Aerodynamic admittance identification method and device for long-span bridges based on operational measured data

Abstract

The present invention provides a method and device for identifying the aerodynamic admittance of a long-span bridge based on operational measured data, relating to the field of bridge safety technology. The method comprises establishing a generalized equation of motion for a long-span bridge under buffeting forces; establishing a bridge finite element model, updating the bridge finite element model using operational measured data to obtain an optimal bridge finite element model; calculating the fluctuating wind coherence function of the long-span bridge main beam; obtaining the three-force coefficient, three-force, and generalized buffeting force of the long-span bridge under different wind attack angles based on the optimal bridge finite element model; performing frequency domain analysis on the generalized equation of motion to obtain a simulated power spectrum density at each point on the long-span bridge main beam; and identifying aerodynamic admittance parameters in a preset aerodynamic admittance function based on the simulated power spectrum density to complete the aerodynamic admittance identification of the long-span bridge. The present invention solves the problem that existing methods are unable to identify the aerodynamic admittance of a long-span bridge during operation.

Description

Technical Field

[0001] The present invention relates to the field of bridge safety technology, and in particular to a method and device for identifying the aerodynamic admittance of a long-span bridge based on operational measured data. Background Art

[0002] Buffeting refers to the random vibrations induced by fluctuating winds on engineering structures. Bridges exposed to naturally fluctuating wind fields inevitably experience buffeting. While buffeting typically does not cause catastrophic damage, large-scale buffeting responses can negatively impact driving safety, comfort, and the fatigue performance of structural components. In the buffeting analysis of long-span bridges, the aerodynamic admittance function plays a crucial role, serving as the transfer function linking the aerodynamic forces acting on the bridge section with the fluctuating wind field of the incoming flow. Therefore, identifying the aerodynamic admittance function has become a crucial step in the buffeting analysis of long-span bridges.

[0003] At present, the identification of aerodynamic admittance function is mostly determined by the pulsating wind speed and pulsating force obtained through wind tunnel test measurements. However, the wind field simulated by the wind tunnel often does not have the large-scale eddies of the actual wind field, which often leads to low aerodynamic admittance values in the low-frequency range. At present, more and more long-span bridges are equipped with long-term health monitoring systems that can measure wind speed and bridge buffeting response on site. Therefore, some scholars have identified aerodynamic admittance by measuring the wind speed and the pulsating force obtained by on-site pressure measurement. However, if the bridge is in the operation stage, the on-site pressure measurement method is difficult to implement, which will make it impossible to identify the aerodynamic admittance of long-span bridges during operation. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for identifying the aerodynamic admittance of a long-span bridge based on operational measured data to improve the above-mentioned problem. To achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a method for identifying the aerodynamic admittance of a long-span bridge based on operational measured data, comprising:

[0006] Based on the parameters of long-span bridges, the generalized motion equations of long-span bridges under buffeting forces are established;

[0007] Establishing a finite element model of the long-span bridge, and updating the finite element model of the bridge using operational measured data of the long-span bridge to obtain an optimal finite element model of the bridge;

[0008] Calculating the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data;

[0009] Obtaining three-force coefficients of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculating three-force and generalized buffeting forces of the main beam of the long-span bridge based on the three-force coefficients;

[0010] Based on the fluctuating wind coherence function, the three-component force and the generalized buffeting force, the generalized motion equation is analyzed in the frequency domain to obtain the simulated power spectrum density of each point on the main beam of the long-span bridge;

[0011] Based on the simulated power spectrum density, the aerodynamic admittance parameters in the preset aerodynamic admittance function are identified, the aerodynamic admittance function is obtained, and the aerodynamic admittance identification of the long-span bridge is completed.

[0012] In a second aspect, the present application also provides a long-span bridge aerodynamic admittance identification device based on operational measured data, comprising:

[0013] The first building block is used to establish the generalized motion equation of the long-span bridge under the buffeting force based on the long-span bridge parameters;

[0014] The second construction module is used to establish a bridge finite element model of the long-span bridge, and update the bridge finite element model through the actual operation measurement data of the long-span bridge to obtain an optimal bridge finite element model;

[0015] A first calculation module is used to calculate the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data;

[0016] a second calculation module, configured to obtain a three-force coefficient of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculate a three-force coefficient and a generalized buffeting force of the main beam of the long-span bridge based on the three-force coefficient;

[0017] A third calculation module is configured to perform frequency domain analysis on the generalized motion equation based on the fluctuating wind coherence function, the three-component force, and the generalized buffeting force to obtain a simulated power spectrum density at each point of the main beam of the long-span bridge;

[0018] The identification module is used to identify the aerodynamic admittance parameters in the preset aerodynamic admittance function based on the simulated power spectrum density, obtain the aerodynamic admittance function, and complete the aerodynamic admittance identification of the long-span bridge.

[0019] The beneficial effects of the present invention are:

[0020] The present invention collects data on bridges during operation to obtain measured operation data. The measured operation data is used to identify the pulsating wind coherence function, the three-component force coefficient of the main beam, and the generalized buffeting force of the long-span bridge. This can indicate the effect of the buffeting force during operation of the long-span bridge. At the same time, the optimization algorithm can accurately identify the aerodynamic admittance function of the long-span bridge in different directions during operation, facilitating subsequent buffeting analysis of the long-span bridge based on the aerodynamic admittance function identified by the present invention.

[0021] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be learned by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 Schematic diagram of the flow of a method for identifying aerodynamic admittance of a long-span bridge based on operational measured data according to an embodiment of the present invention;

[0024] Figure 2 Schematic diagram of the structure of the aerodynamic admittance identification equipment for a large-span bridge based on operational measured data according to an embodiment of the present invention.

[0025] Markings in the figure: 800, aerodynamic admittance identification equipment for large-span bridges based on operational measured data; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0028] Example 1:

[0029] This embodiment provides a method for identifying the aerodynamic admittance of a long-span bridge based on operational measured data.

[0030] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400, step S500, and step S600.

[0031] Step S100: establishing a generalized motion equation of the long-span bridge under buffeting force based on the long-span bridge parameters;

[0032] In this embodiment, the generalized equation of motion is expressed as:

[0033]

[0034] Where, I i represents the generalized mass of the i-th mode, K i represents the reduced frequency of the i-th mode, ω i represents the angular frequency of the i-th mode, D represents the main beam height, U represents the average wind speed, ζ i represents the damping ratio of the i-th mode, ξ i (·), and They represent the generalized displacement, generalized velocity, and generalized acceleration of the i-th order mode, B represents the main beam width, Q i (·) denotes the generalized buffeting force of the i-th mode, and s denotes the dimensionless time.

[0035] In this embodiment, the damping ratio can be obtained by processing the measured buffeting acceleration time history data using a fast Bayesian FFT method, wherein the measured buffeting acceleration time history data is operational measured data of a long-span bridge.

[0036] Step S200: establishing a finite element model of the long-span bridge, and updating the finite element model of the bridge using operational measured data of the long-span bridge to obtain an optimal finite element model of the bridge;

[0037] In this embodiment, measuring points are arranged at one-quarter span, one-half span and three-quarter span of the long-span bridge. At each measuring point, multiple sensors are arranged to collect actual operational data of the long-span bridge during operation.

[0038] The step S200 specifically includes:

[0039] Step S201: Based on the long-span bridge parameters, three-dimensional beam elements are selected to simulate the main beams and pylons of the long-span bridge, spatial rod elements are selected to simulate the cables and hangers of the long-span bridge, and mass elements are used to simulate the auxiliary structures and secondary dead loads of the long-span bridge, thereby obtaining a finite element model of the bridge;

[0040] Step S202: collecting operational measured data of the long-span bridge, wherein the operational measured data includes the measured vertical natural frequency, measured transverse natural frequency, measured torsional natural frequency, measured displacement data, measured environmental parameters, and measured power spectrum density of the long-span bridge;

[0041] Step S203: defining a first objective function based on the operational measured data, and selecting the main beam material density, main beam elastic modulus, main cable material density, and main cable initial stress in the bridge finite element model as update parameters;

[0042] In this embodiment, the first objective function is constructed using measured natural frequency data, wherein the measured natural frequency data includes the measured vertical natural frequency, measured transverse natural frequency, and measured torsional natural frequency of the bridge. The expression of the first objective function is:

[0043]

[0044] Where OBJ Bridge represents the first objective function, f i sim represents the natural frequency of the i-th mode of the updated bridge finite element model, f i mea represents the natural frequency of the i-th mode of the measured frequency data, and n represents the total number of natural frequencies.

[0045] Step S204: updating parameters in the bridge finite element model by using a particle swarm algorithm until the first objective function converges, thereby obtaining an optimal bridge finite element model.

[0046] In this embodiment, a particle swarm algorithm is used to minimize the first objective function, wherein the performance of the particles is associated with the first objective function, and the positions of the particles correspond to the update parameters.

[0047] Step S300: Calculating the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data;

[0048] In this embodiment, based on the conditional simulation method, an iterative approach is adopted to obtain the fluctuating wind coherence function of the entire long-span bridge main beam.

[0049] The Davenport coherence function is selected, and the attenuation constant in the Davenport coherence function is fitted based on the wind speed time history data of the measuring point to obtain a preliminary attenuation constant, and the preliminary coherence function is obtained through the preliminary attenuation constant.

[0050] Based on the preliminary coherence function and wind spectrum information from the measurement points, a conditional simulation of the wind field on the main girder of a long-span bridge is performed. The attenuation constant in the Davenport coherence function is then fitted based on the conditional simulation of the main girder wind field to obtain an updated attenuation constant. These steps are repeated to iteratively determine the optimal attenuation constant and obtain the fluctuating wind coherence function for the entire main girder. The wind speed time history data and wind spectrum information are all measured data from the actual operation of the long-span bridge.

[0051] Step S400: obtaining three-force coefficients of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculating three-force coefficients and generalized buffeting force of the main beam of the long-span bridge based on the three-force coefficients;

[0052] In this embodiment, the three-force coefficient of the main beam is determined by finite element analysis and on-site measured displacement data. The difference between the measured average displacement response of the main beam and the average displacement response of the bridge deck is used as the second objective function. The three-force coefficient is used as the identification parameter, and an optimization algorithm is used to identify the three-force coefficient under different wind attack angles.

[0053] Furthermore, when the average wind speed is low, the flutter derivative of the bridge girder will be very small, and the corresponding self-excited force's impact on the bridge's buffeting can be ignored. Therefore, to eliminate the impact of the self-excited force on the identification of the aerodynamic admittance function, this embodiment identifies the aerodynamic admittance function at low wind speeds.

[0054] The step S400 specifically includes:

[0055] Step S401: Calculating the measured average displacement response of the main beam based on the measured displacement data;

[0056] Step S402: calculating the average displacement response of the bridge deck based on the optimal bridge finite element model;

[0057] Step S403: defining a second objective function based on the measured average displacement response of the main beam and the average displacement response of the bridge deck;

[0058] In this embodiment, the difference between the measured average displacement response of the main beam and the average displacement response of the bridge deck is used as the second objective function.

[0059] Step S404: Using the three-force coefficients as identification parameters of the optimal bridge finite element model, optimizing the identification parameters in the optimal bridge finite element model through an optimization algorithm until the second objective function converges, and obtaining the three-force coefficients under different wind attack angles, wherein the three-force coefficients include a lift coefficient, a drag coefficient, and a moment coefficient.

[0060] The step S400 further includes:

[0061] Step S405: Calculating the slope of the three-force coefficient based on the three-force coefficient;

[0062] Step S406: Calculating the three-component forces of the main beam of the long-span bridge based on the measured environmental parameters, the three-component force coefficient, and the slope of the three-component force coefficient, wherein the measured environmental parameters include the average wind speed, the fluctuating wind speed in the downwind direction, and the fluctuating wind speed in the vertical direction;

[0063] In this embodiment, the three forces of the main beam of a long-span bridge include lift, drag, and moment. The calculation formulas for the lift, drag, and moment are:

[0064]

[0065] Where, L b 、D b and M b Represent lift, drag and torque respectively, C L 、C D and C M They represent the lift coefficient, drag coefficient and moment coefficient respectively, ρ represents the air density, U represents the average wind speed, C L ′、C D ′ and C M ′ represents the slope of lift coefficient, drag coefficient and moment coefficient respectively, B represents the main beam width, u′ represents the fluctuating wind speed in the downwind direction, w′ represents the fluctuating wind speed in the vertical direction, χ L , χ D and χ M are the aerodynamic admittance functions representing the lift, drag, and moment to be identified, respectively.

[0066] Step S407: Obtain the length of the long-span bridge and the vertical mode, lateral mode, and torsional mode of the main beam of the long-span bridge;

[0067] Step S408: Calculate the generalized buffeting force of the main beam of the long-span bridge based on the length of the long-span bridge, the vertical mode, the lateral mode, the torsional mode and the three-part force.

[0068] In this embodiment, the calculation formula for the generalized buffeting force of the main beam of a long-span bridge is:

[0069]

[0070] Where Q i (·) represents the generalized buffeting force of the i-th mode, s represents the dimensionless time, l represents the length of the long-span bridge, h i (·), p i (·) and α i (·) represents the i-th vertical mode, transverse mode and torsional mode of the main beam, x represents the longitudinal coordinate along the length of the main beam, L b (·), D b (·) and M b (·) represent the lift, drag, and moment on the main beam, respectively, and B represents the width of the main beam.

[0071] Step S500: performing frequency domain analysis on the generalized motion equation based on the fluctuating wind coherence function, the three-component force, and the generalized buffeting force to obtain a simulated power spectrum density at each point of the main beam of the long-span bridge;

[0072] The step S500 specifically includes:

[0073] Step S501: Substitute the generalized buffeting force into the generalized motion equation and perform Fourier transform to obtain a frequency domain equation;

[0074] In this embodiment, the generalized motion equation after substituting the generalized buffeting force is subjected to Fourier transform to obtain the frequency domain equation as follows:

[0075]

[0076] Where K i represents the reduced frequency of the i-th order mode, K represents the reduced frequency, ζ i represents the damping ratio of the i-th mode, ρ represents the air density, B represents the main beam width, I i represents the generalized mass of the i-th mode, h i (·), p i (·) and α i (·) represents the i-th vertical mode, transverse mode and torsional mode of the main beam, x represents the longitudinal coordinate along the length of the main beam, and They represent the Fourier transform of the lift, drag and torque on the main beam, Represents the Fourier transform of the generalized displacement of the i-th mode.

[0077] Step S502: Calculating a first power spectrum density of generalized displacement according to random vibration theory and the frequency domain equation;

[0078] In this embodiment, the calculation formula of the first power spectrum density is:

[0079]

[0080] Where, represents the first power spectrum density of the generalized displacement of the i-th order mode, K represents the reduced frequency, K i represents the reduced frequency of the i-th order mode, ρ represents the air density, B represents the main beam width, I i represents the generalized mass of the i-th mode, ζ i represents the damping ratio of the i-th mode, l represents the length of the long-span bridge, S F (x A ,x B ,K) represents the generalized buffeting force at point x under the reduced frequency K A and point x B The cross-spectral density of .

[0081] Step S503: Calculating three-dimensional generalized displacement based on the fluctuating wind coherence function, the three-component force, and the first power spectrum density, where the three-dimensional generalized displacement includes generalized displacement in vertical, lateral, and torsional directions;

[0082] In this embodiment, the expression of the three-dimensional generalized displacement is:

[0083]

[0084]

[0085] r=h,p,α

[0086] a=u,w

[0087] Where, and They represent the generalized displacements in the vertical, lateral, and torsional directions of the i-th order mode, K represents the reduced frequency, and K i represents the reduced frequency of the i-th order mode, ρ represents the air density, B represents the main beam width, I i represents the generalized mass of the i-th mode, ζ i represents the damping ratio of the i-th mode, χ L , χ D and χ M They represent the aerodynamic admittance functions of the lift, drag, and torque to be identified, respectively, and C L 、C D and C M They represent the lift coefficient, drag coefficient and moment coefficient respectively, C L ′、C D ′ and C M ′ represents the slope of lift coefficient, drag coefficient and moment coefficient respectively, S uu(K) and S ww (K) represents the power spectrum density of the fluctuating wind in the downwind direction and vertical direction w under the reduced frequency K, It represents the intermediate vector in direction a and direction r under the reduced frequency K, a and r both represent direction parameters, h represents vertical direction, p represents horizontal direction, α represents torsional direction, u represents the downwind direction of pulsating wind, w represents the vertical direction of pulsating wind, coh a (·) represents the fluctuating wind coherence function, l represents the length of the long-span bridge, h i (·), p i (·) and α i (·) represents the i-th vertical mode, transverse mode and torsional mode of the main beam, respectively, and |·| represents the absolute value.

[0088] Step S504: Calculate the simulated power spectrum density of each point on the main beam of the long-span bridge based on the three-dimensional generalized displacement and the generalized motion equation.

[0089] In this embodiment, the expression of the simulated power spectrum density at each point of the main beam of the long-span bridge is:

[0090]

[0091]

[0092] Where S h (·), S p (·) and S α (·) represents the vertical simulated power spectrum density, lateral simulated power spectrum density and torsional simulated power spectrum density of each point on the main beam of the long-span bridge, x represents the longitudinal coordinate along the main beam length, K represents the reduced frequency, h i (·), p i (·) and α i (·) represents the i-th vertical mode, transverse mode and torsional mode of the main beam, B represents the width of the main beam, and represent the generalized displacements in the vertical, lateral and torsional directions of the i-th mode, respectively.

[0093] Step S600: Based on the simulated power spectrum density, identify the aerodynamic admittance parameters in the preset aerodynamic admittance function, obtain the aerodynamic admittance function, and complete the aerodynamic admittance identification of the long-span bridge.

[0094] The step S600 specifically includes:

[0095] Step S601: defining an aerodynamic admittance objective function based on the measured power spectral density and the simulated power spectral density;

[0096] In this embodiment, the aerodynamic admittance objective function focuses on the difference between the measured power spectrum density and the simulated power spectrum density at the peak point corresponding to the main vibration mode. The expression of the aerodynamic admittance objective function is:

[0097]

[0098] Where OBJ r represents the aerodynamic admittance objective function in the r direction, x j represents the jth measured point, K k represents the reduced frequency of the kth attention, represents the simulated power spectrum density corresponding to the kth reduced frequency of interest at the jth measured point in the r direction, It represents the measured power spectral density corresponding to the kth reduced frequency of interest at the jth measured point in the r direction, where r represents the direction parameter, r = h, p, α, h represents the vertical direction, p represents the horizontal direction, and α represents the torsional direction.

[0099] Step S602: Optimizing the parameters to be fitted in the preset aerodynamic admittance function by using a genetic algorithm until the aerodynamic admittance objective function converges, thereby obtaining aerodynamic admittance parameters;

[0100] In this embodiment, the preset aerodynamic admittance function is represented by the Larose model, wherein the expression of the preset aerodynamic admittance function is:

[0101]

[0102] Where, χ Lu (·) represents the aerodynamic admittance function in the downwind direction of the lift-pulsating wind, χ Lw (·) represents the aerodynamic admittance function in the vertical direction of the lift-pulsating wind, χ Du (·) represents the aerodynamic admittance function in the downwind direction of the resistance pulsating wind, χ Dw (·) represents the aerodynamic admittance function in the vertical direction of the resistance pulsating wind, χ Mu (·) represents the aerodynamic admittance function in the downwind direction of the moment pulsating wind, χ Mw (·) represents the aerodynamic admittance function in the vertical direction of the moment-pulsating wind, a represents the direction parameter, f represents the force parameter, K represents the reduction frequency, γ and β represent the parameters to be fitted, and |·| represents the absolute value.

[0103] Step S603: Substitute the aerodynamic admittance parameter into the preset aerodynamic admittance function to obtain the aerodynamic admittance function of the long-span bridge.

[0104] In summary, the present invention can accurately identify the aerodynamic admittance functions of long-span bridge girders in the vertical, lateral, and torsional directions based on measured data. Furthermore, buffeting analysis of long-span bridges based on the aerodynamic admittance functions identified by the present invention shows a good match between the power spectral density of the measured responses and the power spectral density of the measured responses in numerical simulations.

[0105] Example 2:

[0106] This embodiment provides a device for identifying aerodynamic admittance of a long-span bridge based on operational measured data, the device comprising:

[0107] The first building block is used to establish the generalized motion equation of the long-span bridge under the buffeting force based on the long-span bridge parameters;

[0108] The second construction module is used to establish a bridge finite element model of the long-span bridge, and update the bridge finite element model through the actual operation measurement data of the long-span bridge to obtain an optimal bridge finite element model;

[0109] A first calculation module is used to calculate the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data;

[0110] a second calculation module, configured to obtain a three-force coefficient of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculate a three-force coefficient and a generalized buffeting force of the main beam of the long-span bridge based on the three-force coefficient;

[0111] A third calculation module is configured to perform frequency domain analysis on the generalized motion equation based on the fluctuating wind coherence function, the three-component force, and the generalized buffeting force to obtain a simulated power spectrum density at each point of the main beam of the long-span bridge;

[0112] The identification module is used to identify the aerodynamic admittance parameters in the preset aerodynamic admittance function based on the simulated power spectrum density, obtain the aerodynamic admittance function, and complete the aerodynamic admittance identification of the long-span bridge.

[0113] The second building block includes:

[0114] A construction unit is used to select three-dimensional beam elements to simulate the main beam and bridge tower of the long-span bridge based on the long-span bridge parameters, select spatial rod elements to simulate the cables and hangers of the long-span bridge, and simulate the auxiliary structure and secondary dead load of the long-span bridge through mass elements to obtain a finite element model of the bridge;

[0115] A data acquisition unit is used to collect operational measured data of the long-span bridge, wherein the operational measured data includes the measured vertical natural frequency, measured transverse natural frequency, measured torsional natural frequency, measured displacement data, measured environmental parameters, and measured power spectrum density of the long-span bridge;

[0116] A first definition unit is configured to define a first objective function based on the operational measured data, and select main beam material density, main beam elastic modulus, main cable material density, and main cable initial stress in the bridge finite element model as update parameters;

[0117] The first updating unit is configured to update the update parameters in the bridge finite element model by using a particle swarm algorithm until the first objective function converges to obtain an optimal bridge finite element model.

[0118] The second calculation module includes:

[0119] A first calculation unit is used to calculate the measured average displacement response of the main beam based on the measured displacement data;

[0120] A second calculation unit is used to calculate the average displacement response of the bridge deck based on the optimal bridge finite element model;

[0121] A second definition unit is used to define a second objective function based on the measured average displacement response of the main beam and the average displacement response of the bridge deck;

[0122] a second updating unit, configured to use the three-force coefficients as identification parameters of the optimal bridge finite element model, optimize the identification parameters in the optimal bridge finite element model through an optimization algorithm until the second objective function converges, and obtain the three-force coefficients under different wind attack angles, wherein the three-force coefficients include a lift coefficient, a drag coefficient, and a moment coefficient;

[0123] a third calculation unit, configured to calculate a slope of a three-force coefficient based on the three-force coefficient;

[0124] a fourth calculation unit, configured to calculate the three-component force of the main beam of the long-span bridge based on the measured environmental parameters, the three-component force coefficient, and the slope of the three-component force coefficient, wherein the measured environmental parameters include the average wind speed, the fluctuating wind speed in the downwind direction, and the fluctuating wind speed in the vertical direction;

[0125] A data acquisition unit, used to acquire the length of the long-span bridge and the vertical mode, lateral mode and torsional mode of the main beam of the long-span bridge;

[0126] The fifth calculation unit is used to calculate the generalized buffeting force of the main beam of the long-span bridge based on the length of the long-span bridge, the vertical mode, the lateral mode, the torsional direction mode and the three-part force.

[0127] The third computing module includes:

[0128] a sixth calculation unit, configured to substitute the generalized buffeting force into the generalized motion equation and perform Fourier transform to obtain a frequency domain equation;

[0129] a seventh calculation unit, configured to calculate a first power spectrum density of the generalized displacement according to random vibration theory and the frequency domain equation;

[0130] an eighth calculation unit, configured to calculate a three-dimensional generalized displacement based on the fluctuating wind coherence function, the three-component force, and the first power spectrum density, wherein the three-dimensional generalized displacement includes generalized displacements in vertical, lateral, and torsional directions;

[0131] The ninth calculation unit is used to calculate the simulated power spectrum density of each point on the main beam of the long-span bridge based on the three-dimensional generalized displacement and the generalized motion equation.

[0132] In this embodiment, the identification module includes:

[0133] a third definition unit, configured to define an aerodynamic admittance target function based on the measured power spectral density and the simulated power spectral density;

[0134] A third updating unit is configured to optimize the parameters to be fitted in the preset aerodynamic admittance function by using a genetic algorithm until the aerodynamic admittance objective function converges, thereby obtaining aerodynamic admittance parameters;

[0135] The identification unit is used to substitute the aerodynamic admittance parameter into the preset aerodynamic admittance function to obtain the aerodynamic admittance function of the long-span bridge.

[0136] It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0137] Example 3:

[0138] Corresponding to the above method embodiment, this embodiment also provides a large-span bridge aerodynamic admittance identification device based on operational measured data. The large-span bridge aerodynamic admittance identification device based on operational measured data described below and the large-span bridge aerodynamic admittance identification method based on operational measured data described above can be referenced to each other.

[0139] Figure 2 FIG. 8 is a block diagram of a long-span bridge aerodynamic admittance identification device 800 based on operational measured data according to an exemplary embodiment. Figure 2 As shown, the long-span bridge aerodynamic admittance identification device 800 based on operational measured data may include: a processor 801, a memory 802. The long-span bridge aerodynamic admittance identification device 800 based on operational measured data may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0140] The processor 801 is used to control the overall operation of the large-span bridge aerodynamic admittance identification device 800 based on operational measured data, thereby completing all or part of the steps of the large-span bridge aerodynamic admittance identification method based on operational measured data. The memory 802 is used to store various types of data to support the operation of the large-span bridge aerodynamic admittance identification device 800 based on operational measured data. This data may include, for example, instructions for any application or method operating on the large-span bridge aerodynamic admittance identification device 800 based on operational measured data, as well as application-related data, such as contact information, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the large-span bridge aerodynamic admittance identification device 800 based on operational measured data and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0141] In an exemplary embodiment, the large-span bridge aerodynamic admittance identification device 800 based on operational measured data can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned large-span bridge aerodynamic admittance identification method based on operational measured data.

[0142] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for identifying the aerodynamic admittance of a large-span bridge based on operationally measured data. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the device 800 for identifying the aerodynamic admittance of a large-span bridge based on operationally measured data to implement the aforementioned method for identifying the aerodynamic admittance of a large-span bridge based on operationally measured data.

[0143] Example 4:

[0144] Corresponding to the above method embodiment, this embodiment further provides a readable storage medium. The readable storage medium described below and the aerodynamic admittance identification method for a large-span bridge based on operational measured data described above can refer to each other.

[0145] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the aerodynamic admittance identification method for a long-span bridge based on operational measured data of the above-mentioned method embodiment.

[0146] The readable storage medium may specifically be any readable storage medium that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0147] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0148] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for identifying the aerodynamic admittance of a long-span bridge based on operational measured data, characterized in that: include: Based on the parameters of long-span bridges, the generalized motion equations of long-span bridges under buffeting forces are established; Establishing a finite element model of the long-span bridge, and updating the finite element model of the bridge using operational measured data of the long-span bridge to obtain an optimal finite element model of the bridge; Calculating the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data; Obtaining three-force coefficients of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculating three-force and generalized buffeting forces of the main beam of the long-span bridge based on the three-force coefficients; Based on the fluctuating wind coherence function, the three-component force and the generalized buffeting force, the generalized motion equation is analyzed in the frequency domain to obtain the simulated power spectrum density of each point on the main beam of the long-span bridge; Based on the simulated power spectrum density, the aerodynamic admittance parameters in the preset aerodynamic admittance function are identified to obtain the aerodynamic admittance function, thereby completing the aerodynamic admittance identification of the long-span bridge; The establishing of the bridge finite element model of the long-span bridge, updating the bridge finite element model using operational measured data of the long-span bridge, and obtaining an optimal bridge finite element model includes: Based on the long-span bridge parameters, three-dimensional beam elements are selected to simulate the main beams and bridge towers of the long-span bridge, spatial rod elements are selected to simulate the cables and hangers of the long-span bridge, and mass elements are used to simulate the auxiliary structures and secondary dead loads of the long-span bridge to obtain a finite element model of the bridge; Collecting operational measured data of the long-span bridge, wherein the operational measured data includes the measured vertical natural frequency, measured lateral natural frequency, measured torsional natural frequency, measured displacement data, measured environmental parameters, and measured power spectrum density of the long-span bridge; defining a first objective function based on the operational measured data, and selecting the main beam material density, main beam elastic modulus, main cable material density, and main cable initial stress in the bridge finite element model as update parameters; The update parameters in the bridge finite element model are updated by using a particle swarm algorithm until the first objective function converges, thereby obtaining an optimal bridge finite element model.

2. The aerodynamic admittance identification method of a long-span bridge based on operational measured data according to claim 1 is characterized in that The method of obtaining the three-force coefficients of the long-span bridge under different wind attack angles based on the optimal bridge finite element model includes: Calculating the measured average displacement response of the main beam based on the measured displacement data; Calculating the average displacement response of the bridge deck based on the optimal bridge finite element model; defining a second objective function based on the measured average displacement response of the main beam and the average displacement response of the bridge deck; The three-force coefficients are used as identification parameters of the optimal bridge finite element model. The identification parameters in the optimal bridge finite element model are optimized by an optimization algorithm until the second objective function converges, thereby obtaining the three-force coefficients under different wind attack angles. The three-force coefficients include lift coefficient, drag coefficient and moment coefficient.

3. The aerodynamic admittance identification method of a long-span bridge based on operational measured data according to claim 1 is characterized in that The calculation of the three-force and generalized buffeting force of the main beam of the long-span bridge based on the three-force coefficient includes: Calculating a slope of the three-force coefficient based on the three-force coefficient; Calculating the three-force of the main beam of the long-span bridge based on the measured environmental parameters, the three-force coefficient and the slope of the three-force coefficient, wherein the measured environmental parameters include average wind speed, fluctuating wind speed in the downwind direction and fluctuating wind speed in the vertical direction; Obtain the length of long-span bridges and the vertical mode, lateral mode and torsional mode of the main beam of long-span bridges; The generalized buffeting force of the main beam of the long-span bridge is calculated based on the length of the long-span bridge, the vertical mode, the lateral mode, the torsional direction mode and the three-part force.

4. The aerodynamic admittance identification method of a long-span bridge based on operational measured data according to claim 1 is characterized in that Based on the pulsating wind coherence function, the three-part force and the generalized buffeting force, the generalized motion equation is analyzed in the frequency domain to obtain the simulated power spectrum density of each point on the main beam of the long-span bridge, including: Substituting the generalized buffeting force into the generalized motion equation and performing Fourier transform to obtain a frequency domain equation; Calculating the first power spectral density of the generalized displacement according to random vibration theory and the frequency domain equation; Calculating a three-dimensional generalized displacement based on the fluctuating wind coherence function, the three-component force, and the first power spectrum density, wherein the three-dimensional generalized displacement includes generalized displacements in vertical, lateral, and torsional directions; Based on the three-dimensional generalized displacement and the generalized motion equation, the simulated power spectrum density of each point on the main beam of the long-span bridge is calculated.

5. The aerodynamic admittance identification method of a long-span bridge based on operational measured data according to claim 1 is characterized in that The method of identifying aerodynamic admittance parameters in a preset aerodynamic admittance function based on the simulated power spectrum density to obtain the aerodynamic admittance function includes: defining an aerodynamic admittance objective function based on the measured power spectral density and the simulated power spectral density; Optimizing the parameters to be fitted in the preset aerodynamic admittance function by genetic algorithm until the aerodynamic admittance objective function converges, thereby obtaining the aerodynamic admittance parameters; The aerodynamic admittance parameter is substituted into the preset aerodynamic admittance function to obtain the aerodynamic admittance function of the long-span bridge.

6. A device for identifying aerodynamic admittance of a long-span bridge based on operational measured data, characterized in that: include: The first building block is used to establish the generalized motion equation of the long-span bridge under the buffeting force based on the long-span bridge parameters; The second construction module is used to establish a bridge finite element model of the long-span bridge, and update the bridge finite element model through the actual operation measurement data of the long-span bridge to obtain an optimal bridge finite element model; A first calculation module is used to calculate the fluctuating wind coherence function of the main beam of the long-span bridge based on the operational measured data; a second calculation module, configured to obtain a three-force coefficient of the long-span bridge at different wind attack angles based on the optimal bridge finite element model, and calculate a three-force coefficient and a generalized buffeting force of the main beam of the long-span bridge based on the three-force coefficient; A third calculation module is configured to perform frequency domain analysis on the generalized motion equation based on the fluctuating wind coherence function, the three-component force, and the generalized buffeting force to obtain a simulated power spectrum density at each point of the main beam of the long-span bridge; an identification module, configured to identify aerodynamic admittance parameters in a preset aerodynamic admittance function based on the simulated power spectrum density, obtain the aerodynamic admittance function, and complete aerodynamic admittance identification of a long-span bridge; The second building block includes: A construction unit is used to select three-dimensional beam elements to simulate the main beam and bridge tower of the long-span bridge based on the long-span bridge parameters, select spatial rod elements to simulate the cables and hangers of the long-span bridge, and simulate the auxiliary structure and secondary dead load of the long-span bridge through mass elements to obtain a finite element model of the bridge; A data acquisition unit is used to collect operational measured data of the long-span bridge, wherein the operational measured data includes the measured vertical natural frequency, measured transverse natural frequency, measured torsional natural frequency, measured displacement data, measured environmental parameters, and measured power spectrum density of the long-span bridge; A first definition unit is configured to define a first objective function based on the operational measured data, and select main beam material density, main beam elastic modulus, main cable material density, and main cable initial stress in the bridge finite element model as update parameters; The first updating unit is configured to update the update parameters in the bridge finite element model by using a particle swarm algorithm until the first objective function converges to obtain an optimal bridge finite element model.

7. The aerodynamic admittance identification device for a long-span bridge based on operational measured data according to claim 6 is characterized in that: The second calculation module includes: A first calculation unit is used to calculate the measured average displacement response of the main beam based on the measured displacement data; A second calculation unit is used to calculate the average displacement response of the bridge deck based on the optimal bridge finite element model; A second definition unit is used to define a second objective function based on the measured average displacement response of the main beam and the average displacement response of the bridge deck; a second updating unit, configured to use the three-force coefficients as identification parameters of the optimal bridge finite element model, optimize the identification parameters in the optimal bridge finite element model through an optimization algorithm until the second objective function converges, and obtain the three-force coefficients under different wind attack angles, wherein the three-force coefficients include a lift coefficient, a drag coefficient, and a moment coefficient; a third calculation unit, configured to calculate a slope of a three-force coefficient based on the three-force coefficient; a fourth calculation unit, configured to calculate the three-component force of the main beam of the long-span bridge based on the measured environmental parameters, the three-component force coefficient, and the slope of the three-component force coefficient, wherein the measured environmental parameters include the average wind speed, the fluctuating wind speed in the downwind direction, and the fluctuating wind speed in the vertical direction; A data acquisition unit, used to acquire the length of the long-span bridge and the vertical mode, lateral mode and torsional mode of the main beam of the long-span bridge; The fifth calculation unit is used to calculate the generalized buffeting force of the main beam of the long-span bridge based on the length of the long-span bridge, the vertical mode, the lateral mode, the torsional direction mode and the three-part force.

8. The aerodynamic admittance identification device for a long-span bridge based on operational measured data according to claim 6 is characterized in that: The third computing module includes: a sixth calculation unit, configured to substitute the generalized buffeting force into the generalized motion equation and perform Fourier transform to obtain a frequency domain equation; a seventh calculation unit, configured to calculate a first power spectrum density of the generalized displacement according to random vibration theory and the frequency domain equation; an eighth calculation unit, configured to calculate a three-dimensional generalized displacement based on the fluctuating wind coherence function, the three-component force, and the first power spectrum density, wherein the three-dimensional generalized displacement includes generalized displacements in vertical, lateral, and torsional directions; The ninth calculation unit is used to calculate the simulated power spectrum density of each point on the main beam of the long-span bridge based on the three-dimensional generalized displacement and the generalized motion equation.

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