A bridge wind load monitoring and analysis system
By constructing a digital model of the bridge and updating parameters in real time, the problem that traditional bridge wind vibration monitoring systems cannot adapt to structural degradation has been solved, enabling accurate monitoring and dynamic early warning of bridge wind loads and improving bridge safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIAONING YUNYE INTELLIGENT INFORMATION TECH CO LTD
- Filing Date
- 2026-04-01
- Publication Date
- 2026-06-26
AI Technical Summary
Traditional bridge wind vibration safety monitoring systems rely on fixed safety thresholds, which cannot adapt to the structural degradation of bridges during their service life caused by material aging and environmental erosion, resulting in a decline in early warning effectiveness.
A digital model of the bridge is constructed, which is discretized into wind load elements. By combining material properties and geometric characteristics, a global stiffness and mass matrix is constructed. The RLS-FF algorithm is used to update the model parameters. Dynamic simulation is performed based on measured data, and dynamic thresholds are calculated for early warning.
It enables accurate modeling and real-time parameter updates for bridge wind load monitoring, allowing for timely identification of wind load effects, improving bridge safety, and preventing structural damage or failure.
Smart Images
Figure CN122287455A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of bridge monitoring technology, specifically to a bridge wind load monitoring and analysis system. Background Technology
[0002] As many long-span bridges enter the later stages of operation, performance degradation caused by structural material aging, fatigue accumulation, and environmental erosion has become a significant safety hazard. Wind load, as the controlling load for these lightweight and flexible structures, triggers a dynamic response that is central to safety monitoring. Traditional bridge wind-induced vibration safety monitoring relies on pre-set, design-state-based fixed safety threshold early warning mechanisms. However, the actual dynamic characteristics of bridges are not constant throughout their decades-long lifespan, leading to a significant decrease in the effectiveness of existing systems when applied to bridges in service.
[0003] Existing systems generally use fixed safety thresholds based on design specifications or historical extreme value statistics. When bridge stiffness decreases and damping changes due to degradation, its response under the same wind field becomes more sensitive. Fixed thresholds cannot adjust with structural conditions and may not provide effective early warnings before extreme wind events occur. Summary of the Invention
[0004] In view of the above, it is necessary to provide a bridge wind load monitoring and analysis system to solve the above problems.
[0005] One embodiment of this application provides a bridge wind load monitoring and analysis system, the system comprising: The bridge digital model construction module uses structural mechanics to discretize the target bridge into the main elements that act on the wind load. It integrates the material properties and cross-sectional geometric characteristics of each element to construct a global stiffness matrix and a global mass matrix. Using a damping model, it determines the bridge digital model. The model parameter assimilation module is used to analyze the characteristic parameters of wind field data acquired by sensors on the bridge within a preset time period, obtain the wind load time history, and convert it into the wind load vector of the bridge digital model at the corresponding time based on the static equivalence principle. A linear regression model describing the dynamic equilibrium state of the bridge is constructed based on the measured acceleration data of the bridge using the structural dynamics equations. The RLS-FF algorithm is used to iteratively solve the linear regression model to identify and update the parameters of the bridge digital model. The bridge wind load early warning module performs dynamic simulation based on the updated bridge digital model and the currently measured wind load time history, calculates the theoretical response time history of each node of the bridge in a healthy state, and obtains dynamic thresholds; based on the dynamic thresholds, it monitors and issues early warnings for the bridge wind load.
[0006] The specific steps for constructing the global stiffness matrix and the global mass matrix include: Based on the material properties and cross-sectional geometry of the key connection parts of the target bridge, calculate the element stiffness matrix and element mass matrix of each element; Assemble the element stiffness matrices of all elements according to the node connection relationship. If there is a common connection point between two elements, add the values of the stiffness matrices of the two elements at the corresponding positions of the node to obtain the global stiffness matrix. Using the same method as for obtaining the global stiffness matrix, the global mass matrix is obtained based on the element mass matrix.
[0007] The specific formula for the bridge digital model is as follows: Where E represents the digital model of the bridge. Represents the global quality matrix; , It is a scalar parameter to be estimated. Represents the global stiffness matrix; , and All are damping coefficients; , Both represent parametric models.
[0008] The specific process for obtaining the wind load time history is as follows: Aerodynamic parameters, including drag coefficient, lift coefficient, and torque coefficient, are obtained through wind tunnel testing. The equivalent dynamic wind load components per unit length of the bridge main girder are calculated based on fluid dynamics formulas, including average drag, average lift, and average torque. The obtained equivalent dynamic wind load components are used as the wind load time history.
[0009] The specific process for obtaining the wind load vector is as follows: ;in, This represents the wind load vector at time t. Indicates the force distributed along the bridge direction; Indicates the force distributed across the transverse bridge direction; This indicates the torque distributed along the bridge direction; Indicates the unit length of the bridge; The longitudinal and transverse distributed forces are determined by the components of the average drag and average lift at the corresponding wind angles; the longitudinal distributed torque is determined by the average torque.
[0010] Specifically, the construction of the linear regression model describing the dynamic equilibrium state of the bridge involves: Construct a selection matrix L, which is an N×1 matrix, where N represents the total number of degrees of freedom of all nodes in the finite element model of the bridge, and m represents the number of sensors on the bridge; set the elements at the node degrees of freedom where the acceleration sensor is installed at a preset position on the bridge to 1, and set all other elements to 0. Based on the dynamic equations, we obtain ;in, Represents the observed value at time t. , This represents the acceleration vector composed of the accelerations of all node degrees of freedom at time t; Represents the regression vector. , Let represent the displacement vector consisting of the displacements of all nodal degrees of freedom at time t. This represents the velocity vector composed of the velocities of all node degrees of freedom at time t; Represents a parameter vector. ; This represents the fitting residual term.
[0011] The forgetting factor in the RLS-FF algorithm is determined by the time interval of bridge digital model parameter correction and the periodic evaluation interval of bridge load-bearing capacity.
[0012] Specifically, the calculation of the theoretical response time history of each node of the bridge under a healthy state is as follows: Based on the parameter vector of the bridge digital model after parameter update and the wind load vector obtained at each time step, the structural dynamics equations are solved by step-by-step integration to obtain the theoretical response time history of each node of the bridge under healthy conditions.
[0013] Specifically, the dynamic thresholds include displacement response threshold, acceleration response threshold, and velocity response threshold, which are obtained by using the preset percentile values of acceleration, displacement, and velocity at all integral steps in the theoretical response time history of each node of the bridge under healthy conditions.
[0014] Specifically, the monitoring and early warning of bridge wind load based on the dynamic threshold includes: If the displacement of a bridge node at the current moment is less than or equal to the displacement response threshold, the acceleration is less than or equal to the acceleration response threshold, or the velocity is less than or equal to the velocity response threshold, no warning is triggered; otherwise, a warning is triggered.
[0015] This application has at least the following beneficial effects: The bridge digital model construction module in this application utilizes structural mechanics principles to discretize the target bridge into multiple main elements acting on wind loads. It comprehensively considers the material properties and cross-sectional geometry of each element to construct a global stiffness matrix and a global mass matrix. This precise modeling provides a solid foundation for subsequent analysis, enabling the bridge to accurately reflect its mechanical behavior under wind loads.
[0016] The model parameter assimilation module is responsible for analyzing the characteristic parameters of wind field data acquired by sensors on the bridge within a preset time period, generating wind load time histories, and converting them into wind load vectors for each node in the bridge's digital model. By utilizing the static equivalence principle and structural dynamic equations, this module can construct a linear regression model based on actually measured acceleration data, thereby describing the bridge's dynamic equilibrium state. Iterative solving using the RLS-FF algorithm effectively identifies and updates the parameters of the bridge's digital model, improving its accuracy and real-time performance, and ensuring that the monitored wind loads more closely reflect actual conditions.
[0017] Bridge wind load early warning module: Based on the updated bridge digital model and the currently measured wind load time history, dynamic simulation is performed to calculate the theoretical response time history of each bridge node under healthy conditions, thereby obtaining a dynamic threshold. This dynamic threshold provides an important reference standard for monitoring, enabling the system to issue timely warnings when the actual wind load exceeds the threshold. This mechanism can effectively prevent potential structural damage or failure, improving bridge safety.
[0018] In summary, the various modules work together to form a highly efficient bridge wind load monitoring system through precise modeling, real-time parameter updates, and dynamic early warning. This system can promptly identify and respond to the impact of wind loads on bridges, thereby ensuring the safe operation of bridges. Attached Figure Description
[0019] Figure 1 A block diagram of a bridge wind load monitoring and analysis system provided in this application; Figure 2 The flowchart provided in this application details the specific process for monitoring wind loads on bridges. Detailed Implementation
[0020] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0022] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0024] The following description, in conjunction with the accompanying drawings, details the specific scheme of the bridge wind load monitoring and analysis system provided in this application.
[0025] Please see Figure 1 The diagram illustrates a block diagram of a bridge wind load monitoring and analysis system according to an embodiment of this application. The system includes: a bridge digital model construction module, a model parameter assimilation module, and a bridge wind load early warning module.
[0026] This application first proposes a bridge wind load monitoring and analysis system, applied in the field of bridge monitoring technology, the system comprising: Bridge digital model construction module: Using structural mechanics, the target bridge is discretized into the main elements acting on the wind load. By combining the material properties and cross-sectional geometric characteristics of each element, a global stiffness matrix and a global mass matrix are constructed. Using a damping model, the bridge digital model is determined.
[0027] Because the structural performance (such as overall stiffness and damping characteristics) of long-span bridges gradually degrades due to material aging, fatigue damage, and environmental erosion during long-term service, their dynamic response characteristics under wind loads deviate from the original design state. Therefore, it is necessary to establish a mathematical model that can compare and assimilate real-time monitoring data (acceleration response in this embodiment) with model predictions, and dynamically correct model parameters to continuously track the actual degradation state of the bridge structure. Considering that a bridge is a complex spatial structure, it often exhibits displacement and vibration in various parts when subjected to wind loads. This application selects a finite element model to analyze the wind loads on bridges.
[0028] The target bridge is discretized into main elements acting on wind loads, including beam elements and shell elements, using structural mechanics and the finite element method, thus constructing a finite element model; these elements are connected together through endpoints, and these connection points are called nodes.
[0029] Since wind loads typically act on the surface of a structure (such as bridge decks and main beams), in a finite element model, these loads can be transferred to the nodes according to the equivalence principle, becoming nodal forces. Therefore, wind load analysis actually involves applying forces to the nodes and solving for their motion response. Furthermore, early warning systems usually focus on the overall deformation and vibration of the structure, and these parameters are most intuitively defined at the nodes; therefore, subsequent analyses are all centered on the nodes.
[0030] Based on the as-built drawings, design calculations, and material testing reports of the target bridge, collect the material properties (such as elastic modulus and density) and cross-sectional geometric characteristics (such as area and moment of inertia) of key connection parts (such as bearings). Calculate the element stiffness matrix and element mass matrix for each element. The element stiffness matrix represents the ability of a finite element to resist deformation under stress; the element mass matrix represents the mass distribution and inertial characteristics of a finite element. Assemble the element stiffness matrices of all finite elements according to the node connection relationships. If a node is a common connection point for two elements, the values of the matrices of these two elements at the corresponding positions of that node will be added to obtain the global stiffness matrix. Using the same method as for obtaining the global stiffness matrix, the global mass matrix is obtained based on the element mass matrix. .in and Both are N×N square matrices, where N represents the total number of degrees of freedom of all nodes in the finite element model of the bridge. If the bridge model contains n nodes, and each node contains k degrees of freedom, then... In this embodiment, a three-dimensional bridge model is constructed with 6 degrees of freedom k, including three translational and three rotational degrees of freedom.
[0031] In the global stiffness matrix In, elements Indicates the first When one degree of freedom undergoes a unit displacement (while keeping the displacements of other degrees of freedom zero), in order to maintain equilibrium, it is necessary to... The force applied to each degree of freedom. This value reflects the coupling relationship between the structural stiffness and the degrees of freedom, indicating that a change in the j-th degree of freedom will affect the ith degree of freedom. In a bridge model, the translation of one node may affect the rotation of another node.
[0032] In the global mass matrix In, elements This represents the inertial force generated in the i-th degree of freedom when the j-th degree of freedom has unit acceleration (while keeping the acceleration of other degrees of freedom zero). This value transforms the mass distribution in continuous space into a coefficient relationship between inertial force and acceleration in a linear algebraic system.
[0033] To achieve dynamic updates to the model, it is necessary to update the physical quantities in the model that directly affect the dynamic response: the stiffness matrix. and damping matrix Then, perform parameterization to form the parameter vector θ to be corrected.
[0034] First, a global stiffness scaling factor is introduced. The initial stiffness matrix is parameterized into the following model: in, It is a scalar parameter to be estimated. This indicates that the bridge structure maintains its initial design stiffness; This quantitatively indicates that the overall stiffness of the structure has degraded.
[0035] Secondly, a Rayleigh damping model is adopted, and two damping coefficients to be identified are introduced. and The damping matrix is parameterized as a linear combination of the mass matrix and the stiffness matrix: Among them, the damping coefficient and As bridges age, changes in their energy dissipation characteristics can be effectively reflected, serving as an indicator for sensing bridge performance degradation.
[0036] Finally, we obtain a parameter vector. Controlled bridge digital model ; , Both represent parametric models.
[0037] Model parameter assimilation module: Analyzes the characteristic parameters of wind field data acquired by sensors on the bridge within a preset time period, obtains the wind load time history, and converts it into the wind load vector of the bridge digital model at the corresponding time based on the static equivalence principle. Using the structural dynamics equations, a linear regression model describing the dynamic equilibrium state of the bridge is constructed based on the measured acceleration data of the bridge. The RLS-FF algorithm is used to iteratively solve the linear regression model to identify and update the parameters of the bridge digital model.
[0038] Sensors deployed on the bridge acquire real-time wind field data characteristic parameters at preset intervals, including: average wind speed. Wind angle Average width of the bridge main girder And aerodynamic parameters acquired and stored in advance through wind tunnel tests, including static three-component force coefficients, i.e., drag coefficients, which vary significantly with the angle of attack. Lift coefficient and torque coefficient This is used to quantify the effect of wind load on the bridge cross section in different directions. In this embodiment, the preset duration is 10 minutes, but the implementer can adjust it according to the actual situation. This application does not impose any restrictions on this.
[0039] It should be further explained that the drag coefficient Specifically, it is the ratio of the force acting on an object parallel to the free flow direction to the object's characteristic area and the dynamic pressure of the incoming flow; lift coefficient. Specifically, it is the ratio of the force acting on an object perpendicular to the free flow direction to the object's characteristic area and the dynamic pressure of the incoming flow; torque coefficient. Specifically, it is the ratio of the torque acting on an object to the object's characteristic area, characteristic length, and the dynamic pressure of the incoming flow.
[0040] Since natural wind is highly random, directly inputting unprocessed, completely random wind fields into a finite element model often results in a large computational load, and inverting structural parameters from response data is also problematic. The algorithm would become extremely complex and unstable. Therefore, this application obtains the average wind speed by analyzing the wind field characteristics within a preset time period. and the fluctuating wind speed generated based on the preset wind spectrum Constructing instantaneous wind speed time history Using the instantaneous wind speed time history Calculate the equivalent dynamic wind load components acting per unit length of the bridge main girder, including: average resistance. Average lift and average torque The specific formulas for each are as follows: ; ; in, air density; This represents the instantaneous wind speed time history, where , This represents the average wind speed. This indicates the fluctuating wind speed generated based on a preset wind spectrum; This indicates the width of the main beam of the bridge.
[0041] The equivalent dynamic wind load components calculated above are used to form a dynamic-static combined wind load time history that fluctuates with time. Based on the correspondence between the nodes in the finite element model and the various parts of the bridge, and through the principle of static equivalence, the above-mentioned distributed wind load time history is converted into wind load vectors acting on each node of the model at time t. The length and sampling interval of this time history are completely consistent with the following measured acceleration response data. The specific conversion process is as follows: Let the unit length of the bridge be... At time t, due to the wind direction angle being... The uniformly distributed force generated by the action of wind (from and (components) and distributed torque (from) According to structural mechanics formulas, the equivalent nodal forces and moments at both ends can be obtained. Let the wind direction angle be... Let be the angle between the incoming flow direction and the X-axis (longitudinal to the bridge) of the structural coordinate system. Then the transformation relationship is: Force distributed along the bridge direction: ; Transverse bridge force distribution: ; Vertical force distribution: For static three-component forces, the lift force has been decomposed into the XY plane, so the vertical force distribution is usually not considered. ; Torque distributed around the bridge in the X direction: ; Torque distributed around the reverse bridge direction (Y): ; Furthermore, based on the principle of static equivalence, we obtain: Axial forces at both ends: , , ; Torque around the x-axis at both ends: ; Bending moments about the z-axis at both ends: , ; The local wind load vector at time t can be obtained by following the steps above. : In actual testing, the number of nodes n in the finite element model is often much larger than the number of sensors m actually installed. Therefore, this application pre-arranges m sensors evenly at the actual bridge locations corresponding to the nodes in the finite element model. For any selected sensor, a selection matrix L is constructed to ensure the authenticity of information transmission. The selection matrix L is an N×1 matrix in which only the degrees of freedom of the node corresponding to the selected sensor is set to 1, and all other positions are set to 0.
[0042] Based on the spatial location of each node in the bridge model, the local wind load vectors of all nodes in the bridge finite element model are assembled into a wind load vector. Its dimensions and total degrees of freedom Consistent.
[0043] The parameterized dynamic equations can be expressed as: Multiply both sides of the formula by one on the left. By mapping the measured acceleration data, we obtain: in, This represents the acceleration vector composed of the accelerations of all node degrees of freedom at time t; The velocity vector at time t represents the velocity of all nodes in terms of their degrees of freedom. This represents the displacement vector composed of the displacements of all nodal degrees of freedom at time t. Both the velocity vector and the displacement vector are derived from the acceleration. Obtained by numerical integration; This represents the equivalent nodal wind load vector acting on all nodes of the bridge finite element model at time t. It's important to note that low-frequency drift is a common problem when integrating to obtain velocity and displacement. To improve the accuracy and stability of the calculation, bandpass filtering of the acceleration data is usually required. In this embodiment, a frequency range of 0.1Hz to 10Hz is used to effectively remove low-frequency noise below 0.1Hz and high-frequency interference above 10Hz, thereby optimizing the subsequent integration results.
[0044] The left side of the equation consists of three known scalars multiplied by an unknown parameter, while the right side contains a single known scalar. Definition: Observations: ; Regression vector: ; Its physical meaning is: it includes displacement terms related to stiffness. The velocity term related to quality Velocity terms related to stiffness The weighted scalar value. It represents the current state of motion. Below, the structure has parameters , , Sensitivity.
[0045] Parameter vector: ; Finally, the standard linear equation is obtained: ,in It is a fitting residual term introduced to accommodate model errors and measurement noise.
[0046] The RLS-FF algorithm takes real-time monitoring data and model prediction information as input and outputs updated bridge digital model parameters.
[0047] At each time t, the following recursive calculation is performed: (1) Initialize the parameter estimation error covariance matrix It is a 3×3 identity matrix; (2) Calculate the gain matrix, the specific formula is: in, This represents the gain matrix at sampling time t; The parameter estimation error covariance matrix for the previous time step is (3×3); Forgetting factor, through To obtain, This indicates the time interval for correcting the parameters of the bridge digital model, which is preset to 24 hours. The periodic evaluation interval for the load-bearing capacity of the target bridge in the performance evaluation specification is generally set at 2-3 years in common engineering specifications. In this embodiment, it is set at 2 years, or 17,544 hours. This represents the regression vector at sampling time t.
[0048] (3) Update parameter estimates: in, Parameter estimation at time t-1; The prediction error for new observations based on old parameters is updated using this residual driving parameter.
[0049] (4) Update the error covariance matrix: After the update is completed, the parameter estimate at time t will be obtained. and its error covariance matrix .
[0050] (5) Update the parameters of the bridge digital model: The updated digital model of the bridge is Since wind loads are mainly determined by the bridge's structural shape and real-time wind speed images, and are unrelated to the bridge's own mass, the mass matrix is used... It is considered unchanged. Its corresponding parameter vector is updated as follows: In the initial state, the bridge's initial stiffness is consistent with the design, therefore... The initial value is 1; depending on the bridge type and material, a typical modal damping ratio is selected (e.g., 0.5-1% for steel structures, 1-2% for concrete structures). For concrete bridges... , .
[0051] At this point, the digital model can be updated in real time in terms of stiffness and damping properties as the bridge structure changes.
[0052] Bridge wind load early warning module: Based on the updated bridge digital model and the current measured wind load time history, dynamic simulation is performed to calculate the theoretical response time history of each node of the bridge in a healthy state and obtain dynamic thresholds; based on the dynamic thresholds, the bridge wind load is monitored and early warning is provided.
[0053] Parameter vector based on the updated bridge digital model With the known wind load vector The structural dynamics equations are solved using a general step-by-step integration method. In this embodiment, the Newmark-β method is employed, with an integration step size of 0.01 s, to calculate the theoretical response time histories of each node of the bridge under the current structural state and wind load scenario. ,in, It is the theoretical response time history of the node at time t, which includes the acceleration, displacement and velocity of the node at all integration steps.
[0054] Setting a dynamic response threshold defines a reasonable and expected upper limit for the fluctuation of the response value under specific wind conditions and structural states. Responses exceeding this threshold are considered low-probability anomalies requiring attention. In the calculated theoretical response time history, the 95th percentile means that 95% of the response data points during that period are below this value, which can cover the normal fluctuation range of the structure under stable wind conditions. Therefore, this embodiment uses the 95th percentile as the dynamic threshold. Taking the displacement response time history of one node of the bridge, and the theoretical response time history of that node, at time t as an example: The theoretical response time history for this period The 95th percentile of the displacement values (i.e., the elements in the second row) for all integration steps of this array is calculated as the displacement response threshold under the current bridge performance state, denoted as . Using the same method as the displacement threshold, the acceleration response threshold of this node is calculated respectively. Speed response threshold .
[0055] Finally, for each time period, an array of bridge structural response thresholds can be obtained. Thus, a time series of bridge structural response thresholds is constructed. .
[0056] In this application, the sensor data acquisition frequency on the bridge is 50Hz, including displacement. acceleration ,speed Then, the sliding window method is used to dynamically detect the real-time data. In this embodiment, the window duration is set to 10 minutes, and the sliding step size is 10 minutes.
[0057] Taking one of the time windows as an example: Calculate all displacements within the window. acceleration ,speed maximum value , , ; Window over-limit judgment: based on displacement response As a key indicator for judgment; when When the bridge node displacement exceeds the threshold, the structure has undergone static or quasi-static deformation exceeding safety limits, directly threatening structural safety and triggering a red alert; when , or When the wind speed is high, it indicates that the bridge structure is severely affected by the wind field and is moving rapidly, accumulating kinetic energy. However, if the displacement has not yet exceeded the threshold, it is a precursor to deformation of the bridge structure, triggering a yellow warning; otherwise, no warning is triggered.
[0058] Based on this judgment index, the degree of influence of the wind field on the bridge structure is monitored once every preset time interval, so as to realize the monitoring and analysis of the wind load on the bridge; in this embodiment, the preset time interval is ten minutes.
[0059] The specific flowchart for monitoring wind loads on bridges is as follows: Figure 2 As shown.
[0060] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0061] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A bridge wind load monitoring analysis system, characterized by, The system includes: The bridge digital model construction module uses structural mechanics to discretize the target bridge into the main elements acting on the wind load. It integrates the material properties and cross-sectional geometric characteristics of each element to construct a global stiffness matrix and a global mass matrix. Using a damping model, it determines the bridge digital model. The model parameter assimilation module is used to analyze the characteristic parameters of wind field data acquired by sensors on the bridge within a preset time period, obtain the wind load time history, and convert it into the wind load vector of the bridge digital model at the corresponding time based on the static equivalence principle. A linear regression model describing the dynamic equilibrium state of the bridge is constructed based on the measured acceleration data of the bridge using the structural dynamics equations. The RLS-FF algorithm is used to iteratively solve the linear regression model to identify and update the parameters of the bridge digital model. The bridge wind load early warning module performs dynamic simulation based on the updated bridge digital model and the currently measured wind load time history, calculates the theoretical response time history of each node of the bridge in a healthy state, and obtains dynamic thresholds; based on the dynamic thresholds, it monitors and issues early warnings for the bridge wind load.
2. A bridge wind load monitoring analysis system as claimed in claim 1, wherein, The specific steps for constructing the global stiffness matrix and global mass matrix include: Based on the material properties and cross-sectional geometry of the key connection parts of the target bridge, calculate the element stiffness matrix and element mass matrix of each element; Assemble the element stiffness matrices of all elements according to the node connection relationship. If there is a node that is commonly connected to two elements, add the values of the two element stiffness matrices at the corresponding positions of the node to obtain the global stiffness matrix. Using the same method as for obtaining the global stiffness matrix, the global mass matrix is obtained based on the element mass matrix.
3. A bridge wind load monitoring analysis system as in claim 1, wherein, The specific formula of the bridge digital model is: ; wherein E represents the bridge digital model, represents a global mass matrix; , is a scalar parameter to be estimated, represents a global stiffness matrix; , and are damping coefficients; , both represent a parameterized model.
4. A bridge wind load monitoring analysis system as in claim 1, wherein, The specific process for obtaining the wind load time history is as follows: Aerodynamic parameters, including drag coefficient, lift coefficient, and torque coefficient, are obtained through wind tunnel testing. The equivalent dynamic wind load components per unit length of the bridge main girder are calculated based on fluid dynamics formulas, including average drag, average lift, and average torque. The obtained equivalent dynamic wind load components are used as the wind load time history.
5. A bridge wind load monitoring analysis system as claimed in claim 3, wherein, The specific process for obtaining the wind load vector is as follows: local wind load vector of each node at time t The formula is: wherein, represents a longitudinal distribution force; represents a transverse distribution force; represents a longitudinal distribution torque; represents a unit length of the bridge; The longitudinal and transverse distributed forces are determined by the components of the average drag and average lift at the corresponding wind angles; the longitudinal distributed torque is determined by the average torque. According to the spatial position of each node in the bridge model, the local wind load vectors of all nodes of the bridge finite element model are assembled into a wind load vector .
6. A bridge wind load monitoring analysis system as claimed in claim 5, wherein, The construction of the linear regression model describing the dynamic equilibrium state of the bridge is specifically as follows: For any selected sensor, construct a selection matrix L, which is an N×1 matrix, where N represents the total number of degrees of freedom of all nodes in the finite element model of the bridge; set the elements at the node degrees of freedom where the selected sensor is installed at a preset position on the bridge to 1, and set all other elements to 0. Based on the dynamic equation, a linear regression model is obtained ; wherein, represents the observation value at time t, , represents the acceleration vector composed of the acceleration of all node degrees of freedom at time t; represents the regression vector, , represents the displacement vector composed of the displacement of all node degrees of freedom at time t, represents the velocity vector composed of the velocity of all node degrees of freedom at time t; represents the parameter vector, ; represents the fitting residual term.
7. A bridge wind load monitoring analysis system as in claim 1, wherein, The forgetting factor in the RLS-FF algorithm is determined by the time interval of bridge digital model parameter correction and the periodic evaluation interval of bridge load-bearing capacity.
8. A bridge wind load monitoring analysis system as in claim 1, wherein, The theoretical response time history of each node of the bridge under healthy conditions is calculated as follows: Based on the parameter vector of the bridge digital model after parameter update and the wind load vector obtained at each time step, the structural dynamics equations are solved by step-by-step integration to obtain the theoretical response time history of each node of the bridge under healthy conditions.
9. A bridge wind load monitoring analysis system as in claim 1, wherein, The dynamic thresholds specifically include displacement response threshold, acceleration response threshold, and velocity response threshold, which are obtained by using the preset percentile values of acceleration, displacement, and velocity at all integral steps in the theoretical response time history of each node of the bridge under healthy conditions.
10. A bridge wind load monitoring analysis system as claimed in claim 9, wherein, The monitoring and early warning of bridge wind load based on the dynamic threshold specifically includes: If the displacement of a bridge node at the current moment is less than or equal to the displacement response threshold, the acceleration is less than or equal to the acceleration response threshold, or the velocity is less than or equal to the velocity response threshold, no warning is triggered; otherwise, a warning is triggered.