Vibration control method and system for steel-concrete composite box girder bridge

Abstract

The embodiment of the application discloses a kind of steel-concrete composite box girder bridge vibration control method and system, vibration control method is carried out by establishing the spatial finite element model of interface slip and shear hysteresis effect to consider accurately vehicle-bridge coupling vibration analysis, and the initial optimal parameter group of multiple tuned mass damper is optimized and determined based on analysis result and is installed tuning;In bridge operation stage, vibration is monitored in real time by sensor system, and damper parameter is dynamically adjusted according to the deviation of measured data and target value by controller, to realize adaptive control.So, the application combines high-precision simulation, optimization design and intelligent control, overcomes the problem that control effect is not good due to model simplification and fixed parameter in traditional method, and significantly improves the accuracy and environmental adaptability of vibration suppression.

Description

Technical Field

[0001] This invention relates to the field of vibration control technology for bridge engineering structures, and in particular to a vibration control method and system for a steel-concrete composite box girder bridge. Background Technology

[0002] Steel-concrete composite box girder bridges are widely used in modern railway and highway bridges due to their excellent mechanical properties and economic efficiency. However, under dynamic loads such as those from high-speed trains, complex interface slip effects occur at the interface between the steel beams and the concrete bridge deck, while the wide flanges of the box girder also induce significant shear lag effects. These nonlinear effects cause a large deviation between the actual dynamic response of the bridge and the results of traditional theoretical calculations, posing challenges to the safety and ride comfort of the bridge. To suppress vibration, multiple tuned mass dampers (MTMDs) have proven to be an effective passive control device. However, traditional MTMD parameter design is usually based on simplified bridge models and fails to fully consider the impact of the aforementioned interface slip and shear lag effects on the actual dynamic characteristics of the bridge, resulting in vibration reduction effects that often fall short of expectations in practical applications. Furthermore, the train loads (such as train formation and speed) borne by the bridge during operation are dynamically changing, while the parameters of traditional MTMDs remain fixed once installed, making them unable to adapt to such time-varying load conditions. This leads to a decrease in control effectiveness and insufficient adaptive capability under non-design conditions. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a vibration control method, device, equipment and storage medium for steel-concrete composite box girder bridges, in order to solve the technical problem that traditional vibration control methods have unsatisfactory control effects due to their failure to accurately consider nonlinear effects such as bridge interface slippage and shear hysteresis and the time-varying nature of loads.

[0004] To achieve the above objectives, the technical solution created by this invention is implemented as follows: In a first aspect, the present invention provides a vibration control method for a steel-concrete composite box girder bridge, comprising the following steps: S1. Collect the geometric dimensions, material properties and interface connection parameters of the steel-concrete composite box girder bridge. Based on the geometric dimensions, material properties and interface connection parameters, establish a spatial finite element model considering interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate interface friction and deformation evolution, and the shear lag effect is characterized by the width-to-span ratio. S2. Based on the aforementioned spatial finite element model, load parameters including different train formations, speeds, and densities, as well as track irregularity excitations, are input to perform vehicle-bridge coupled vibration analysis and obtain vertical displacement and acceleration time history data at key locations on the bridge. S3. Based on the vertical displacement and acceleration time history data, construct an objective function, and use an optimization algorithm to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio, and damping ratio. S4. Install the multi-tuned mass damper at the location of maximum vibration response of the bridge, and perform initial tuning according to the initial optimal parameter set; During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with a preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control.

[0005] Furthermore, in step S1, the nonlinear dynamic constitutive relation is characterized by the Bouc-Wen model, which simulates stiffness degradation and energy dissipation during the slip process. When the Bouc-Wen model is used, the shear force generated by the interface slip effect Characterized by the solutions to the following system of equations: ; ; in, The total shear force at the interface; This refers to the amount of interface sliding. This refers to the interface scrolling speed; This is the initial elastic stiffness; The ratio of post-yield stiffness to initial stiffness, ranging from... ; Let be a dimensionless internal state variable describing hysteresis behavior; Dimensionless parameters for controlling the shape and properties of hysteresis loops; Dimensionless internal state variables The derivative with respect to time.

[0006] Furthermore, in step S2, the vehicle-bridge coupled vibration analysis is performed through the following steps: S201. The train is simplified into a multi-rigid-body system consisting of a car body, bogies and wheelsets. The car body and bogies are connected by secondary suspension spring damping elements, and the bogies and wheelsets are connected by primary suspension spring damping elements. Based on the multibody dynamics theory, the dynamic equations of the vehicle system with the displacement, velocity and acceleration of each degree of freedom of the train as the basic unknowns are established. S202. Based on the aforementioned spatial finite element model, by assembling the overall mass matrix, damping matrix, and stiffness matrix, a dynamic equation for the bridge system with bridge node displacement, velocity, and acceleration as the basic unknowns is established. S203. Based on the wheel-rail interaction theory, establish the geometric compatibility conditions and mechanical equilibrium relationship between the wheelset and the rail. Couple the vehicle system dynamic equation and the bridge system dynamic equation into a time-varying nonlinear dynamic system through the geometric compatibility conditions and mechanical equilibrium relationship. Solve the time-varying nonlinear dynamic system using the numerical integration method to obtain the vertical displacement and acceleration time history data of key positions of the bridge.

[0007] Furthermore, in step S201, the vehicle system dynamics equations are expressed as: ; in, These are the vehicle's mass matrix, damping matrix, and stiffness matrix, respectively. These are the vehicle's displacement vector, velocity vector, and acceleration vector, respectively. This is the wheel-rail interaction force vector; In step S202, the dynamic equations of the bridge system are expressed as follows: ; in, These are the bridge's mass matrix, damping matrix, and stiffness matrix, respectively. These are the bridge's displacement vector, velocity vector, and acceleration vector, respectively. This is the force vector exerted by the wheel on the bridge. In step S203, the geometric compatibility condition is: the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the corresponding position of the bridge and the track irregularity value; The mechanical equilibrium relationship is as follows: the force exerted by the wheel on the rail and the reaction force exerted by the rail on the wheel are equal in magnitude and opposite in direction. Furthermore, in step S3, based on the vertical displacement and acceleration time history data, an objective function is constructed with the goal of suppressing bridge vibration. Its expression is: ; in, Acceleration time history data for key locations on the bridge; Vertical displacement at key locations on the bridge; and These are the weighting coefficients for acceleration and displacement, respectively, and they satisfy the normalization condition. ; An optimization algorithm is used to minimize the objective function. To achieve the objective, the initial optimal parameter set for the multi-tuned mass damper was calculated, the initial optimal parameter set including the total mass ratio. Tuning frequency ratio Damping ratio ; The optimization algorithm establishes the correlation between parameter optimization and vehicle-bridge coupled vibration analysis through the following process: For each set of parameters to be optimized, substitute it into the dynamic model of the multi-tuned mass damper and couple it into the vehicle-bridge coupled vibration analysis, and re-execute the vibration response calculation; By comparing the objective functions corresponding to different parameter combinations The parameters are iteratively updated along the descent direction of the objective function's output value until the convergence criterion is met. The optimization algorithm employs a pattern search method, performing iterative searches within a set parameter range, and comparing the objective functions corresponding to different parameter combinations. The output value of the objective function is selected to make the objective function... The parameter combination with the smallest output value is taken as the initial optimal parameter set; The ranges of the parameters are as follows: The parameter combination is as follows: .

[0008] Furthermore, the operating parameters are specified as the damping coefficients of the multi-tuned mass damper. The initial damping coefficient of the multi-tuned mass damper is continuously adjusted via an electro-hydraulic servo actuator. The parameters are set according to the initial optimal parameter set. The controller employs a proportional-integral-derivative (PI-DI) control algorithm, based on the deviation between real-time vibration data and the target vibration response value. Calculate the required real-time damping coefficient Its control law is defined by the following formula:

[0009] in, for The real-time damping coefficient that needs to be applied at any given time is the output of the controller; Set the initial damping coefficient of the damper for the initial optimal parameter set; for The control deviation at time is denoted as _____. , The preset target vibration response value is a constant. for The bridge vibration response value is measured in real time by a sensor system deployed on the bridge. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; This is a time variable, representing the current moment in the operation of the control system; The variable is the integral variable, representing the time from the start time 0 of the control system to the current time. The time span between; For control deviation function In the integral variable The function value at time t, its functional relationship with same; Indicates the integral variable The differential; Represents the time variable The differential.

[0010] Secondly, the present invention provides a vibration control device for a steel-concrete composite box girder bridge, comprising: The data acquisition module collects the geometric dimensions, material properties, and interface connection parameters of the steel-concrete composite box girder bridge. Based on the geometric dimensions, material properties, and interface connection parameters, it establishes a spatial finite element model that considers interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate interface friction and deformation evolution, and the shear lag effect is characterized by the width-to-span ratio. The acquisition module, based on the spatial finite element model, inputs load parameters including different train formations, speeds and densities, as well as track irregularity excitations, to perform vehicle-bridge coupled vibration analysis and acquire vertical displacement and acceleration time history data at key locations on the bridge. The construction module constructs an objective function based on the vertical displacement and acceleration time history data, and uses an optimization algorithm to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio, and damping ratio. The installation module is used to install the multi-tuned mass damper at the location of maximum vibration response of the bridge and to perform initial tuning according to the initial optimal parameter set. During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with a preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control.

[0011] Thirdly, the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the vibration control method for steel-concrete composite box girder bridges as described in the first aspect of the invention.

[0012] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the vibration control method for a steel-concrete composite box girder bridge as described in the first aspect of the invention.

[0013] By establishing a spatial finite element model that considers interface slip and shear lag effects, the nonlinear mechanical behavior of steel-concrete composite structures is accurately captured through the above technical solution. This provides a high-precision calculation basis for subsequent vibration analysis. Based on this model, vehicle-bridge coupled vibration analysis can accurately obtain the actual dynamic response of the bridge under train loads, overcoming the calculation bias caused by traditional models. Using this accurate response data, an objective function is constructed and optimized to obtain the initial optimal parameters of the multi-tuned mass damper. This ensures that the vibration reduction device has optimal passive control performance from the beginning of installation. Furthermore, by monitoring the bridge vibration response in real time and dynamically adjusting the damper parameters using an adaptive control algorithm, the control system can actively adapt to the time-varying characteristics of train loads during operation. This forms a complete technical closed loop from accurate modeling and optimized design to intelligent control, thereby significantly improving the reliability and environmental adaptability of vibration control for steel-concrete composite box girder bridges. Attached Figure Description

[0014] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the vibration control method for a steel-concrete composite box girder bridge provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the vibration control device for a steel-concrete composite box girder bridge provided in Embodiment 2 of the present invention. Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0015] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0016] To facilitate a clear description of the technical solutions in the embodiments of the present invention, some terms and technologies involved in the embodiments of the present invention will be briefly introduced below: Steel-concrete composite box girder bridges: These are bridge structures composed of reinforced concrete bridge decks and steel box girders connected by shear-resistant connectors to share the load. Their core characteristic lies in the complementary effect of the superior compressive strength of concrete and the excellent tensile strength of steel.

[0017] Spatial finite element model: This model discretizes the bridge structure into a large number of tiny elements with geometric dimensions (such as beam elements, plate elements, and solid elements), connected by nodes. Each element is given realistic material properties, enabling the simulation of the structure's mechanical response in three spatial directions.

[0018] Interface slip effect: refers to the small relative displacement that occurs at the interface between the steel beam and the concrete slab in a steel-concrete composite beam under load. This slip leads to stiffness degradation and increased deformation of the composite beam, and is a key nonlinear factor affecting the accurate calculation of composite beams. This invention accurately characterizes it by introducing a nonlinear dynamic constitutive relation.

[0019] Shear lag effect: also known as "shear hysteresis," in wide box girders, when the beam is subjected to bending, the normal stress distribution in the flanges is uneven, and the stress in the flanges far from the web is less than the value calculated according to simple beam theory. This invention quantifies its impact on overall stiffness using the key geometric parameter of the width-to-span ratio (the ratio of bridge width to span).

[0020] Nonlinear dynamic constitutive relations refer to mathematical models used to describe the relationship between force and deformation of materials or contact interfaces under stress, as well as factors such as load history and velocity. In this invention, they specifically refer to the relations used to simulate the complex mechanical behavior of friction, slippage, stiffness degradation, and energy dissipation at steel-concrete interfaces, specifically employing the Bouc-Wen model.

[0021] Vehicle-bridge coupled vibration analysis: a dynamic analysis method that treats the vehicle model and the bridge model as an interacting whole system. The analysis considers not only the impact of bridge deformation on vehicle dynamics but also the impact of the dynamic loads generated by vehicle motion on bridge vibration, linking the two through wheel-rail interaction for solution.

[0022] Multiple tuned mass dampers (TMDs) are passive vibration control devices composed of multiple TMDs with different natural frequencies connected in parallel. Their working principle is to counteract structural vibration energy through the inertial force of the mass blocks. Due to their wider frequency coverage, they are more adaptable to frequency variations compared to a single TMD.

[0023] Adaptive vibration control: an intelligent control strategy whose core lies in real-time monitoring of the vibration response of the controlled structure (bridge) and automatically adjusting the operating parameters of the control device (such as MTMD) online according to the deviation between the monitoring data and the preset target, so that the system can adapt to changes in external loads (such as different trains) and always maintain excellent control performance.

[0024] Geometric compatibility condition: In a vehicle-bridge coupled system, this refers to the coordination relationship of displacements at the contact point between the wheelset and the rail. This invention assumes close wheel-rail contact, meaning the vertical displacement of the wheelset must be equal to the sum of the vertical displacement of the corresponding point on the bridge and the rail irregularity. This is the bridge that couples two dynamic equations.

[0025] Mechanical equilibrium relationship: In a vehicle-bridge coupled system, this refers to the interaction relationship of forces at the wheel-rail contact point. That is, the force exerted by the wheel on the rail and the reaction force exerted by the rail on the wheel are equal in magnitude and opposite in direction. This is the basis for deriving the dynamic equations of the vehicle and the bridge.

[0026] Proportional-Integral-Derivative (PID) control algorithm: one of the most widely used feedback control laws. The controller output consists of three parts based on the real-time deviation signal: the proportional term reflects the current deviation and determines the response speed; the integral term accumulates historical deviations and is used to eliminate steady-state errors; and the derivative term predicts the trend of deviation changes and increases system damping.

[0027] In existing technologies, passive tuned mass dampers (such as multiple tuned mass dampers, MTMDs) are a widely used technique for vibration control of long-span bridges, especially steel-concrete composite box girder bridges. Due to their simple structure, high reliability, and lack of external energy requirements, they play a crucial role in suppressing excessive bridge vibrations caused by dynamic loads such as trains and wind loads. However, existing vibration control methods based on TMDs / MTMDs often fail to achieve the theoretically expected control results when applied to steel-concrete composite box girder bridges, which exhibit significant nonlinear mechanical characteristics. The main bottlenecks lie in three areas: model accuracy, device design methods, and environmental adaptability.

[0028] First, existing technologies for establishing bridge dynamic models for vibration analysis and controller design typically employ highly simplified linear or equivalent beam models. These models often neglect the nonlinear slip effects at the interface between the steel beam and the concrete slab, and fail to accurately account for the significant shear lag effect in wide box girder sections. Since interface slip in composite beams leads to degradation of the overall structural stiffness, and shear lag affects its stress distribution and dynamic characteristics, ignoring these key nonlinear factors results in a fundamental deviation between the theoretical model and the actual dynamic response of the bridge. Consequently, any subsequent analysis and design based on this model is grounded in an inaccurate physical foundation from the outset.

[0029] Secondly, in the parameter design phase of MTMD, existing methods mostly rely on natural frequencies and mode shapes calculated from simplified bridge models, and their optimization objective functions are often based on idealized dynamic responses. This disconnect between the "design model" and the "actual structure" means that the determined optimal MTMD parameters (such as tuning frequency and damping ratio) are not optimal solutions for the actual dynamic characteristics of the bridge. Even if the device is carefully manufactured and installed, due to inherent deviations in its design basis, "detuning" often occurs in practical applications, meaning that the optimal operating frequency band of the MTMD does not match the actual dominant vibration frequency of the bridge, thus greatly weakening its passive vibration reduction potential.

[0030] Furthermore, most existing technical solutions use MTMDs as passive control devices with fixed parameters, lacking the ability to self-adjust according to actual load conditions during bridge operation. The train loads (such as different train formations, speeds, and axle loads) experienced by steel-concrete composite box girder bridges during operation are dynamically changing, leading to alterations in the bridge's vibration characteristics. Fixed-parameter MTMDs only exhibit optimal performance near the design conditions; their control effectiveness significantly decreases once load conditions deviate from the design conditions. This inherent limitation in adapting to time-varying loads restricts their reliability and robustness in complex operating environments.

[0031] In summary, there is an urgent need for a vibration control method for steel-concrete composite box girder bridges that can address the unsatisfactory control effects of traditional vibration control methods due to their failure to accurately consider nonlinear effects such as bridge interface slippage and shear lag, as well as the time-varying nature of loads. This method should be able to accurately characterize the nonlinear mechanical behavior of the composite girder and, based on this, construct an intelligent vibration control strategy that can dynamically adjust according to the actual state of the bridge.

[0032] Based on this, embodiments of the present invention provide a vibration control method, device, electronic device, and storage medium for a steel-concrete composite box girder bridge. This vibration control method can be used in the field of vibration control technology for bridge engineering structures, aiming to solve or improve the above-mentioned deficiencies of the prior art.

[0033] Example 1 Figure 1 This is a flowchart illustrating the vibration control method for a steel-concrete composite box girder bridge provided in Embodiment 1 of the present invention. Figure 1 As shown, the vibration control method for a steel-concrete composite box girder bridge includes the following steps: S1. Collect the geometric dimensions, material properties and interface connection parameters of the steel-concrete composite box girder bridge. Based on the geometric dimensions, material properties and interface connection parameters, establish a spatial finite element model considering the interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate the evolution of interface friction and deformation, and the shear lag effect is characterized by the width-to-span ratio. Specifically, in step S1, the geometric dimensions, material properties, and interface connection parameters required for model construction are obtained based on design data and measured data. On this basis, a spatial finite element model of the bridge is established. This spatial finite element model is characterized by simultaneously considering interface slip and shear lag effects. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation, which can simulate the friction and deformation evolution behavior of the interface during the stress process; the shear lag effect is characterized by the span-to-width ratio, a geometric parameter.

[0034] S2. Based on the spatial finite element model, load parameters including different train formations, speeds and densities, as well as track irregularity excitations, are input to perform vehicle-bridge coupled vibration analysis and obtain vertical displacement and acceleration time history data at key locations on the bridge. Specifically, the purpose of step S2 is to obtain the dynamic response of the bridge under a specified load. In step S2, load parameters including different train formations, speeds, and densities, as well as track irregularity excitations, are input into the spatial finite element model established in step S1. Through vehicle-bridge coupled vibration analysis, the response of the vehicle system and the bridge system under dynamic interaction is calculated. This analysis can obtain vertical displacement and acceleration data of key bridge locations (such as the mid-span section) under train loads.

[0035] S3. Based on the vertical displacement and acceleration time history data, construct the objective function, and use the optimization algorithm to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio and damping ratio. Specifically, in step S3, an objective function is constructed using the vertical displacement and acceleration data obtained in step S2. An optimization algorithm is then used to minimize this objective function, thereby calculating the initial optimal parameter set for the multi-tuned mass damper. This parameter set includes the total mass ratio, tuning frequency ratio, and damping ratio.

[0036] S4. Install a multi-tuned mass damper at the location of maximum vibration response of the bridge and perform initial tuning according to the initial optimal parameter set. During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with the preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control.

[0037] Specifically, in step S4, a multi-tuned mass damper is first installed at the location of maximum bridge vibration response, and initial tuning is performed based on the initial optimal parameter set obtained in step S3. Subsequently, during bridge operation, vibration is monitored in real time by a sensor system, real-time vibration data is acquired and fed back to the controller. The controller compares the real-time data with the preset target vibration response value, generates control commands based on the deviation, and dynamically adjusts the damper's operating parameters to achieve adaptive vibration control. Step S4 completes the entire process from device installation and initial setup to adaptive control.

[0038] Therefore, the vibration control method for steel-concrete composite box girder bridges provided by this invention systematically solves the technical problems in existing technologies, such as inaccurate design basis due to model simplification and the difficulty of fixed-parameter devices adapting to time-varying loads, by constructing a complete technical chain from high-precision modeling, dynamic response analysis, vibration reduction device parameter optimization to adaptive control. This method has the following significant advantages: First, in the model establishment stage (step S1), this invention accurately characterizes the slip effect at the steel-concrete interface by introducing a nonlinear dynamic constitutive relation and uses the span-to-width ratio to characterize the shear lag effect, thereby establishing a spatial finite element model that reflects the true stress characteristics of the composite beam. This high-precision model effectively overcomes the theoretical biases of traditional simplified models and provides a reliable physical basis for all subsequent analyses.

[0039] Secondly, in the dynamic response acquisition stage (step S2), vehicle-bridge coupled vibration analysis is performed based on the aforementioned high-precision model. This accurately simulates the interaction between the train load and the bridge, obtaining real vertical displacement and acceleration data at key locations on the bridge. This step ensures that the input data used for vibration reduction device design has high reliability, avoiding design deviations caused by inaccurate input data from the outset.

[0040] In the device parameter optimization stage (step S3), an objective function is constructed using real dynamic response data, and an optimization algorithm is used to solve for the optimal parameter combination of the multi-tuned mass damper (including total mass ratio, tuning frequency ratio, and damping ratio). This design process ensures that the parameters of the vibration reduction device are set based on the actual dynamic characteristics of the bridge, guaranteeing that the passive control performance of the device in the initial state is theoretically optimal.

[0041] Finally, in the control implementation phase (step S4), this method does not remain passive control. Instead, it monitors the vibration response in real time during operation, and the controller dynamically adjusts the damper's operating parameters based on the deviation between the measured data and the target value. This adaptive control mechanism enables the system to proactively adapt to changes in external excitations such as train load, effectively compensating for the performance degradation of the initial fixed parameters outside the design conditions. This forms a closed loop where "precise initial design" and "online intelligent adjustment" reinforce each other, ultimately achieving continuous and stable vibration control under all operating conditions.

[0042] In some implementations, in step S1, the nonlinear dynamic constitutive relation is characterized using the Bouc-Wen model, which simulates stiffness degradation and energy dissipation during the slip process. When using the Bouc-Wen model, the shear force generated by the interface slip effect Characterized by the solutions to the following system of equations: ; ; in, The total shear force at the interface; This refers to the amount of interface sliding. This refers to the interface scrolling speed; This is the initial elastic stiffness; The ratio of post-yield stiffness to initial stiffness, ranging from... ; Let be a dimensionless internal state variable describing hysteresis behavior; Dimensionless parameters for controlling the shape and properties of hysteresis loops; Dimensionless internal state variables The derivative with respect to time.

[0043] When implementing this in a spatial finite element model, it is necessary to set up dedicated interface elements or contact elements at the location representing the steel-concrete interface, and implant the aforementioned Bouc-Wen model as the constitutive relation (i.e., force-slip relation) for this element. In the dynamic time history analysis, for each interface element, at each time step, based on the current calculation results (interface slip), and interface scrolling speed The dimensionless internal state variables are solved using numerical integration methods (such as the fourth-order Runge-Kutta method). Then the total shear force at the interface is calculated. The total shear force at this interface It will participate in the equilibrium iteration of the entire structural system as an internal force of the unit. In some implementations, in step S2, the vehicle-bridge coupled vibration analysis is performed through the following steps, which constitute a complete dynamic simulation process: S201. Establishment of vehicle system dynamics equations: The train is simplified as a multi-rigid-body system. Specifically, each car is modeled as a system consisting of a car body, bogie, and wheelsets. The car body and bogie are connected by secondary suspension spring-damping elements, and the bogie and wheelsets are connected by primary suspension spring-damping elements. Based on multibody dynamics theory (such as the Lagrange equations), the dynamic equations of the entire vehicle system are established, and their standard form is: ; in, These are the vehicle's mass matrix, damping matrix, and stiffness matrix, respectively. These are the vehicle's displacement vector, velocity vector, and acceleration vector, respectively, encompassing the degrees of freedom of the vehicle body, bogie, and wheelsets. This is the wheel-rail interaction force vector; S202. Establishment of the dynamic equations of the bridge system: Based on the spatial finite element model, by assembling the overall mass matrix, damping matrix, and stiffness matrix, the dynamic equations of the bridge system with bridge node displacement, velocity, and acceleration as the basic unknowns are established: ; in, These are the bridge's mass matrix, damping matrix, and stiffness matrix, respectively. These are the bridge's displacement vector, velocity vector, and acceleration vector, respectively. This is the force vector exerted by the wheel on the bridge. S203, System Coupling and Solution: Based on the theory of wheel-rail interaction, geometric compatibility conditions and mechanical equilibrium relationships between wheelsets and rails are established. The vehicle system dynamic equations and bridge system dynamic equations are coupled into a time-varying nonlinear dynamic system through geometric compatibility conditions and mechanical equilibrium relationships. The time-varying nonlinear dynamic system is solved using numerical integration methods to obtain the time history data of vertical displacement and acceleration at key locations on the bridge. The coupling conditions include: (1) The geometric compatibility condition is: the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the corresponding position of the bridge and the track irregularity. Specifically, assuming close wheel-rail contact, the first... Vertical displacement of each wheelset satisfy: ; in, The bridge is at the current position of the wheelset. The vertical displacement at the point (obtained by interpolation of the bridge displacement vector). It is the known value of track irregularity.

[0044] (2) The mechanical equilibrium relationship is: the force exerted by the wheel on the rail and the reaction force exerted by the rail on the wheel are equal in magnitude and opposite in direction. Specifically, the first... The force of each wheel on the bridge The reaction force of the rail on the wheel Satisfying the action-reaction relationship:

[0045] For example, by using the aforementioned geometric compatibility conditions and mechanical equilibrium relationships as constraints, the vehicle system dynamics equations and the bridge system dynamics equations are combined to form a unified time-varying nonlinear dynamics system. The solution to this coupled system is performed using numerical integration methods (such as the Newmark-β method), and its specific iterative process is as follows: Starting from the initial state, the total time for the train to cross the bridge is divided into several small time steps. Within each time step, the following calculations are performed sequentially: a) Based on the current position of the vehicle and the deformation of the bridge, and according to the geometric compatibility conditions, determine the contact point position between each wheelset and the rail and its corresponding track irregularity value. b) Based on the updated wheel-rail contact state and according to the mechanical equilibrium relationship, calculate the wheel-rail interaction force vector at the current moment using a wheel-rail force model (such as linear Hertzian contact theory). According to the principle of action and reaction, the force vector of the wheel on the bridge is... and Equal in size, opposite in direction; c) Calculate the force vector of the wheel on the bridge. Substituting the known load terms into the bridge system dynamics equations On the right side, simultaneously, the wheel-rail interaction force vector Substitute the equations into the right side of the vehicle system dynamics equations, and then use numerical integration methods (such as the Newmark-β method) to solve the coupled system, and calculate the displacement, velocity and acceleration responses of the vehicle and bridge systems at the next time step; d) Using the calculated displacement and velocity at the new moment as initial conditions, advance the time by one step and repeat steps a) to c) until the entire process of the train crossing the bridge is simulated.

[0046] Based on this, through the above iterative process, the vertical displacement and acceleration time history data of key locations on the bridge can finally be obtained.

[0047] In some implementations, in step S3, based on the vertical displacement and acceleration time history data, an objective function is constructed with the goal of suppressing bridge vibration. Its expression is: ; in, Acceleration time history data for key locations on the bridge; Vertical displacement at key locations on the bridge; and These are the weighting coefficients for acceleration and displacement, respectively, and they satisfy the normalization condition. ; Wherein, objective function The physical meaning of is a comprehensive evaluation index for the vibration response of bridges.

[0048] An optimization algorithm is used to minimize the objective function. To achieve the objective, an initial optimal set of parameters for a multi-tuned mass damper (MTMD) was calculated, including the total mass ratio. Tuning frequency ratio Damping ratio ; The optimization algorithm establishes the correlation between parameter optimization and vehicle-bridge coupled vibration analysis through the following process: (1) Parameter transformation: For each set of parameters to be optimized (total mass ratio) Tuning frequency ratio Damping ratio First, the dimensionless parameters are converted into the physical parameters of MTMD: Total mass ,in The generalized mass of the bridge's main girder; stiffness Among them, the tuning frequency , The target control frequency for the bridge; Damping coefficient .

[0049] (2) Establishment of the MTMD dynamic model: The dynamic behavior of the multi-tuned mass damper is described by the following equations of motion: ; in, Let be the displacement of the damper mass relative to the bridge. The acceleration at the bridge mounting point.

[0050] (3) System Coupling: The MTMD model is coupled to the vehicle-bridge coupled vibration analysis in step S202. Specifically, the reaction term generated by MTMD is added to the bridge system dynamic equations. The bridge system equations are then corrected as follows: ; in, This represents the force exerted by the damper on the bridge.

[0051] (4) Vibration response recalculation: For each set of parameters, the vehicle-bridge coupled vibration analysis is re-executed, and the coupled system is solved by numerical integration method (such as Newmark-β method) to obtain the vertical displacement and acceleration time history data of the bridge under the parameter combination.

[0052] (5) Calculation of objective function: Based on the newly obtained vibration response data, calculate the objective function. The output value.

[0053] (6) Optimization iteration: Using the pattern search method, within the range of parameters... Perform an iterative search. By comparing the output values ​​corresponding to different parameter combinations, update the parameters along the descent direction of the output value until the convergence criterion is met. Then, select the parameter combination that minimizes the output value. .

[0054] The above process ensures a close connection between parameter optimization and vehicle-bridge coupling analysis. By repeatedly simulating and evaluating the effect of the parameters, the optimal solution for the actual dynamic characteristics of the bridge is obtained.

[0055] In step S4, the specific implementation of the dynamic adjustment process is as follows: The operating parameter is specified as the damping coefficient of the multituned mass damper. In practice, the valve opening or orifice area inside the damper can be adjusted via an electro-hydraulic servo actuator to steplessly and continuously change the damping force, thereby achieving the desired damping coefficient. Real-time adjustment. Initial damping coefficient of the multi-tuned mass damper. The optimal damping ratio needs to be calculated based on the initial optimal parameter set obtained in step S3. Configure the settings. The configuration formula is based on TMD theory and is typically as follows: ,in and These represent the stiffness and mass of the MTMD element, respectively.

[0056] The controller employs a proportional-integral-derivative (PID) control algorithm, which is based on the deviation between real-time vibration data and the target vibration response value. Calculate the required real-time damping coefficient Its control law is defined by the following formula:

[0057] in, for The real-time damping coefficient that needs to be applied at any given time is the output of the controller; Set the initial damping coefficient of the damper for the initial optimal parameter set; for The control deviation at time is denoted as _____. , The preset target vibration response value is a constant. for The bridge vibration response value is measured in real time by a sensor system deployed on the bridge. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; This is a time variable, representing the current moment in the operation of the control system; The variable is the integral variable, representing the time from the start time 0 of the control system to the current time. The time span between; For control deviation function In the integral variable The function value at time t, its functional relationship with same; Indicates the integral variable The differential; Represents the time variable The differential.

[0058] Example 2 Figure 2 This is a structural schematic diagram of the vibration control device 100 for a steel-concrete composite box girder bridge provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the vibration control device 100 for a steel-concrete composite box girder bridge provided in Embodiment 2 of the present invention includes a data acquisition module 110, an acquisition module 120, a construction module 130, and an installation module 140. Among them, the acquisition module 110 acquires the geometric dimensions, material properties and interface connection parameters of the steel-concrete composite box girder bridge, and establishes a spatial finite element model considering the interface slip effect and shear lag effect based on the geometric dimensions, material properties and interface connection parameters. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate the evolution of interface friction and deformation, and the shear lag effect is characterized by the width-to-span ratio. The acquisition module 120, based on the spatial finite element model, takes load parameters including different train formations, speeds and densities, as well as track irregularity excitations, as input to perform vehicle-bridge coupled vibration analysis and acquire vertical displacement and acceleration time history data at key locations on the bridge. Module 130 is constructed to build an objective function based on vertical displacement and acceleration time history data, and an optimization algorithm is used to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio and damping ratio. Install module 140 by installing a multi-tuned mass damper at the location of maximum vibration response of the bridge and performing initial tuning according to the initial optimal parameter set; During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with the preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control.

[0059] The coolant flow distribution device for the liquid-cooled server provided in this embodiment 2 can realize the coolant flow distribution method for the liquid-cooled server in embodiment 1, and has the same technical effect as embodiment 1.

[0060] Example 3 Figure 3 This is a structural diagram of an electronic device according to Embodiment 3 of the present invention. Figure 3 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 3 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0061] like Figure 3 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0062] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0063] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0064] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard disk drives"). Disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disk drives for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0065] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0066] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12 / server / computer, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 3 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 3As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0067] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the vibration control method for steel-concrete composite box girder bridges provided in the embodiments of the present invention.

[0068] Example 4 Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the vibration control method for a steel-concrete composite box girder bridge as provided in the above embodiments.

[0069] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0071] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A vibration control method for a steel-concrete composite box girder bridge, characterized in that, Includes the following steps: S1. Collect the geometric dimensions, material properties and interface connection parameters of the steel-concrete composite box girder bridge. Based on the geometric dimensions, material properties and interface connection parameters, establish a spatial finite element model considering interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate interface friction and deformation evolution, and the shear lag effect is characterized by the width-to-span ratio. S2. Based on the aforementioned spatial finite element model, load parameters including different train formations, speeds, and densities, as well as track irregularity excitations, are input to perform vehicle-bridge coupled vibration analysis and obtain vertical displacement and acceleration time history data at key locations on the bridge. S3. Based on the vertical displacement and acceleration time history data, construct an objective function, and use an optimization algorithm to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio, and damping ratio. S4. Install the multi-tuned mass damper at the location of maximum vibration response of the bridge, and perform initial tuning according to the initial optimal parameter set; During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with a preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control. In step S1, the nonlinear dynamic constitutive relation is characterized by the Bouc-Wen model, which simulates stiffness degradation and energy dissipation during the slip process, and special interface elements or contact elements are set at the position representing the steel-concrete interface to embed the Bouc-Wen model. When the Bouc-Wen model is used, the shear force generated by the interface slip effect Characterized by the solutions to the following system of equations: ； ； in, The total shear force at the interface; This refers to the amount of interface sliding. This refers to the interface scrolling speed; This is the initial elastic stiffness; The ratio of post-yield stiffness to initial stiffness, ranging from... ; Let be a dimensionless internal state variable describing hysteresis behavior; Dimensionless parameters for controlling the shape and properties of hysteresis loops; Dimensionless internal state variables The derivative with respect to time; The operating parameter is specified as the damping coefficient of the multituned mass damper. By adjusting the valve opening or throttling orifice area in the internal oil circuit of the damper using an electro-hydraulic servo actuator, the damping force can be changed steplessly and continuously, thereby achieving the desired damping coefficient. Real-time adjustment of the initial damping coefficient of the multi-tuned mass damper The parameters are set according to the initial optimal parameter set. The controller employs a proportional-integral-derivative (PI-DI) control algorithm, based on the deviation between real-time vibration data and the target vibration response value. Calculate the required real-time damping coefficient Its control law is defined by the following formula: in, for The real-time damping coefficient that needs to be applied at any given time is the output of the controller; Set the initial damping coefficient of the damper for the initial optimal parameter set; for The control deviation at time is denoted as _____. , The preset target vibration response value is a constant. for The bridge vibration response value is measured in real time by a sensor system deployed on the bridge. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; This is a time variable, representing the current moment in the operation of the control system; The variable is the integral variable, representing the time from the start time 0 of the control system to the current time. The time span between; For control deviation function In the integral variable The function value at time t, its functional relationship with same; Indicates the integral variable The differential; Represents the time variable The differential; In step S3, based on the vertical displacement and acceleration time history data, an objective function is constructed with the goal of suppressing bridge vibration. Its expression is: ； in, Acceleration time history data for key locations on the bridge; Vertical displacement at key locations on the bridge; and These are the weighting coefficients for acceleration and displacement, respectively, and they satisfy the normalization condition. ; An optimization algorithm is used to minimize the objective function. To achieve the objective, the initial optimal parameter set for the multi-tuned mass damper was calculated, the initial optimal parameter set including the total mass ratio. Tuning frequency ratio Damping ratio ; The optimization algorithm establishes the correlation between parameter optimization and vehicle-bridge coupled vibration analysis through the following process: For each set of parameters to be optimized, substitute it into the dynamic model of the multi-tuned mass damper and couple it into the vehicle-bridge coupled vibration analysis, and re-execute the vibration response calculation; By comparing the objective functions corresponding to different parameter combinations The output value, along the objective function The output value is iteratively updated in the descent direction of the parameters until the convergence criterion is met; The optimization algorithm employs a pattern search method, performing iterative searches within a set parameter range, and comparing the objective functions corresponding to different parameter combinations. The output value of the objective function is selected to make the objective function... The parameter combination with the smallest output value is taken as the initial optimal parameter set; The ranges of the parameters are as follows: The parameter combination is as follows: .

2. The vibration control method for steel-concrete composite box girder bridge according to claim 1, characterized in that, In step S2, the vehicle-bridge coupled vibration analysis is performed through the following steps: S201. The train is simplified into a multi-rigid-body system consisting of a car body, bogies and wheelsets. The car body and bogies are connected by secondary suspension spring damping elements, and the bogies and wheelsets are connected by primary suspension spring damping elements. Based on the multibody dynamics theory, the dynamic equations of the vehicle system with the displacement, velocity and acceleration of each degree of freedom of the train as the basic unknowns are established. S202. Based on the aforementioned spatial finite element model, by assembling the overall mass matrix, damping matrix, and stiffness matrix, a dynamic equation for the bridge system with bridge node displacement, velocity, and acceleration as the basic unknowns is established. S203. Based on the wheel-rail interaction theory, establish the geometric compatibility conditions and mechanical equilibrium relationship between the wheelset and the rail. Couple the vehicle system dynamic equation and the bridge system dynamic equation into a time-varying nonlinear dynamic system through the geometric compatibility conditions and mechanical equilibrium relationship. Solve the time-varying nonlinear dynamic system using the numerical integration method to obtain the vertical displacement and acceleration time history data of key positions of the bridge.

3. The vibration control method for steel-concrete composite box girder bridge according to claim 2, characterized in that, In step S201, the vehicle system dynamics equations are expressed as follows: ； in, These are the vehicle's mass matrix, damping matrix, and stiffness matrix, respectively. These are the vehicle's displacement vector, velocity vector, and acceleration vector, respectively. This is the wheel-rail interaction force vector; In step S202, the dynamic equations of the bridge system are expressed as follows: ； in, These are the bridge's mass matrix, damping matrix, and stiffness matrix, respectively. These are the bridge's displacement vector, velocity vector, and acceleration vector, respectively. This is the force vector exerted by the wheel on the bridge. In step S203, the geometric compatibility condition is: the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the corresponding position of the bridge and the track irregularity value; The mechanical equilibrium relationship is as follows: the force exerted by the wheel on the rail and the reaction force exerted by the rail on the wheel are equal in magnitude and opposite in direction.

4. A vibration control system for a steel-concrete composite box girder bridge, employing the vibration control method for a steel-concrete composite box girder bridge as described in claim 1, characterized in that, include: The data acquisition module collects the geometric dimensions, material properties, and interface connection parameters of the steel-concrete composite box girder bridge. Based on the geometric dimensions, material properties, and interface connection parameters, it establishes a spatial finite element model that considers interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relation to simulate interface friction and deformation evolution, and the shear lag effect is characterized by the width-to-span ratio. The acquisition module, based on the spatial finite element model, inputs load parameters including different train formations, speeds and densities, as well as track irregularity excitations, to perform vehicle-bridge coupled vibration analysis and acquire vertical displacement and acceleration time history data at key locations on the bridge. The construction module constructs an objective function based on the vertical displacement and acceleration time history data, and uses an optimization algorithm to minimize the objective function to calculate the initial optimal parameter set of the multi-tuned mass damper. The initial optimal parameter set includes the total mass ratio, tuning frequency ratio, and damping ratio. The installation module is used to install the multi-tuned mass damper at the location of maximum vibration response of the bridge and to perform initial tuning according to the initial optimal parameter set. During bridge operation, a sensor system deployed on the bridge monitors the vibration response in real time, acquires real-time vibration data, and feeds it back to the controller. The controller compares the real-time vibration data with a preset target vibration response value and generates control commands based on the deviation between the two to dynamically adjust the operating parameters of the multi-tuned mass damper, thereby achieving adaptive vibration control.

5. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the vibration control method for steel-concrete composite box girder bridge as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vibration control method for a steel-concrete composite box girder bridge as described in any one of claims 1 to 3.

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