and determine whether the deviation between the output geometric state and the design form converges within the preset tolerance to obtain the final construction positioning data.

[0045] Finally, the optimization verification in step S600 constitutes the closed-loop quality control logic of the entire method, aiming to ensure the reliability and engineering applicability of the positioning inversion results. This step uses the theoretical adjustment parameters calculated in step S500 as a modified assumed initial construction state, and re-introduces them into the complete forward-coupled simulation process starting from dynamic wind field analysis for verification calculation. This process simulates how the structure will deform in the subsequent real environment if construction positioning is performed according to these modified parameters. By comparing the difference between the final geometric shape obtained from this verification simulation and the ideal design shape, the actual compensation effect of this set of adjustment parameters can be quantitatively evaluated. If this difference does not meet the preset engineering accuracy tolerance requirements, this deviation information can be used as feedback to guide further iterative optimization of the adjustment parameters. Only when the verification results meet the convergence conditions will the corresponding construction positioning data be finally output, thus theoretically ensuring that the positioning instructions before construction have already included accurate compensation for deformations caused by the complex time-varying external environment and construction activities.

[0046] Further, step S200 includes steps S210 to S230.

[0047] Step S210: Construct the flow field calculation domain on the structural surface based on the first information. Reconstruct the aerodynamic shape surface of the unfinished bridge structure from the discrete point cloud, and generate a three-dimensional spatial mesh for fluid calculation based on the aerodynamic shape surface. The flow field calculation model is obtained.

[0048] Step S220: Calculate wind load based on the flow field calculation model and the second information. By using real-time wind speed and wind direction time series data as time-varying boundary conditions at the flow field inlet, solve the unsteady Navier-Stokes equations to obtain the time-varying wind pressure distribution field acting on the aerodynamic surface of the bridge structure, and obtain the dynamic wind load dataset.

[0049] Step S230: Perform dynamic deformation simulation of point cloud based on dynamic wind load dataset and first information. By mapping the time-varying wind pressure distribution field into a spatial force system acting on each point cloud, and calculating the displacement response based on the local stiffness characteristics of the bridge structure represented by the point cloud, the original point cloud is driven to undergo synchronous deformation to obtain a dynamic geometric model.

[0050] Specifically, step S210, which processes the raw laser point cloud data, does not merely use these massive discrete point clouds for visualization or simple modeling. Instead, it reconstructs a continuous, closed outer surface model of the bridge structure using 3D surface reconstruction technology. This model is defined as an aerodynamic surface, used to describe the physical boundary of the structure's contact with the air. Subsequently, based on this surface, a multi-layered, dense-to-sparse 3D volumetric mesh network is generated in its external space using mesh generation technology. This network is the flow field calculation model, which constructs a virtual spatial domain for calculating the airflow motion around the structure. This step directly constructs the fluid analysis model based on the real geometric shape of the construction site, which may contain construction errors or temporary structures, rather than an idealized design drawing model. This ensures that the input geometric conditions for subsequent wind load analysis are spatially consistent with the actual site conditions.

[0051] Based on this, step S220 sets the wind speed time series data as time-varying conditions on the inlet boundary of the aforementioned flow field calculation model. The core calculation process is to numerically solve the fundamental physical laws describing viscous air flow, namely the unsteady Navier-Stokes equations, within the entire three-dimensional volumetric mesh domain under the drive of these boundary conditions. By solving this equation, the instantaneous pressure values ​​acting on every tiny region of the real aerodynamic shape surface generated in step S210 at each calculation moment can be obtained. The collection of all these time-varying pressure values ​​constitutes the time-varying wind pressure distribution field covering the structural surface, i.e., the dynamic wind load dataset. This step elevates the wind load assessment of the structure from traditional static or quasi-static approximate calculations based on standard empirical formulas to high-fidelity numerical simulations based on computational fluid dynamics, directly coupled with the real time-varying wind field and the real structural shape. This can capture the unsteady and non-uniform wind pressure characteristics caused by wind turbulence and the complex shape of the structure. Preferably, in the scenario of railway bridge wind field simulation, the assumption of incompressible flow is adopted, and the unsteady Navier-Stokes equations are expressed as:

[0052] ;

[0053] In the formula, Indicates the density of air; This represents the partial derivative with respect to time, i.e., the rate of change of the velocity field with time. Represents a spatial coordinate vector; It is a time variable; Indicates at time Any point within the computational domain Above, the instantaneous motion state of air particles; This is the convective acceleration term, representing momentum transport caused by non-uniform velocity distribution in the flow field; For pressure scalar field, representing the instantaneous wind pressure acting on the bridge surface, the set of instantaneous wind pressure at all locations and times constitutes the dynamic wind load dataset; Indicates the dynamic viscosity of air; It is a vector differential operator used to calculate the spatial rate of change of field quantities; The Laplace operator is defined as follows: In this equation, it is used to describe the momentum diffusion effect caused by air viscosity; This represents the volume force vector per unit volume (such as gravity). In this embodiment, by numerically solving this unsteady Navier-Stokes equations over the entire domain and combining them with time-varying inlet conditions defined by measured wind data, the time-varying wind pressure distribution field covering the bridge surface can be calculated. This elevates wind load assessment from a static approximation to a dynamic simulation level directly coupled with the actual wind field and structural shape.

[0054] Step S230 couples the aforementioned physical process with the structural mechanical response. This is achieved by mapping and converting the time-varying wind pressure distribution field calculated in step S220, according to its location of action, one by one, into a three-dimensional force vector acting on each spatial point in the original laser point cloud data, thus forming a time-varying spatial force system. To calculate the structural deformation caused by this force system, this step does not re-establish an independent structural finite element model, but rather utilizes the geometric and attribute information inherent in the original point cloud data itself. By assigning mechanical parameters reflecting the local stiffness characteristics of the bridge structure to the point set in the point cloud data, such as local cross-sectional properties inferred based on point cloud density and prior knowledge, the displacement response generated can be directly calculated on this point cloud model based on the applied dynamic wind pressure spatial force system. This process drives the coordinates of each original point cloud to move according to mechanical laws, and the synchronous displacement of all points ultimately forms a deformed point cloud set, i.e., a dynamic geometric model. This step simplifies the efficient "fluid-structure interaction" simulation process: instead of data mapping and iterative solving between complex fluid and structural meshes, wind pressure loads are directly applied to the point cloud representing the structure, and the deformed geometry is calculated in real-time based on the local properties of the point cloud. This enables rapid assessment of real-time structural configuration changes caused by wind loads, allowing subsequent construction positioning corrections to fully consider the impact of wind-induced deformation. Preferably, to achieve the above-mentioned fluid-structure interaction simulation, the surface loads obtained from fluid calculations are converted into nodal loads of the structural point cloud model, and the deformation is solved directly using the geometric properties of the point cloud, as follows:

[0055] First, the transformation from continuous wind pressure field to discrete point cloud nodal force was completed using the point cloud nodal wind load mapping equation, expressed as:

[0056] ;

[0057] In the formula, Indicates time , acting on the Three-dimensional nodal force vectors on an original laser point cloud; Indicates time Located at the The location of the original laser point cloud on the structural surface The instantaneous wind pressure scalar value at the location is directly derived from the dynamic wind load dataset; For the first The equivalent bearing area of ​​the original laser point cloud is not an attribute of the point cloud itself, but is allocated from the structural surface model reconstructed from the original dense point cloud, representing the size of the local surface area where the point is located. For in position The unit normal vector perpendicular to the aerodynamic surface of the bridge structure. This equation simplifies the load transfer path, bypassing the complex data mapping process between different grids in existing fluid-structure interaction methods. It directly and accurately converts the distributed wind pressure obtained from computational fluid dynamics (CFD) into a concentrated force system acting on the discrete point cloud model, based on the geometric position and local surface properties of each point cloud.

[0058] Then, using the displacement response equation based on the local properties of the point cloud, the quasi-static deformation of the discrete point cloud system under wind load is described. The equation is expressed as:

[0059] ;

[0060] In the formula, Indicates time , No. The three-dimensional displacement vector of the original laser point cloud; For the first The local flexibility matrix (3x3 matrix) of the original laser point cloud location is determined based on the local stiffness characteristics of the bridge structure where the point cloud is located. This is inferred and assigned by information such as the spatial distribution density of the point cloud and prior component cross-sectional properties (such as the flange and web areas of the steel box girder), which quantitatively describes the ability of the structure near the point to resist deformation. Indicates time , acting on the The equation transforms the original laser point cloud data into a 3D nodal force vector, which provides a computable mechanical response to the point cloud-spring system. This is achieved by assigning a flexibility matrix containing actual structural stiffness information to each point cloud. It can directly determine the applied nodal force. Calculate the displacement of each point This allows for the simulation of the overall deformation of the bridge under wind load.

[0061] Further, step S300 includes steps S310 to S330.

[0062] Step S310: Reconstruct the three-dimensional transient temperature field inside the structure based on the dynamic geometric model and third information. By combining the time-series temperature data with the structural geometry, spatial orientation and solar radiation model, calculate the three-dimensional temperature gradient distribution inside the bridge structure caused by non-uniform solar radiation and heat conduction, and obtain the transient temperature field.

[0063] Step S320: Perform thermal strain simulation based on transient temperature field. By defining thermal expansion coefficient functions for different material regions and different components in the bridge structure, calculate the unconstrained thermal strain field generated by the combined effect of temperature gradient and non-uniform thermal expansion characteristics, and obtain the initial thermal strain distribution.

[0064] Step S330: Based on the initial thermal strain distribution and dynamic geometric model, perform constraint thermal stress solution and displacement calculation. By applying the initial thermal strain as a load to the structural finite element model and solving the stress balance equation based on the actual connection and support conditions of the structure, the structural displacement field caused by constraint thermal stress is obtained. The displacement field is the geometric state under the influence of temperature.

[0065] Specifically, the core of step S310 is to construct a three-dimensional transient temperature field within the structure. This step deeply couples the time-series temperature data with the precise structural shape represented by the dynamic geometric model, the spatial three-dimensional orientation information of the components, and the solar radiation physical model calculated based on geographical location, time, and date. By solving the transient heat conduction equation, the three-dimensional temperature gradient field, which is spatially continuous and evolves over time, is calculated, formed by multiple physical processes such as non-uniform solar radiation that varies with time, convective heat transfer with ambient air, and internal heat conduction in various parts of the bridge. This transient temperature field can accurately characterize the significant temperature differences between the top and bottom plates of the steel beams, and between the concrete bridge deck and the steel beams, caused by differences in solar angle, shading, and material heat capacity. This is the physical basis for accurately analyzing the temperature effects of railway bridges, especially large-span steel-concrete composite structures. The above-mentioned reconstruction process of the three-dimensional transient temperature field within the structure can be achieved by solving the following governing equations:

[0066] ;

[0067] In the formula, For in position The density of the material varies between the steel and concrete components of a steel-concrete composite bridge. For in position The specific heat capacity of the material; In time Located in the internal space of the structure The temperature scalar at a given point, its spatial distribution and temporal evolution together constitute the transient temperature field; The partial derivative of temperature with respect to time characterizes the transient properties of the temperature field; For in position Thermal conductivity of the material; It is a vector differential operator used to calculate the spatial rate of change of field quantities; This is a thermal conduction term used to describe the diffusion of heat within the structure due to a temperature gradient. Indicates the location and time The volumetric heat generation rate due to solar radiation absorption is calculated by integrating real-time time-series temperature data, structural surface geometry and spatial orientation, geographical location, date and time, and a solar radiation model, while also considering the shading effect between components. By solving this equation across the entire structural domain and considering boundary conditions for convective heat transfer with ambient air, the transient temperature field with a significant three-dimensional gradient formed by the combined effects of sunlight, shading, material differences, and heat conduction under real meteorological conditions can be simulated, providing accurate input for subsequent thermal deformation analysis.

[0068] Based on this, step S320 performs thermal strain simulation. The processing takes into account the characteristics of bridge structures composed of multiple materials and varying component geometric properties. Different material regions and structural components in the dynamic geometric model are assigned thermal expansion coefficient functions that strictly correspond to the material's physical properties. Then, the temperature change value at each point in the transient temperature field calculated in step S310 is multiplied by the thermal expansion coefficient of the corresponding material and direction. This yields the microscopic strain distribution within the entire structural space, assuming that each part can expand freely without constraint—that is, the unconstrained thermal strain field. This step clearly distinguishes the differences in thermal expansion behavior between steel and concrete, and between the longitudinal and vertical directions of the bridge, rather than using a single, average thermal expansion coefficient. This approach can realistically reflect the free deformation trend under the combined effects of material inhomogeneity and temperature gradients. Below are some representative examples of thermal expansion coefficient functions:

[0069] The function of the coefficient of thermal expansion for orthotropic steel plates with flanges of rolled steel beams:

[0070] ;

[0071] In the formula, Indicates the point located in the flange region of the steel beam. The thermal expansion coefficient tensor at that location; This indicates the coefficient of thermal expansion of steel along the rolling direction (usually the longitudinal direction of a bridge); This represents the coefficient of thermal expansion of steel perpendicular to the rolling direction (both horizontal and vertical). This function describes the anisotropy of thermal expansion behavior in rolled steel sheets due to the directionality of the microstructure.

[0072] The thermal expansion coefficient function of homogeneous isotropic concrete:

[0073] ;

[0074] In the formula, Indicates the location of the point in the concrete bridge deck or pier area. The coefficient of thermal expansion at that location; This represents the scalar coefficient of thermal expansion of concrete, and its value is related to that of steel. or The significant differences are the fundamental reason why steel-concrete composite structures experience significant constraint stresses under temperature variations. This function represents the typical thermophysical properties of concrete materials in bridge structures.

[0075] Equivalent thermal expansion coefficient transition function of steel-concrete joint:

[0076] ;

[0077] In the formula, This indicates a point located at the junction of the steel beam and the concrete bridge deck (such as near shear connectors). The equivalent thermal expansion coefficient at that location; From point The minimum distance vector to the interface; This represents the coefficient of thermal expansion of steel perpendicular to the rolling direction; This represents the scalar coefficient of thermal expansion of concrete. This is a smooth transition function, such as the Sigmoid function, whose value continuously varies from 0 (pure concrete side) to 1 (pure steel side). This function is used in modeling to handle the non-abrupt equivalent thermodynamic behavior of steel and concrete in the bonding region due to microscopic connections and interactions.

[0078] Step S330 involves solving for constrained thermal stress and calculating displacement. The principle is that, under actual construction connection conditions and boundary support conditions, the various parts of the structure cannot freely achieve the unconstrained thermal strain calculated in step S320. This step treats the initial thermal strain distribution as a self-balancing initial strain load, applying it to a structural mechanics analysis model that reflects the actual stiffness characteristics, connection methods, and support constraints of the structure. By solving for the stress and displacement fields that satisfy the structural force equilibrium conditions, deformation compatibility conditions, and boundary constraint conditions under this initial strain load, the true mechanical response of the structure under temperature changes is finally obtained. The calculated structural displacement field represents the position and shape changes ultimately caused by temperature changes under the actual constraint state of the structure. This displacement field, superimposed on the dynamic geometric model, constitutes the geometric state under the influence of temperature. This step, from free strain to constrained response, reveals the significant secondary internal forces and deformations of large and complex bridge structures under temperature loads due to their statically indeterminate constraints and material differences, highlighting a key mechanical aspect that must be considered for achieving high-precision construction positioning. This step can be expressed as solving the following mechanical governing equations:

[0079] Finite element equations for solving constrained thermal stress and displacement:

[0080] ;

[0081] Among them, the equivalent thermal load vector Calculated from the initial thermal strain field:

[0082] ;

[0083] In the formula, The overall stiffness matrix integrates the element connection relationships and material elasticity matrices of each part of the structure as defined by the dynamic geometric model. (Distinguishing between steel and concrete), and the actual connection methods and support constraints, with the constraints addressed by processing the stiffness matrix. and displacement vector The corresponding degrees of freedom are used to achieve this, which is the root cause of preventing the free thermal expansion of the structure and generating constraint stress; Let be the global nodal displacement vector, and its solution is the actual displacement field generated by the structure under the combined action of temperature change and constraints; The equivalent thermal load vector converts the initial thermal strain distribution (i.e., the unconstrained thermal strain field) into forces acting on the nodes of the finite element model; Represents the element strain-displacement relationship matrix; Represents the elastic matrix of the element material; It represents the initial thermal strain field inside the unit, including information on material differences and temperature gradients; Indicates matrix transpose; Indicates the cell number index; Indicates the first The spatial area occupied by each unit; The differential symbol; Indicates all units In its area Integrate within the range and sum the results.

[0084] Further, step S400 includes steps S410 to S430.

[0085] Step S410: Based on the fourth information, simulate the contact force between the bridge erecting machine and the bridge. Based on the geometric and mechanical relationship of the hydraulic actuator of the bridge erecting machine, the time-series hydraulic data is inverted and calculated into the real-time support reaction force of each leg on the top surface of the bridge and the concentrated moving load generated by the movement of the crane, so as to obtain the dynamic force dataset of the construction machinery.

[0086] Step S420: Based on the dynamic force dataset of construction machinery and the geometric state under the influence of temperature, dynamic contact boundary conditions are mapped. Based on the relative spatial pose of the bridge erecting machine and the unfinished bridge structure, the time-series force data is allocated and mapped to the corresponding contact element nodes on the finite element model of the bridge structure to obtain the time-varying node load boundary conditions.

[0087] Step S430: Solve the deformation response based on the time-varying nodal load boundary conditions. Obtain the predicted deformation field by solving the structural displacement time history under the combined action of the time-varying boundary conditions and the current temperature stress state through step-by-step integration.

[0088] Specifically, step S410 converts the time-series hydraulic pressure data of the bridge erecting machine's hydraulic system from equipment status signals into forces acting on the bridge structure. This step establishes a deterministic physical conversion model between hydraulic pressure and output force or torque based on the specific geometric dimensions of the bridge erecting machine's hydraulic actuators, the cylinder's effective area, and its lever transmission relationship. Through this model, the real-time hydraulic pressure data sequence is inverted and calculated into real-time vertical and horizontal support reactions acting on a specified area of ​​the bridge's top surface by each outrigger, as well as the concentrated moving load applied to the track by the crane's wheel assembly during movement. This yields a complete dataset of dynamic forces acting on the bridge during the construction machinery's work cycle, varying over time. This step does not rely on theoretical estimations or simplification assumptions, but directly utilizes the hydraulic system—the closest physical signal to the force source—to accurately obtain the time-history characteristics of the construction live load, providing a realistic load input for subsequent time-history analysis.

[0089] The goal of step S420 is to address the problem of how to accurately apply dynamic forces in space to a bridge structure model that has already deformed due to temperature effects. This step is based on the relative spatial orientation between the bridge erecting machine and the unfinished bridge structure, which has changed due to temperature-induced geometric changes. The process begins by identifying the nodal regions corresponding to the actual contact positions of the support plates of the bridge erecting machine's legs and the wheels of the overhead crane on the finite element model of the bridge structure. Then, the time-series force data calculated in step S410, including the magnitude, direction, and point of application of the forces, is dynamically allocated and transformed into time-varying nodal forces and moments on these specific contact nodes based on the aforementioned spatial correspondence. This process ultimately generates a set of nodal load boundary conditions that directly correspond to the degrees of freedom of the nodals in the finite element model and vary over time. This step achieves precise positioning of the dynamic loads of the construction machinery on the structural calculation model, ensuring the mechanical consistency between the load transfer path and the actual site conditions.

[0090] Finally, step S430 solves for the time-varying deformation response caused by construction loads on a structural state coupled with temperature effects. This process is based on the structural stiffness and mass matrices corresponding to the geometric state, and uses the time-varying nodal load boundary conditions generated in step S420 as external excitation inputs. The calculation process employs direct integration, performing step-by-step numerical integration to solve the dynamic equations of motion of the structure. Within each micro-hour time step, it solves for the new acceleration, velocity, and displacement responses of the structure under the combined action of nodal loads and existing temperature stresses at the current moment, based on the structural displacement and velocity states at the previous moment. By traversing the entire time history of the construction load action, the complete history of displacement over time in each degree of freedom of the bridge structure from the start of construction to the current moment under the coupled action of temperature and dynamic construction mechanical loads is finally calculated, i.e., the predicted deformation field. This step completes the coupled analysis of the initial deformation of the environmental temperature field and the incremental deformation of the dynamic construction loads, and the output results accurately characterize the evolution of the structural configuration from the current state under complex time-varying conditions. Preferably, step S430 can be achieved by solving the structural dynamics equations of motion of coupled temperature stress and construction load and by performing integral recursion using the Newmark-β method.

[0091] The equations of motion for structural dynamics are expressed as follows:

[0092] ;

[0093] In the formula, For the overall quality matrix; , , They are time The global nodal displacement vector, global nodal velocity vector, and global nodal acceleration vector are given. The core field variable to be solved is the complete time history evolution of which is the predicted deformation field, used to describe the motion trajectory of all nodes as time changes; The overall damping matrix is ​​used to describe the energy dissipation of a structure during vibration (such as internal friction of materials, friction of connectors, etc.), and is usually constructed based on models such as Rayleigh damping. The overall stiffness matrix; This represents a time-varying nodal load vector, used to describe the dynamic forces of construction machinery such as bridge erecting machines as they change over time. This represents the residual temperature load vector, which equates the existing temperature stresses (initial stresses) in the structure to nodal forces, serving as the initial load condition for dynamic analysis. Together they constitute the total incentive.

[0094] Next, at discrete time points and The updates to displacement, velocity, and acceleration are performed according to the following recursive relationship:

[0095] ;

[0096] ;

[0097] Simultaneous dynamic equations in The format of time:

[0098] ;

[0099] Solve together displacement at time ,speed and acceleration .

[0100] In the formula, This is the time step for numerical integration; It is a time variable; , These are the parameters of the Newmark-β integral method, used to control the numerical accuracy and stability of the algorithm; For the overall quality matrix; Overall damping matrix; The overall stiffness matrix; Represents the residual temperature load vector; for The nodal displacement vector at time t; In order to be in The nodal velocity vector at time t; In order to be in The nodal acceleration vector at time t; In order to be in The nodal displacement vector at time t; In order to be in The nodal velocity vector at time t; In order to be in The nodal acceleration vector at time t; In order to be in The time-varying nodal load vector at each microsecond. Internally, the algorithm is based on the known structural state from the previous time step. Taking into account the external construction load at the current moment and constant temperature residual force Under the influence of the load, the new state at the current moment is solved. By traversing the entire construction load time history, the complete time series of the structural displacement response is finally obtained. That is, to predict the deformation field.

[0101] Further, step S500 includes steps S510 to S530.

[0102] Step S510: Calculate the geometric difference of control points based on the predicted deformation field. Extract the geometric coordinates of each control point of the bridge under the predicted deformation state and compare them with the coordinates of the corresponding points on the design theoretical alignment to obtain the spatial coordinate difference vector of all control points.

[0103] Step S520: Construct construction configuration inversion constraints based on spatial coordinate difference vector. By considering the coordinate difference of each control point as the result of the combined effect of its initial geometric position, current construction load and predicted deformation, and combining the bridge alignment parameters, establish a set of geometric compatibility equations describing the relationship between the initial position, predicted deformation and design position to obtain the inversion mathematical model.

[0104] Step S530: Solve for the correction vector based on the inversion mathematical model. By minimizing the initial position correction of all control points as the optimization objective, the adjustment parameters for construction positioning are calculated.

[0105] Specifically, step S510 quantifies the comprehensive predicted deformation field into geometric deviation information specific to key locations that can be used to guide construction. This step extracts the geometric coordinates corresponding to the construction control point locations from the predicted deformed structural geometry and spatially compares these coordinates with the ideal coordinates of the corresponding points on the theoretical alignment of the bridge design. The three-dimensional coordinate difference between the design location and the predicted deformation location of each control point constitutes a spatial coordinate difference vector. The set of difference vectors for all control points mathematically and precisely characterizes the overall deviation of the final structural form from the design target under the predicted load conditions.

[0106] Step S520 aims to construct a mathematical model that can deduce the cause from the result. Its basic principle is to understand the spatial coordinate difference of each control point as a result of three factors: the unknown initial construction location of the point, the known set of loads acting on the structure, and the known predicted deformation response produced by these loads. This step, combined with the bridge's design alignment parameters, establishes a geometric compatibility equation for each control point. This equation describes the geometric relationship that must be satisfied between its design coordinates, the initial coordinates to be determined, and the predicted deformation displacement generated from these initial coordinates under known loads. By simultaneously solving these equations for all control points, an inverse mathematical model is formed with the initial coordinates of all control points as unknowns. This step transforms the construction positioning problem from an experience-based geometric adjustment into a mathematically defined inverse problem solution. The geometric compatibility equations establish a mathematical process for solving the initial installation location from the final deformation result.

[0107] For the There are 3 construction control points whose geometric relationships satisfy:

[0108] ;

[0109] All By simultaneously solving the equations for the control points, we obtain the geometric compatibility equations:

[0110] ;

[0111] In the formula, For the first The design coordinate vectors of each control point are determined based on the bridge design theory alignment and represent the final target positions that need to be achieved during construction. For the first The initial construction coordinate vector of each control point indicates the spatial location where the point should be placed when it is not under load (or only under a known initial load); For the first The predicted deformation displacement vector of each control point represents the displacement from the initial position. Departure, in the comprehensive load set The displacement produced at this point under the action; The comprehensive load set includes all load conditions that have been predicted through simulation, such as wind load, temperature load, and construction machinery load. For all The total set of unknowns is composed of the initial coordinate vectors of each control point; This represents the total number of control points. The design coordinate vector for the first control point; The design coordinate vector for the first control point; For the first Design coordinate vectors for each control point; This is the initial construction coordinate vector for the first control point; This is the initial construction coordinate vector for the second control point; For the first Initial construction coordinate vectors of each control point; This is the predicted deformation displacement vector for the first control point; This is the predicted deformation displacement vector for the second control point; For the first The predicted deformation displacement vector of each control point.

[0112] Finally, step S530 solves the aforementioned inversion mathematical model to obtain specific construction guidance parameters. Since the equation set may be overdetermined or have multiple solutions, this step seeks the most reasonable solution by defining an optimization objective. This optimization objective is typically set as a metric that minimizes the initial position corrections required for all control points, preferably such as the sum of squares of the correction vectors at each point. Under this objective, the geometric compatibility equation set established in step S520 is solved, ultimately yielding a set of initial coordinate corrections for each control point that ensure the structure best matches the design alignment after predicted deformation. This set of corrections constitutes the adjustment parameters for construction positioning. The mathematical expression of the optimization problem is as follows:

[0113] ;

[0114] In the formula, For the first Initial construction coordinate vectors of each control point; For all The total set of unknowns is composed of the initial coordinate vectors of each control point; For the first The reference coordinate vector of each control point can be taken as an initial estimate determined based on the design alignment or the current measured state, and used as a reference benchmark to minimize the adjustment amount in the optimization. To optimize the objective function, it is defined as the sum of squares of the initial coordinate corrections (i.e., the deviations from the reference coordinates) of all control points; is the Euclidean norm of a vector, used to calculate the length of the vector; This represents the total number of control points. This is the index of the control point.

[0115] Further, step S600 includes steps S610 to S630.

[0116] Step S610: Update the initial construction configuration according to the adjustment parameters. By applying the correction vector to the initial geometric position coordinates of the unfinished bridge structure, the configuration represented by the laser point cloud data in the first information is globally corrected to obtain the updated initial construction configuration.

[0117] Step S620: Perform multiphysics coupling simulation iteration based on the updated initial construction configuration. By substituting the updated initial construction configuration into the simulation model of the coupling effect of wind load, temperature effect and construction load, calculate the comprehensive deformation response of the structure under the new initial conditions and obtain the updated predicted deformation field.

[0118] Step S630: Based on the updated predicted deformation field, perform convergence determination. Calculate the spatial deviation between the geometric position of the bridge control point under the updated predicted deformation state and the corresponding point position of the design theoretical alignment, and determine whether the norm of the deviation vector is less than the preset convergence threshold. Output the final construction positioning data.

[0119] Specifically, step S610 first performs a configuration update operation, using the correction vectors of each control point as input for geometric transformation to systematically adjust the spatial configuration defined by the original laser point cloud data. This process translates the three-dimensional coordinates of each point according to the correction vector of its control region, thereby generating a theoretically pre-corrected three-dimensional point cloud model. This model represents the updated initial construction configuration recommended to offset predicted deformation.

[0120] Based on the updated initial construction configuration, step S620 restarts a complete forward coupled simulation to verify the correction effect. This new configuration is used as the initial geometric condition and re-introduced into the complete coupled simulation process, from dynamic wind load calculation to construction load time history analysis. On the updated geometry, the new wind-induced deformation, temperature deformation, and load response of the structure under the same environmental and construction load sequence are recalculated. By solving this series of coupled physical field equations, a latest comprehensive deformation response of the structure under the new assumed initial conditions is finally obtained, i.e., the updated predicted deformation field. This step is essentially a forward verification of the inversion solution in step S500, simulating the scenario of "how the structure would actually deform if constructed with these parameters."

[0121] Finally, step S630 performs convergence determination and data output. From the updated predicted deformation field, the geometric coordinates of key control points are extracted again, and the spatial deviation vector between them and the theoretical design alignment coordinates is calculated. By calculating the norm of this deviation vector, the overall closeness of the predicted final shape to the design target after one round of correction can be quantitatively evaluated. This norm value is compared with a preset convergence threshold representing the construction accuracy requirements. If the norm is less than the threshold, the updated initial construction configuration is deemed to meet the accuracy requirements, and it is output as the final construction positioning data. If not, the new deviation vector can be used as input and fed back to a new round of inversion optimization iteration. This step achieves a self-calibration function, ensuring that the output positioning parameters reach the optimal level at the theoretical simulation level through iterative loops, thus theoretically guaranteeing the accuracy of the bridge alignment before construction.

[0122] Example 2:

[0123] like Figure 2 As shown in the figure, this embodiment provides a construction positioning system for railway bridges, the system including:

[0124] The acquisition module 901 is used to acquire the first, second, third and fourth information of the construction site of the target railway bridge. The first information is the laser point cloud data of the current bridge's incomplete structural geometry. The second information is the real-time wind speed and wind direction time series data. The third information is the ambient temperature time series data. The fourth information is the real-time hydraulic pressure data of the main beam support legs of the bridge erecting machine and the traveling mechanism of the overhead crane.

[0125] The reconstruction module 902 is used to perform dynamic geometric reconstruction based on the first information and the second information. By inputting the point cloud data sequence into the fluid dynamics model, the unsteady wind pressure distribution on the structural surface is calculated to drive the geometric deformation of the point cloud, thereby obtaining the dynamic geometric model.

[0126] Simulation module 903 is used to perform thermal deformation simulation based on dynamic geometric model and third information. By establishing the mapping relationship between the temperature field of railway bridge and the non-uniform thermal expansion coefficient of material, it calculates the displacement field generated by constrained thermal stress inside the structure and obtains the geometric state under the influence of temperature.

[0127] Prediction module 904 is used to simulate construction load based on the geometric state under temperature influence and fourth information. By mapping real-time hydraulic data to dynamic boundary loads in the contact area between the bridge erecting machine and the bridge structure, it solves the time-varying deformation response of the structure under dynamic load and obtains the predicted deformation field.

[0128] The inversion module 905 is used to perform positioning inversion based on the predicted deformation field. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning.

[0129] The verification module 906 is used to perform optimization verification based on the adjusted parameters. By taking the correction vector as input for real-time simulation, it determines whether the deviation between the output geometric state and the design form converges within the preset tolerance, and obtains the final construction positioning data.

[0130] In one specific embodiment of this application, the reconstruction module 902 includes:

[0131] The first reconstruction unit is used to construct the flow field calculation domain of the structural surface based on the first information. It reconstructs the aerodynamic shape surface of the unfinished bridge structure from the discrete point cloud and generates a three-dimensional spatial mesh for fluid calculation based on the aerodynamic shape surface. The flow field calculation model is obtained.

[0132] The second reconstruction unit is used to calculate wind load based on the flow field calculation model and the second information. By using real-time wind speed and wind direction time series data as the time-varying boundary conditions of the flow field inlet, the unsteady Navier-Stokes equations are solved to obtain the time-varying wind pressure distribution field acting on the aerodynamic surface of the bridge structure, and the dynamic wind load dataset is obtained.

[0133] The third reconstruction unit is used to perform dynamic deformation simulation of point clouds based on the dynamic wind load dataset and the first information. By mapping the time-varying wind pressure distribution field into a spatial force system acting on each point cloud, and calculating the displacement response based on the local stiffness characteristics of the bridge structure represented by the point cloud, the original point cloud is driven to undergo synchronous deformation to obtain a dynamic geometric model.

[0134] In one specific embodiment of this application, the simulation module 903 includes:

[0135] The first simulation unit is used to reconstruct the three-dimensional transient temperature field inside the structure based on the dynamic geometric model and third information. By combining the time-series temperature data with the structural geometry, spatial orientation and solar radiation model, the three-dimensional temperature gradient distribution inside the bridge structure caused by non-uniform solar radiation and heat conduction is calculated to obtain the transient temperature field.

[0136] The second simulation unit is used to perform thermal strain simulation based on the transient temperature field. By defining thermal expansion coefficient functions for different material regions and different components in the bridge structure, it calculates the unconstrained thermal strain field generated by the combined effect of temperature gradient and non-uniform thermal expansion characteristics, and obtains the initial thermal strain distribution.

[0137] The third simulation unit is used to solve for constrained thermal stress and calculate displacement based on the initial thermal strain distribution and dynamic geometric model. By applying the initial thermal strain as a load to the structural finite element model and solving the stress balance equation based on the actual connection and support conditions of the structure, the structural displacement field caused by constrained thermal stress is obtained. The displacement field is the geometric state under the influence of temperature.

[0138] In one specific embodiment of this application, the prediction module 904 includes:

[0139] The first prediction unit is used to simulate the contact force between the bridge erecting machine and the bridge based on the fourth information. Based on the geometric and mechanical relationship of the hydraulic actuator of the bridge erecting machine, the time-series hydraulic data is inverted and calculated into the real-time support reaction force of each leg on the top surface of the bridge and the concentrated moving load generated by the movement of the crane, so as to obtain the dynamic force dataset of the construction machinery.

[0140] The second prediction unit is used to perform dynamic contact boundary condition mapping based on the dynamic force dataset of construction machinery and the geometric state under the influence of temperature. Based on the relative spatial pose of the bridge erecting machine and the unfinished bridge structure, the time-series force data is allocated and mapped to the corresponding contact element nodes on the finite element model of the bridge structure to obtain the time-varying node load boundary conditions.

[0141] The third prediction unit is used to solve the deformation response based on the time-varying nodal load boundary conditions. The predicted deformation field is obtained by solving the structural displacement time history under the combined action of time-varying boundary conditions and current temperature stress state through step-by-step integration.

[0142] In one specific embodiment of this application, the inversion module 905 includes:

[0143] The first inversion unit is used to calculate the geometric difference of control points based on the predicted deformation field. By extracting the geometric coordinates of each control point of the bridge under the predicted deformation state and comparing them with the coordinates of the corresponding points on the design theoretical alignment, the spatial coordinate difference vector of all control points is obtained.

[0144] The second inversion unit is used to construct the construction configuration inversion constraint based on the spatial coordinate difference vector. By considering the coordinate difference of each control point as the result of the combined effect of its initial geometric position, current construction load and predicted deformation, a set of geometric compatibility equations describing the relationship between the initial position, predicted deformation and design position is established in combination with the bridge alignment parameters to obtain the inversion mathematical model.

[0145] The third inversion unit is used to solve for the correction vector based on the inversion mathematical model. By minimizing the initial position correction of all control points as the optimization objective, the adjustment parameters for construction positioning are calculated.

[0146] In one specific embodiment of this application, the verification module 906 includes:

[0147] The first verification unit is used to update the initial construction configuration according to the adjustment parameters. By applying the correction vector to the initial geometric position coordinates of the unfinished bridge structure, the configuration represented by the laser point cloud data in the first information is globally corrected to obtain the updated initial construction configuration.

[0148] The second verification unit is used to perform multiphysics coupling simulation iteration based on the updated initial construction configuration. By substituting the updated initial construction configuration into the simulation model of the coupling effect of wind load, temperature effect and construction load, the comprehensive deformation response of the structure under the new initial conditions is calculated to obtain the updated predicted deformation field.

[0149] The third verification unit is used to determine the convergence based on the updated predicted deformation field. It calculates the spatial deviation between the geometric position of the bridge control point under the updated predicted deformation state and the corresponding position of the point in the design theoretical alignment, and determines whether the norm of the deviation vector is less than the preset convergence threshold, and outputs the final construction positioning data.

[0150] Example 3:

[0151] Corresponding to the above method embodiments, this embodiment also provides a construction positioning device for railway bridges. The construction positioning device for railway bridges described below and the construction positioning method for railway bridges described above can be referred to in correspondence.

[0152] Figure 3 This is a block diagram illustrating a construction positioning device 800 for a railway bridge according to an exemplary embodiment. Figure 3 As shown, the construction positioning device 800 for railway bridges may include a processor 801 and a memory 802. The construction positioning device 800 for railway bridges may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0153] The processor 801 controls the overall operation of the railway bridge construction positioning device 800 to complete all or part of the steps in the railway bridge construction positioning method described above. The memory 802 stores various types of data to support the operation of the railway bridge construction positioning device 800. This data may include, for example, instructions for any application or method operating on the railway bridge construction positioning device 800, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the railway bridge construction positioning device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0154] In an exemplary embodiment, a construction positioning device 800 for a railway bridge may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned construction positioning method for a railway bridge.

[0155] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for construction positioning of a railway bridge. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of a railway bridge construction positioning device 800 to complete the above-described method for construction positioning of a railway bridge.

[0156] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A construction positioning method for railway bridges, characterized in that, include: The system acquires first, second, third, and fourth information about the construction site of the target railway bridge. The first information is laser point cloud data of the incomplete geometric shape of the current bridge structure. The second information is real-time wind speed and direction time series data. The third information is ambient temperature time series data. The fourth information is real-time hydraulic pressure data of the main beam support legs of the bridge erecting machine and the traveling mechanism of the overhead crane. Based on the first and second information, dynamic geometric reconstruction is performed. By inputting the point cloud data sequence into the fluid dynamics model, the geometric deformation of the point cloud driven by the unsteady wind pressure distribution on the structural surface is calculated, and a dynamic geometric model is obtained. Thermal deformation simulation is performed based on the dynamic geometric model and the third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, the displacement field generated by the constraint thermal stress inside the structure is calculated, and the geometric state under the influence of temperature is obtained. Based on the geometric state under the influence of temperature and the fourth information, the construction load simulation is carried out. By mapping the real-time hydraulic data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, the time-varying deformation response of the structure under the dynamic load is solved to obtain the predicted deformation field. Based on the predicted deformation field, the positioning inversion is performed. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning. The optimization and verification are carried out based on the adjustment parameters. By using the correction vector as input for real-time simulation, it is determined whether the deviation between the output geometric state and the design form converges within the preset tolerance, and the final construction positioning data is obtained.

2. The construction positioning method for railway bridges according to claim 1, characterized in that, Based on the first and second information, dynamic geometric reconstruction is performed. This involves inputting a point cloud data sequence into a fluid dynamics model to calculate the geometric deformation of the point cloud driven by the unsteady wind pressure distribution on the structural surface, resulting in a dynamic geometric model, including: Based on the first information, a flow field calculation domain is constructed on the structural surface. The aerodynamic shape surface of the unfinished bridge structure is reconstructed from the discrete point cloud, and a three-dimensional spatial mesh for fluid calculation is generated by outward extension based on the aerodynamic shape surface, thus obtaining the flow field calculation model. Based on the flow field calculation model and the second information, wind load is calculated. By using real-time wind speed and wind direction time series data as time-varying boundary conditions at the flow field inlet, the unsteady Navier-Stokes equations are solved to obtain the time-varying wind pressure distribution field acting on the aerodynamic surface of the bridge structure, and a dynamic wind load dataset is obtained. Based on the dynamic wind load dataset and the first information, point cloud dynamic deformation simulation is performed. By mapping the time-varying wind pressure distribution field into a spatial force system acting on each point cloud, and calculating the displacement response based on the local stiffness characteristics of the bridge structure represented by the point cloud, the original point cloud is driven to undergo synchronous deformation, thus obtaining a dynamic geometric model.

3. The construction positioning method for railway bridges according to claim 1, characterized in that, Thermal deformation simulation is performed based on the dynamic geometric model and the third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, the displacement field generated by the constrained thermal stress inside the structure is calculated, and the geometric state under the influence of temperature is obtained, including: Based on the dynamic geometric model and the third information, the three-dimensional transient temperature field inside the structure is reconstructed. By combining the time-series temperature data with the structural geometry, spatial orientation and solar radiation model, the three-dimensional temperature gradient distribution inside the bridge structure caused by non-uniform solar radiation and heat conduction is calculated to obtain the transient temperature field. Thermal strain simulation is performed based on the transient temperature field. By defining thermal expansion coefficient functions for different material regions and different components in the bridge structure, the unconstrained thermal strain field generated by the combined effect of temperature gradient and non-uniform thermal expansion characteristics is calculated, and the initial thermal strain distribution is obtained. Based on the initial thermal strain distribution and the dynamic geometric model, the constrained thermal stress is solved and the displacement is calculated. The initial thermal strain is applied as a load to the structural finite element model, and the stress balance equation is solved based on the actual connection and support conditions of the structure to obtain the structural displacement field caused by the constrained thermal stress. The displacement field is the geometric state under the influence of temperature.

4. The construction positioning method for railway bridges according to claim 1, characterized in that, Based on the geometric state under the influence of temperature and the fourth information, construction load simulation is performed. By mapping real-time hydraulic pressure data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, the time-varying deformation response of the structure under dynamic load is solved to obtain the predicted deformation field, including: Based on the fourth information, the contact force simulation between the bridge erecting machine and the bridge is carried out. Based on the geometric and mechanical relationship of the hydraulic actuator of the bridge erecting machine, the time-series hydraulic data is inverted and calculated into the real-time support reaction force of each leg on the top surface of the bridge and the concentrated moving load generated by the movement of the crane, so as to obtain the dynamic force dataset of the construction machinery. Based on the dynamic force dataset of the construction machinery and the geometric state under the influence of temperature, dynamic contact boundary conditions are mapped. Based on the relative spatial pose of the bridge erecting machine and the unfinished bridge structure, the time-series force data is allocated and mapped to the corresponding contact element nodes on the finite element model of the bridge structure to obtain the time-varying node load boundary conditions. The deformation response is solved based on the time-varying nodal load boundary conditions. The predicted deformation field is obtained by solving the structural displacement time history under the combined action of the time-varying boundary conditions and the current temperature stress state through step-by-step integration.

5. The construction positioning method for railway bridges according to claim 1, characterized in that, Based on the predicted deformation field, a positioning inversion is performed. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning, including: Based on the predicted deformation field, the geometric difference of the control points is calculated. By extracting the geometric coordinates of each control point of the bridge under the predicted deformation state and comparing them with the coordinates of the corresponding points on the design theoretical alignment, the spatial coordinate difference vector of all control points is obtained. Based on the spatial coordinate difference vector, the construction configuration inversion constraint is constructed. By considering the coordinate difference of each control point as the result of the combined effect of its initial geometric position, current construction load and predicted deformation, and combining the bridge alignment parameters, a set of geometric compatibility equations describing the relationship between the initial position, predicted deformation and design position is established to obtain the inversion mathematical model. The correction vector is solved based on the inversion mathematical model. The adjustment parameters for construction positioning are calculated by minimizing the initial position correction of all control points as the optimization objective.

6. A construction positioning system for railway bridges, characterized in that, include: The acquisition module is used to acquire first, second, third and fourth information of the construction site of the target railway bridge. The first information is laser point cloud data of the incomplete structural geometry of the current bridge. The second information is real-time wind speed and wind direction time series data. The third information is ambient temperature time series data. The fourth information is real-time hydraulic pressure data of the main beam support legs of the bridge erecting machine and the traveling mechanism of the overhead crane. The reconstruction module is used to perform dynamic geometric reconstruction based on the first information and the second information. It calculates the geometric deformation of the point cloud by inputting the point cloud data sequence into the fluid dynamics model to drive the unsteady wind pressure distribution on the structural surface, thereby obtaining the dynamic geometric model. The simulation module is used to perform thermal deformation simulation based on the dynamic geometric model and the third information. By establishing the mapping relationship between the temperature field of the railway bridge and the non-uniform thermal expansion coefficient of the material, the displacement field generated by the constraint thermal stress inside the structure is calculated, and the geometric state under the influence of temperature is obtained. The prediction module is used to simulate construction loads based on the geometric state under the influence of temperature and the fourth information. By mapping real-time hydraulic data to the dynamic boundary load of the contact area between the bridge erecting machine and the bridge structure, the time-varying deformation response of the structure under dynamic load is solved to obtain the predicted deformation field. The inversion module is used to perform positioning inversion based on the predicted deformation field. By establishing a set of geometric constraint equations between the bridge design geometry and the predicted deformation field, the correction vector of the initial geometric position is solved to obtain the adjustment parameters for construction positioning. The verification module is used to perform optimization verification based on the adjustment parameters. By taking the correction vector as input for real-time simulation, it determines whether the deviation between the output geometric state and the design form converges within the preset tolerance, and obtains the final construction positioning data.

7. The construction positioning system for railway bridges according to claim 6, characterized in that, The reconstruction module includes: The first reconstruction unit is used to construct the flow field calculation domain of the structural surface according to the first information. It reconstructs the aerodynamic shape surface of the unfinished bridge structure from the discrete point cloud and generates a three-dimensional spatial mesh for fluid calculation based on the aerodynamic shape surface. The flow field calculation model is obtained. The second reconstruction unit is used to calculate wind load based on the flow field calculation model and the second information. By using real-time wind speed and wind direction time series data as time-varying boundary conditions at the flow field inlet, the unsteady Navier-Stokes equations are solved to obtain the time-varying wind pressure distribution field acting on the aerodynamic surface of the bridge structure, and a dynamic wind load dataset is obtained. The third reconstruction unit is used to perform point cloud dynamic deformation simulation based on the dynamic wind load dataset and the first information. By mapping the time-varying wind pressure distribution field into a spatial force system acting on each point cloud, and calculating the displacement response based on the local stiffness characteristics of the bridge structure represented by the point cloud, the original point cloud is driven to undergo synchronous deformation to obtain a dynamic geometric model.

8. The construction positioning system for railway bridges according to claim 6, characterized in that, The simulation module includes: The first simulation unit is used to reconstruct the three-dimensional transient temperature field inside the structure based on the dynamic geometric model and the third information. By combining the time-series temperature data with the structural geometry, spatial orientation and solar radiation model, the three-dimensional temperature gradient distribution inside the bridge structure caused by non-uniform solar radiation and heat conduction is calculated to obtain the transient temperature field. The second simulation unit is used to perform thermal strain simulation based on the transient temperature field. By defining thermal expansion coefficient functions for different material regions and different components in the bridge structure, it calculates the unconstrained thermal strain field generated by the combined effect of temperature gradient and non-uniform thermal expansion characteristics, and obtains the initial thermal strain distribution. The third simulation unit is used to solve for constrained thermal stress and calculate displacement based on the initial thermal strain distribution and the dynamic geometric model. By applying the initial thermal strain as a load to the structural finite element model and solving the stress balance equation based on the actual connection and support conditions of the structure, the structural displacement field caused by constrained thermal stress is obtained. The displacement field is the geometric state under the influence of temperature.

9. The construction positioning system for railway bridges according to claim 6, characterized in that, The prediction module includes: The first prediction unit is used to simulate the contact force between the bridge erecting machine and the bridge based on the fourth information. Based on the geometric and mechanical relationship of the hydraulic actuator of the bridge erecting machine, the time-series hydraulic data is inverted and calculated into the real-time support reaction force of each leg on the top surface of the bridge and the concentrated moving load generated by the movement of the crane, so as to obtain the dynamic force dataset of the construction machinery. The second prediction unit is used to perform dynamic contact boundary condition mapping based on the dynamic force dataset of the construction machinery and the geometric state under the influence of temperature. Based on the relative spatial pose of the bridge erecting machine and the unfinished bridge structure, the time-series force data is allocated and mapped to the corresponding contact element nodes on the finite element model of the bridge structure to obtain the time-varying node load boundary conditions. The third prediction unit is used to solve the deformation response based on the time-varying nodal load boundary conditions. By step-by-step integration, the structural displacement time history under the combined action of time-varying boundary conditions and current temperature stress state is solved to obtain the predicted deformation field.

10. The construction positioning system for railway bridges according to claim 6, characterized in that, The inversion module includes: The first inversion unit is used to calculate the geometric difference of control points based on the predicted deformation field. By extracting the geometric coordinates of each control point of the bridge under the predicted deformation state and comparing them with the coordinates of the corresponding points on the design theoretical alignment, the spatial coordinate difference vector of all control points is obtained. The second inversion unit is used to construct construction configuration inversion constraints based on the spatial coordinate difference vector. By considering the coordinate difference of each control point as the result of the combined effect of its initial geometric position, current construction load and predicted deformation, a set of geometric compatibility equations describing the relationship between the initial position, predicted deformation and design position is established in combination with the bridge alignment parameters to obtain the inversion mathematical model. The third inversion unit is used to solve the correction vector according to the inversion mathematical model. By minimizing the initial position correction of all control points as the optimization objective, the adjustment parameters for construction positioning are calculated.

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