A method, device, and medium for predicting and calculating hanging basket structures based on multi-source data.
By integrating multi-source data to establish a dynamic correction mechanism, the problem of error deviation in the calculation of the hanging basket structure was solved, and high-precision simulation and real-time tracking of the stress state of the hanging basket were realized, ensuring the safety and accuracy of cantilever casting construction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA RAILWAY NO10 ENGINEERING GROUP THIRD CONSTRUCTION CO LTD
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-09
AI Technical Summary
Existing calculation methods for hanging basket structures cannot fully consider the processing errors of members, the dispersion of material properties, and the uncertainty of on-site boundary conditions. This results in significant deviations between the calculation model and the actual stress state, making it impossible to monitor and warn of abnormal conditions in real time, which affects the safety control and alignment accuracy of cantilever casting construction.
By integrating multi-source data from the design, fabrication, and on-site data of the hanging basket, a dynamic correction mechanism is established to achieve high-precision simulation and real-time tracking of the hanging basket's stress state. This includes collecting and cleaning data, establishing a standardized parameter library, constructing a mechanical model, correcting the stiffness matrix and load vector, and iteratively optimizing parameters using dynamic construction data to ensure that the calculation results are consistent with the measured values.
It achieves high-precision simulation of the stress state of the hanging basket, ensuring the structural safety and precise control of the construction alignment, timely warning of potential risks, and avoiding engineering accidents.
Smart Images
Figure CN122174389A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of structural calculation technology for bridge cantilever construction equipment, specifically a method, equipment, and medium for predictive calculation of hanging basket structures based on multi-source data. Background Technology
[0002] Cantilever construction is currently the mainstream construction method for bridge projects such as long-span prestressed concrete continuous beam bridges, continuous rigid frame bridges, and T-shaped rigid frame bridges. The formwork, as the core load-bearing equipment in cantilever construction, must travel along the beam track to sequentially complete all procedures, including formwork support, rebar tying, concrete pouring, and prestressing tensioning for each beam segment. The strength, stiffness, stability, and overturning resistance of the formwork directly determine the safety management, bridge alignment accuracy, and construction efficiency of the bridge construction. According to current national standards, before designing, processing, and using the formwork, structural stress calculations under all construction conditions must be completed to ensure that all performance indicators meet the standards and construction requirements. Formwork structural calculation has become an indispensable core technical process in bridge cantilever construction.
[0003] As my country's bridge engineering develops towards longer spans, complex terrains, lightweight construction, and customization, the formwork structure has evolved from the traditional truss structure to various forms such as rhomboid formwork, triangular formwork, bowstring formwork, cable-stayed formwork, and cable-stayed formwork. Different types of formwork exhibit significant differences in force transmission paths, component composition, boundary conditions, and construction conditions, demonstrating substantial non-standard customization characteristics. Currently, the mainstream formwork structure calculation techniques in the industry are mainly divided into two categories: one is the analytical calculation method based on classical structural mechanics, which simplifies the formwork structure into a statically determinate or low-order statically indeterminate mechanical model and completes the preliminary stress verification of the main truss using manual calculation or simple tabular calculations; the other is the single-project-specific modeling and calculation method based on general-purpose finite element software, which uses finite element software such as ANSYS, MIDAS / Civil, and ABAQUS to manually complete the entire process of geometric modeling, mesh generation, material definition, load application, boundary condition setting, working condition calculation, and result extraction for the specific formwork structure of a particular project.
[0004] Existing calculation methods for cantilevered formwork structures rely heavily on theoretical analysis based on design parameters, making it difficult to fully consider factors such as member processing errors, material property dispersion, and uncertainties in on-site boundary conditions. This results in significant deviations between the calculation model and the actual stress state. Furthermore, traditional methods typically employ offline analysis, making it impossible to dynamically calibrate the model based on real-time monitoring data during construction. This hinders the accurate tracking of the structural mechanical behavior evolution under different working conditions and prevents timely warnings when abnormal conditions such as loose joints or overload occur, posing significant risks to the safety control and alignment accuracy of cantilevered construction. Summary of the Invention
[0005] This invention provides a method, equipment, and medium for predicting and calculating hanging basket structures based on multi-source data. By integrating multi-source data from design, processing, and on-site operations and introducing a dynamic correction mechanism, it achieves high-precision simulation and real-time tracking of the stress state of the hanging basket, ultimately achieving precise control over structural safety and construction alignment, thereby solving the problems in the background technology.
[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A method for predicting and calculating hanging basket structures based on multi-source data, comprising the following steps performed using computer equipment: S1 collects hanging basket design parameters, actual processing data, and real-time on-site monitoring data. All data are cleaned, normalized, and feature-mapped to form a standardized parameter library in a unified format. The on-site measured data includes unloaded static data and construction dynamic data; S2. Based on the standardized parameter library, a complete structural mechanical model of the hanging basket is established, the overall stiffness matrix of the structure is calculated, the initial design load is applied, and the initial nodal displacements and internal forces of each member of the hanging basket are obtained by solving the static equilibrium equation. S3. The structural stiffness matrix is corrected using the actual measured data of the processing, and the actual load vector is corrected using the unloaded static data. Based on the corrected stiffness matrix and load vector, the structural mechanics equations are solved again to obtain the corrected hanging basket node displacements and member internal forces. S4 uses construction dynamic data as constraints to compare the corrected hanging basket node displacements and member internal forces to calculate the deviation. If the deviation exceeds the threshold, the structural parameters are iteratively corrected, the solution is recalculated and compared, until the calculation results are consistent with the measured values. Finally, the overall displacement data of the hanging basket and the internal force data of each member are output.
[0007] Preferably, in S1, rigid usage boundaries are set for the collected unloaded static data and construction dynamic data: the unloaded static data is only used for the load vector correction in S3, and the construction dynamic data is only used for the constraint conditions in S4. The design parameters of the hanging basket include the geometric dimensions of the hanging basket structural members, material design parameters, and design loads; the geometric dimensions of the hanging basket structural members include length L and moment of inertia H. The material design parameters include the design elastic modulus. ; The measured processing data includes the measured elastic modulus of the hanging basket pole material. Measured deviation of moment of inertia of member cross section Initial micro-bending sag of the rod , where i represents the i-th member; The unloaded static data includes measured displacement and strain values at multiple measuring points on the hanging basket structure under unloaded conditions after installation; the construction dynamic data includes measured displacement and strain values at the same measuring points collected during construction as under unloaded conditions. and real-time load data .
[0008] Preferably, the specific implementation steps of S2 are as follows: S21. Based on the design parameters of the hanging basket in the standardized parameter library, the entire structure of the hanging basket is discretized into beam elements to determine the topological connection relationship and initial boundary conditions of each discrete element. S22, calculate the element stiffness matrix of each discrete element, and assemble all element stiffness matrices into the overall structural reference stiffness matrix using the direct stiffness method. Complete the construction of the complete structural mechanical model of the hanging basket; S23, Based on the design loads in the standardized parameter library, establish the initial load vector. Apply it to the mechanical model of the already constructed hanging basket structure; S24, Establish the static equilibrium equations And solve for the initial node displacement vector of the hanging basket. With the internal forces of the rod ,in, Let be the initial node displacement vector of the hanging basket.
[0009] Preferably, in S22, an initial defect correction factor is introduced to optimize the element stiffness matrix: First, based on the initial micro-bending sag of the rod. Calculate the initial defect correction factor :
[0010] in, Let be the design length of the i-th member, ideally a straight rod. It degenerates into a traditional unit if initial micro-bending data is not collected, then... Traditional units can be used directly; Then, the element stiffness matrix is established using the optimized Euler-Bernoulli beam element:
[0011] in, Let be the initial defect correction factor for the i-th member; Let be the measured initial micro-bending sagitta of the i-th member. Let be the design section moment of inertia of the i-th member.
[0012] Preferably, the specific implementation steps of S3 are as follows: S31, based on the actual measurement data from the fabrication process, performs physical corrections on the overall structural reference stiffness matrix obtained in S2 to determine the material and geometric properties, resulting in a first-order corrected stiffness matrix. ; S32, based on the measured displacement and strain values of multiple hanging basket structure measuring points in the unloaded static data, corrects the initial design load vector by removing load terms that did not actually occur and supplementing the actual permanent load values that occurred on site, thus obtaining a corrected load vector that matches the unloaded state on site. ; S33, based on a first-order modified stiffness matrix Introducing static boundary correction coefficient vector By fine-tuning the boundary constraints, the final corrected stiffness matrix is obtained. ;
[0013] in, The initial value is 1. The final value is obtained by least-squares fitting of the deviation between the measured displacement and strain values in the unloaded static data and the displacement calculated by the initial correction model. The deviation of the displacement calculated by the initial correction model is [value missing], and the initial correction model is a first-order corrected stiffness matrix. The corresponding model, the difference between the displacement calculated under no-load conditions and the actual measured displacement under no-load static conditions; ⊙ represents the element-by-element correction operator; S34, Establish the modified static equilibrium equations The corrected displacement vector of the hanging basket node is obtained by solving the problem. With the internal forces of each member ,in, This is the corrected nodal displacement vector.
[0014] Preferably, in S31, the formula for calculating the physical property correction of the overall structural stiffness matrix is:
[0015] in, This is a first-order stiffness matrix correction; Let E be the factory measured elastic modulus of the rod material, and let E be the design elastic modulus of the rod material. Let be the design section moment of inertia of the i-th member; Let be the measured deviation of the moment of inertia of the i-th member.
[0016] Preferably, in S4, the deviation calculation uses the relative deviation norm of the measuring point displacement as the criterion, and the calculation formula is as follows:
[0017]
[0018] Where r is the displacement deviation vector of the key measuring point; The displacement calculation values of the measuring points corresponding to the construction dynamic data in the corrected results obtained from S3. Describing the 2-norm, The 2-norm of the deviation vector; Let be the 2-norm of the measured displacement vector; This is the relative deviation norm.
[0019] Preferably, in S4, the specific implementation steps after calculating the deviation are as follows: S41, Set the preset convergence threshold ε and the maximum number of iterations, and calculate the relative deviation norm of the measurement points. ,like Output the overall displacement data of the hanging basket and the internal force data of each member; S42, if Using the static boundary correction coefficient vector in S3 Define a dynamic mechanical behavior correction parameter vector as the initial value. Construct the least squares objective function The calculation formula is:
[0020] in, For the current correction parameter vector Calculate the displacement value at the measuring point below; S43, the optimal correction parameter vector is solved iteratively using the Gauss-Newton method with a damping term. The iterative update formula is:
[0021] in, Let be the correction parameter vector for the (a+1)th iteration, where 'a' represents the iteration number. Let be the correction parameter vector for the a-th iteration; Let be the Jacobian sensitivity matrix for the k-th iteration, T denotes the transpose of the matrix; λ is the damping factor to prevent iteration divergence; o is the identity matrix;
[0022] Among them, the Boolean matrix extracted from measurement point C has only the position elements corresponding to the key measurement point degrees of freedom set to 1, and the rest set to 0; For the current correction parameters Below, the displacement vector of all nodal nodes of the hanging basket structure; The parameter vector is corrected for dynamic mechanical behavior and continuously updated during the iteration process; when Take initial value hour;
[0023] Right now Equal to the nodal displacements calculated in S3 ; S44, after each iteration, the structural stiffness matrix is updated synchronously. The structural mechanics equations are solved again to obtain the iterated nodal displacements and member internal forces, and the relative deviation norm is calculated again. S45, when satisfied Alternatively, when the number of iterations reaches the preset maximum limit, stop the iteration and output the final overall displacement data of the hanging basket and the internal force data of each member; Corrected parameters based on eventual convergence The final corrected stiffness matrix is obtained by combining construction dynamic data and real-time load data. The precise displacements and stresses of all nodes and members of the hanging basket structure are obtained by solving the model. At the same time, based on the converged model, the prediction results of subsequent unconstructed conditions are calculated.
[0024] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.
[0025] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.
[0026] As can be seen from the above technical solution compared with the prior art, the present invention has the following beneficial effects: 1. This invention integrates design, processing, and on-site measurement data to progressively correct the structural stiffness matrix and load vector, eliminating calculation errors caused by geometric deviations of members, material differences, and uncertainties in boundary conditions. This achieves high-precision simulation of the stress state of the hanging basket, providing a reliable basis for the alignment control and structural safety of cantilever construction.
[0027] 2. By introducing construction dynamic data as constraints, this invention iteratively optimizes the stiffness correction parameters of key nodes, realizes real-time tracking and dynamic calibration of the mechanical behavior of the hanging basket, accurately predicts the displacement and internal force changes of subsequent construction conditions, provides timely warning of potential risks, and effectively avoids engineering accidents caused by loose connections or overload. Attached Figure Description
[0028] Figure 1 This is a schematic diagram outlining the method steps in an embodiment of the present invention; Figure 2 This is a schematic diagram of the iterative verification logic flow in an embodiment of the present invention. Detailed Implementation
[0029] 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 some embodiments of the present invention, but not all embodiments.
[0030] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.
[0031] This invention provides a method for predicting and calculating hanging basket structures based on multi-source data, which involves performing the following steps using a computer device: Figure 1 As shown: S1 collects hanging basket design parameters, actual processing data, and real-time on-site monitoring data. All data is cleaned, normalized, and feature-mapped to form a standardized parameter library with a unified format. The actual on-site measurement data includes unloaded static data and construction dynamic data. S2. Based on the standardized parameter library, a complete structural mechanical model of the hanging basket is established, the overall stiffness matrix of the structure is calculated, the initial design load is applied, and the initial nodal displacements and internal forces of each member of the hanging basket are obtained by solving the static equilibrium equation. S3. The structural stiffness matrix is corrected using the actual measured data of the processing, and the actual load vector is corrected using the unloaded static data. Based on the corrected stiffness matrix and load vector, the structural mechanics equations are solved again to obtain the corrected hanging basket node displacements and member internal forces. S4 uses construction dynamic data as constraints to compare the corrected hanging basket node displacements and member internal forces to calculate the deviation. If the deviation exceeds the threshold, the structural parameters are iteratively corrected, the solution is recalculated and compared, until the calculation results are consistent with the measured values. Finally, the overall displacement data of the hanging basket and the internal force data of each member are output.
[0032] Example: In this embodiment, the main bridge of a major cross-river bridge adopts a three-span prestressed concrete variable cross-section continuous box girder (60+100+60)m, constructed using the cantilever casting method. The hanging basket structure is a rhomboid truss type, with a total design weight of approximately 80t and a maximum cantilever length of 5m, serving as the core load-bearing facility for the cantilever construction. To ensure construction safety and accuracy, the hanging basket structure prediction and calculation method based on multi-source data proposed in this invention is used to accurately simulate and dynamically correct the mechanical behavior of the hanging basket throughout the entire process.
[0033] The overall process of this invention is as follows: Figure 1 As shown, implementation begins: data collection and standardization; Design parameters for the hanging basket: Extract the geometric dimensions and material parameters of each member of the hanging basket from the construction drawing design documents. Taking the upper chord of the main truss as an example, HN400×200 hot-rolled H-beams are used, with a length L = 6.2, a design moment of inertia H, a design elastic modulus E, and design loads including the self-weight of the formwork (15t), construction equipment (5t), and the wet weight of the concrete to be poured, etc., based on the most unfavorable working conditions.
[0034] Actual processing measurement data: Before leaving the factory, the rods are sampled and tested, and the following data are recorded for each rod: Measured modulus of elasticity: Ultrasonic testing was used, yielding a modulus of 205 GPa for the upper chord of the main truss, and between 204 and 207 GPa for the remaining members. Measured deviation of moment of inertia: Cross-sectional dimensions were measured using vernier calipers, and the actual moment of inertia was calculated. Initial micro-bending sag: The degree of bending in the members was measured using a string line method.
[0035] Static data under no-load conditions: After the formwork is assembled and before the first concrete pour, measuring points are set up at key locations of the formwork: ① Front midpoint (displacement measuring point D1, using a high-precision total station with an accuracy of 0.5mm); ② Main truss nodes (displacement measuring points D2~D4); ③ Connection between the sling and the bottom formwork. Displacement (e.g., measured deflection of D1 is 0.4mm, measured settlement of D2 is 0.1mm) and strain values (S1~S8 are all close to zero, indicating no abnormalities under no-load conditions) are collected at each measuring point under no-load conditions.
[0036] Construction dynamic data: During the pouring of segments such as Block 1 and Block 2, displacement and strain of the same batch of measuring points were collected simultaneously, and real-time loads were recorded. For example, during the pouring of Block 1, the wet weight of the concrete was 120t, and the load of construction personnel and equipment was 8t. The measured deflection at point D1 was 13.2mm, and the deflection at point D2 was 5.1mm. All real-time loads were recorded through weighbridges, hydraulic gauges, etc., and converted into nodal load vectors.
[0037] All data were cleaned, outliers were removed, normalized, and standardized to mm, N, MPa units. After establishing the correspondence between measurement points and finite element model nodes with the feature mapping, the data were stored in a standardized parameter library.
[0038] Initial model establishment and solution; Discretization and boundary conditions: For example, the hanging basket structure can be discretized into 56 beam elements and 43 nodes; the topological connection relationship is determined: the main truss nodes are rigidly connected, the slings and the bottom basket are hinged, the rear anchor point is fixed to constrain all degrees of freedom, and the front support only constrains vertical displacement to simulate the roller support. The element type adopted is Euler-Bernoulli beam element.
[0039] Considering the element stiffness matrix of initial imperfections: Taking web member #08 as an example, calculate the initial imperfection correction factor: Calculate the element stiffness matrix of each discrete element, and assemble all element stiffness matrices into the overall structural reference stiffness matrix using the direct stiffness method. Complete the construction of the complete structural mechanical model of the hanging basket; Introduce an initial defect correction factor to optimize the element stiffness matrix: First, based on the initial micro-bending sag of the rod. Calculate the initial defect correction factor :
[0040] in, Let be the design length of the i-th member, ideally a straight rod. It degenerates into a traditional unit if initial micro-bending data is not collected, then... We directly use traditional elements; in the calculation, the curvature is very small, almost 1.
[0041] For example, the stiffness of horizontal strut #12, which exhibits significant bending, remains close to 1, indicating that initial bending has a negligible impact on stiffness under typical machining accuracy. If significant bending occurs due to transportation, the stiffness can be reduced to 0.98, which is then non-negligible. The optimized Euler-Bernoulli beam element formula is used to establish the stiffness matrix of each element:
[0042] in, Let be the initial defect correction factor for the i-th member; Let be the measured initial micro-bending sagitta of the i-th member. Let be the design section moment of inertia of the i-th member.
[0043] Initial Loads and Solution: Establish the initial load vector based on the design loads, including the formwork self-weight of 15t and construction equipment weight of 5t, and solve the static equilibrium equations: Based on the design loads in the standardized parameter library, an initial load vector is established. Apply it to the mechanical model of the already constructed hanging basket structure; Establish static equilibrium equations And solve for the initial node displacement vector of the hanging basket. With the internal forces of the rod ,in, Let be the initial node displacement vector of the hanging basket.
[0044] The initial nodal displacements and internal forces of each member are obtained, such as the axial force of the upper chord of the main truss, which is -280kN, and the compression member. At this point, the model reflects the ideal response under the design load, which is unrelated to the actual measurement under no-load conditions.
[0045] Based on the actual fabrication data, the overall structural reference stiffness matrix obtained in S2 is physically corrected for material and geometric properties to obtain the first-order corrected stiffness matrix. ; Based on the measured displacement and strain values of multiple hanging basket structure measuring points in the unloaded static data, the initial design load vector is corrected by removing load terms that have not actually occurred and supplementing the permanent load values that actually occurred on site, thus obtaining a corrected load vector that matches the unloaded state on site. ; Based on the first-order modified stiffness matrix Introducing static boundary correction coefficient vector By fine-tuning the boundary constraints, the final corrected stiffness matrix is obtained. ;
[0046] in, The initial value is 1. The final value is obtained by least-squares fitting of the deviation between the measured displacement and strain values in the unloaded static data and the displacement calculated by the initial correction model. The deviation of the displacement calculated by the initial correction model is [value missing], and the initial correction model is a first-order corrected stiffness matrix. The corresponding model, the difference between the displacement calculated under no-load conditions and the actual measured displacement under no-load static conditions; ⊙ represents the element-by-element correction operator; Establish the modified static equilibrium equations The corrected displacement vector of the hanging basket node is obtained by solving the problem. With the internal forces of each member ,in, This is the corrected nodal displacement vector.
[0047] The formula for calculating the physical property correction of the overall structural stiffness matrix is as follows:
[0048] in, This is a first-order stiffness matrix correction; Let E be the factory measured elastic modulus of the rod material, and let E be the design elastic modulus of the rod material. Let be the design section moment of inertia of the i-th member; Let be the measured deviation of the moment of inertia of the i-th member.
[0049] The deviation calculation uses the relative deviation norm of the measured point displacement as the criterion, and the calculation formula is as follows:
[0050]
[0051] Where r is the displacement deviation vector of the key measuring point; The displacement calculation values of the measuring points corresponding to the construction dynamic data in the corrected results obtained from S3. Describing the 2-norm, The 2-norm of the deviation vector; Let be the 2-norm of the measured displacement vector; This is the relative deviation norm.
[0052] After calculating the deviation, as follows Figure 2 As shown, the specific implementation steps are as follows: Set the preset convergence threshold ε and the maximum number of iterations, and calculate the relative deviation norm of the measurement points. ,like Output the overall displacement data of the hanging basket and the internal force data of each member; like Using the static boundary correction coefficient vector in S3 Define a dynamic mechanical behavior correction parameter vector as the initial value. Construct the least squares objective function The calculation formula is:
[0053] in, For the current correction parameter vector Calculate the displacement value at the measuring point below; The optimal correction parameter vector is solved iteratively using the Gauss-Newton method with damping terms. The iterative update formula is as follows:
[0054] in, Let be the correction parameter vector for the (a+1)th iteration, where 'a' represents the iteration number. Let be the correction parameter vector for the a-th iteration; Let be the Jacobian sensitivity matrix for the k-th iteration, T denotes the transpose of the matrix; λ is the damping factor to prevent iteration divergence; o is the identity matrix;
[0055] Among them, the Boolean matrix extracted from measurement point C has only the position elements corresponding to the key measurement point degrees of freedom set to 1, and the rest set to 0; For the current correction parameters Below, the displacement vector of all nodal nodes of the hanging basket structure; The parameter vector is corrected for dynamic mechanical behavior and continuously updated during the iteration process; when Take initial value hour;
[0056] Right now Equal to the nodal displacements calculated in S3 ; After each iteration, the structural stiffness matrix is updated synchronously. The structural mechanics equations are solved again to obtain the iterated nodal displacements and member internal forces, and the relative deviation norm is calculated again. When satisfied Alternatively, when the number of iterations reaches the preset maximum limit, stop the iteration and output the final overall displacement data of the hanging basket and the internal force data of each member; Corrected parameters based on eventual convergence The final corrected stiffness matrix is obtained by combining construction dynamic data and real-time load data. The precise displacements and stresses of all nodes and members of the hanging basket structure are obtained by solving the model. At the same time, based on the converged model, the prediction results of subsequent unconstructed conditions are calculated.
[0057] This embodiment fully demonstrates the specific implementation process of the hanging basket structure calculation method based on multi-source data, and verifies the feasibility and superiority of the method through actual engineering data. This method not only improves calculation accuracy but also promptly identifies potential safety hazards, providing reliable technical support for similar cantilever bridge construction.
[0058] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.
[0059] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.
[0060] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the hanging basket structure prediction calculation methods based on multi-source data in the above embodiments.
[0061] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.
[0062] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.
[0063] For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media.
[0064] The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).
[0065] It should be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.
[0066] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0067] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0068] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A method for predicting and calculating hanging basket structures based on multi-source data, characterized in that, Perform the following steps using a computer device: S1 collects hanging basket design parameters, actual processing data, and real-time on-site monitoring data. All data are cleaned, normalized, and feature-mapped to form a standardized parameter library in a unified format. The on-site measured data includes unloaded static data and construction dynamic data; S2. Based on the standardized parameter library, a complete structural mechanical model of the hanging basket is established, the overall stiffness matrix of the structure is calculated, the initial design load is applied, and the initial nodal displacements and internal forces of each member of the hanging basket are obtained by solving the static equilibrium equation. S3. The structural stiffness matrix is corrected using the actual measured data of the processing, and the actual load vector is corrected using the unloaded static data. Based on the corrected stiffness matrix and load vector, the structural mechanics equations are solved again to obtain the corrected hanging basket node displacements and member internal forces. S4 uses construction dynamic data as constraints to compare the corrected hanging basket node displacements and member internal forces to calculate the deviation. If the deviation exceeds the threshold, the structural parameters are iteratively corrected, the solution is recalculated and compared, until the calculation results are consistent with the measured values. Finally, the overall displacement data of the hanging basket and the internal force data of each member are output.
2. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 1, characterized in that: In S1, rigid usage boundaries are set for the collected unloaded static data and construction dynamic data: the unloaded static data is only used for the load vector correction in S3, and the construction dynamic data is only used for the constraint conditions in S4. The design parameters of the hanging basket include the geometric dimensions of the hanging basket structural members, material design parameters, and design loads; the geometric dimensions of the hanging basket structural members include length L and moment of inertia H. The material design parameters include the design elastic modulus. ; The measured processing data includes the measured elastic modulus of the hanging basket pole material. Measured deviation of moment of inertia of member cross section Initial micro-bending sag of the rod , where i represents the i-th member; The unloaded static data includes measured displacement and strain values at multiple measuring points on the hanging basket structure under unloaded conditions after installation; the construction dynamic data includes measured displacement and strain values at the same measuring points collected during construction as under unloaded conditions. and real-time load data .
3. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 2, characterized in that: The specific implementation steps of S2 are as follows: S21. Based on the design parameters of the hanging basket in the standardized parameter library, the entire structure of the hanging basket is discretized into beam elements to determine the topological connection relationship and initial boundary conditions of each discrete element. S22, calculate the element stiffness matrix of each discrete element, and assemble all element stiffness matrices into the overall structural reference stiffness matrix using the direct stiffness method. Complete the construction of the complete structural mechanical model of the hanging basket; S23, Based on the design loads in the standardized parameter library, establish the initial load vector. Apply it to the mechanical model of the already constructed hanging basket structure; S24, Establish the static equilibrium equations And solve for the initial node displacement vector of the hanging basket. With the internal forces of the rod ,in, Let be the initial node displacement vector of the hanging basket.
4. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 3, characterized in that: In step S22, an initial defect correction factor is introduced to optimize the unit stiffness matrix: First, based on the initial micro-bending sag of the rod. Calculate the initial defect correction factor : in, Let be the design length of the i-th member, ideally a straight rod. It degenerates into a traditional unit if initial micro-bending data is not collected, then... Traditional units can be used directly; Then, the element stiffness matrix is established using the optimized Euler-Bernoulli beam element: in, Let be the initial defect correction factor for the i-th member; Let be the measured initial micro-bending height of the i-th member. Let be the moment of inertia of the design section of the i-th member.
5. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 4, characterized in that: The specific implementation steps of S3 are as follows: S31, based on the actual measurement data from the fabrication process, performs physical corrections on the overall structural reference stiffness matrix obtained in S2 to determine the material and geometric properties, resulting in a first-order corrected stiffness matrix. ; S32, based on the measured displacement and strain values of multiple hanging basket structure measuring points in the unloaded static data, corrects the initial design load vector by removing load terms that did not actually occur and supplementing the actual permanent load values that occurred on site, thus obtaining a corrected load vector that matches the unloaded state on site. ; S33, based on a first-order modified stiffness matrix Introducing static boundary correction coefficient vector By fine-tuning the boundary constraints, the final corrected stiffness matrix is obtained. ; in, The initial value is 1. The final value is obtained by least-squares fitting of the deviation between the measured displacement and strain values in the unloaded static data and the displacement calculated by the initial correction model. The deviation of the displacement calculated by the initial correction model is [value missing], and the initial correction model is a first-order corrected stiffness matrix. The corresponding model, the difference between the displacement calculated under no-load conditions and the actual measured displacement under no-load static conditions; ⊙ represents the element-by-element correction operator; S34, Establish the modified static equilibrium equations The corrected displacement vector of the hanging basket node is obtained by solving the problem. With the internal forces of each member ,in, This is the corrected nodal displacement vector.
6. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 5, characterized in that: In S31, the formula for calculating the physical property correction of the overall structural stiffness matrix is as follows: in, This is a first-order stiffness matrix correction; Let E be the factory measured elastic modulus of the rod material, and let E be the design elastic modulus of the rod material. Let be the design section moment of inertia of the i-th member; Let be the measured deviation of the moment of inertia of the i-th member.
7. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 6, characterized in that: In step S4, the deviation calculation uses the relative deviation norm of the measuring point displacement as the criterion, and the calculation formula is as follows: Where r is the displacement deviation vector of the key measuring point; The displacement calculation values of the measuring points corresponding to the construction dynamic data in the corrected results obtained from S3. Describing the 2-norm, The 2-norm of the deviation vector; Let be the 2-norm of the measured displacement vector; This is the relative deviation norm.
8. The method for predicting and calculating hanging basket structures based on multi-source data as described in claim 7, characterized in that: In step S4, the specific implementation steps after calculating the deviation are as follows: S41, Set the preset convergence threshold ε and the maximum number of iterations, and calculate the relative deviation norm of the measurement points. ,like Output the overall displacement data of the hanging basket and the internal force data of each member; S42, if Using the static boundary correction coefficient vector in S3 Define a dynamic mechanical behavior correction parameter vector as the initial value. Construct the least squares objective function The calculation formula is: in, For the current correction parameter vector Calculate the displacement value at the measuring point below; S43, the optimal correction parameter vector is solved iteratively using the Gauss-Newton method with a damping term. The iterative update formula is: in, Let be the correction parameter vector for the (a+1)th iteration, where 'a' represents the iteration number. Let be the correction parameter vector for the a-th iteration; Let be the Jacobian sensitivity matrix for the k-th iteration, T denotes the transpose of the matrix; λ is the damping factor to prevent iteration divergence; o is the identity matrix; Among them, the Boolean matrix extracted from measurement point C has only the position elements corresponding to the key measurement point degrees of freedom set to 1, and the rest set to 0; For the current correction parameters Below, the displacement vector of all nodal nodes of the hanging basket structure; The parameter vector is corrected for dynamic mechanical behavior and continuously updated during the iteration process; when Take initial value hour; Right now Equal to the nodal displacements calculated in S3 ; S44, after each iteration, the structural stiffness matrix is updated synchronously. The structural mechanics equations are solved again to obtain the iterated nodal displacements and member internal forces, and the relative deviation norm is calculated again. S45, when satisfied Alternatively, when the number of iterations reaches the preset maximum limit, stop the iteration and output the final overall displacement data of the hanging basket and the internal force data of each member; Corrected parameters based on eventual convergence The final corrected stiffness matrix is obtained by combining construction dynamic data and real-time load data. The precise displacements and stresses of all nodes and members of the hanging basket structure are obtained by solving the model. At the same time, based on the converged model, the prediction results of subsequent unconstructed conditions are calculated.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.