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

Abstract

The embodiment of the invention discloses a vibration control method and system for a steel-concrete combined box girder bridge, and the method comprises the steps: carrying out the vehicle-bridge coupling vibration analysis through building a space finite element model which accurately considers the interface slippage and shear hysteresis effect; an initial optimal parameter group of the multi-tuned mass damper is optimized and determined based on an analysis result, and installation tuning is carried out; in the operation stage of the bridge, vibration is monitored in real time through the sensor system, parameters of the damper are dynamically adjusted by the controller according to the deviation between measured data and a target value, and self-adaptive control is achieved. Thus, high-precision simulation, optimization design and intelligent control are combined, the problem that the control effect is poor due to model simplification and fixed parameters in a traditional method is solved, and vibration suppression accuracy and environmental adaptability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge engineering structure vibration control, and in particular to a vibration control method and system for a steel-concrete composite box girder bridge. BACKGROUND

[0002] Steel-concrete composite box girder bridges are widely used in modern railway and highway bridges due to their excellent mechanical properties and economy. However, under the action of dynamic loads such as high-speed trains, complex interface slip effects occur at the interface connection between the steel girder and the concrete bridge deck of such bridges, and the wide flange of the box girder also causes 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 (MTMD) have been proven to be an effective passive control device. However, traditional MTMD parameter design is usually based on simplified bridge models and does not fully consider the effects of interface slip and shear lag on the true dynamic characteristics of the bridge, resulting in a vibration reduction effect that often fails to meet expectations in actual applications. In addition, the train loads (such as marshalling and speed) that the bridge experiences during operation are dynamic, and once installed, the parameters of traditional MTMD are fixed and cannot adapt to such time-varying load conditions, leading to a decline in control effectiveness and insufficient adaptive ability under non-design conditions. SUMMARY

[0003] Therefore, the embodiments of the present application provide a vibration control method, device, and equipment for a steel-concrete composite box girder bridge and a storage medium to solve the technical problem of unsatisfactory control effectiveness of traditional vibration control methods due to the failure to accurately consider nonlinear effects such as bridge interface slip and shear lag and load time variation.

[0004] To achieve the above-mentioned purpose, the technical solution of the present application is as follows: In a first aspect, the present application provides a vibration control method for a steel-concrete composite box girder bridge, comprising the following steps: S1, collecting the geometric dimensions, material properties, and interface connection parameters of the steel-concrete composite box girder bridge, and establishing a spatial finite element model considering interface slip effects and shear lag effects based on the geometric dimensions, material properties, and interface connection parameters, wherein the interface slip effects are characterized by introducing a nonlinear dynamic constitutive relationship that simulates interface friction and deformation evolution, and the shear lag effects are characterized by a width-to-span ratio; S2, based on the spatial finite element model, inputting load parameters including different train marshalling, speed, and density, and track irregularity excitation, performing vehicle-bridge coupled vibration analysis, and obtaining vertical displacement and acceleration time history data of key positions of the bridge; S3, constructing a target function based on the vertical displacement and the acceleration time history data, and using an optimization algorithm to minimize the target function, to calculate an initial optimal parameter set of the multiple tuned mass damper, the initial optimal parameter set including a total mass ratio, a tuned frequency ratio and a damping ratio; S4, installing the multiple tuned mass damper at a position of maximum vibration response of the bridge, and initially tuning according to the initial optimal parameter set; During the operation of the bridge, the vibration response is monitored in real time by a sensor system arranged on the bridge, real-time vibration data are obtained and fed back to a controller, the controller compares the real-time vibration data with a preset target vibration response value, generates a control instruction according to the deviation between the two, and dynamically adjusts the working parameters of the multiple tuned mass damper to achieve adaptive vibration control.

[0005] Further, in step S1, the nonlinear dynamic constitutive relation is characterized by a Bouc-Wen model simulating the stiffness degradation and energy dissipation in the process of slip; When the Bouc-Wen model is used, the shear force generated by the interface slip effect is characterized by the solution of the following equation set: ; ; wherein, is the total shear force of the interface; is the interface slip amount; is the interface slip velocity; is the initial elastic stiffness; is the ratio of the post-yield stiffness to the initial stiffness, ranging from 0 to 1; ; is a dimensionless internal state variable describing the hysteresis behavior; is a dimensionless parameter controlling the shape and characteristics of the hysteresis loop; is a dimensionless internal state variable derivative with respect to time.

[0006] Further, in step S2, the train-bridge coupled vibration analysis is achieved by the following steps: S201, simplifying the train into a multi-rigid-body system composed of a car body, a bogie and a wheelset, connecting the car body and the bogie through two secondary suspension spring-damper elements, connecting the bogie and the wheelset through one primary suspension spring-damper element, and establishing a vehicle system dynamics equation based on the multi-body dynamics theory, with the displacement, velocity and acceleration of each degree of freedom of the train as the basic unknown quantities; S202, based on the space finite element model, through the group set integral mass matrix, damping matrix and stiffness matrix, the bridge system dynamics equation is established with bridge node displacement, velocity, acceleration as basic unknown quantity; S203, based on wheel-rail interaction theory, the geometric compatibility condition and mechanical equilibrium relationship between wheel set and rail are established, the vehicle system dynamics equation and the bridge system dynamics equation are coupled into time-varying nonlinear dynamics system through the geometric compatibility condition and mechanical equilibrium relationship, and the time-varying nonlinear dynamics system is solved by using numerical integration method, so as to obtain the vertical displacement and acceleration time history data of the bridge key position.

[0007] Further, in step S201, the vehicle system dynamics equation is expressed as: Among them, The mass matrix, damping matrix and stiffness matrix of the vehicle respectively; The displacement vector, velocity vector and acceleration vector of the vehicle respectively; The wheel-rail interaction force vector; In step S202, the bridge system dynamics equation is expressed as: Among them, The mass matrix, damping matrix and stiffness matrix of the bridge respectively; The displacement vector, velocity vector and acceleration vector of the bridge respectively; The force vector of the wheel to the bridge; In step S203, the geometric compatibility condition is that the vertical displacement of the wheel set 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 that the force of the wheel acting on the rail and the reaction force of the rail on the wheel are equal in size and opposite in direction. Further, in step S3, based on the vertical displacement and acceleration time history data, a target function is constructed with the bridge vibration suppression as the optimization objective , the expression is: Among them, The acceleration time history data of the bridge key position; The vertical displacement of the bridge key position; And The weight coefficients of acceleration and displacement respectively, and satisfy the normalization condition ; An optimization algorithm is used to minimize the target function ​​​To achieve the goal, the initial optimal parameter set of the multiple tuned mass damper is calculated, which includes total mass ratio , tuned frequency ratio and damping ratio ; Wherein, the optimization algorithm establishes the correlation between parameter optimization and vehicle-bridge coupled vibration analysis through the following process: For each combination of parameter set to be optimized, it is substituted into the dynamic model of the multiple tuned mass damper and coupled to the vehicle-bridge coupled vibration analysis, and the vibration response calculation is re-executed; By comparing the output values of the objective function corresponding to different parameter combinations, the parameters are iteratively updated in the direction of the output value of the objective function, until the convergence criterion is met; The optimization algorithm uses the pattern search method to iteratively search within the set parameter range, selects the parameter combination that minimizes the output value of the objective function as the initial optimal parameter set by comparing the output values of the objective function corresponding to different parameter combinations; Wherein, the parameter range is respectively: ; the parameter combination is: .

[0008] Further, the working parameter is specified as the damping coefficient of the multiple tuned mass damper, which is continuously adjusted by an electro-hydraulic servo actuator, and the initial damping coefficient of the multiple tuned mass damper is set according to the initial optimal parameter set; The controller uses a proportional-integral-derivative control algorithm to calculate the required real-time damping coefficient according to the deviation between real-time vibration data and target vibration response value, and its control law is defined as follows:

[0009] Wherein, is the real-time damping coefficient required at time, which is the output of the controller; is the initial damping coefficient of the damper set by the initial optimal parameter set; is the control deviation at time, whose value is , is the preset target vibration response value, which is a constant; is 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] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication; The memory stores computer-executable instructions; When the processor executes the computer-executable instructions stored in the memory, the method for vibration control of the steel-concrete composite box girder bridge of the first aspect is implemented.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method for vibration control of the steel-concrete composite box girder bridge of the first aspect is implemented.

[0013] Through the above technical solution, by establishing a spatial finite element model considering the interface slip effect and the shear lag effect, the nonlinear mechanical behavior of the steel-concrete composite structure is accurately captured, providing a high-precision calculation basis for subsequent vibration analysis. Based on this model, the real dynamic response of the bridge under train load can be accurately obtained through vehicle-bridge coupling vibration analysis, overcoming the calculation deviation caused by traditional models. The initial optimal parameters of the multiple tuned mass damper are obtained by constructing the objective function with the accurate response data and optimizing, ensuring that the vibration reduction device has optimal passive control performance from the beginning of installation. Moreover, by real-time monitoring of the bridge vibration response and dynamically adjusting the damper parameters using the adaptive control algorithm, the control system can actively adapt to the time-varying characteristics of the train load in the operation process, forming a complete technical closed loop from accurate modeling, optimization design to intelligent control, thereby significantly improving the effect reliability and environmental adaptability of the vibration control of the steel-concrete composite box girder bridge. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which form a part of the present application, are used to provide further understanding of the present application, and the illustrative embodiments thereof and their descriptions are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings: Figure 1 Flowchart of the method for vibration control of the steel-concrete composite box girder bridge provided for the first embodiment of the present application; Figure 2 Structure diagram of the vibration control device for the steel-concrete composite box girder bridge provided for the second embodiment of the present application; Figure 3 Structure diagram of the electronic device hardware provided for the third embodiment of the present application. DETAILED DESCRIPTION

[0015] The application will be described in further detail below with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application. In addition, it should be noted that only parts related to the application are shown in the drawings for the convenience of description, rather than all structures.

[0016] In order to clearly describe the technical solutions of the embodiments of the application, the following briefly introduces some terms and technologies involved in the embodiments of the application: Steel-concrete composite box girder bridge: refers to a bridge structure jointly stressed by a reinforced concrete bridge deck and a steel box girder combined through shear connectors. The core feature lies in the complementary effect of good compressive performance of concrete and excellent tensile performance of steel.

[0017] Spatial finite element model: the model discretizes the bridge structure into a large number of small units with geometric dimensions (such as beam elements, plate elements, solid elements), and connects them through nodes. Each unit is assigned real material properties, which can simulate the mechanical response of the structure in three spatial directions.

[0018] Interface slip effect: refers to the relative small displacement of the steel girder and the concrete slab at the interface in the steel-concrete composite beam under the action of load. This slip will lead to stiffness degradation and deformation increase of the composite beam, and is a key nonlinear factor affecting the accurate calculation of the composite beam. The application accurately characterizes it by introducing a nonlinear dynamic constitutive relationship.

[0019] Shear lag effect: also known as "shear lag", in a wide box girder, when the beam is bent, the normal stress in the flange plate is unevenly distributed, and the stress of the flange far from the web will be less than the calculated value according to the simple beam theory. The application quantifies its influence on the overall stiffness through the key geometric parameter of the width-to-span ratio (the ratio of the bridge width to the span).

[0020] Nonlinear dynamic constitutive relationship: refers to a mathematical model used to describe the relationship between force and deformation of materials or contact interfaces during loading, which changes with load history, velocity and other factors. In the application, it specifically refers to a relationship for simulating the complex mechanical behavior of steel-concrete interface friction, slip, stiffness degradation and energy dissipation, and the Bouc-Wen model is used.

[0021] Vehicle-bridge coupled vibration analysis: a dynamic analysis method that considers the vehicle model and the bridge model as an interactive whole system. When analyzing, not only the influence of bridge deformation on vehicle dynamics is considered, but also the influence of vehicle motion generated dynamic load on bridge vibration is considered, and the two are connected through wheel-rail interaction to solve.

[0022] Multiple tuned mass damper: A passive vibration control device composed of multiple tuned mass dampers (TMDs) with different natural frequencies connected in parallel. Its working principle is to counteract the structural vibration energy through the inertial force of the mass block. Compared with a single TMD, it has stronger adaptability to frequency changes due to a wider frequency band covered.

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

[0024] Geometric compatibility condition: In the vehicle-bridge coupling system, it refers to the coordination relationship of displacements at the wheel-rail contact points. In this invention, it is assumed that the wheel-rail is in close contact, i.e. 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 track irregularity value, which is the bridge connecting the two dynamic equations.

[0025] Mechanical equilibrium relationship: In the vehicle-bridge coupling system, it refers to the interaction relationship of forces at the wheel-rail contact points. That is, the force of the wheel on the rail and the reaction force of the rail on the wheel are equal in size and opposite in direction, which is the basis for solving the vehicle and bridge dynamics equations simultaneously.

[0026] Proportional-integral-derivative control algorithm: A widely used feedback control law, i.e. the output of the controller consists of three parts: the proportional term reacts to the current deviation, determines the response speed; the integral term accumulates historical deviations to eliminate steady-state errors; the derivative term predicts the trend of deviation change to increase system damping.

[0027] In the prior art, passive tuned mass damper (such as multiple tuned mass damper, MTMD) is a widely used technical means for vibration control of long-span bridges, especially steel-concrete composite box girder bridges. Due to its simple structure, high reliability and no need for external energy, it plays an important role in suppressing excessive vibration of bridges caused by train, wind load and other dynamic loads. However, the existing vibration control method based on TMD / MTMD often fails to achieve the theoretical expectation when applied to steel-concrete composite box girder bridges, which have significant nonlinear mechanical characteristics. The main bottlenecks are reflected in the model accuracy, device design method and environmental adaptability.

[0028] Firstly, the prior art usually adopts a highly simplified linear or equivalent beam model to establish the dynamic model of the bridge for vibration analysis and controller design. Such a model often ignores the nonlinear effect of interface slip between the steel beam and the concrete slab and fails to accurately consider the significant shear lag effect in the wide box girder section. The interface slip of the composite beam leads to the degradation of the overall stiffness of the structure, while the shear lag effect affects the stress distribution and dynamic characteristics of the structure. Ignoring these key nonlinear factors causes essential deviation between the theoretical model and the actual dynamic response of the bridge, resulting in that any subsequent analysis and design based on the model are built on an inaccurate physical basis from the beginning.

[0029] Secondly, in the parameter design of the MTMD, the existing method mostly relies on the natural frequency and mode shape calculated by the simplified model of the bridge, and the optimization objective function is often based on idealized dynamic response. The disconnection between the "design model" and the "actual structure" leads to the fact that the optimal parameters (such as tuning frequency and damping ratio) of the MTMD are not the optimal solution for the real dynamic characteristics of the bridge. Even if the device is carefully manufactured and installed, due to the deviation in the design basis itself, the "detuning" phenomenon often occurs in actual application, that is, the optimal working frequency band of the MTMD does not match the actual main frequency of the bridge vibration, thereby greatly weakening the potential of passive vibration reduction.

[0030] Thirdly, the existing technical solutions mostly use the MTMD as a passive control device with fixed parameters, lacking the ability to self-adjust according to the actual load condition during the operation of the bridge. The train load (such as different formation, speed, and axle load) borne by the steel-concrete composite box girder bridge during operation is dynamically changing, which can lead to changes in the vibration characteristics of the bridge. The MTMD with fixed parameters only has optimal performance near the design working condition, and its control effect will decrease significantly once the load condition deviates from the design working condition. This inherent defect of being unable to adapt to time-varying loads limits its 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 solve the problem of unsatisfactory control effect caused by the failure of traditional vibration control methods to accurately consider the nonlinear effects of interface slip, shear lag, and load time variation. The method can accurately depict the nonlinear mechanical behavior of the composite beam and construct an intelligent vibration control strategy that can dynamically adjust according to the actual state of the bridge.

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

[0033] Embodiment one Figure 1 A flowchart of a vibration control method for a steel-concrete composite box girder bridge according to Embodiment One of the present application is shown in FIG. 1. The vibration control method for a steel-concrete composite box girder bridge includes the following steps: Figure 1 S1, collecting geometric dimensions, material properties and interface connection parameters of the steel-concrete composite box girder bridge, and establishing a spatial finite element model considering interface slip effect and shear lag effect based on the geometric dimensions, material properties and interface connection parameters, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by a width-to-span ratio; Specifically, in step S1, the geometric dimensions, material properties and interface connection parameters required for model construction are obtained according to design data and measured data, and on this basis, a spatial finite element model of the bridge is established. The spatial finite element model has the characteristics of considering both interface slip effect and shear lag effect. The interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship, which can simulate the friction and deformation evolution behavior of the interface under stress; the shear lag effect is characterized by the width-to-span ratio.

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

[0035] S3, constructing a target function based on the vertical displacement and acceleration time history data, and using an optimization algorithm to minimize the target function to calculate an initial optimal parameter set of the multiple tuned mass dampers, the initial optimal parameter set including a total mass ratio, a tuned frequency ratio and a damping ratio; Specifically, in step S3, a target function is constructed using the vertical displacement and acceleration data obtained in step S2, and an optimization algorithm is used to minimize the target function to calculate an initial optimal parameter set of the multiple tuned mass dampers. The parameter set includes a total mass ratio, a tuned frequency ratio and a damping ratio.

[0036] ​S4, installing multiple tuned mass dampers at the maximum vibration response position of the bridge, and performing initial tuning according to the initial optimal parameter set; During the operation of the bridge, the vibration response is monitored in real time by the sensor system arranged on the bridge, real-time vibration data are obtained and fed back to the controller, the controller compares the real-time vibration data with the preset target vibration response value, generates a control instruction according to the deviation, and dynamically adjusts the working parameters of the multiple tuned mass dampers, so that adaptive vibration control is realized.

[0037] Specifically, in step S4, the multiple tuned mass dampers are first installed at the maximum vibration response position of the bridge, and initial tuning is performed according to the initial optimal parameter set obtained in step S3. Subsequently, during the operation of the bridge, the vibration is monitored in real time by the sensor system, real-time vibration data are obtained and fed back to the controller. The controller compares the real-time data with the preset target vibration response value, generates a control instruction according to the deviation, dynamically adjusts the working parameters of the dampers, and realizes adaptive vibration control. Step S4 completes the entire process from device installation, initial setting to adaptive control.

[0038] Therefore, the vibration control method for the steel-concrete composite box girder bridge provided by the present application solves the technical problems in the prior art that the design basis is inaccurate due to model simplification, and that the fixed parameter device is difficult to adapt 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. The method has the following significant advantages: First, in the model establishment stage (step S1), the present application accurately characterizes the slip effect of the steel-concrete interface by introducing a nonlinear dynamic constitutive relationship, and characterizes the shear lag effect by using a width-span ratio, thereby establishing a spatial finite element model that can reflect the true stress characteristics of the composite beam. This high-precision model effectively overcomes the theoretical deviation of traditional simplified models, providing a reliable physical basis for all subsequent analyses.

[0039] Secondly, in the dynamic response acquisition stage (step S2), based on the above high-precision model, vehicle-bridge coupling vibration analysis is performed, which can accurately simulate the interaction between train load and bridge, and obtain the real vertical displacement and acceleration data of the bridge at key positions. This step ensures that the input data for the vibration reduction device design has high credibility, avoiding design deviation caused by inaccurate input data from the source.

[0040] In the device parameter optimization stage (step S3), the objective function is constructed by using the real dynamic response data, and the optimal parameter combination (including total mass ratio, tuning frequency ratio and damping ratio) of the multiple tuned mass damper is solved by using the optimization algorithm. This design process makes the parameters of the vibration reduction device set based on the real dynamic characteristics of the bridge, ensuring that the passive control performance of the device in the initial state is theoretically optimal.

[0041] Finally, in the control implementation stage (step S4), the method does not stop at passive control, but through real-time monitoring of the vibration response during operation, and the controller dynamically adjusts the working parameters of the damper according to the deviation between the measured data and the target value. This adaptive control mechanism enables the system to actively adapt to changes in external excitations such as train loads, effectively compensating for the performance degradation of the initial fixed parameters outside the design working conditions, forming a closed loop of "precise initial design" and "online intelligent adjustment" to enhance each other, and ultimately achieving continuous and stable vibration control effect in all working conditions.

[0042] In some embodiments, in step S1, the nonlinear dynamic constitutive relationship is characterized by the Bouc-Wen model which simulates the stiffness degradation and energy dissipation during slip; When the Bouc-Wen model is used, the shear force generated by the interface slip effect is characterized by the solution of the following equation set: ; ; wherein, is the total shear force of the interface; is the interface slip amount; is the interface slip velocity; is the initial elastic stiffness; is the ratio of post-yield stiffness to initial stiffness, ranging from 0 to 1; ; is a dimensionless internal state variable describing the hysteresis behavior; is a dimensionless parameter controlling the shape and characteristics of the hysteresis loop; is the dimensionless internal state variable derivative with respect to time.

[0043] When implemented in a spatial finite element model, a special interface element or contact element needs to be set at the position representing the steel-concrete interface, and the above Bouc-Wen model is implanted as the constitutive relationship (i.e. force-slip relationship) of the element. In dynamic time history analysis, for each interface element, at each time step, the current calculation results (interface slip amount and interface slip velocity ), the dimensionless internal state variables are solved by numerical integration method (such as fourth-order Runge-Kutta method), and then the total shear force of the interface is calculated The total shear force of the interface will participate in the balance iteration of the whole structure system In some embodiments, in step S2, the vehicle-bridge coupled vibration analysis is realized by the following steps, which constitute a complete dynamic simulation process: S201, vehicle system dynamics equation establishment: The train is simplified as a multi-rigid-body system, specifically, each vehicle is modeled as a system composed of a car body, a bogie and a wheelset, the car body is connected with the bogie through a secondary suspension spring-damping element, and the bogie is connected with the wheelset through a primary suspension spring-damping element. Based on the multi-body dynamics theory (such as Lagrange equation), the vehicle system dynamics equation of the whole vehicle is established, and its standard form is: ; wherein, are the mass matrix, the damping matrix and the stiffness matrix of the vehicle respectively; are the displacement vector, the velocity vector and the acceleration vector of the vehicle respectively, which contain the degrees of freedom of the car body, the bogie and the wheelset; is the wheel-rail interaction force vector; S202, bridge system dynamics equation establishment: Based on the spatial finite element model, the global mass matrix, the damping matrix and the stiffness matrix are assembled to establish the bridge system dynamics equation with the bridge node displacement, velocity and acceleration as the basic unknown quantities: ; wherein, are the mass matrix, the damping matrix and the stiffness matrix of the bridge respectively; are the displacement vector, the velocity vector and the acceleration vector of the bridge respectively; is the force vector of the wheelset acting on the bridge; S203, system coupling and solving: Based on the wheel-rail interaction theory, the geometric compatibility conditions and the mechanical equilibrium relationship between the wheelset and the rail are established, the vehicle system dynamics equation and the bridge system dynamics equation are coupled into a time-varying nonlinear dynamics system through the geometric compatibility conditions and the mechanical equilibrium relationship, and the time-varying nonlinear dynamics system is solved by using the numerical integration method, so as to obtain the vertical displacement and acceleration time history data of the bridge key positions, wherein the coupling conditions include: (1) The geometric compatibility condition is that the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the bridge at the corresponding position and the track irregularity value, specifically, it is assumed that the wheel and rail are in close contact, the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the bridge at the corresponding position and the track irregularity value, and the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the bridge at the corresponding position and the track irregularity value. Vertical displacement of wheelset Satisfies: ; Wherein, is the vertical displacement of the bridge at the current position of the wheelset (obtained by bridge displacement vector interpolation), is the known track irregularity value.

[0044] (2) The mechanical equilibrium relationship is that the force of the wheel acting on the rail and the counterforce of the rail acting on the wheel are equal in size and opposite in direction, specifically, the force of the first wheelset acting on the bridge and the counterforce of the rail acting on the wheel satisfy the action and reaction relationship:

[0045] Exemplarily, the above geometric compatibility conditions and mechanical equilibrium relationship are taken as constraints, the vehicle system dynamics equation and the bridge system dynamics equation are coupled to form a unified time-varying nonlinear dynamics system. The solution of the coupled system is carried out by using a numerical integration method (such as the Newmark-β method), and the specific iteration process is as follows: starting from the initial state, the total time of the train passing through the bridge is divided into a plurality of small time steps, and in each time step, the following calculations are performed in sequence: a) According to the position of the vehicle and the deformation mode of the bridge at the current time, the contact point position of each wheelset and the corresponding track irregularity value are determined according to the geometric compatibility condition; b) Based on the updated wheel-rail contact state and according to the mechanical equilibrium relationship, the wheel-rail interaction force vector at the current time is calculated through the wheel-rail force model (such as the linear Hertz contact theory); According to the action and reaction principle, the force vector of the wheel acting on the bridge is equal in size and opposite in direction to ; c) The calculated force vector of the wheel acting on the bridge is taken as a known load term and substituted into the right side of the bridge system dynamics equation , and the wheel-rail interaction force vector is substituted into the right side of the vehicle system dynamics equation, then the coupled system is solved by using a numerical integration method (such as the Newmark-β method) to calculate the displacement, velocity and acceleration response of the vehicle and bridge system at the next time step; d) The calculated displacement and velocity at the new time are taken as initial conditions, the time is advanced by one step, and steps a) to c) are repeated until the entire process of the train passing through the bridge is simulated.​

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

[0047] In some embodiments, in step S3, based on the vertical displacement and acceleration time history data, a target function is constructed with the optimization objective of suppressing the vibration of the bridge , the expression of which is: ; wherein, is the acceleration time history data of the key position of the bridge; is the vertical displacement of the key position of the bridge; and are the weight coefficients of the acceleration and displacement, respectively, and satisfy the normalization condition ; wherein, the physical meaning of the target function is a comprehensive evaluation index of the vibration response of the bridge.

[0048] An optimization algorithm is used to minimize the target function as the target, to calculate the initial optimal parameter set of the multiple tuned mass damper (MTMD), which includes the total mass ratio , the tuned frequency ratio and the damping ratio ; wherein, the optimization algorithm establishes the correlation between parameter optimization and vehicle-bridge coupled vibration analysis through the following process: (1) Parameter conversion: for each set of parameter combinations to be optimized (total mass ratio , tuned frequency ratio and damping ratio ), first convert the dimensionless parameters into physical parameters of the MTMD: total mass , wherein is the generalized mass of the bridge main beam; stiffness , wherein the tuned frequency , is the target control frequency of the bridge; damping coefficient .

[0049] (2) Establishment of the dynamic model of the MTMD: the dynamic behavior of the multiple tuned mass damper is described by the following motion equation: ; wherein, is the displacement of the damper mass relative to the bridge, is the acceleration of the bridge installation point.

[0050] (3) System coupling: coupling the MTMD model into the vehicle-bridge coupled vibration analysis in step S202. Specifically, an anti-force term generated by the MTMD is added into the bridge system dynamics equation. The bridge system equation is revised as: ; where, is the force exerted by the damper on the bridge.

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

[0052] (5) Objective function calculation: based on the newly obtained vibration response data, the output value of the objective function is calculated.

[0053] (6) Optimization iteration: using the pattern search method, an iterative search is performed within the range of the parameters . By comparing the output values corresponding to different parameter combinations, the parameters are updated in the direction of the output value decrease until the convergence criterion is met. The parameter combination that minimizes the output value at this time .

[0054] The above process ensures the close association of parameter optimization and vehicle-bridge coupled analysis, and the optimal solution for the real dynamic characteristics of the bridge is obtained by repeatedly evaluating the effect of the parameters.

[0055] In step S4, the specific implementation of the dynamic adjustment process is as follows: The working parameter is specified as the damping coefficient of the multiple tuned mass damper . In specific implementation, the valve opening or orifice area of the damper internal oil circuit can be adjusted by an electro-hydraulic servo actuator, thereby continuously and steplessly changing the damping force to realize real-time adjustment of the damping coefficient . The initial damping coefficient of the multiple tuned mass damper needs to be set according to the optimal damping ratio in the initial optimal parameter combination calculated in step S3 . The setting formula is based on the TMD theory and is usually , where and are the stiffness and mass of the MTMD unit, respectively.

[0056] The controller uses a proportional-integral-derivative control algorithm (PID control algorithm), which calculates the required real-time damping coefficient according to the deviation between the real-time vibration data and the target vibration response value 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 space finite element model, inputs load parameters containing different train formations, speeds and densities and track irregularity excitations, performs train-bridge coupled vibration analysis, and acquires vertical displacement and acceleration time history data of a bridge key position; The construction module 130 constructs an objective function based on the vertical displacement and acceleration time history data, and calculates an initial optimal parameter set of the multiple tuned mass dampers by using an optimization algorithm to minimize the objective function, wherein the initial optimal parameter set includes a total mass ratio, a tuned frequency ratio and a damping ratio; The installation module 140 installs the multiple tuned mass dampers at the maximum vibration response position of the bridge, and performs initial tuning according to the initial optimal parameter set; During the operation of the bridge, the vibration response is monitored in real time through the sensor system arranged on the bridge, real-time vibration data are acquired and fed back to the controller, the controller compares the real-time vibration data with the preset target vibration response value, generates a control instruction according to the deviation between the two, and dynamically adjusts the working parameters of the multiple tuned mass dampers to realize adaptive vibration control.

[0059] The cooling liquid flow distribution device of the liquid cooling server provided in the embodiment two can realize the cooling liquid flow distribution method of the liquid cooling server in the embodiment one, and has the same technical effects as the embodiment one.

[0060] Embodiment three Figure 3 A structure diagram of an electronic device according to the embodiment three of the present application, Figure 3 A block diagram of an exemplary electronic device 12 suitable for implementing an embodiment of the present application is shown. Figure 3 The electronic device 12 shown is merely one example and should not be taken as limiting the functionality or use of embodiments of the present application.

[0061] As Figure 3 shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 can include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 to the processing unit 16.

[0062] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics bus, a processor or local bus using any of a variety of bus architectures including, for example, an industry standard architecture (ISA), micro-channel architecture (MAC), enhanced ISA (EISA), Video Electronics Standards Association (VESA) local bus, and a 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 is located either internally or externally to electronic device 12, including both volatile and nonvolatile media, removable and non-removable media.

[0064] System memory 28 can 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 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (typically called a "hard drive"). A magnetic disk drive can also be provided for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive can be provided for reading from or writing to a removable, non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media). In these instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

[0065] Program / utility 40, having a set (at least one) of program modules 42, can be stored in, for example, system memory 28 by way of example, such programs can include an operating system, one or more application programs, other program modules, and program data, each or a combination thereof, used by the processes of embodiments of the application. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0066] Electronic device 12 can also communicate with one or more external devices 14 such as a keyboard or pointing device, a display 24, etc. and also with one or more devices that enable a user to interact with electronic device 12 / server / computer and / or any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication can occur via input / output (I / O) interface(s) 22. Still yet, electronic device 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via network adapter 20. As depicted, network adapter 20 communicates with the other components of electronic device 12 via bus 18. It should be appreciated that although not shown, other hardware and / or software components that are used in connection with the Figure 3 illustrated, network adapter 20 communicates with the other components of electronic device 12 via bus 18. It should be appreciated that although not shown, other hardware and / or software components that are used in connection with the Figure 3Other hardware and / or software modules can be used in conjunction with electronic device 12, as shown in FIG. 1, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0067] Processing unit 16 performs various function applications and data processing by running programs stored in system memory 28, such as implementing the vibration control method of the steel-concrete composite box girder bridge according to the embodiments of the present application.

[0068] Embodiment Four The embodiments of the present application also provide a storage medium containing computer executable instructions, which, when executed by a computer processor, are used to perform the vibration control method of the steel-concrete composite box girder bridge according to the above embodiments.

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

[0070] The computer readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave, in which computer readable program code is embodied. Such propagated data signals can take a wide variety of forms, including but not limited to electro-magnetic signals, optical signals, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a storage medium, that is capable of storing the program for use by or in connection with the instruction execution system, apparatus or device.

[0071] The program code contained on the computer readable medium can be transmitted using any suitable medium, including, but not limited to, wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the above.

[0072] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

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

Claims

1. A method of vibration control of a steel-concrete composite box girder bridge, characterized by, The method comprises the following steps: S1, collecting the geometric size, material properties and interface connection parameters of a steel-concrete composite box girder bridge, and establishing a spatial finite element model considering interface slip effect and shear lag effect based on the geometric size, material properties and interface connection parameters, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by a width-to-span ratio; S2, based on the spatial finite element model, inputting load parameters including different train formations, speeds and densities and track irregularity excitations, performing vehicle-bridge coupled vibration analysis, and obtaining vertical displacement and acceleration time history data of the bridge at key positions; S3, based on the vertical displacement and acceleration time history data, constructing an objective function, and using an optimization algorithm to minimize the objective function to obtain an initial optimal parameter set of the multiple tuned mass dampers, the initial optimal parameter set including a total mass ratio, a tuned frequency ratio and a damping ratio; S4, installing the multiple tuned mass dampers at the maximum vibration response position of the bridge and tuning the multiple tuned mass dampers according to the initial optimal parameter set. During the operation of the bridge, a sensor system arranged on the bridge is used to monitor the vibration response in real time, real-time vibration data are obtained and fed back to a controller, the controller compares the real-time vibration data with a preset target vibration response value, generates a control instruction according to the deviation between the two, dynamically adjusts the working parameters of the multiple tuned mass dampers, and realizes adaptive vibration control.

2. The vibration control method of a steel-concrete composite box girder bridge according to claim 1, characterized by: In step S1, the nonlinear dynamic constitutive relationship is characterized by a Bouc-Wen model simulating stiffness degradation and energy dissipation in the slip process; When the Bouc-Wen model is adopted, the shear force generated by the interface slip effect is characterized by the solution of the following system of equations: ； ； where, is the total interfacial shear force; is the interfacial slip; is the interfacial slip velocity; is the initial elastic stiffness; is the ratio of post-yield stiffness to initial stiffness, in the range ; is a dimensionless internal state variable that describes the hysteretic behavior; is a dimensionless parameter that controls the shape and characteristics of the hysteretic loop; is the dimensionless internal state variable derivative with respect to time.

3. The vibration control method of a steel-concrete composite box girder bridge according to claim 1, characterized by, In step S2, the vehicle-bridge coupled vibration analysis is realized by the following steps: S201, simplifying a train into a multi-rigid-body system composed of a car body, a bogie and a wheel set, connecting the car body and the bogie through secondary suspension spring-damping elements, connecting the bogie and the wheel set through primary suspension spring-damping elements, and establishing a vehicle system dynamics equation taking the displacement, velocity and acceleration of each degree of freedom of the train as basic unknown quantities based on the multi-body dynamics theory; S202, based on the spatial finite element model, establishing a bridge system dynamics equation taking the displacement, velocity and acceleration of the bridge nodes as basic unknown quantities by assembling the mass matrix, the damping matrix and the stiffness matrix; S203, based on the wheel-rail interaction theory, establishing geometric compatibility conditions and mechanical equilibrium relationships between the wheel set and the rail, coupling the vehicle system dynamics equation and the bridge system dynamics equation into a time-varying nonlinear dynamics system through the geometric compatibility conditions and the mechanical equilibrium relationships, and solving the time-varying nonlinear dynamics system by using a numerical integration method, thereby obtaining the vertical displacement and acceleration time history data of the bridge at key positions.

4. The vibration control method of a steel-concrete composite box girder bridge according to claim 3, characterized by, In step S201, the vehicle system dynamics equation is represented as: ； wherein, M, C, K are respectively the mass matrix, the damping matrix and the stiffness matrix of the vehicle; X, V, A are respectively the displacement vector, the velocity vector and the acceleration vector of the vehicle; F is the wheel-rail interaction force vector; In step S202, the bridge system dynamics equation is represented as: ； wherein, M, C, and K are respectively the mass matrix, the damping matrix, and the stiffness matrix of the bridge; X, V, and A are respectively the displacement vector, the velocity vector, and the acceleration vector of the bridge; F is the force vector of the vehicle on the bridge; In step S203, the geometric compatibility condition is that the vertical displacement of the wheelset is equal to the sum of the vertical displacement of the bridge at the corresponding position and the track irregularity value; The mechanical equilibrium relationship is that the force of the wheel acting on the rail is equal in size and opposite in direction to the reaction force of the rail on the wheel.

5. The vibration control method of a steel-concrete composite box girder bridge according to claim 3, characterized by, In step S3, a target function is constructed with the vertical displacement and acceleration time history data as the optimization objective to suppress the bridge vibration The expression is: ； wherein, is the acceleration time history data at the critical location of the bridge; is the vertical displacement at the critical location of the bridge; and are the weight coefficients of acceleration and displacement respectively, and satisfy the normalization condition ; using an optimization algorithm to minimize an objective function To this end, an initial optimal set of parameters of a multiple tuned mass damper is calculated, said initial optimal set of parameters comprising a total mass ratio , a tuned frequency ratio and a 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 parameter combinations to be optimized, substitute it into the dynamic model of the multiple tuned mass damper and couple it to the vehicle-bridge coupled vibration analysis to re-execute the vibration response calculation. By comparing the output values of the objective function corresponding to different parameter combinations , the parameters are iteratively updated in the direction of the output value decrease of the objective function , until the convergence criterion is met. The optimization algorithm adopts a pattern search method, iteratively searches in a set parameter range, selects a parameter combination that minimizes the output value of the target function as the initial optimal parameter combination by comparing the output values of the target function corresponding to different parameter combinations . ​ Wherein, the parameter ranges are respectively: ; the parameter combination is: .

6. The vibration control method of a steel-concrete composite box girder bridge according to claim 1, characterized by: the working parameters specify the damping coefficients of the multiple tuned mass dampers the initial damping coefficients of the multiple tuned mass dampers are continuously adjusted by electro-hydraulic servo actuators are set in accordance with the initial optimal parameter set The controller employs a proportional-integral-derivative control algorithm to adjust the real-time damping coefficient based on the deviation of the real-time vibration data from the target vibration response value calculating a real-time damping coefficient whose control law is defined by the following equation: ； wherein, is the real-time damping coefficient to be applied at the moment, is the output of the controller; setting an initial damping coefficient of the damper for the initial optimal parameter set; is the control deviation at the moment, whose value is , is a preset target vibration response value, which is a constant; is the bridge vibration response value measured at the moment by the sensor system arranged on the bridge; is a proportional gain coefficient; is an integral gain coefficient; is a derivative gain coefficient; is a time variable representing the current time instant at which the control system is running; 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.

7. A vibration control device for a steel-concrete composite box girder bridge, characterized by It comprises: The acquisition module 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, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by the width-span ratio. The acquisition module 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, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by the width-span ratio. The acquisition module 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, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by the width-span ratio. The acquisition module 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, wherein the interface slip effect is characterized by introducing a nonlinear dynamic constitutive relationship simulating interface friction and deformation evolution, and the shear lag effect is characterized by the width-span ratio. During the operation of the bridge, the sensor system arranged on the bridge is used to monitor the vibration response in real time, real-time vibration data is acquired and fed back to the controller, the controller compares the real-time vibration data with the preset target vibration response value, generates a control instruction according to the deviation between the two, dynamically adjusts the working parameters of the multiple tuned mass damper, and realizes adaptive vibration control.

8. An electronic device, comprising: It comprises: A processor and a memory connected in communication with the processor; The memory stores computer execution instructions; When the processor executes the computer execution instructions stored in the memory, it is used to realize the vibration control method of the steel-concrete composite box girder bridge as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, it is used to realize the vibration control method of the steel-concrete composite box girder bridge as claimed in any one of claims 1 to 6.

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