Simulation test system and method for aeroelastic model of girder of truss girder bridge

Abstract

The invention discloses an aeroelastic model simulation test system and method for a girder of a truss girder bridge. The aeroelastic model simulation test system comprises a wind tunnel environment module, a wind speed control module, an aeroelastic model and an evaluation module, the control module is electrically connected with the elastic adjusting pieces and used for adjusting the elastic coefficients of the elastic adjusting pieces in real time; the test generation module is internally provided with a truss girder bridge structure performance degradation model, generates an elastic coefficient change sequence of the elastic adjusting part and outputs a plurality of test schemes; and the data execution module is linked with the test generation module and the wind speed control module, sequentially loads the elastic coefficients corresponding to the test schemes, triggers a wind tunnel test and records a critical wind speed theoretical value. According to the technical scheme provided by the invention, a refined analysis basis can be provided for wind-induced vibration safety evaluation of the existing truss girder bridge, and the technical problem that the time-varying characteristics of a traditional homogenization model and a real bridge are not matched is effectively solved.

Description

Technical Field

[0001] This application relates to the technical field of wind tunnel tests. Specifically, it relates to a pneumo-elastic model simulation test system and method for the main girder of a truss girder bridge. Background Technique

[0002] Long-span bridges are prone to aerodynamic instability phenomena such as flutter, vortex-induced vibration, and buffeting under the action of natural wind loads. In severe cases, it may lead to dynamic instability of the structure. To ensure the safety of the bridge structure, it is necessary to verify through pneumo-elastic model wind tunnel tests. This test determines the critical instability wind speed of the structure by establishing a scaled model of the bridge, simulating different wind speed levels and airflow attack angles in the wind tunnel environment, and gradually testing, so as to evaluate the matching of the design scheme with the wind environment parameters of the construction site.

[0003] Current pneumo-elastic model wind tunnel tests usually adopt idealized assumption conditions: setting each connection part of the model to a homogenized state for testing. However, in actual engineering, the bridge structure will show dynamic change characteristics during the operation period: First, there are significant differences in the aging rates of different connection components, and non-uniform structural damage is likely to occur in key parts such as welds and anchoring nodes; Second, during the periodic inspection and maintenance process, the implementation degree and timeliness differences of maintenance measures will lead to changes in the distribution of the structural elastic coefficient; Finally, the combined action of these factors makes the overall wind resistance performance of the bridge show time-varying characteristics.

[0004] The limitation of the existing test method is that it assumes that the degradation of the elastic coefficients of each connection node of the model remains synchronized, and the tensile / shear strength of the structure maintains a homogenized state. This idealized assumption is essentially different from the evolution law of the mechanical characteristics of the actual bridge, resulting in a significant deviation between the theoretical value of the critical wind speed obtained from the test and the failure wind speed threshold of the actual operating bridge, directly affecting the accuracy of the wind-induced vibration safety assessment. Summary of the Invention

[0005] The content part of this application is used to briefly introduce ideas, which will be described in detail in the specific implementation part later. The content part of this application is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] As the first aspect of this application, to solve the technical problems mentioned in the above background technique part, some embodiments of this application provide a pneumo-elastic model simulation test system for the main girder of a truss girder bridge, including: A wind tunnel environment module for constructing a test environment simulating a natural wind field. The wind tunnel environment module includes an adjustable attack angle device and a turbulence generation unit; A wind speed control module connected to the wind tunnel environment module for adjusting the wind tunnel wind speed through a variable-frequency motor and real-time feedback of wind speed data; The aeroelastic model includes a plurality of segment modules distributed longitudinally along the main beam, and a connecting component connecting two adjacent segment modules; An evaluation module, including a laser displacement meter and a strain sensor arranged on each segment module, for measuring dynamic deformation data of the aeroelastic model; in, The connection component comprises: Two free end connecting parts are respectively fixed to the end faces of adjacent segment modules; The elastic adjusting member is an elastic damper with an adjustable elastic coefficient, and both ends are rigidly connected to the free end connecting portion; The aeroelastic model simulation test system also includes: A control module, electrically connected to each elastic adjusting member, for adjusting the elastic coefficient of the elastic adjusting member in real time; The test generation module has a built-in truss beam bridge structure performance degradation model, generates a sequence of elastic coefficient changes of elastic adjustment parts, and outputs multiple test plans; The data execution module is linked with the test generation module and the wind speed control module to sequentially load the elastic coefficient variation sequence corresponding to each test scheme, trigger the wind tunnel test and record the critical wind speed theoretical value.

[0007] This technical solution can quantitatively simulate the elastic coefficient degradation characteristics of connection components at different service stages by constructing a truss beam bridge structural performance degradation model, and generate a connection elastic coefficient system with time-varying characteristics based on this. By establishing a multi-dimensional structural performance degradation database, the differentiated aging status of various connection nodes during the entire life cycle of the bridge can be accurately reproduced in the wind tunnel test, and then the wind resistance performance test of multiple working conditions coupling can be carried out. Compared with the traditional method, the technical advantages of this solution are reflected in: by introducing a structural time-varying mechanical model, the nonlinear elastic coefficient degradation law of key connection parts such as weld fatigue damage and bolt preload relaxation can be systematically reflected. During the test, the elastic constraint conditions of each connection node can be dynamically adjusted, and a quantitative mapping relationship between the degree of aging and wind resistance performance can be established, thereby significantly improving the completeness of the test data and the working condition coverage rate. This technology can provide a refined analysis basis for the wind-induced vibration safety assessment of existing truss beam bridges, and effectively solve the technical problem of the mismatch between the traditional homogenized model and the time-varying characteristics of the real bridge.

[0008] When the truss girder bridge undergoes structural deformation under strong wind loads, the mechanical property degradation of its connection structure presents a continuous and progressive damage accumulation process, rather than the discrete state jump of "normal - yield - fracture" preset in the traditional model. The existing modeling method of simplifying each connection unit into a discretized performance state has essential defects: First, it fails to reflect the degradation characteristics of non - linear elastic coefficients such as bolt loosening and micro - slip on the contact surface; second, it ignores the coupling effect between different damage modes and the time - varying characteristics of the damage rate; third, it severs the dynamic correlation between local damage of components and the overall structural response. This discretization treatment of the continuous damage process will lead to the loss of simulation accuracy of the structural performance degradation model, ultimately causing the dynamic response data obtained from wind tunnel tests to deviate from the true evolution law and reducing the credibility of the service safety assessment results.

[0009] Therefore, the present application provides the following technical solutions: The structural performance degradation model of the truss girder bridge includes several hidden states corresponding to aging information, and the probability transition information between the hidden states.

[0010] In the technical solutions provided by the present application, the aging process of the connection part of the truss girder bridge is described by several continuous hidden states, and then the change of the connection part is described by the probability transition information. Therefore, it can dynamically show the accumulation process of metal damage in the connection part, enabling the simulation test to accurately reflect the evolution law of the actual dynamic response data.

[0011] The composite load borne by the connection component includes three - dimensional dynamic components - the aerodynamic tension directly induced by the wind speed time - history, the continuous axial force generated by the self - weight of the structure, and the inertial alternating stress caused by the bridge vibration. The time - domain coupling effect of this multi - source excitation will result in: First, the relaxation rate of the bolt pre - tightening force is non - linearly correlated with the wind speed pulsation characteristics; second, the micro - slip displacement on the contact surface and the structural swing amplitude form a positive feedback mechanism; third, the corrosion damage process is controlled by both the stress amplitude and the number of cycles. When using the static equivalent load simulation method, it is impossible to reproduce the time - varying characteristics of the tensile - shear - bending composite stress field in actual wind - induced vibration; while the conventional dynamic loading device is difficult to accurately decouple the phase relationship between the inertial force and the aerodynamic force. This directly leads to a significant difference between the component stress spectrum obtained in the test and the actual service conditions of the real bridge, causing the simulation result of the damage accumulation rate of the connection node to deviate from the true evolution law, and ultimately affecting the reliability of the structural wind resistance safety assessment.

[0012] Furthermore, the construction method of the structural performance degradation model of the truss girder bridge is as follows: S1: Set several continuous aging states q, the number of aging states is Q, and q represents the index of the aging state; S2: Set the state variable R t ,R tDenote the state variable at time t, R t ={ra t , rb t , rc t}, where ra t denotes the accumulated displacement within time t; , denotes the integral of the displacement length X of the connection component with respect to time from 0 to time t; h ; rb t denotes the number of times the connection component is stressed within time t; rc t denotes the time during which the stress on the connection component exceeds the stress threshold within time t; S3: Set the initial state probability distribution P of the aging state, P = {p1, … p q … p Q}, q ∈ [1, Q], where p q is the probability of the q-th aging state occurring; S4: Establish the probability transition matrix W of the aging state, W = {w qs}, w qs represents the probability of transitioning from aging state q to aging state s, s ∈ [1, Q]; s > q; S5: Construct the state variable probability matrix E, E = {b q (R t )} represents the probability density function of the connection component in each aging state; ; where M represents the number of mixture Gaussian components in each aging state, g qm represents the weight of the m-th Gaussian distribution in aging state q; represents the m-th Gaussian distribution function in aging state q, u qm and L qm are the mean vector and covariance vector respectively; ; where, represents the matrix transformation symbol, P represents the initial state probability distribution, and exp represents the power operation of the base e of the natural logarithm; S6: Co-model the probability transition matrix W and the state variable probability matrix E to generate a structural performance degradation model for the truss beam bridge.

[0013] This technical solution realizes the computable characterization of the structural damage evolution mechanism by constructing a multi-level degradation model based on the theory of stochastic processes. Specifically, data assimilation technology is used to establish the coupling relationship between the state transition probability matrix W and the observation state probability matrix E, forming a damage evolution analysis system driven by a double matrix: 1), The state transition probability matrix W uses the Markov chain modeling method to quantitatively characterize the microscopic defects of the connecting members under different stress levels; 2), The observation state probability matrix E is based on establishing the dynamic mapping relationship between the macroscopic mechanical responses of the members (including measurable parameters such as strain energy density and residual displacement) and the implicit damage state. This method can transform the complex damage path of the connection node (such as the competition mechanism between stress corrosion crack propagation and fatigue damage) into a computable stochastic process, significantly improving the simulation accuracy of the structural response under the coupling action of multiple physical fields in the wind tunnel test.

[0014] Furthermore, S6 includes the following steps: S61: Construct an initial truss girder bridge structural performance degradation model λ; λ = (P, W, E); Among them, P = {1, 0, 0, 0}, W = [w qs Q×Q , Q = 4, the constraint conditions of W are: (1) w qs > 0, s > q, that is, the aging state can only transfer to the aging state with a larger number; (2) w qs = 0, s ≤ q, that is, the aging state cannot transfer to the aging state with a smaller number; (3) , that is, the sum of the probabilities of the aging state q transferring out is 1; E = {b q (R t )}; S62: Train the initial truss girder bridge structural performance degradation model λ to find the best model parameters to maximize the likelihood probability of the observation sequence.

[0015] Furthermore, S62 includes the following steps: S621: Initialize the training parameters and the maximum number of iterations. The training parameters include the initial probability transition matrix W, the initial state variable probability matrix E, and the change threshold; S622: Update the probability transition matrix W: S623: Update the state variable probability matrix E; S624: Repeat S622 and S623 for iterative repetition until the likelihood probability of the observation sequence is less than the change threshold or the maximum number of iterations is reached. ​

[0016] Further, it is divided into 4 aging states according to the proportion of the effective connection length of the pneumatic model connection component under conventional test conditions.

[0017] In the technical solution provided by the present application, dividing the aging state according to the elastic coefficient of the connection component can accurately divide each state, avoiding the problem that the distinction degree between adjacent states is not high and making the model too redundant.

[0018] Further, the force threshold is the tensile force of the connection component within the maximum elastic limit.

[0019] When the connection component is subjected to a tensile force within the force threshold, it can recover. When it is subjected to a tensile force outside the force threshold, the recovery ability of the connection component gradually decreases, so that the aging process of the connection component can be gradually described.

[0020] When a truss girder bridge is damaged in strong wind weather, there will be an obvious short-board effect, that is, as long as the connection components in a certain area are broken, the stability of the truss girder bridge will collapse rapidly. Therefore, when conducting the wind tunnel test of the truss girder bridge, it is actually to test the minimum wind force environment that can be withstood during the normal service period of the truss girder bridge. At present, the wind tunnel tests are all simulation tests considering that the structural performance of the connection components is consistent and the structure does not change. In actual work, the accuracy is not high. Therefore, the present application provides the following technical solution: Further, the test generation module includes: A model construction unit with a structural performance degradation model of the truss girder bridge built in; A scheme generation unit that sets an aging state for each connection component based on the particle swarm algorithm to generate 1 simulation scheme; A simulation unit that presets the elastic coefficients in each aging state; sets the aging states of the connection components in the simulation scheme as the aging states to generate a test scheme; A probability fusion unit that incorporates the transition probability of each aging state into the test scheme according to the structural performance degradation model of the truss girder bridge.

[0021] This technical solution realizes the refined simulation of the degradation process of the bridge connection node by constructing a parameterized dynamic elastic coefficient model and a probability fusion mechanism. The specific implementation process includes two core modules: 1). Based on the damage state-elastic coefficient mapping criterion, the aging state of each connection component in the simulation model is quantified as a time-varying elastic coefficient, and the elastic coefficient will be dynamically adjusted to accurately characterize the non-linear response process of the connection interface from micro-slip to macro-displacement; 2). Establish a probability fusion analysis unit. By integrating the state transition probability, a multi-scale damage evolution prediction model is constructed, which can dynamically correct the degradation path of the elastic coefficient of each node under different environmental excitations. This method effectively overcomes the error caused by the idealized treatment of the elastic coefficient of the connection node in the conventional wind tunnel test, enabling the test model to accurately reproduce the slip displacement time history curve and hysteretic energy dissipation characteristics of the connection part under the action of strong wind, and significantly improving the engineering credibility of the determination of the structural dynamic instability critical point.

[0022] There are multiple connection components in the main girder of the truss girder bridge. The aging degree of each connection component during use is unpredictable individually. If the aging state of each connection component is set to the lowest, it obviously does not conform to the actual operation and maintenance situation of the main girder of the truss girder bridge. If all possible combinations of aging states are traversed, it will lead to too many wind tunnel tests and reduce the test efficiency. Therefore, the present application provides the following technical solutions: Furthermore, set an aging score for each aging state and set a score threshold; The scheme generation unit continuously generates simulation schemes on the premise that the sum of the aging scores in the simulation scheme is higher than the score threshold.

[0023] Setting the score threshold can imitate the actual aging situation of the main girder of the truss girder bridge during normal service, and thus obtain the wind tunnel test results that most conform to the actual operation situation while reducing the number of simulations.

[0024] As the second aspect of the present application, a method for simulating the aeroelastic model test of the main girder of a truss girder bridge is provided, and the simulation test of the aeroelastic model of the main girder of the truss girder bridge is carried out by using the aforementioned aeroelastic model simulation test system for the main girder of the truss girder bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting a part of the present application are used to provide a further understanding of the present application, making other features, objectives, and advantages of the present application more obvious. The schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation of the present application.

[0026] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the elements and components are not necessarily drawn to scale.

[0027] In the drawings: Figure 1 It is a structural schematic diagram of the aeroelastic model simulation test system for the main girder of the truss girder bridge.

[0028] Figure 2 It is a logic diagram for generating the theoretical value of the critical wind speed by the aeroelastic model simulation test system for the main girder of the truss girder bridge.

[0029] Figure 3 It is the nominal stress-strain curve of Q235 steel.

[0030] Figure 4 It is the flowchart for generating the theoretical value of the critical wind speed in Embodiment 2. Detailed implementation manners

[0031] Hereinafter, embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not used to limit the protection scope of the present application.

[0032] In addition, it should be noted that for the convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0033] Hereinafter, the present application will be described in detail with reference to the drawings and in combination with the embodiments.

[0034] Embodiment 1:

[0035] Referring to Figures 1 - 2 , the aeroelastic model simulation test system for the main girder of a truss girder bridge includes: a wind tunnel environment module, a wind speed control module, an aeroelastic model, an evaluation module, a control module, a test generation module, and a data execution module. Among them: the wind tunnel environment module is used to construct a test environment simulating the natural wind field, and the wind tunnel environment module includes an adjustable angle of attack device and a turbulence generation unit; the wind speed control module is connected to the wind tunnel environment module and is used to adjust the wind tunnel wind speed through a variable frequency motor and real-time feedback the wind speed data; the aeroelastic model includes a plurality of segment modules longitudinally distributed along the main girder and a connection component connecting two adjacent segment modules; the evaluation module includes laser displacement gauges and strain sensors arranged on each segment module and is used to measure the dynamic deformation data of the aeroelastic model and generate a theoretical value of the critical wind speed when the dynamic deformation data reaches a preset maximum value. The control module is electrically connected to each elastic adjustment member and is used to adjust the elastic coefficient of the elastic adjustment member in real time; the test generation module has a structural performance degradation model of a truss girder bridge built in, generates a change sequence of the elastic coefficient of the elastic adjustment member, and outputs a plurality of test schemes; the data execution module is linked with the test generation module and the wind speed control module, sequentially loads the change sequence of the elastic coefficient corresponding to each test scheme, triggers the wind tunnel test and records the theoretical value of the critical wind speed.

[0036] The wind tunnel environment module, the wind speed control module, the aeroelastic model, and the evaluation module are all prior arts. The key point of this application is to adjust the aging state of the aeroelastic model so as to obtain the theoretical value of the lowest critical wind speed of the truss girder bridge within the normal service life range.

[0037] The following is the implementation method of how to adjust the aging state of the aeroelastic model and how to obtain the theoretical value of the lowest critical wind speed of the truss girder bridge within the normal service life range.

[0038] The connection component includes a free end connection part and an elastic connection member. The elastic connection member is a double connection shaft cylinder with adjustable pressure. The free end connection part is respectively the connection parts of the two connection shafts of the elastic connection member. By adjusting the air pressure in the cylinder, the connection elastic coefficient of the elastic connection member can be adjusted. The key point of this application is to convert the rigid connection member in the original aeroelastic model into an elastic connection member with adjustable elasticity.

[0039] As Figure 3 shown: Figure 3 It is the nominal stress-strain curve of Q235 steel. This steel has different deformation characteristics under different loads. When the load is less than 24, Q235 steel is within the elastic limit; when the load exceeds 38, Q235 steel begins to gradually yield and loses its load-bearing capacity. The elastic connection member in this solution needs to simulate the elastic change process of the connection member of the aeroelastic model. Given the known load and tensile amount, the displacement of the free end can be controlled by adjusting the hydraulic pressure or air pressure, thereby simulating this curve.

[0040] The test generation module includes: a model construction unit, a scheme generation unit, a simulation unit, and a probability fusion unit. The model construction unit has a built-in structural performance degradation model of the truss girder bridge; the scheme generation unit sets an aging state for each connection component based on the particle swarm algorithm to generate 1 simulation scheme; the simulation unit presets the elastic coefficients under each aging state. Set the aging state of each connection component in the simulation scheme to the corresponding elastic coefficient to generate a test scheme; the probability fusion unit incorporates the transition probability of each aging state into the test scheme according to the structural performance degradation model of the truss girder bridge.

[0041] For example: The aeroelastic model has a total of 10 connection components. Elastic coefficients are set for these 10 connection components respectively, and they are placed in the wind tunnel environment module for testing, and then 1 theoretical value of the critical wind speed can be obtained. Multiple theoretical values can be obtained through repeated tests, and finally the most suitable value is selected as the theoretical value of the critical wind speed.

[0042] In this application, the model construction unit can dynamically reflect the performance degradation characteristics of the connection components during use based on the truss girder bridge structure performance degradation model. The simulation unit can generate the elastic coefficient after degradation in real time when the connection components degrade, so as to realize the dynamic adjustment of the simulation scheme. The core of this scheme is to establish the corresponding relationship between the elastic coefficient of the elastic adjusting part and the force characteristics of the connection components in different aging states. The specific implementation scheme is as follows: Specifically: the truss girder bridge structure performance degradation model includes several hidden states corresponding to aging information, and the probability transition information between each hidden state.

[0043] The construction method of the truss girder bridge structure performance degradation model is as follows: S1: Set several consecutive aging states q, the number of aging states is Q, and q represents the index of the aging state.

[0044] Under normal test conditions, the pneumatic model connection components are divided into 4 aging states according to the effective connection length ratio. Specifically, the essential reason for the collapse of the truss girder bridge due to the influence of air flow is the fracture failure of the connection components. The connection components will continuously age during service, resulting in a gradual attenuation of their tensile strength. In this scheme, the aging process of the connection components is divided into 4 aging states. To quantitatively characterize these 4 states, they are divided according to the effective connection length ratio. Taking a steel connection component with a standard width of 10 cmm as an example: by prefabricating standardized notches of 1 cmm, 2 cmm, and 3 cmm (or retaining effective lengths of 9 cmm, 8 cmm, and 7 cmm), typical working conditions with 100%, 90%, 80%, and 70% effective connection lengths are formed respectively. The design principle of this scheme is based on the material performance degradation mechanism: the aging of the connection components is essentially an irreversible deterioration process of the material's load-bearing performance, which is specifically manifested as the continuous reduction of the volume of the steel structure with effective connection ability. By prefabricating standardized notches, multiple groups of sample data in different aging states can be quickly generated in the test environment, significantly improving the data acquisition efficiency for subsequent model training.

[0045] S2: Set the state variable R t , R t represents the state variable at time t, R t ={ra t , rb t , rc t}, where, ra t represents the accumulated displacement within time t; , represents the integral of the displacement length X h of the connection component from 0 to time t with respect to time; rb tIndicates the number of times the connected component is subjected to force within time t; the count is triggered by the strain sensor threshold.

[0046] rc t It indicates the time when the stress of the connected component exceeds the stress threshold within time t, and the statistical stress amplitude exceeds the design threshold.

[0047] In this scheme, the state variables are physical quantities that can be measured during pneumatic tests, which are directly measured by the laser displacement meter and strain sensor on the evaluation module.

[0048] State variable R t The method of obtaining is as follows: a uniaxial quasi-static tensile test is performed on the connection components of the standard aeroelastic model, and a step-by-step incremental load is applied through a servo loading system: the load range covers the elastic stage (σ≤240MPaσ≤240MPa), the plastic development stage (240MPa<σ≤380MPa) and the failure stage (σ>380MPa), where σ is the load.

[0049] For example, after applying a standard tensile load for a specific period of time to a certain connection component, the corresponding state variable value can be measured through mechanical analysis. When the component is in the second aging state, this state variable is classified as the second aging state characterization parameter. After accumulating data through a large number of experiments, sufficient sample data can be obtained for each aging state. For each aging state, the probability distribution characteristics of its state variable are the Markov transition characteristics that characterize the state. Therefore, the state variable R t Essentially, it carries the implicit feature information of the connection component migrating from aging state 1 to aging state 2.

[0050] S3: Set the initial state probability distribution P of the aging state, P={p1, ...p q …p Q}, q∈[1,Q], where p q is the probability of the qth aging state occurring, and the initial state probability distribution P = {1, 0, 0, 0}.

[0051] When connected components age, they gradually move from the first aging state to the last aging state. This process is irreversible and will definitely happen, but the timing of individual transitions cannot be accurately predicted. Therefore, using transition probabilities to describe this state transition process is more accurate.

[0052] S4: Establish the probability transfer matrix W of the aging state, W={w qs},w qs represents the probability of transitioning from aging state q to aging state s, s∈[1,Q]; s>q; S5: Construct the state variable probability matrix E, E={b q (Rt represents the probability density function of the connection component in each aging state; ; where M represents the number of Gaussian mixture components included in each aging state, m represents the index of the Gaussian mixture model component, m ∈ [1, M], and g qm represents the weight of the m-th Gaussian distribution in aging state q; represents the m-th Gaussian distribution function in aging state q, and u qm and L qm are the mean vector and covariance vector respectively; ; where represents the matrix transformation symbol, P represents the initial state probability distribution, and exp represents the power operation of the base e of the natural logarithm.

[0053] The state variable is only an observed value, and there is no clear corresponding relationship between the state variable and the aging state. However, according to the aging law, it can be known that the distribution of the state variables corresponding to each aging state has a pattern. The probability density function in this solution actually describes the distribution of the state variables in the corresponding aging state.

[0054] S6: Co-model the probability transition matrix W and the state variable probability matrix E to generate the structural performance degradation model of the truss beam bridge.

[0055] The probability transition matrix W describes the implicit change information between aging states, while the state variable probability matrix E describes the internal relationship between the observed value of the state variable and the aging state. Therefore, S6 specifically includes the following steps: S61: Construct an initial structural performance degradation model λ of the truss beam bridge; λ = (P, W, E); where P = {1, 0, 0, 0}, W = [w qs Q×Q , Q = 4, and the constraint conditions of W are: (1) w qs > 0, s > q, that is, the aging state can only transfer to an aging state with a larger number; (2) w qs = 0, s ≤ q, that is, the aging state cannot transfer to an aging state with a smaller number; (3) , that is, the sum of the probabilities of the aging state q transferring out is 1; E = {b q (R t )}; ​S62: Train the initial structural performance degradation model λ of the truss girder bridge to find the optimal model parameters to maximize the likelihood probability of the observation sequence; Specifically, S62 includes the following steps: S621: Initialize the training parameters and the maximum number of iterations. The training parameters include the initial state distribution, the probability transition matrix W, the state variable probability matrix E, and the change threshold; S622: Update the probability transition matrix W: Calculate the forward vector using the forward algorithm ; ; where, R1…R t represents the data sequence of the state variable from time 1 to time t, and | represents the conditional probability symbol; q t =q means being in the aging state q at time t; Calculate the likelihood probability P(R 1;T |λ); ; where, T represents the total duration of the data sequence, represents the forward vector at the final time T; Calculate the backward variable ; , is the backward variable, representing the conditional probability of observing the data sequence from time t to T; Iteratively update the probability transition matrix W and the state variable probability matrix E through expectation maximization; ; : represents the probability of the aging state q transferring to the aging state s after the update of the probability transition matrix update; represents the probability of transferring from the aging state q to the aging state s at time t; ; represents the probability of being in the aging state q at time t; ; According to Update the probability transition matrix W; S623: Update the state variable probability matrix E.

[0056] Specifically, it includes the following steps: Update the Gaussian distribution weight g qm ; ; Among them, is the updated Gaussian distribution weight, represents the posterior probability that the state variable at time t belongs to the m-th Gaussian component in the aging state q; ; ; ; represents a multivariate Gaussian distribution, represents the index of the Gaussian mixture model (GMM) component, ∈ [1, M], represents the -th mean vector of the Gaussian component in the aging state q; represents the -th covariance vector of the Gaussian component in the aging state q; is the updated mean vector, is the updated covariance vector; According to update the state variable probability matrix E.

[0057] S624: Repeat S622 and S623 for iterative repetition until the likelihood probability of the observation sequence is less than the change threshold or the maximum number of iterations is reached.

[0058] After the construction of the probability transition matrix W and the state variable probability matrix E is completed, the mapping relationship between the two can be established. Based on this, the aging state with the highest corresponding probability can be parsed through the input state variable, thereby revealing the implicit association mechanism between the state variable and the aging state.

[0059] Furthermore, the simulation unit sets an elastic coefficient for each aging state. The simulation unit configures an exclusive elastic parameter system for each aging state: when the load on the connection component is within the force threshold, the system simulates the normal elastic deformation of the steel structure. As the load increases, the connection component will simulate plastic deformation. When the load continues to increase, the connection component will simulate the yield phenomenon. During the entire simulation process, only the elastic coefficient needs to be adjusted. In this solution, 4 aging states are set, and each aging state includes a force threshold, a load threshold, and a collapse threshold. When the load on the connection component is within the force threshold, the connection component maintains the initial elastic coefficient; when the load on the connection component enters the load threshold range, an elastic coefficient attenuation mechanism is implemented; when the load on the connection component crosses the collapse threshold, the tensile degree of the connection component is negatively correlated with the bearing capacity; when it completely exceeds the collapse threshold, the elastic coefficient is reset to zero and the connection constraint is released to achieve free end movement. In this way, the simulation unit only needs to record the aging state set for each connection component, and then map the corresponding force threshold, load threshold, and collapse threshold of this aging state to the elastic coefficient of this connection component.

[0060] The aging state on the connection component is an initial setting. During the wind tunnel test, when it is necessary to continue the simulation test, the aging transition process of the connection component needs to be simulated. For this purpose, the probability fusion unit will record the corresponding state variable R of each connection component during the wind tunnel test t changes, and generate the most likely aging state of each connection component according to the new state variable.

[0061] For example: a certain connection component is set to the first aging state, and then this aging state is input into the structural performance degradation model of the truss girder bridge to obtain the initial state variable R of this connection component t . During the wind tunnel test, the tensile force on this connection component increases, so its state variable R t starts to increase. The increased R t is input into the structural performance degradation model of the girder bridge to obtain the probability that this connection component transfers to the second aging state backward is 60%. Obtain the probabilities of all connection components transferring to the next aging state backward, and then adjust the aging states of all connection components according to these probabilities, and further adjust the force threshold, load threshold, and collapse threshold of all connection components. Therefore, the aging process of the bridge can be simulated in the wind tunnel test.

[0062] Example 2:

[0063] Example 1 provides how to simulate the wind resistance test of the main girder of a truss girder bridge during the aging process through a connection component with a controllable elastic coefficient. However, in practice, there are numerous connection components on the main girder of a truss girder bridge, and there are a large number of possible test schemes. To find the theoretical value of the lowest critical wind speed when the main girder of a truss girder bridge is operating normally, the present invention also provides Example 2, referring to Figure 4 , Example 2 provides the following technical solution: Set an aging score for each aging state and set a score threshold; the scheme generation unit continuously generates simulation schemes on the premise that the sum of the aging scores in the simulation scheme is higher than the score threshold.

[0064] For example, the first aging state is set to 4 points, the second aging state is set to 3 points, the third aging state is set to 2 points, and the fourth aging state is set to 1 point.

[0065] The setting of the score threshold needs to be combined with the actual engineering requirements and is usually associated with the maintenance standard of the main girder of a truss girder bridge. In this embodiment, the specification requires that the connection components of the main girder should be maintained above the third aging state, but the actual operation and maintenance data show that the distribution of its aging state presents an approximate normal distribution characteristic: mainly concentrated in the second and third aging states (accounting for about 68%), and a small amount is distributed in the first and fourth aging states (each accounting for about 16%). Setting the score threshold based on this distribution law can effectively exclude extreme working conditions that are impossible to occur under normal maintenance conditions (such as all components being in the third and fourth aging states).

[0066] Considering the mechanical sensitivity of the main girder of a truss girder bridge, the sudden fracture of key connection components may cause structural instability. To capture such non-linear failure modes and determine the theoretical value of the lowest critical wind speed, the following technical route is adopted in this scheme: The scheme generation unit generates a new simulation scheme based on the following steps: Z1: Generate a sorting sequence in ascending order according to the geometric distance between each connection component and the center line of the main girder; The smaller the central support structure of the main girder of a truss girder bridge, the greater the stress it receives in practice. Therefore, sort each connection component from low to high, and the serial number of the connection component can reflect its stress condition.

[0067] Z2: Set the mean value of the aging score and the standard deviation of the aging score; The distribution of the aging states of each connection component in the set simulation scheme needs to satisfy the mean value of the aging score and the standard deviation of the aging score. The mean value of the aging score describes the central position of the aging state, and the standard deviation describes the situation of the aging state spreading outside the mean value. The mean value of the aging score is actually another expression of the score threshold.

[0068] In this solution, it is necessary to find the theoretical value of the lowest critical wind speed under normal conditions. Therefore, after setting the score threshold, it is necessary to make the sum of the aging scores of each connection component exactly equal to the score threshold. For this purpose, directly setting the average value of the aging scores will have higher representativeness.

[0069] The above three data are all set according to requirements. In this solution, there are 20 connection components, the average value of the aging score is 3 points, the score threshold is 60 points, and the standard deviation can be any value between 1 and 2.

[0070] Z3: According to the average value of the aging scores and the standard deviation of the aging scores, obtain the probability density of each aging state, and calculate the number of connection components J1, J2, J3, J4 corresponding to each aging state according to the probability density; After obtaining the average value and the standard deviation, the normal distribution curve of the aging state distribution can be drawn, and then the probability density of each aging state can be obtained. After knowing the total number of connection components, the number of connection components corresponding to each aging state can be obtained.

[0071] Z4: Set J1 first particles, J2 second particles, J3 third particles, and J4 fourth particles, and randomly place the first particles, second particles, third particles, and fourth particles into the sorting sequence to generate a simulation scheme. The positions of each first particle, second particle, third particle, and fourth particle do not overlap. Send the simulation scheme to the simulation unit to generate a test scheme, and obtain 1 theoretical value of the critical wind speed; The number of first particles corresponds to the number of the first aging state. Therefore, putting the first particle into the sorting sequence indicates that the connection component corresponding to the position of the first particle in the sorting sequence is in the first aging state. In this way, by putting all the first particles, second particles, third particles, and fourth particles into the sorting sequence, 1 simulation scheme can be generated.

[0072] In practice, in order to reduce the number of particles, the reverse filling method can be adopted - giving priority to allocating the positions of non-dominant particles, and the remaining positions will automatically belong to the largest state.

[0073] For example, in this application, the number of second particles is the largest. Therefore, only the first particles, third particles, and fourth particles can be set. After the first particles, third particles, and fourth particles are spread out, the remaining positions in the sorting sequence can be set to the second aging state.

[0074] Z5: Configure the objective function f(x); ; Wherein, x is the input of the objective function f(x), Y1 and Y2 are preset weight coefficients, Y1 < Y2, Y1 + Y2 = 1, d represents the index of the connection component, D represents the total number of connection components, and h d represents the aging score of the d-th connection component, and c d represents the serial number of the d-th connection component in the sorting sequence, and F is the theoretical value of the critical wind speed of this wind tunnel test.

[0075] Thus, in the objective function f(x), if the theoretical value of the critical wind speed is smaller, then f(x) is larger. At the same time, if the connection components with severe aging are closer to the middle position of the main girder of the truss girder bridge, f(x) is larger. Therefore, the objective function f(x) set in this solution will guide the connection components with severe aging to approach the middle position of the main girder of the truss girder bridge during the iteration process, while trying to find the lowest theoretical value of the critical wind speed.

[0076] Z6: Set the maximum number of iterations, update the particle position based on the maximum objective function, repeat the aforementioned steps Z4 - Z5, and perform B iterations continuously. B is a positive integer until the maximum number of iterations is reached, or f(x) reaches the threshold. When updating the particle position, random updates can be performed by setting the speed, updating the distance, etc.

[0077] In the technical solution provided by this application, the particle swarm algorithm is used for continuous iterative operations, and it can quickly find the lowest theoretical value of the critical wind speed of the main girder of the truss girder bridge under daily maintenance work.

[0078] Embodiment 3:

[0079] A method for simulating the aeroelastic model of the main girder of a truss girder bridge, using the aeroelastic model simulation system of the main girder of the truss girder bridge described in Embodiment 1 to perform the simulation test on the aeroelastic model of the main girder of the truss girder bridge.

[0080] The above description is only some preferred embodiments of this application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of this application.

Claims

1. An aeroelastic model simulation test system for the main girder of a truss girder bridge, comprising: A wind tunnel environment module, a wind speed control module, an aeroelastic model, and an evaluation module; The aeroelastic model includes a plurality of segment modules longitudinally distributed along the main beam, and a connection component connecting two adjacent segment modules; It is characterized in that: The connection component includes: Two free-end connection parts, respectively fixed to the end faces of adjacent segment modules; An elastic adjustment member, which is an elastic damper with an adjustable elastic coefficient, and both ends are rigidly connected to the free-end connection parts; The aeroelastic model simulation test system for the main beam of the truss beam bridge further includes: A control module, electrically connected to each elastic adjustment member, for adjusting the elastic coefficient of the elastic adjustment member in real time; A test generation module, which has a structural performance degradation model of the truss beam bridge built in, generates a sequence of changes in the elastic coefficient of the connection component in different aging states, and outputs a plurality of test schemes; A data execution module, which is linked with the test generation module and the wind speed control module, sequentially loads the sequence of changes in the elastic coefficient corresponding to each test scheme, triggers the wind tunnel test and records the theoretical value of the critical wind speed.

2. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 1, characterized in that: The structural performance degradation model of the truss beam bridge includes several hidden states corresponding to aging information, and the probability transition information between the hidden states.

3. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 2, wherein: The construction method of the structural performance degradation model of the truss beam bridge is as follows: S1: Set several consecutive aging states q, the number of aging states is Q, and q represents the index of the aging state; S2: Set the status variable R t , R t represents the status variable at time t, R t = {ra t , rb t , rc t}, where ra t represents the accumulated displacement within time t; , represents the integral of the displacement length X of the connecting component over the time period from 0 to t; h with respect to time; rb t represents the number of times the connecting component is stressed within time t; rc t Indicates the time when the force on the connecting component exceeds the force threshold within time t; S3: Set the initial state probability distribution P of the aging state, P = {p1, … p q … p Q}, q ∈ [1, Q], where p q is the probability of the q-th aging state occurring; S4: Establish a probability transition matrix W for the aging state, W = {w qs}, where w qs represents the probability of transitioning from the aging state q to the aging state s, s ∈ [1, Q]; s > q; S5: Construct the state variable probability matrix E, where E = {b q (R t )} represents the probability density function of the connection components in each aging state; ； where M represents the number of Gaussian mixture components included in each aging state, and g qm represents the weight of the m-th Gaussian distribution of the aging state q; represents the m-th Gaussian distribution function of the aging state q, and u qm and L qm are the mean vector and covariance vector respectively; ； Among them, represents the matrix transformation symbol, P represents the initial state probability distribution, and exp represents the power operation of the base e of the natural logarithm; S6: Co-model the probability transition matrix W and the state variable probability matrix E to generate the structural performance degradation model of the truss beam bridge.

4. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 3, characterized in that: S6 includes the following steps: S61: Construct an initial structural performance degradation model λ of the truss beam bridge; λ = (P, W, E); where, P = {1, 0, 0, 0}, W = [w qs Q×Q , Q = 4, the constraint condition of W is:​ w qs > 0, s > q, that is, the aging state can only transfer to an aging state with a larger number; w qs = 0, s ≤ q, that is, the aging state cannot transition to an aging state with a smaller number; , that is, the sum of the probabilities of the aging state q transferring out is 1; E = {b q (R t )}; S62: Train the initial structural performance degradation model λ of the truss beam bridge to find the best model parameters to maximize the likelihood probability of the observation sequence.

5. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 4, characterized in that: S62 includes the following steps: S621: Initialize the training parameters and the maximum number of iterations. The training parameters include the initial probability transition matrix W, the initial state variable probability matrix E, and the change threshold; S622: Update the probability transition matrix W: S623: Update the state variable probability matrix E; S624: Repeat S622 and S623 for iterative repetition until the likelihood probability of the observation sequence is less than the change threshold or the maximum number of iterations is reached.

6. The aeroelastic model simulation test system for the main beam of the truss beam bridge according to claim 5, characterized in that: S622: Specifically includes; Calculate the forward vector using the forward algorithm ; ； wherein, R1…R t represents the data sequence of the state variable from time 1 to time t, and | represents the conditional probability symbol; q t = q represents the aging state q at time t; Calculate the likelihood probability P(R 1;T |λ); ； where T represents the total duration of the data sequence, represents the forward vector at the final moment T; Calculate the backward variable ; ; is a backward variable, representing the conditional probability of observing the data sequence from time t to T; Iteratively update the probability transition matrix W and the state variable probability matrix E through expectation maximization; ； : represents the probability that the aging state q transfers to the aging state s after the update of the updated probability transition matrix; Denote the probability of transferring from the aging state q to the aging state s at time t; ； Represents the probability of being in the aging state q at time t; Represents the probability of being in the aging state q at time t; ； According to Update the probability transition matrix W; S623: Specifically includes: Update the Gaussian distribution weights g qm ; ； Among them, is the updated Gaussian distribution weight, represents the posterior probability that the state variable at time t belongs to the m-th Gaussian component in the aging state q; ； ； ； represents a multivariate Gaussian distribution, represents the index of a Gaussian mixture model (GMM) component, represents the th mean vector of the Gaussian component in the aging state q; represents the th covariance vector of the Gaussian component in the aging state q; is the updated mean vector, is the updated covariance vector; According to Update the state variable probability matrix E.

7. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 3, characterized in that: Divided into 4 aging states according to the proportion of the effective connection length of the connection component of the pneumatic model under conventional test conditions.

8. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to any one of claims 1 to 7, characterized in that: The test generation module includes: A model construction unit, which has a structural performance degradation model of the truss beam bridge built in; A scheme generation unit, which sets an aging state for each connection component based on the particle swarm algorithm to generate 1 simulation scheme; A simulation unit, which presets the elastic coefficients in each aging state, and sets the aging state of each connection component in the simulation scheme to the corresponding elastic coefficient to generate a test scheme; A probability fusion unit incorporates the transition probability of each aging state into the test scheme according to the structural performance degradation model of the truss girder bridge.

9. The aeroelastic model simulation test system for the main girder of a truss girder bridge according to claim 8, wherein: An aging score is set for each aging state, and a score threshold is set; The scheme generation unit continuously generates simulation schemes on the premise that the sum of the aging scores in the simulation scheme is higher than the score threshold.

10. A method for simulating an aeroelastic model test of a main girder of a truss girder bridge, characterized in that, Use the aeroelastic model simulation test system for the main girder of a truss girder bridge according to any one of claims 1 to 9 to conduct a simulation test on the aeroelastic model of the main girder of a truss girder bridge.

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