Stay cable force and girder line shape cooperative control method of extradosed cable-stayed bridge

Abstract

The invention discloses a cooperative control method for stay cable force and main beam line shape of an extradosed cable-stayed bridge. The cooperative control method comprises the steps that cable force data, main beam line shape data and main beam section internal force monitoring data in the construction stage are collected; based on a preset cable force adjustment judgment criterion, whether cable force adjustment needs to be started or not is judged; when triggering conditions are met, a geometric nonlinear analysis model considering cable-beam-tower cooperative deformation is established, and a cable force-main beam line shape sensitivity influence matrix is generated; screening K stay cables as key adjustment objects by adopting an influence matrix column vector L2 norm sorting method, and performing global optimization based on a simulated annealing algorithm to obtain an optimal displacement mean square error and an optimal cable force adjustment amount; and according to the key adjustment object and the cable force adjustment amount, multi-cable synchronous grading tensioning work is carried out. The invention provides a systematic and intelligent solution for the construction control of the extradosed cable-stayed bridge, and has remarkable economic benefits and social benefits.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of cable tensioning, and particularly relates to a method for collaborative control of cable force and main beam alignment of a low-tower cable-stayed bridge. BACKGROUND

[0002] Low-tower cable-stayed bridges have become the mainstream bridge type for river-crossing and valley-crossing projects due to their economic efficiency and large-span advantage. However, the construction process of low-tower cable-stayed bridges faces the core problem of strong coupling between cable force and main beam alignment: the control difficulty is aggravated by the low-tower characteristics: the low-tower results in a small cable angle and a high proportion of horizontal cable force components, and the sensitivity of the main beam alignment to cable force changes is significantly improved (a unit cable force deviation of ±5% can cause an alignment deviation of ≥30mm); multiple-stage dynamic adjustment is required: the construction process needs to go through tens of cable force tensioning, and the traditional static control method relying on experience thresholds (such as a fixed cable force tolerance of ±10%) is prone to cumulative errors due to factors such as concrete shrinkage and temperature load, causing the deviation of the bridge alignment from the design target to exceed the limit (domestic statistics show that ≥40% of bridges need to be adjusted).

[0003] Under this background, the dynamic and intelligent realization of cable force-alignment collaborative control is a key technical breakthrough for ensuring the safety of the structure and the accuracy of the bridge.

[0004] The current construction control method has systematic defects: data perception and decision-making are fragmented: cable force monitoring relies on jack oil pressure gauges (precision ±2%) or vibration frequency methods (error >5% under environmental noise interference), and is not linked with real-time linkage analysis of main beam alignment and cross-sectional internal force; the adjustment triggering mechanism is rigid: only single-index thresholds (such as cable force overage or alignment over-limit) are used for judgment, ignoring the coupling effect of multiple parameters (such as not timely warning of main beam bending moment over-limit); the model simplification leads to distortion: the conventional linear analysis model ignores the cable-beam-tower collaborative deformation (some bridge measurements show that the geometric nonlinear effect causes a cable force transmission efficiency loss of 12%); the influence matrix relies on design stage theoretical values and is not dynamically updated with the construction process, and the sensitivity calculation deviation accumulates to ±25%; the adjustment strategy is locally optimal: manual experience is used to select cable adjustment objects (such as only adjusting the cable with the largest deviation), which causes the problem of "repairing the west to compensate for the east"; and there is a lack of global optimization algorithm support, making it difficult to achieve multi-objective balance between smoothness (alignment error ≤L / 4000), cable force balance (dispersion coefficient ≤3%) and construction efficiency (tensioning stroke ≤equipment upper limit). SUMMARY

[0005] Therefore, the present application provides a method for collaborative control of cable force and main beam alignment of a low-tower cable-stayed bridge, which provides a systematic and intelligent solution for the construction control of low-tower cable-stayed bridges, and has significant economic and social benefits.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The application provides a cable force and main beam line shape collaborative control method for a low-tower cable-stayed bridge. Step S1, real-time collection of cable force data, main beam line shape data and main beam section internal force monitoring data in the construction stage is performed. Step S2, whether the cable force adjustment needs to be started is judged based on a preset cable force adjustment judgment criterion, wherein the judgment criterion is set as a dynamic updating multi-threshold trigger mechanism, and the dynamic updating multi-threshold trigger mechanism includes real-time coupling calculation of a main beam absolute elevation deviation threshold value, a cable force deviation threshold value and a main beam section bending moment tolerance value. Step S3, when the trigger condition is met, a geometric nonlinearity analysis model considering cable-beam-tower collaborative deformation is established by taking the current construction stage measured data as a boundary condition, and a cable force-main beam line shape sensitivity influence matrix is generated. Step S4, an influence matrix array vector L2 norm sorting method is adopted, K cables with an influence coefficient absolute value cumulative value exceeding a span related threshold value are selected as key adjustment objects, global optimization is performed based on a simulated annealing algorithm, an objective function is an optimization index of a weighted combination of a comprehensive displacement mean square error and a cable force adjustment amount, and constraint conditions include main beam line shape smoothness, cable force balance degree and tensioning equipment stroke limitation. Step S5, multi-cable synchronous staged tensioning work is performed according to the key adjustment objects and the cable force adjustment amount.

[0007] Preferably, the cable force data is obtained through a pressure sensor arranged at the anchoring end of the cable, the main beam line shape data is measured through a combination of a total station and an inclination sensor, and the internal force monitoring data is obtained through a fiber bragg grating strain sensor embedded in a key section of the main beam.

[0008] Preferably, the trigger condition of the cable force adjustment judgment criterion is defined as any one of the following three conditions being met: The absolute value of the main beam elevation deviation is greater than 1 / 5000 of the span, and the height difference change rate between adjacent measuring points exceeds 0.5mm / m; Or, the relative deviation of the measured cable force value of a single cable and the design target value exceeds 10%, or the adjacent cable force difference degree reaches 150% of the design allowable value; Or, the bending moment monitoring values of the main beam key sections including the midspan, 1 / 4 span and tower root exceed 80% of the section resistance design value, and the bending moment gradient change rate exceeds a preset safety threshold value.

[0009] Preferably, step S3 specifically includes the following steps: A geometric nonlinear analysis model of the low tower cable-stayed bridge is constructed, the stayed cables, the main girder and the tower are divided into multiple rigid domains and flexible domains, the rigid domains are used to simulate the local rigid body behavior of the structure, the flexible domains are used to consider the material nonlinear and geometric nonlinear effects, a geometric nonlinear shape function considering the second order effect is introduced, the cooperative deformation characteristics of the cable-girder-tower system are described through the nonlinear finite element equation set based on the virtual work principle; The current construction stage measured data are processed through multi-source data fusion, the stayed cable force monitoring data, the main girder linear monitoring data and the main girder section internal force monitoring data are integrated; Based on the fused data, the nonlinear finite element equation set of the cable-girder-tower cooperative deformation is solved by using the improved Newton-Raphson iteration method; In the iteration solving process, the active control strategy is adopted to adjust the constraint conditions of the stayed cable force and the main girder linear in real time, so as to ensure that the model can dynamically adapt to various complex working conditions in the construction process; Based on the geometric nonlinear analysis model, the sensitivity analysis technology in the finite element method is used to calculate the stayed cable force-main girder linear sensitivity influence matrix reflecting the specific influence degree of the stayed cable force change on the main girder linear, wherein the column of the influence matrix is the displacement response value of each main girder measuring point obtained by the finite element sensitivity analysis for the unit stayed cable force change of a single stayed cable.

[0010] Preferably, in step S4, when the influence matrix column vector L2 norm sorting method is used to screen the key adjustment object, the following operations are performed: The L2 norm of each column vector in the influence matrix is calculated, and the L2 norm reflects the comprehensive influence degree of the stayed cable on the main girder linear; The column vectors are sorted in descending order according to the size of the L2 norm, and the first K stayed cables whose influence coefficient absolute value cumulative value exceeds the preset span related threshold value are screened out; The K stayed cables screened out are grouped according to the force point distribution of the stayed cables; In step S5, when the stayed cable force is adjusted by implementing the multi-stayed cable synchronous staged tensioning work, the cooperative adjustment strategy is used for the stayed cables in the same group to utilize their mutual influence and cooperation during the adjustment of the stayed cable force.

[0011] Preferably, when the stayed cable force is adjusted by implementing the multi-stayed cable synchronous staged tensioning work, the following operations are performed: The key adjustment object and the corresponding K stayed cable grouping result are determined, and the cooperative adjustment strategy is used for the stayed cables in the same group; The stayed cable force adjustment amount of each stayed cable is determined respectively, the maximum stayed cable force adjustment amount is determined according to the statistical result of the stayed cable force adjustment amount, the staged tensioning times and the proportional coefficient of each tensioning are determined based on the preset maximum stayed cable force adjustment amount-staged tensioning strategy; According to the hierarchical tensioning times and the proportional coefficient of each tensioning, the cable force adjustment amount of all the stay cables is respectively decomposed into multiple hierarchical tensioning amounts; The intelligent tensioning equipment is adopted to synchronously and hierarchically tension the key adjustment objects, and the tensioning operation is sequentially performed according to the hierarchical tensioning amounts until the multi-cable synchronous hierarchical tensioning work is completed.

[0012] Preferably, the global optimization based on the simulated annealing algorithm in the step S4 further comprises the following optimization steps: The parameters of the simulated annealing algorithm are initialized, including the initial temperature, the cooling rate, the iteration number and the termination temperature, and the parameter range is set according to the scale and complexity of the cable-stayed bridge; An initial solution is generated, a group of cable force adjustment amounts are randomly generated as the starting point of the simulated annealing algorithm, and the objective function value corresponding to the initial solution is calculated; In each iteration process, a neighborhood solution is generated by random disturbance, the current cable force adjustment amount is slightly changed randomly to generate a new cable force adjustment amount combination; The objective function value of the neighborhood solution is calculated, the change direction of the objective function value is judged, and it is decided whether to accept the neighborhood solution or not; If the optimization index improves, the neighborhood solution is accepted as the current solution; If the optimization index deteriorates, the neighborhood solution is accepted with a preset probability, and the probability gradually decreases with the decrease of the temperature; The temperature parameter is updated, the temperature is reduced according to the preset cooling rate, and the iteration process is repeated until the temperature reaches the termination temperature or the preset convergence condition is met; The global optimal solution is recorded, and the cable force adjustment amount combination with the minimum objective function value found in the iteration process is taken as the final cable force adjustment scheme.

[0013] The present application at least has the following beneficial effects: 1. The present application realizes the collaborative control of the stay cable force and the main beam line shape in the construction process of the low tower cable-stayed bridge, effectively improves the construction precision and the structural safety.

[0014] 2. The introduction of the geometric nonlinear analysis model and the sensitivity influence matrix makes the cable force adjustment scheme more scientific and reasonable, fully considers the interaction between the cable-beam-tower, and improves the accuracy and reliability of the model.

[0015] 3. Through the influence matrix array vector L2 norm sorting method, the first K stay cables with the absolute value of the influence coefficient exceeding the related threshold value of the span are selected as the key adjustment objects, to ensure the pertinence and efficiency of the adjustment work.

[0016] 4. Using the simulated annealing algorithm for global optimization, the optimization index of the weighted combination of displacement mean square error and cable force adjustment amount is determined to meet the constraints of main beam linear smoothness, cable force balance and tensioning equipment stroke limit, etc.

[0017] 5. The multi-cable synchronous staged tensioning strategy not only ensures the uniformity and gradualness of cable force adjustment, but also reduces the impact on the structure and improves the stability of the construction process.

[0018] Other advantages, objects, and features of the application will be set forth in the following specification and will be apparent to those skilled in the art from the teachings of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the specification. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to make the purposes, technical solutions and beneficial effects of the present application clearer, the present application provides the following drawings for illustration: Figure 1 A flowchart of the steps of the cable force and main beam linear collaborative control method for a low-tower cable-stayed bridge in an embodiment of the present application; Figure 2 A flowchart of the steps of the influence matrix generation in an embodiment of the present application; Figure 3 A flowchart of the steps of the multi-cable synchronous staged tensioning work in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0021] The present application provides a cable force and main beam linear collaborative control method for a low-tower cable-stayed bridge, referring to Figure 1 , comprising: Step S1, real-time acquisition of cable force data, main beam linear data and main beam section internal force monitoring data in the construction stage; Step S2, based on the preset cable force adjustment judgment criterion, it is judged whether the cable force adjustment needs to be started, wherein the judgment criterion is set as a dynamic updating multi-threshold trigger mechanism, including real-time coupled calculation of main beam absolute elevation deviation threshold, cable force deviation threshold and main beam section moment tolerance value; Step S3, when the trigger condition is met, a geometric nonlinear analysis model considering cable-beam-tower collaborative deformation is established with the current construction stage measured data as the boundary condition, and a cable force-main beam linear sensitivity influence matrix is generated; Step S4: Using the L2 norm sorting method of the column vector of the influence matrix, the top K stay cables whose cumulative absolute values ​​of influence coefficients exceed the span-related threshold are selected as key adjustment targets. Global optimization is performed based on the simulated annealing algorithm. The objective function is the optimization index of the weighted combination of the comprehensive displacement mean square error and the cable force adjustment. The constraints include the smoothness of the main beam alignment, the cable force balance and the travel limit of the tensioning equipment. Step S5: Implement multi-cable synchronous graded tensioning based on the key adjustment objects and cable force adjustment amounts.

[0022] The working principle and beneficial effects of the above technical solution are as follows: This invention achieves comprehensive perception of the bridge construction status by real-time collection of cable force data, main beam alignment data, and main beam section internal force monitoring data during the construction phase. Based on a preset, dynamically updated multi-threshold triggering mechanism, the absolute elevation deviation threshold, cable force deviation threshold, and main beam section bending moment tolerance value of the main beam are calculated in real time to accurately determine whether cable force adjustment needs to be initiated. When the triggering conditions are met, the measured data of the current construction phase are used as boundary conditions to establish a geometric nonlinear analysis model considering the coordinated deformation of the cable-beam-tower system, and a cable force-main beam alignment sensitivity influence matrix is ​​generated to quantify the impact of cable force changes on the main beam alignment. By using the L2 norm sorting method of the influence matrix column vectors, the top K stay cables whose cumulative absolute values ​​of influence coefficients exceed the span-related thresholds are selected as key adjustment targets to ensure the targeted and efficient nature of the adjustment work. This invention utilizes a simulated annealing algorithm for global optimization, combining the weighted optimization index of displacement root mean square error and cable force adjustment amount. Under constraints such as main girder alignment smoothness, cable force balance, and tensioning equipment travel limitations, the optimal cable force adjustment scheme is determined. Finally, based on the key adjustment objects and cable force adjustment amounts, multi-cable synchronous graded tensioning is implemented to achieve precise control of cable force and accurate shaping of the main girder alignment. This invention realizes the coordinated control of cable force and main girder alignment during the construction of low-tower cable-stayed bridges, effectively improving construction accuracy and structural safety. Real-time data acquisition and dynamic threshold judgment enable timely and accurate cable force adjustment, avoiding the problem of inaccurate adjustment timing in traditional methods. The introduction of a geometric nonlinear analysis model and a sensitivity influence matrix makes the cable force adjustment scheme more scientific and reasonable, fully considering the interaction between cables, beams, and towers, improving the accuracy and reliability of the model. The global optimization process based on the simulated annealing algorithm ensures the optimal allocation of cable force adjustment amounts, balancing the relationship between comprehensive displacement and cable force adjustment amounts, and avoiding the problem of local optima. The multi-cable synchronous graded tensioning strategy not only ensures the uniformity and gradualness of cable force adjustment but also reduces the impact on the structure and improves the stability of the construction process. This invention provides a systematic and intelligent solution for the construction control of low-tower cable-stayed bridges, with significant economic and social benefits.

[0023] In one specific embodiment, cable tension data is acquired through pressure sensors installed at the anchorage ends of the stay cables. High-precision pressure sensors are installed at the anchorage ends of each stay cable; these sensors can detect real-time tension changes in the stay cables and convert the tension data into electrical signals, which are then transmitted to the data acquisition system. The system performs preliminary data processing, including filtering and amplification, to improve the accuracy and reliability of the data.

[0024] The main girder alignment data is measured using a combination of a total station and tilt sensors: the total station, set on the pier or stable foundation, can accurately measure the three-dimensional coordinates of multiple measuring points on the main girder. The tilt sensors are installed at key locations on the main girder to measure its tilt angle. The combination of these two methods reflects both the overall alignment changes of the main girder and captures subtle local displacements and rotations.

[0025] Internal force monitoring data is acquired through fiber optic strain sensors embedded in key sections of the main beam. These sensors can monitor the strain of the main beam section in real time. The fiber optic strain sensors sense strain based on changes in the reflected wavelength of the fiber optic grating, offering advantages such as high precision, resistance to electromagnetic interference, and long-term stability. The strain data is transmitted to the data acquisition system via optical fiber.

[0026] The collected data is transmitted to the central control system via wired or wireless communication. The central control system integrates and analyzes the data, and based on preset judgment criteria and optimization algorithms, achieves precise and coordinated control of the cable tension and main beam alignment.

[0027] In one specific embodiment, the triggering condition for the cable tension adjustment determination criterion is defined as the fulfillment of any one of the following three conditions: The absolute value of the elevation deviation of the main beam is greater than 1 / 5000 of the span, and the rate of change of elevation difference between adjacent measuring points exceeds 0.5 mm / m. This indicates that the alignment deviation of the main beam has exceeded the allowable range, which may affect the overall structure and stability of the bridge. Alternatively, if the relative deviation between the measured cable force value of a single stay cable and the design target value exceeds 10%, or the difference in cable force between adjacent cables reaches 150% of the design allowable value, this indicates that the cable force of the stay cable deviates significantly from the design requirements, which may affect the stress distribution and alignment of the main beam. Alternatively, if the bending moment monitoring values ​​of key sections of the main beam, including the mid-span, 1 / 4-span, and tower root, exceed 80% of the section resistance design value, and the bending moment gradient change rate exceeds the preset safety threshold, this indicates that the stress on key parts of the main beam is close to the design limit, and there may be structural safety risks.

[0028] By setting the above triggering conditions, it is possible to determine in a timely and accurate manner whether cable tension adjustment is needed, thereby ensuring the construction quality and safety of the low-tower cable-stayed bridge.

[0029] In a preferred embodiment, refer to Figure 2 Step S3 specifically includes the following steps: A geometric nonlinear analysis model of a low-tower cable-stayed bridge is constructed, dividing the cable-stayed bridge, main beam, and tower into multiple rigid and flexible domains. The rigid domain is used to simulate the local rigid body behavior of the structure, while the flexible domain is used to consider material nonlinearity and geometric nonlinearity effects. A geometric nonlinear shape function considering second-order effects is introduced, and the cooperative deformation characteristics of the cable-beam-tower system are described by a set of nonlinear finite element equations based on the principle of virtual work. Multi-source data fusion processing is performed on the measured data during the current construction phase, integrating cable force monitoring data, main beam alignment monitoring data, and main beam section internal force monitoring data. Based on the fused data, the nonlinear finite element equations for the coordinated deformation of the cable-beam-tower were solved using the improved Newton-Raphson iterative method. During the iterative solution process, an active control strategy is adopted to adjust the constraints of cable force and main beam alignment in real time, ensuring that the model can dynamically adapt to various complex working conditions during construction. Based on a geometric nonlinear analysis model, the sensitivity analysis technique in the finite element method is used to calculate the cable force-main beam alignment sensitivity influence matrix, which reflects the specific influence of the cable force change on the main beam alignment. The columns of the influence matrix are the displacement response values ​​of each measuring point on the main beam obtained by finite element sensitivity analysis for the unit cable force change of a single cable.

[0030] The working principle and beneficial effects of the above technical solution are as follows: When constructing the geometric nonlinear analysis model of a low-tower cable-stayed bridge, the stay cables, main beam, and tower are divided into multiple rigid and flexible domains. The rigid domains are used to simulate the local rigid body behavior of the structure, while the flexible domains are used to consider material nonlinearity and geometric nonlinearity effects. A geometric nonlinear shape function considering second-order effects is introduced, and the cooperative deformation characteristics of the cable-beam-tower system are described through a set of nonlinear finite element equations based on the principle of virtual work. This set of nonlinear finite element equations can be expressed as: in, Here is the tangent stiffness matrix. For the iterative displacement increment, For nodal load vectors, This is the node load vector for the current iteration.

[0031] Multi-source data fusion processing is performed on the measured data during the current construction phase, integrating cable tension monitoring data, main beam alignment monitoring data, and main beam section internal force monitoring data. A weighted average algorithm is used to improve data accuracy and eliminate inconsistencies between data points. Assume the weights of the cable tension monitoring data are... The weight of the main beam alignment monitoring data is: The weight of the monitoring data of internal forces in the main beam section is: The merged data can then be represented as: in, For the merged data, Soli Linear Internal strength These are cable tension monitoring data, main beam alignment monitoring data, and main beam section internal force monitoring data, respectively.

[0032] Based on the fused data, an improved Newton-Raphson iterative method is used to solve the nonlinear finite element equations for the coordinated deformation of the cable-beam-tower system. In each iteration, the true solution is gradually approximated by updating the tangent stiffness matrix and nodal load vectors. The iterative formula of the improved Newton-Raphson iterative method is as follows: in, For the first The displacement vector of the next iteration. For the first The displacement vector of the next iteration. For the first The displacement increment of the next iteration.

[0033] During the iterative solution process, an active control strategy is adopted to adjust the constraints of cable forces and main beam alignment in real time, ensuring that the model can dynamically adapt to various complex working conditions during construction. Specifically, based on the monitoring data and model prediction results of the current construction stage, the constraints of cable forces and main beam alignment are dynamically adjusted to ensure the accuracy and adaptability of the model.

[0034] Based on a geometric nonlinear analysis model, sensitivity analysis techniques from the finite element method were used to calculate the cable force-main beam alignment sensitivity influence matrix, which reflects the specific impact of changes in cable force on the main beam alignment. The columns of this matrix represent the displacement response values ​​at each measuring point on the main beam obtained through finite element sensitivity analysis for a single cable force change. The sensitivity influence matrix can be expressed as: in, and These are the measuring points of the main beam. exist and Displacement response in the direction, For cable stays The change in cable force.

[0035] In a preferred embodiment, step S4, when screening key adjustment objects using the L2 norm sorting method of the influence matrix column vectors, includes the following: Calculate the L2 norm of each column vector in the influence matrix. The L2 norm reflects the overall influence of the stay cable on the main beam alignment. Sort the column vectors in descending order according to the size of the L2 norm, and select the first K stay cables whose cumulative absolute value of influence coefficient exceeds the preset span correlation threshold. The selected K stay cables are grouped according to the distribution of their stress points; In step S5, when adjusting the cable force during the multi-cable synchronous graded tensioning operation, a coordinated adjustment strategy is adopted for the stay cables in the same group to utilize their mutual influence and synergistic effect when adjusting the cable force.

[0036] The working principle and beneficial effects of the above technical solution are as follows: Calculate the L2 weighted norm index of each column vector (corresponding to a single cable-stayed cable) in the influence matrix: in, Let L2 norm be the column vector corresponding to the j-th cable-stayed bridge. The weighting factor is based on the structural characteristics of low-tower cable-stayed bridges, and its calculation formula is as follows: in, Let j be the angle between the j-th stay cable and the main beam. This is the horizontal distance from the anchor point of the cable to the mid-span. For bridge span; Set dynamic filtering thresholds: , The total number of stay cables, only when The cable-stayed bridge was included in the candidate set; according to The candidate set is sorted from largest to smallest value, and the top K stay cables are selected as the key adjustment targets, where the value of K is determined according to the following rules: in, This represents the maximum absolute deviation of the elevation of each measuring point on the main beam during the current construction phase.

[0037] In a preferred embodiment, refer to Figure 3 When adjusting cable tension during multi-cable synchronous graded tensioning, the following operations are performed: Identify the key adjustment targets and the corresponding K cable grouping results, and adopt a coordinated adjustment strategy for the cables in the same group; The cable tension adjustment amount for each cable is determined separately. The maximum cable tension adjustment amount is determined based on the statistical results of the cable tension adjustment amount. Based on the preset maximum cable tension adjustment amount-stage tensioning strategy, the number of stage tensioning steps and the proportional coefficient of each tensioning step are determined. Based on the number of tensioning stages and the proportional coefficient of each tensioning stage, the cable force adjustment of all stay cables is decomposed into multiple tensioning stages. Intelligent tensioning equipment is used to perform synchronous and graded tensioning on key adjustment objects. Tensioning operations are carried out sequentially according to the graded tensioning amount until the multi-cable synchronous and graded tensioning work is completed.

[0038] The working principle and beneficial effects of the above technical solution are as follows: When implementing multi-cable synchronous graded tensioning, a coordinated adjustment strategy is adopted for the stay cables within the same group, utilizing their mutual influence and synergistic effect to adjust the cable force. Specific operations include: determining the key adjustment objects and the corresponding K stay cable grouping results; adopting a coordinated adjustment strategy for the stay cables within the same group; determining the cable force adjustment amount for each stay cable; determining the maximum cable force adjustment amount based on the statistical results of the cable force adjustment amount; determining the number of graded tensioning steps and the proportional coefficient for each tensioning step based on the preset maximum cable force adjustment amount and graded tensioning strategy; decomposing the cable force adjustment amount of all stay cables into multiple graded tensioning amounts based on the number of graded tensioning steps and the proportional coefficient for each tensioning step; using intelligent tensioning equipment to synchronously grade tension the key adjustment objects, performing tensioning operations sequentially according to the graded tensioning amounts until the multi-cable synchronous graded tensioning work is completed. The calculation and sorting of the L2 norm can accurately screen out the stay cables that have the greatest impact on the main beam alignment, ensuring the targeted and effective nature of the adjustment work. By grouping the stay cables and employing a coordinated adjustment strategy, the mutual influence between them was fully utilized, resulting in more uniform and stable cable force adjustment. This avoided localized stress concentrations and abrupt changes in the main girder's alignment, improving the stability and safety of the construction process. The application of staged tensioning and intelligent tensioning equipment not only reduced the number of adjustments and workload but also significantly saved construction time and material costs, improving overall construction efficiency. This refined adjustment method helps optimize the main girder's alignment and internal force distribution, reducing the difficulty and cost of later adjustments, effectively improving the bridge's construction quality and durability, and ensuring the structure's safety and reliability.

[0039] In a preferred embodiment, step S4, when performing global optimization based on the simulated annealing algorithm, further includes the following optimization steps: Initialize the parameters of the simulated annealing algorithm, including the initial temperature, cooling rate, number of iterations, and termination temperature, and set the parameter range according to the scale and complexity of the cable-stayed bridge; Generate an initial solution and randomly generate a set of cable force adjustment amounts (solution vectors). , representing the cable tension adjustment of the K key stay cables (unit: kN), serves as the starting point for the simulated annealing algorithm and calculates the objective function value corresponding to the initial solution: in, The predicted elevation of the main beam at the i-th measuring point is represented by the influence matrix. For the design elevation, This is the baseline value for the allowable deviation of elevation. This is the maximum allowable adjustment for a single cable (set based on the performance of the tensioning equipment). For the difference in cable tension adjustment, a penalty item is applied. Let J be the anchor point distance between cable j and cable k. , as well as All represent weighting coefficients, set to 1.0, 0.4, and 0.2 respectively; Constraint settings, cable tension adjustment constraint: Main beam alignment smoothness constraint: rate of change of elevation difference between adjacent measuring points , For the spacing between measuring points, the travel constraint of the tensioning equipment is as follows: ,in, For cable force to stroke conversion factor, This refers to the upper limit of the total stroke of the synchronous tensioning equipment; SA algorithm execution flow: Initialize temperature Markov chain length ; In each iteration, a neighborhood solution is generated through random perturbation or non-uniform Gaussian perturbation, which makes a small random change to the current cable force adjustment, resulting in a new combination of cable force adjustments; the new solution components Among them, the amplitude of the disturbance , This represents the current iteration number; Annealing Plan: ,when The iteration terminates at the specified time. Calculate the objective function value of the neighborhood solution, determine the direction of change of the objective function value, and decide whether to accept the neighborhood solution; If the objective function value decreases, the optimization index improves. If so, then the solution in the neighborhood is accepted as the current solution; If the objective function value increases and the optimization index deteriorates, then... Then, with a preset probability Accepting neighborhood solutions, the probability of which gradually decreases as the temperature decreases; To prevent local optima offset; Update the temperature parameters, reduce the temperature according to the preset cooling rate, and repeat the iterative process until the temperature reaches the termination temperature or meets the preset convergence condition. Record the global optimal solution and the combination of cable force adjustment amounts with the minimum objective function value found during the iteration process, as the final cable force adjustment scheme.

[0040] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge, characterized in that, include: Step S1: Real-time collection of cable force data, main beam alignment data, and main beam section internal force monitoring data during the construction phase; Step S2: Based on the preset cable force adjustment judgment criteria, determine whether cable force adjustment needs to be initiated. The judgment criteria are set as a dynamically updated multi-threshold triggering mechanism, including real-time coupled calculation of the main beam absolute elevation deviation threshold, cable force deviation threshold and the main beam section bending moment tolerance value. Step S3: When the triggering conditions are met, a geometric nonlinear analysis model considering the coordinated deformation of cable-beam-tower is established using the measured data of the current construction stage as boundary conditions, and the cable force-main beam alignment sensitivity influence matrix is ​​generated. Step S4: Using the L2 norm sorting method of the column vector of the influence matrix, the top K stay cables whose cumulative absolute values ​​of influence coefficients exceed the span-related threshold are selected as key adjustment targets. Global optimization is performed based on the simulated annealing algorithm. The objective function is the optimization index of the weighted combination of the comprehensive displacement mean square error and the cable force adjustment. The constraints include the smoothness of the main beam alignment, the cable force balance and the travel limit of the tensioning equipment. Step S5: Implement multi-cable synchronous graded tensioning based on the key adjustment objects and cable force adjustment amounts.

2. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 1, characterized in that, The cable force data is obtained through pressure sensors installed at the anchorage ends of the stay cables, the main beam alignment data is measured using a combination of a total station and an inclination sensor, and the internal force monitoring data is obtained through fiber optic strain sensors embedded in key sections of the main beam.

3. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 1, characterized in that, The trigger condition for the cable tension adjustment criterion is defined as the fulfillment of any one of the following three conditions: The absolute value of the main beam elevation deviation is greater than 1 / 5000 of the span, and the rate of change of elevation difference between adjacent measuring points exceeds 0.5 mm / m; Alternatively, the relative deviation between the measured cable force value of a single cable and the design target value exceeds 10%, or the difference in force between adjacent cables reaches 150% of the design allowable value; Alternatively, the bending moment monitoring values ​​at key sections of the main beam, including mid-span, 1 / 4 span, and tower root, exceed 80% of the section resistance design value, and the bending moment gradient change rate exceeds the preset safety threshold.

4. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 1, characterized in that, Step S3 specifically includes the following steps: A geometric nonlinear analysis model of a low-tower cable-stayed bridge is constructed, dividing the cable-stayed bridge, main beam, and tower into multiple rigid and flexible domains. The rigid domain is used to simulate the local rigid body behavior of the structure, while the flexible domain is used to consider material nonlinearity and geometric nonlinearity effects. A geometric nonlinear shape function considering second-order effects is introduced, and the cooperative deformation characteristics of the cable-beam-tower system are described by a set of nonlinear finite element equations based on the principle of virtual work. Multi-source data fusion processing is performed on the measured data during the current construction phase, integrating cable force monitoring data, main beam alignment monitoring data, and main beam section internal force monitoring data. Based on the fused data, the nonlinear finite element equations for the coordinated deformation of the cable-beam-tower were solved using the improved Newton-Raphson iterative method. During the iterative solution process, an active control strategy is adopted to adjust the constraints of cable force and main beam alignment in real time, ensuring that the model can dynamically adapt to various complex working conditions during construction. Based on a geometric nonlinear analysis model, the sensitivity analysis technique in the finite element method is used to calculate the cable force-main beam alignment sensitivity influence matrix, which reflects the specific influence of the cable force change on the main beam alignment. The columns of the influence matrix are the displacement response values ​​of each measuring point on the main beam obtained by finite element sensitivity analysis for the unit cable force change of a single cable.

5. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 1, characterized in that, In step S4, when using the L2 norm sorting method of the influence matrix column vectors to screen key adjustment objects, the following steps are performed: Calculate the L2 norm of each column vector in the influence matrix. The L2 norm reflects the degree of comprehensive influence of the stay cables on the main beam alignment. Sort the column vectors in descending order according to the size of the L2 norm, and select the first K stay cables whose cumulative absolute value of influence coefficient exceeds the preset span correlation threshold. The selected K stay cables are grouped according to the distribution of their stress points; In step S5, when adjusting the cable force during the multi-cable synchronous graded tensioning operation, a coordinated adjustment strategy is adopted for the stay cables in the same group to utilize their mutual influence and synergistic effect when adjusting the cable force.

6. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 5, characterized in that, When adjusting cable tension during multi-cable synchronous graded tensioning, the following operations are performed: Identify the key adjustment targets and the corresponding K cable grouping results, and adopt a coordinated adjustment strategy for the cables in the same group; The cable tension adjustment amount for each cable is determined separately. The maximum cable tension adjustment amount is determined based on the statistical results of the cable tension adjustment amount. Based on the preset maximum cable tension adjustment amount-stage tensioning strategy, the number of stage tensioning steps and the proportional coefficient of each tensioning step are determined. Based on the number of tensioning stages and the proportional coefficient of each tensioning stage, the cable force adjustment of all stay cables is decomposed into multiple tensioning stages. Intelligent tensioning equipment is used to perform synchronous and graded tensioning on key adjustment objects. Tensioning operations are carried out sequentially according to the graded tensioning amount until the multi-cable synchronous and graded tensioning work is completed.

7. The method for coordinated control of cable tension and main girder alignment in a low-tower cable-stayed bridge according to claim 1, characterized in that, Step S4, which involves global optimization based on the simulated annealing algorithm, also includes the following optimization steps: Initialize the parameters of the simulated annealing algorithm, including the initial temperature, cooling rate, number of iterations, and termination temperature, and set the parameter range according to the scale and complexity of the cable-stayed bridge; Generate an initial solution by randomly generating a set of cable force adjustment values ​​as the starting point for the simulated annealing algorithm, and calculate the objective function value corresponding to the initial solution; In each iteration, a neighborhood solution is generated through random perturbation, and the current cable force adjustment is changed slightly by random variation to generate a new combination of cable force adjustment. Calculate the objective function value of the neighborhood solution, determine the direction of change of the objective function value, and decide whether to accept the neighborhood solution; If the objective function value decreases and the optimization index improves, then the neighborhood solution is accepted as the current solution. If the objective function value increases and the optimization index deteriorates, then the neighborhood solution is accepted with a preset probability, which gradually decreases as the temperature decreases. Update the temperature parameters, reduce the temperature according to the preset cooling rate, and repeat the iterative process until the temperature reaches the termination temperature or meets the preset convergence condition. Record the global optimal solution and the combination of cable force adjustment amounts with the minimum objective function value found during the iteration process, as the final cable force adjustment scheme.

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