Mountainous large-span cable-stayed bridge construction period wind-induced vibration control method

Abstract

This invention discloses a method for controlling wind-induced vibration during the construction of a long-span cable-stayed bridge in mountainous areas, comprising: obtaining the average wind speed profile at the construction site; constructing a fluctuating wind speed power spectrum model and a coherence function model at the construction site; extracting the generalized transfer function and correlation reduction factor to correct the fluctuating wind speed power spectrum model and the coherence function model; constructing a power spectral density matrix based on the corrected fluctuating wind speed power spectrum model and the coherence function model at the construction site; determining the phase angle and weighting coefficient of each frequency component after decomposition and generating harmonic components and weighted superposition to generate fluctuating wind speed time history samples at various points in the main beam and bridge tower space; obtaining buffeting force load and static wind load; obtaining self-excited force load at various points in the main beam; constructing finite element models of the target bridge under different construction states; using the superposition of buffeting force load, static wind load, and self-excited force load as input load time history parameters to simulate the vibration time-domain response of the target bridge under different construction states and optimize vibration control measures.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering technology, and in particular to a method for controlling wind-induced vibration during the construction of long-span cable-stayed bridges in mountainous areas. Background Technology

[0002] Long-span bridges, especially cable-stayed bridges with high piers and long spans, are constructed in mountainous areas with complex terrain and variable wind conditions. During the construction phase (particularly the cantilever construction phase), their structural stiffness and frequency are relatively low, making them particularly sensitive to wind loads. Wind-induced vibration responses are significant and directly affect the safety and control of structural construction. Therefore, accurately predicting and assessing the wind-induced vibration response (especially buffeting response) during bridge construction is crucial.

[0003] Currently, the main methods for analyzing the buffeting response of long-span bridges are time-domain and frequency-domain methods. Frequency-domain methods typically employ random vibration theory and spectral analysis, providing statistical characteristic values ​​of the structural response (such as mean and root mean square), but they struggle to obtain detailed time-history response processes. Time-domain analysis methods, by simulating time-history samples of wind loads and combining them with a finite element model (FEM) for dynamic analysis, can directly obtain time-history information of the structural response. This allows for consideration of complex factors such as geometric nonlinearity and damping variations, and provides a direct reflection of the structure's dynamic behavior.

[0004] However, existing time-domain analysis methods for the buffeting response of long-span bridges mostly rely on quasi-steady theory assumptions when simulating input wind loads. This assumption holds that the aerodynamic characteristics of the buffeting acting on the structural cross-section (such as energy spectrum distribution and spatial correlation) are completely consistent with the characteristics of the incoming turbulent flow (pulsating wind). However, in reality, especially for bridge cross-sections with poor streamlines or complex auxiliary components, and in mountainous wind fields with large turbulence intensity and scale, the spatiotemporal characteristics of the incoming turbulent flow change as it passes over the bridge cross-section. This leads to significant differences between the actual buffeting force and the incoming pulsating wind in terms of energy spectrum distribution and spatial correlation. Traditional time-domain analysis methods ignore this difference, directly using the pulsating wind speed time history generated based on the characteristics of the incoming wind field (such as the Kaimal spectrum, Davenport coherence function, etc.) to calculate the buffeting force. This results in a large deviation between the calculated buffeting force time history load and the actual situation, thus affecting the accuracy of the buffeting response analysis results and potentially posing a threat to the wind resistance safety assessment during bridge construction.

[0005] In addition, there are some existing studies on wind-induced vibration control during bridge construction, such as Chinese invention patent application CN 112906260A, which discloses a control method combining vertical cable-stayed bridges and a pendulum-type TMD (Transient Dynamic Damping). This method establishes a finite element model incorporating control measures and uses buffeting time-domain analysis and aeroelastic wind tunnel tests to evaluate and verify the vibration reduction effect of the proposed control measures.

[0006] However, while these methods employ time-domain buffeting analysis as an evaluation tool, their buffeting response calculations typically still rely on traditional quasi-steady theory to determine the buffeting loads acting on the structure. This means that even for evaluating advanced control measures, the buffeting load calculation methods themselves fail to overcome the inherent limitations of the quasi-steady assumptions, namely, they fail to fully consider the differences between the incoming flow turbulence characteristics and the actual aerodynamic characteristics in terms of spectral energy distribution and spatial correlation. Furthermore, although these methods may utilize wind tunnel tests (such as aeroelastic model tests) to verify results or correct structural model parameters, their purpose is not to improve the accuracy of buffeting load simulations from the input end.

[0007] Therefore, existing technologies for time-domain analysis of buffeting during bridge construction (especially in mountainous areas with complex wind conditions) generally lack a method that can fundamentally correct the buffeting load input signal, effectively incorporate aerodynamic unsteady effects, and thus improve analysis accuracy, regardless of whether the purpose is to directly predict the response or evaluate the effectiveness of control measures. This leaves uncertainty in the assessment of the structure's wind resistance performance and the prediction of the effectiveness of control measures. Summary of the Invention

[0008] To address the shortcomings of the existing technologies, this invention aims to provide a method that fully considers the influence of incoming turbulence, corrects the differences in energy spectrum distribution characteristics and spatial correlation between incoming turbulence and aerodynamic forces by introducing a generalized transfer function and a correlation reduction factor, generates improved fluctuating wind speed time history samples, obtains more accurate buffeting force time history load input, and thus improves the accuracy of vibration time domain response analysis of target bridges under different construction states.

[0009] To achieve the above-mentioned objectives, the technical solution provided by this invention includes:

[0010] Methods for controlling wind-induced vibration during the construction of long-span cable-stayed bridges in mountainous areas, including the following steps:

[0011] S1. Obtain the average wind speed profile at the construction site and construct the pulsating wind speed power spectrum model and coherence function model at the construction site.

[0012] S2. Based on the segmental model wind tunnel test data, extract the generalized transfer function and correlation reduction factor, and modify the fluctuating wind speed self-power spectrum model and coherence function model.

[0013] S3. Construct a power spectral density matrix based on the modified power spectrum model and coherence function model of the pulsating wind speed at the construction site. After Cholesky decomposition, determine the phase angle and weighting coefficient of each frequency component and generate harmonic components. Weight all harmonic components and superimpose them to generate pulsating wind speed time history samples at various points in the space of the main beam and bridge tower.

[0014] S4. Obtain the buffeting force load based on the pulsed wind speed time history sample; obtain the static wind load based on the average wind speed profile at the construction site; obtain the self-excited force load at each point on the main beam.

[0015] S5. Construct finite element models of the target bridge under different construction conditions, and use the superposition of the buffeting force load, static wind load and self-excited force load as input load time history parameters to simulate the vibration time domain response of the target bridge under different construction conditions.

[0016] S6. Optimize vibration control measures based on the vibration time-domain response of the target bridge under different construction conditions.

[0017] Preferably, the method for extracting the generalized transfer function and correlation reduction factor in step S2 includes:

[0018] S201. Use a passive wind field simulation device to generate test wind fields with different turbulence parameters, and measure the pulsating wind speed point spectrum and cross-sectional correlation coefficient.

[0019] S202. Measure the buffeting force spectrum and cross-sectional correlation coefficient on the segmental model in the experimental turbulent wind field using a segmental model pressure or force measuring device;

[0020] S203. Based on the fluctuating wind speed point spectrum and the buffeting force point spectrum, construct a function of normalized buffeting force point spectrum with frequency, cross-sectional characteristic size and turbulence integral scale, and fit the generalized transfer function T(n);

[0021] S204. Integrate the cross-sectional correlation coefficients of fluctuating wind speed and buffeting force to obtain the ratio of the cross-sectional correlation lengths of fluctuating wind speed and buffeting force, and obtain the correlation reduction factor R based on this ratio. cov .

[0022] Preferably, the method for correcting the power spectrum model and coherence function model of fluctuating wind speed in step S2 includes:

[0023] The correction function for the power spectrum of fluctuating wind speed is:

[0024] S u,w-m (n)=S u,w (n)·T(n); where S u,w (n) represent the power spectra of longitudinal and vertical pulsating wind speeds at height z, respectively;

[0025] The correction function for the coherence function is:

[0026] Among them, C z Δy is the attenuation coefficient, n is the physical frequency, and U(z) is the average wind speed at height z.

[0027] Preferably, the construction states of the target bridge include: the maximum double cantilever construction state, the single cantilever mid-term construction state, and the maximum single cantilever construction state.

[0028] Preferably, the method for obtaining the buffeting force load based on the fluctuating wind speed time history sample in step S4 includes:

[0029] The expression for buffeting force under pulsating wind is constructed based on the quasi-steady method:

[0030]

[0031] in, M b (t) represents the time histories of vertical buffeting force, downwind buffeting force, and buffeting moment, respectively; ρ is the air density; U is the average wind speed; B is the width of the main beam section; C D C L and C M These are the drag, lift, and lift moment coefficients, respectively; C′ D C′ L and C′ M , respectively, are the slopes of the lift, drag, and lift moment coefficient curves; u(t) and w(t) are the downwind wind speed and vertical fluctuating wind speed output from the fluctuating wind speed time history sample, respectively.

[0032] Preferably, the method for obtaining the static wind load based on the average wind speed profile at the construction site in step S4 includes:

[0033] The wind load acting on the target bridge by the average wind at the construction site is considered as the static wind load, and the expression for the static wind load is constructed as follows:

[0034]

[0035]

[0036] Among them, F D F L F M These represent wind resistance, wind lift, and wind moment, respectively; ρ is air density; U is average wind speed; α0 is average angle of attack; C D C L and C M These are the drag, lift, and lift moment coefficients, respectively; D and B are the height and width of the main beam section, respectively.

[0037] Beneficial effects

[0038] This invention modifies the traditional fluctuating wind speed self-power spectrum and coherence function models by introducing a generalized transfer function and correlation reduction factor extracted from segmental model wind tunnel tests. This overcomes the limitations of the quasi-steady theoretical assumptions in existing technologies and effectively considers the differences in energy spectrum distribution and spatial correlation between the incoming turbulent flow and the actual buffeting aerodynamic forces acting on the bridge cross section. The fluctuating wind speed time history samples generated based on the modified wind field model can more realistically reflect the actual aerodynamic loading characteristics, thereby obtaining a more accurate buffeting force time history load input, ultimately significantly improving the accuracy and reliability of the buffeting time-domain response analysis results.

[0039] This invention does not directly simulate the complex buffeting force spectrum and correlations. Instead, it indirectly reflects the actual characteristics of aerodynamic forces by modifying relatively mature and readily available wind field model parameters (power spectrum and coherence function). Then, it uses standard methods such as harmonic synthesis to generate optimized wind speed time histories. This approach makes the generated buffeting force time histories more statistically consistent with reality, making them more reasonable and effective inputs for finite element time-domain analysis, thus improving the scientific rigor of the entire analysis process. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a preferred embodiment of the wind-induced vibration control method for a long-span cable-stayed bridge in mountainous areas during construction. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings. In the description of this invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0042] Example 1

[0043] like Figure 1 As shown in the figure, this embodiment provides a method for controlling wind-induced vibration during the construction period of a long-span cable-stayed bridge in mountainous areas, including the following steps:

[0044] S1. Obtain the average wind speed profile at the construction site and construct the pulsating wind speed power spectrum model and coherence function model for the construction site. The average wind speed profile, also known as the wind speed profile or wind profile, refers to the distribution curve of wind speed with altitude. It is affected by topography, stratification stability, and weather patterns, exhibiting different distribution patterns in the vertical direction, and has various mathematical expressions. The variation of average wind speed along altitude in the atmospheric boundary layer, i.e., the wind profile, can be described by either a logarithmic law or an exponential law. To adapt to the wind flow characteristics in mountainous areas, considering the roughness and complexity of mountainous terrain, in some preferred embodiments, a logarithmic law is used to describe the average wind speed U(z) at different heights z:

[0045]

[0046] Where k is the Kármán constant, with a value of approximately 0.4; z0 is the surface roughness height; u * Let U be the flow shear velocity. Based on the average wind speed U(z1) at the ground roughness height z0 and the reference height z1 of 10m at the bridge site, the average wind speed U(z) at any height z can be obtained:

[0047]

[0048] The self-power spectrum of fluctuating wind speed characterizes the energy frequency distribution characteristics of fluctuating wind speed. Different types of self-power spectrum models can be selected depending on the scenario. To describe the random fluctuation or turbulence characteristics of wind, the Kaimal spectral model is used to describe the self-power spectrum of fluctuating wind speed:

[0049]

[0050] Among them, S u,w (n) represent the longitudinal and vertical pulsating wind speed autopower spectra at height z, respectively, and σ u,w Let f be the root mean square of the longitudinal and vertical pulsation velocities, n be the physical frequency, and f be the Monning coordinate, which is equal to f = nz / U(z).

[0051] The coherence function of fluctuating wind speed characterizes the correlation between different frequency components of fluctuating wind speed. Different types of coherence function models can be selected depending on the scenario. To characterize the spatial correlation between fluctuating wind speed components of the same frequency n at different points in space (e.g., two points Δy apart in the span of a bridge), the Davenport exponential coherence function model is used to describe the coherence function of fluctuating wind speed.

[0052]

[0053] In the formula, Coh(n) is the coherence function of fluctuating wind speed at height z, Δy is the cross-sectional spacing, and C z This is the attenuation coefficient.

[0054] S2. Based on the segmental model wind tunnel test data, extract the generalized transfer function and correlation reduction factor, and modify the fluctuating wind speed self-power spectrum model and coherence function model.

[0055] It should be understood that for long-span bridge main beams with non-streamlined cross-sections or complex auxiliary components, and in mountainous wind environments where turbulence intensity and scale are typically large, the simple quasi-steady assumption (i.e., that the instantaneous aerodynamic response is proportional to and completely consistent with the fluctuations in incoming wind speed) is often insufficiently accurate. Therefore, this invention considers a correction mechanism based on segmental model wind tunnel test data. By extracting two key physical quantities, the generalized transfer function and the correlation reduction factor, this mechanism quantifies and compensates for the deviation between the theoretical model and the actual aerodynamic effects.

[0056] Specifically, the generalized transfer function (GJF) primarily addresses the differences in energy spectrum distribution between the incoming turbulent flow (pulsating wind) and the resulting buffeting aerodynamic forces. The efficiency of transferring pulsating wind components of different frequencies to the aerodynamic forces is not entirely consistent; some frequency components may be amplified while others may be weakened. This frequency-dependent transfer characteristic is described by the GJF. In contrast, the correlation reduction factor focuses on handling the differences in spatial correlation between the two. Pulsating winds acting at different locations along the bridge span inherently possess a certain spatial correlation (described by the coherence function), but after interacting with the complex three-dimensional flow across the bridge cross-section, the resulting buffeting force typically exhibits a further reduction in spatial correlation. This degree of correlation reduction is quantified by the correlation reduction factor. By considering these two differences separately, this step can more comprehensively capture the true statistical characteristics of the aerodynamic forces.

[0057] In some preferred embodiments, specific methods for extracting the generalized transfer function and correlation reduction factor based on wind tunnel tests are provided, including:

[0058] S201. Generate test wind fields with different turbulence parameters using a passive wind field simulation device, and measure the fluctuating wind speed point spectrum and transverse correlation coefficient. The passive wind field simulation device includes, but is not limited to, passive simulation devices such as grids, wedges, and rough elements. The turbulence parameters include, but are not limited to, turbulence intensity and integral scale.

[0059] S202. Measure the buffeting force spectrum and span correlation coefficient on the segmental model in a test turbulent wind field using a segmental model pressure or force measuring device. The segmental model is a bridge model made to geometric scale, representing a typical cross-section of the main beam. The buffeting force spectrum can be obtained through direct measurement or by pressure integration.

[0060] S203. Based on the fluctuating wind speed point spectrum and the buffeting force point spectrum, construct a normalized function of the buffeting force point spectrum relative to frequency, cross-sectional characteristic size, and turbulence integral scale, and fit the generalized transfer function T(n). The normalization of the buffeting force point spectrum can be achieved by calculating the ratio of the fluctuating wind speed point spectrum data to the buffeting force point spectrum data. The fitting can be implemented using commonly used finite element analysis software in this field.

[0061] S204. Integrate the cross-sectional correlation coefficients of fluctuating wind speed and buffeting force to obtain the ratio of the cross-sectional correlation lengths of fluctuating wind speed and buffeting force, and obtain the correlation reduction factor R based on this ratio. cov The correlation reduction factor R cov This reflects the degree of attenuation of the aerodynamic spatial correlation relative to the incoming airflow correlation.

[0062] Because turbulence is a multi-scale, three-dimensional unsteady flow, its spatiotemporal characteristics change as it flows over an object, resulting in differences in the energy spectrum distribution and spatial correlation between the incoming turbulence and aerodynamic forces. However, directly measuring these differences in the time domain is very difficult and hard to describe using mathematical models. Therefore, this invention performs measurements in the frequency domain and corrects the autopower spectrum and coherence function of the fluctuating wind speed accordingly. This allows the differences in energy spectrum distribution and spatial correlation between the incoming turbulence and aerodynamic forces to be reflected in the fluctuating wind speed time history signal, thereby obtaining a more accurate time history load of the buffeting force.

[0063] By measuring frequency domain statistical characteristics, which are relatively easier to achieve in a wind tunnel, the differences in energy spectrum and spatial correlation between aerodynamic forces and incoming wind are captured. These differences are then fed back to the fluctuating wind speed autopower spectrum model and coherence function model established in step S1 through correction factors T(n) and Rcov. In some preferred embodiments, methods for correcting the fluctuating wind speed autopower spectrum model and coherence function model are provided, specifically including:

[0064] The correction function for the power spectrum of fluctuating wind speed is:

[0065] S u,w-m (n)=S u,w (n)·T(n); where S u,w (n) represent the power spectra of longitudinal and vertical pulsating wind speeds at height z, respectively;

[0066] The correction function for the coherence function is:

[0067] Among them, C z Δy is the attenuation coefficient, n is the physical frequency, and U(z) is the average wind speed at height z.

[0068] With the above corrections, the fluctuating wind speed time history signal generated subsequently based on the corrected spectrum and coherence function, although still in the form of a wind speed time history, has its inherent statistical characteristics that indirectly reflect the spectrum and spatial correlation characteristics of the real aerodynamic forces, thus making the final calculated buffeting force time history load more accurate and reliable.

[0069] S3. Construct a power spectral density matrix based on the modified power spectrum model and coherence function model of the pulsating wind speed at the construction site. After Cholesky decomposition, determine the phase angle and weighting coefficient of each frequency component and generate harmonic components. Weight all harmonic components and superimpose them to generate pulsating wind speed time history samples at various points in the space of the main beam and bridge tower.

[0070] It should be understood that, since it is necessary to simulate the wind loads acting on large structures (such as the main beams of cable-stayed bridges and tall bridge towers) along their spatial distribution, it is necessary to consider the fluctuating wind speeds at multiple key points and their spatial correlations. This invention uses a power spectral density matrix to describe multi-point random wind fields, which fully encapsulates the energy distribution and spatial correlation characteristics of the target wind field in the frequency domain.

[0071] Furthermore, the symmetric positive definite power spectral density matrix is ​​decomposed into the product of a lower triangular matrix and its conjugate transpose through Cholesky decomposition. This transforms the problem of generating correlated stochastic processes into generating uncorrelated stochastic processes. The desired spatial correlation is then achieved through linear transformation of the lower triangular matrix. During this process, the amplitude (or weighting coefficient) of each discrete frequency component needs to be determined, typically by the elements of the decomposed matrix and the frequency step size. Simultaneously, a random phase angle (usually uniformly distributed between 0 and 2π) needs to be assigned to each frequency component at each point to ensure that the final synthesized time history exhibits the statistical characteristics of a stochastic process, rather than a deterministic periodic signal. These amplitudes and phase angles collectively define a series of harmonic components (i.e., sine or cosine waves) with specific frequencies and spatial correlation. Those skilled in the art will recognize that the above method can be efficiently implemented using mature computational software (such as MATLAB) and its built-in function libraries; this invention does not impose further limitations.

[0072] S4. Obtain the buffeting load based on the pulsating wind speed time history sample; obtain the static wind load based on the average wind speed profile at the construction site; obtain the self-excited load at each point on the main beam.

[0073] The method for obtaining the buffeting force load based on the time history sample of the fluctuating wind speed includes:

[0074] The expression for buffeting force under pulsating wind is constructed based on the quasi-steady method:

[0075]

[0076] in, M b (t) represents the time histories of vertical buffeting force, downwind buffeting force, and buffeting moment, respectively; ρ is the air density; U is the average wind speed; B is the width of the main beam section; C D C L and C M These are the drag, lift, and lift moment coefficients, respectively; C′ D C′ L and C′ M , respectively, are the slopes of the lift, drag, and lift moment coefficient curves; u(t) and w(t) are the downwind wind speed and vertical fluctuating wind speed output from the fluctuating wind speed time history sample, respectively.

[0077] The method for obtaining static wind load based on the average wind speed profile at the construction site includes:

[0078] The wind load acting on the target bridge by the average wind at the construction site is considered as the static wind load, and the expression for the static wind load is constructed as follows:

[0079]

[0080] Among them, F D F L F M These represent wind resistance, wind lift, and wind moment, respectively; ρ is air density; U is average wind speed; α0 is average angle of attack; C D C L and C M These are the drag, lift, and lift moment coefficients, respectively; D and B are the height and width of the main beam section, respectively.

[0081] Those skilled in the art will understand that the self-excited load is not directly caused by the external wind field, but rather generated by the interaction between the bridge structure's own vibrational motion (such as vertical bending, torsion, lateral bending, etc.) and the surrounding airflow. When the bridge vibrates, its motion relative to the air changes the effective wind speed and angle of attack acting on the cross-section, thereby generating additional aerodynamic forces, i.e., self-excited forces, that are related to the motion state (velocity, displacement, acceleration). Self-excited forces may act as damping forces (dissipating vibration energy) or as negative damping forces (inputting energy into the vibration system), the latter potentially leading to catastrophic aeroelastic instability (such as flutter). In time-domain analysis, a common method for obtaining self-excited loads is to use the impulse response function (IRF) method, such as the Lin model or the time-domain convolution model proposed by Scanlan and developed by others, to convert the flutter derivative measured in the frequency domain into a time-domain impulse response function, and then calculate the self-excited force time history through convolution integral, which depends on the structure's motion history at all previous moments. This part of the technical steps can be calculated by those skilled in the art with reference to existing technologies, and this invention does not impose further limitations.

[0082] S5. Construct finite element models of the target bridge under different construction conditions, and use the superposition of the buffeting force load, static wind load and self-excited force load as input load time history parameters to simulate the vibration time domain response of the target bridge under different construction conditions.

[0083] Specifically, finite element models of the target bridge under different construction conditions can be established using the ANSYS APDL programming language. By changing parameters such as structural damping ratio, incoming flow angle of attack, and wind speed, the time-domain response of the cable-stayed bridge under different construction conditions can be simulated under different parameters.

[0084] S6. Optimize vibration control measures based on the vibration time-domain response of the target bridge under different construction conditions.

[0085] Those skilled in the art will understand that the vibration control measures aim to reduce the buffeting response of the bridge structure during construction, ensuring construction safety and the long-term stability of the bridge structure. Conventional vibration control measures in the art include:

[0086] 1. Bridge structure design optimization: By optimizing the design parameters of the bridge structure, the stiffness and damping of the bridge structure can be improved, and its flutter sensitivity can be reduced.

[0087] 2. Innovative Temporary Support System: A temporary support system was designed to address the characteristics of mountainous canyon terrain, effectively resisting wind loads and reducing the flutter response of the bridge structure.

[0088] 3. Improved construction techniques: Advanced construction techniques and equipment are adopted to reduce vibration and disturbance during construction and minimize the impact on the bridge structure.

[0089] 4. Application of active control technology: Introduce advanced active control technologies, such as tuned mass dampers (TMD) and active mass dampers (AMD), to actively control the buffeting response of the bridge structure and further reduce its buffeting response.

[0090] Since the design of specific vibration control measures is not the focus of this invention, it will not be elaborated upon. Those skilled in the art can analyze the vibration time-domain response of the target bridge under different construction states to formulate vibration reduction measures that meet different construction locations, different bridge structures and different design requirements. This invention does not intend to further limit this part of the content.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling wind-induced vibration during the construction of long-span cable-stayed bridges in mountainous areas, characterized in that, Including the following steps: S1. Obtain the average wind speed profile at the construction site and construct the pulsating wind speed power spectrum model and coherence function model at the construction site. S2. Based on the segmental model wind tunnel test data, extract the generalized transfer function and correlation reduction factor, and modify the fluctuating wind speed self-power spectrum model and coherence function model. S3. Construct a power spectral density matrix based on the modified power spectrum model and coherence function model of the pulsating wind speed at the construction site. After Cholesky decomposition, determine the phase angle and weighting coefficient of each frequency component and generate harmonic components. Weight all harmonic components and superimpose them to generate pulsating wind speed time history samples at various points in the space of the main beam and bridge tower. S4. Obtain the buffeting force load based on the pulsed wind speed time history sample; obtain the static wind load based on the average wind speed profile at the construction site; obtain the self-excited force load at each point on the main beam. S5. Construct finite element models of the target bridge under different construction conditions, and use the superposition of the buffeting force load, static wind load and self-excited force load as input load time history parameters to simulate the vibration time domain response of the target bridge under different construction conditions. S6. Optimize vibration control measures based on the vibration time-domain response of the target bridge under different construction states; The extraction methods for the generalized transfer function and correlation reduction factor mentioned in step S2 include: S201. Use a passive wind field simulation device to generate test wind fields with different turbulence parameters, and measure the pulsating wind speed point spectrum and cross-sectional correlation coefficient. S202. Measure the buffeting force spectrum and cross-sectional correlation coefficient on the segmental model in the experimental turbulent wind field using a segmental model pressure or force measuring device; S203. Based on the fluctuating wind speed point spectrum and the buffeting force point spectrum, construct a function of normalized buffeting force point spectrum with frequency, cross-sectional characteristic size and turbulence integral scale, and fit the generalized transfer function T(n); S204. Integrate the cross-sectional correlation coefficients of fluctuating wind speed and buffeting force to obtain the ratio of the cross-sectional correlation lengths of fluctuating wind speed and buffeting force, and obtain the correlation reduction factor R based on this ratio. cov ; The method for correcting the self-power spectrum model and coherence function model of fluctuating wind speed in step S2 includes: The correction function for the power spectrum of fluctuating wind speed is: Among them, S u,w (n) represent the power spectra of longitudinal and vertical pulsating wind speeds at height z, respectively; The correction function for the coherence function is: Among them, C z Δy is the attenuation coefficient, n is the physical frequency, and U(z) is the average wind speed at height z.

2. The method for controlling wind-induced vibration during the construction period of a long-span cable-stayed bridge in mountainous areas as described in claim 1, characterized in that, The construction states of the target bridge include: the maximum double cantilever construction state, the single cantilever mid-term construction state, and the maximum single cantilever construction state.

3. The method for controlling wind-induced vibration during the construction period of a long-span cable-stayed bridge in mountainous areas as described in claim 1, characterized in that, The method for obtaining the buffeting force load based on the fluctuating wind speed time history sample in step S4 includes: The expression for buffeting force under pulsating wind is constructed based on the quasi-steady method: ； ； ； in, , , These are the time histories of vertical buffeting force, downwind buffeting force, and buffeting moment, respectively. U is the air density; U is the average wind speed; B is the width of the main beam section. , and These are the drag, lift, and lift moment coefficients, respectively. , and , respectively, are the slopes of the lift, drag, and lift moment coefficient curves; u(t) and w(t) are the downwind wind speed and vertical fluctuating wind speed output from the fluctuating wind speed time history sample, respectively.

4. The method for controlling wind-induced vibration during the construction period of a long-span cable-stayed bridge in mountainous areas as described in claim 1, characterized in that, The method for obtaining the static wind load based on the average wind speed profile at the construction site, as described in step S4, includes: The wind load acting on the target bridge by the average wind at the construction site is considered as the static wind load, and the expression for the static wind load is constructed as follows: ； ； ； in, , , These are respectively wind resistance, wind lift, and wind moment; U is the air density; U is the average wind speed; α0 is the average angle of attack. , and These are the drag, lift, and lift moment coefficients, respectively; D and B are the height and width of the main beam section, respectively.

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