Stay cable vortex-induced vibration batch automatic identification method

Abstract

The invention relates to a stay cable vortex-induced vibration batch automatic identification method, which comprises the following steps of: firstly, automatically extracting wave crests of vibration signals of a plurality of stay cables in batches through wave crest batch automatic detection; then linear regression is carried out on the normalized wave crest, and the slope of linear regression and the standard deviation of the peak residual error are extracted in batches; finally, the vibration states of all the stay cables are judged at a time through the slope and the threshold value of the standard deviation, and automatic batch identification of vortex-induced vibration of the stay cables is achieved. The method solves the problems that a traditional vortex-induced vibration recognition method can only recognize the vortex-induced vibration of one stay cable at a time, the recognition efficiency is low, and the resource demand is large, can be used for online recognition of the vortex-induced vibration of the stay cables of a bridge health monitoring system and automatic analysis of long-term monitoring data of the stay cables, and improves the recognition efficiency of the vortex-induced vibration of the stay cables.

Description

Technical Field

[0001] This application relates to the field of bridge health monitoring and identification of vortex-induced vibration of stay cables, specifically, to a method for batch automatic identification of vortex-induced vibration of stay cables. Background Technology

[0002] Vortex-induced vibration (VEV) of stay cables is a typical abnormal vibration of stay cables. Monitoring and identifying VEV of stay cables is a crucial requirement in bridge health monitoring. Existing methods for identifying VEV of stay cables mainly include displacement reconstruction based on acceleration integrals, decision tree methods based on historical data, and automatic clustering methods based on time-frequency domain characteristic indices. These methods are effective in identifying VEV of main beams and individual stay cables, accurately identifying VEV of stay cables. However, in routine monitoring, stay cables are usually monitored in batches, and existing methods can only analyze one cable at a time. In online monitoring, existing methods require parallel analysis to analyze the vibration state of all monitored stay cables, which is very resource-intensive. In long-term offline data analysis, a cyclical analysis approach can be used to analyze all stay cables, but this method has low timeliness and is very computationally expensive. Summary of the Invention

[0003] To overcome at least one deficiency in the prior art, this application provides a batch automatic identification method for vortex-induced vibration of cable-stayed bridges.

[0004] Firstly, a method for batch automatic identification of vortex-induced vibration of cable-stayed bridges is provided, including: Obtain the original vibration signals of multiple stay cables and establish the original signal matrix; determine the peak positions based on the original signal matrix and establish the peak matrix. The elements in the peak matrix are numbered and normalized to obtain a normalized time matrix. The mean of each column element in the peak matrix is ​​calculated to form a peak mean row vector. This peak mean row vector is then expanded to the same dimension as the peak matrix to obtain the peak mean matrix. Based on the normalized time matrix, the peak mean matrix, and the peak matrix, the linear regression slope vector and the linear regression intercept matrix are calculated. The trend term of the peaks is removed from the linear regression slope vector and the linear regression intercept matrix to obtain the residual matrix. Calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector; Each column element of the standard residual vector and each column element of the linear regression slope vector are compared with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

[0005] In one embodiment, determining the peak position based on the original signal matrix and establishing the peak matrix includes: The forward difference matrix and the backward difference matrix are obtained from the original signal matrix; Set the elements less than 0 in the forward and backward difference matrices to 0, and calculate the product of the forward and backward difference matrices to obtain the product matrix. Determine the positions of non-zero elements in the product matrix, extract the corresponding elements from the original signal matrix, and obtain the peak matrix.

[0006] In one embodiment, the linear regression slope vector and the linear regression intercept matrix are calculated based on the normalized time matrix, the peak mean matrix, and the peak matrix; including: Calculate the mean of each column of the normalized time matrix to obtain the time mean vector, and expand the time mean vector to obtain the time mean matrix. The linear regression slope vector is:

[0007] in, This is the linear regression slope vector. For the normalized time matrix, This is a time mean matrix. For the peak matrix, The peak mean matrix, for The Middle i The elements of the column, sum represents the summation by column of the matrix; Calculate the linear regression intercept row vector:

[0008] in, This is the linear regression intercept row vector. This is the linear regression slope vector. It is a time mean vector; The linear regression intercept row vector is expanded to obtain the linear regression intercept matrix.

[0009] In one embodiment, the residual matrix is ​​obtained by removing the trend term of the peak from the linear regression slope vector and the linear regression intercept matrix, including:

[0010] in, The residual matrix is... For the peak matrix, This is the linear regression slope vector. This is the intercept matrix for linear regression.

[0011] In one embodiment, each column element of the standard residual vector and each column element of the linear regression slope vector are compared with the corresponding thresholds to obtain the identification result of the vortex-induced vibration of each stay cable, including: if Then the vibration is random vibration; where, The first standard residual vector i Column elements; if and Then the vibration is in the stable stage of vortex-induced vibration; among which, The first of the linear regression slope vectors i Column elements; if and Then the vibration is in the vortex-induced vibration formation stage; if and Then the vibration is in the dissipation stage of vortex-induced vibration.

[0012] Secondly, a batch automatic identification device for vortex-induced vibration of cable-stayed bridges is provided, comprising: The peak matrix establishment module is used to acquire the original vibration signals of multiple stay cables and establish the original signal matrix; based on the original signal matrix, the peak positions are determined and the peak matrix is ​​established. The peak trend term removal module is used to number the elements in the peak matrix and normalize the numbers to obtain a normalized time matrix; calculate the mean of each column element in the peak matrix to form a peak mean row vector, and expand the peak mean row vector to the same dimension as the peak matrix to obtain the peak mean matrix; calculate the linear regression slope vector and linear regression intercept matrix based on the normalized time matrix, peak mean matrix, and peak matrix; remove the peak trend term based on the linear regression slope vector and linear regression intercept matrix to obtain the residual matrix; The standard residual vector determination module is used to calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector. The vibration identification module is used to compare each column element of the standard residual vector and each column element of the linear regression slope vector with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

[0013] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for batch automatic identification of vortex-induced vibrations in cable-stayed bridges.

[0014] Fourthly, embodiments of this application provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described method for batch automatic identification of vortex-induced vibration of cable-stayed bridges.

[0015] Compared with existing technologies, this application has the following advantages: First, it automatically extracts the peaks of vibration signals from multiple stay cables in batches through peak automatic detection; then, it performs linear regression on the normalized peaks to extract the slope and standard deviation of the peak residuals in batches; finally, it determines the vibration state of all stay cables at once by using thresholds for the slope and standard deviation, thus achieving batch automatic identification of vortex-induced vibration of stay cables. This application solves the problems of traditional vortex-induced vibration identification methods, which can only identify the vortex-induced vibration of one stay cable at a time, resulting in low identification efficiency and high resource requirements. It can be used for online identification of vortex-induced vibration of stay cables in bridge health monitoring systems and automatic analysis of long-term monitoring data of stay cables, improving the efficiency of vortex-induced vibration identification of stay cables. Attached Figure Description

[0016] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings: Figure 1 A flowchart of a method for batch automatic identification of vortex-induced vibration in cable-stayed bridges is shown. Figure 2 A diagram showing the arrangement of the stay cable sensors is provided. Figure 3 The results of a day's batch identification of vortex-induced vibrations in the cable-stayed bridge are shown. Figure 4 The results of the index changes of cable No. 2 in the first 50 minutes are shown; Figure 5 The original signal and peak detection results at time 1 are shown; Figure 6 The original signal and peak detection results at time 3 are shown; Figure 7 The original time history data for time 1 is shown; Figure 8 The original time history data for time 2 is shown; Figure 9 The original time history data for time 3 is shown; Figure 10 The original time history data for time 4 is shown. Detailed Implementation

[0017] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0018] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution of this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0019] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0020] This application provides a method for batch automatic identification of vortex-induced vibration in cable-stayed bridges. Figure 1 A flowchart of a batch automatic identification method for vortex-induced vibration of cable-stayed bridges is shown. (See attached diagram) Figure 1 The method mainly includes the following steps: Step S1: Obtain the original vibration signals of multiple stay cables and establish the original signal matrix. Original signal matrix Each column represents the original vibration signal of a cable-stayed cable; the position of the peak is determined based on the original signal matrix, and a peak matrix is ​​established.

[0021]

[0022] in, Original signal matrix The i List, For the first i The first in the list k One data point, m This represents the number of data points in each column of the signal, which is also the number of rows in the original signal matrix. n This represents the number of cables in the cable-stayed bridge, which is also the number of columns in the original signal matrix.

[0023] Specifically, based on the original signal matrix Obtain the forward difference matrix and backward difference matrix ;

[0024]

[0025] in, Represents the original signal matrix 2 to m- 1 line.

[0026] Set all elements less than 0 in the forward and backward difference matrices to 0, and then calculate the product of the forward and backward difference matrices to obtain the product matrix. ; , This represents the matrix dot product.

[0027] By determining the positions of non-zero elements in the product matrix, and extracting the corresponding elements from the original signal matrix, the peak matrix is ​​obtained. Here, when the number of elements in each column is inconsistent, it is padded with 0. The matrix dimension is... .

[0028]

[0029] Among them, the peak matrix Positions without elements are filled with 0. Crest matrix The i List, for The Middle k Data.

[0030] Step S2: Number the elements in the peak matrix and normalize the numbers to obtain a normalized time matrix; calculate the mean of each column element in the peak matrix to form a peak mean row vector, and expand the peak mean row vector to the same dimension as the peak matrix to obtain the peak mean matrix; calculate the linear regression slope vector and the linear regression intercept matrix based on the normalized time matrix, the peak mean matrix, and the peak matrix; remove the trend term of the peaks based on the linear regression slope vector and the linear regression intercept matrix to obtain the residual matrix.

[0031] Here, the peak matrix Each column of the matrix is ​​numbered starting from 1. Then, each number is divided by the number of non-zero elements in that column to normalize the matrix, resulting in a normalized time matrix. The purpose of normalization is to unify the regression slopes of peak values ​​for different oscillation periods to the same comparison standard.

[0032]

[0033] in, For the normalized time matrix The i List, k The number of non-zero elements in the first column.

[0034] Then, calculate the peak matrix. The mean of each column element is used to form a peak mean row vector. This peak mean row vector is then expanded to the same dimension as the peak matrix to obtain the peak mean matrix. , will with In a matrix, the element at the position corresponding to the 0 element is set to 0:

[0035] in, Peak mean matrix The i List, Every element in is the same (except for the one in the middle). The elements at the positions corresponding to the 0 elements in the matrix are all peak matrices. No. i The mean of the column elements.

[0036] Specifically, the mean of each column of the time matrix is ​​calculated to obtain the time mean vector. The time mean vector is expanded to obtain the time mean matrix. , will with In a matrix, the element at the position corresponding to the 0 element is set to 0:

[0037] here, Time mean matrix The i List, Every element in is the same (except for the one in the middle). The elements at the positions corresponding to the 0th element in the matrix are all elements of the time matrix. i The mean of the column.

[0038] The linear regression slope vector is:

[0039] in, This is the linear regression slope vector. For the normalized time matrix, This is a time mean matrix. For the peak matrix, The peak mean matrix, for The Middle i The elements of a column, sum represents the summation of the matrix by columns.

[0040] The linear regression intercept row vector is:

[0041] in, This is the linear regression intercept row vector. This is the linear regression slope vector. This is a time mean vector.

[0042] Linear regression intercept row vector By extending the matrix, we obtain the linear regression intercept matrix. .

[0043]

[0044] in, The intercept matrix for linear regression The i List, Each element in the vector is identical, representing the nth row of the linear regression intercept vector. i The elements of the column.

[0045] The residual matrix is:

[0046] in, The residual matrix is... For the peak matrix, This is the linear regression slope vector. This is the intercept matrix for linear regression.

[0047] Step S3: Calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector. ,in, The first standard residual vector i Column elements.

[0048] Step S4: Compare each column element of the standard residual vector and each column element of the linear regression slope vector with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

[0049] Specifically, if Then the vibration is random vibration; where, The first standard residual vector i Column elements if and Then the vibration is in the stable stage of vortex-induced vibration; among which, The first of the linear regression slope vectors i Column elements; if and Then the vibration is in the vortex-induced vibration formation stage; if and Then the vibration is in the dissipation stage of vortex-induced vibration.

[0050] The threshold value is determined as follows: Random vibration amplitudes do not have a stable trend of change. Therefore, the normalized amplitude must be a random number with a mean of 0 and an amplitude fluctuating between 0 and 1.0, corresponding to a standard deviation of approximately 0.29.

[0051] The amplitudes of vortex-induced vibrations exhibit trends during both the formation and dissipation stages. The formation stage is defined as a process where the ratio of the minimum to the maximum amplitude is greater than 0.6 and the amplitude increases linearly, while the dissipation stage is defined as a process where the amplitude decreases linearly. After removing the trend term, the normalized amplitudes from both the formation and dissipation stages become a set of randomly distributed data with a mean of 0 and a maximum fluctuation of 0.4, corresponding to a standard deviation of approximately 0.12.

[0052] The amplitude of vortex-induced vibration in the stable stage varies randomly within a certain range. The vibration in the stable stage is defined as the ratio of the minimum amplitude to the maximum amplitude being greater than 0.7 and the amplitude having no trend term. The normalized amplitude must be a random number with a mean of 0 and an amplitude fluctuating between 0.7 and 1.0, with a corresponding standard deviation of approximately 0.09.

[0053] Therefore, by comparing the standard deviation of the amplitude after removing the trend term with the value of 0.12, it can be determined whether it is random vibration or eddy-induced vibration.

[0054] The threshold value is determined as follows: The amplitude of vortex-induced vibration in its stable phase fluctuates between 0.7 and 1.0. Considering extreme cases, the amplitude in the stable phase fluctuates linearly, corresponding to a slope of -0.3 to 0.3. Therefore, the stable phase of vortex-induced vibration occurs when the slope is in the range of -0.3 to 0.3. When the slope is greater than 0.3, it is the vortex-induced vibration formation phase, and when it is less than -0.3, it is the vortex-induced vibration dissipation phase.

[0055] The above thresholds are not strictly fixed. Engineers can determine appropriate thresholds based on the leniency of the target recognition results and the value selection logic.

[0056] This embodiment first automatically extracts the peaks of vibration signals from multiple stay cables through batch automatic peak detection. Then, it performs linear regression on the normalized peaks, extracting the slope and standard deviation of the peak residuals in batches. Finally, by using thresholds for the slope and standard deviation, it determines the vibration state of all stay cables at once, achieving batch automatic identification of vortex-induced vibration in stay cables. This embodiment solves the problems of traditional vortex-induced vibration identification methods, which can only identify the vortex-induced vibration of one stay cable at a time, resulting in low identification efficiency and high resource requirements. It can be used for online identification of vortex-induced vibration in stay cables in bridge health monitoring systems and automatic analysis of long-term monitoring data of stay cables, improving the efficiency of vortex-induced vibration identification.

[0057] Figure 2 The diagram shows the arrangement of the cable-stayed bridge sensors. The background cable-stayed bridge is equipped with a bridge health monitoring system, in which accelerometers are installed on 12 cables to monitor the vibration status of the cables. The accelerometers are sampled at a frequency of 50Hz, and each analysis uses 1 minute of data, simultaneously identifying the vibration status of all 12 cables.

[0058] Figure 3 The results of a day's batch identification of vortex-induced vibration of the cable-stayed bridge are shown, with different colors representing different vibration states.

[0059] Figure 4 The results of the index changes of the No. 2 cable-stayed bridge over the first 50 minutes are shown, where the dashed lines represent the thresholds for standard deviation and slope, respectively. Four time points were selected, and the identification results were verified using acceleration time history results. The identification results for times 1 to 4 represent random vibration, vortex-induced vibration formation stage, vortex-induced vibration stabilization stage, and vortex-induced vibration decay stage, respectively.

[0060] Figure 5 The original signal and peak detection results at time 1 are shown, where the yellow highlighted position represents the peak position. The peak position matches the peak position of the time history signal, proving the accuracy of the peak detection method of this application.

[0061] Figure 6 The original signal and peak detection results at time 3 are shown, where the yellow highlighted position represents the peak position. The peak position matches the peak position of the time history signal, proving the accuracy of the peak detection method of this application.

[0062] Figure 7 The original time history data for time 1 is shown, which exhibits random vibration characteristics. The identification results are consistent with the data characteristics, proving the accuracy of the identification results.

[0063] Figure 8 The original time history data for time 2 is shown, according to Figure 8 The amplitude increases linearly, which is consistent with the characteristics of the formation stage of vortex-induced vibration. The identification results are consistent with the data characteristics, proving the accuracy of the identification results.

[0064] Figure 9 The original time history data for time 3 is shown, according to Figure 9 The amplitude is relatively stable, fluctuating within a small range, which is consistent with the characteristics of the stable stage of vortex-induced vibration. The identification results are consistent with the data characteristics, proving the accuracy of the identification results.

[0065] Figure 10 The raw time history data for time 4 is shown, according to Figure 10 The amplitude decreased linearly, which is consistent with the characteristics of the dissipation stage of vortex-induced vibration. The identification results are consistent with the data characteristics, proving the accuracy of the identification results.

[0066] Based on the same inventive concept as the automatic batch identification method for vortex-induced vibration of cable-stayed bridges, this embodiment also provides a corresponding automatic batch identification device for vortex-induced vibration of cable-stayed bridges, including: The peak matrix establishment module is used to acquire the original vibration signals of multiple stay cables and establish the original signal matrix; based on the original signal matrix, the peak positions are determined and the peak matrix is ​​established. The peak trend term removal module is used to number the elements in the peak matrix and normalize the numbers to obtain a normalized time matrix; calculate the mean of each column element in the peak matrix to form a peak mean row vector, and expand the peak mean row vector to the same dimension as the peak matrix to obtain the peak mean matrix; calculate the linear regression slope vector and linear regression intercept matrix based on the normalized time matrix, peak mean matrix, and peak matrix; remove the peak trend term based on the linear regression slope vector and linear regression intercept matrix to obtain the residual matrix; The standard residual vector determination module is used to calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector. The vibration identification module is used to compare each column element of the standard residual vector and each column element of the linear regression slope vector with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

[0067] The automatic batch identification device for vortex-induced vibration of cable-stayed bridges in this embodiment has the same inventive concept as the automatic batch identification method for vortex-induced vibration of cable-stayed bridges described above. Therefore, the specific implementation of this device can be found in the embodiment section of the automatic batch identification method for vortex-induced vibration of cable-stayed bridges described above, and its technical effects correspond to the technical effects of the above method, so it will not be repeated here.

[0068] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method for batch automatic identification of vortex-induced vibration of cable-stayed bridges.

[0069] This application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described method for batch automatic identification of vortex-induced vibration of cable-stayed bridges.

[0070] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for batch automatic identification of vortex-induced vibration of cable-stayed bridges, characterized in that, include: Obtain the original vibration signals of multiple stay cables and establish the original signal matrix; Determine the peak positions based on the original signal matrix and establish a peak matrix; The elements in the peak matrix are numbered, and the numbering is normalized to obtain a normalized time matrix; Calculate the mean of each column element of the peak matrix to form a peak mean row vector. Expand the peak mean row vector to the same dimension as the peak matrix to obtain the peak mean matrix. Calculate the linear regression slope vector and the linear regression intercept matrix based on the normalized time matrix, the peak mean matrix, and the peak matrix. The residual matrix is ​​obtained by removing the trend term of the peak from the linear regression slope vector and the linear regression intercept matrix. Calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector; Each column element of the standard residual vector and each column element of the linear regression slope vector are compared with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

2. The method as described in claim 1, characterized in that, in, Determine the peak position based on the original signal matrix and establish the peak matrix, including: The forward difference matrix and the backward difference matrix are obtained based on the original signal matrix; Set the elements less than 0 in the forward difference matrix and the backward difference matrix to 0, and calculate the product of the forward difference matrix and the backward difference matrix to obtain the product matrix; The positions of non-zero elements in the product matrix are determined, and the corresponding elements in the original signal matrix are extracted to obtain the peak matrix.

3. The method as described in claim 1, characterized in that, in, Based on the normalized time matrix, the peak mean matrix, and the peak matrix, calculate the linear regression slope vector and the linear regression intercept matrix; including: Calculate the mean of each column of the normalized time matrix to obtain a time mean vector, and expand the time mean vector to obtain a time mean matrix; The linear regression slope vector is: in, This is the slope vector of the linear regression. For the normalized time matrix, This is a time mean matrix. For the peak matrix, The peak mean matrix, for The Middle i The elements of the column, sum represents the summation by column of the matrix; Calculate the linear regression intercept row vector: in, This is the linear regression intercept row vector. This is the slope vector of the linear regression. It is a time mean vector; The linear regression intercept row vector is expanded to obtain the linear regression intercept matrix.

4. The method as described in claim 1, characterized in that, in, Based on the linear regression slope vector and linear regression intercept matrix, after removing the trend term of the peak, the residual matrix is ​​obtained, including: in, The residual matrix is... For the peak matrix, This is the slope vector of the linear regression. This is the intercept matrix for linear regression.

5. The method as described in claim 1, characterized in that, in, Each column element of the standard residual vector and each column element of the linear regression slope vector are compared with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable, including: if Then the vibration is random vibration; where, The first standard residual vector i Column elements; if and Then the vibration is in the stable stage of vortex-induced vibration; among which, The first of the linear regression slope vectors i Column elements; if and Then the vibration is in the vortex-induced vibration formation stage; if and Then the vibration is in the dissipation stage of vortex-induced vibration.

6. A batch automatic identification device for vortex-induced vibration of cable-stayed bridges, characterized in that, include: The peak matrix establishment module is used to acquire the original vibration signals of multiple stay cables and establish the original signal matrix. Determine the peak positions based on the original signal matrix and establish a peak matrix; The peak trend term removal module is used to number the elements in the peak matrix and normalize the numbering to obtain a normalized time matrix. Calculate the mean of each column element of the peak matrix to form a peak mean row vector. Expand the peak mean row vector to the same dimension as the peak matrix to obtain the peak mean matrix. Calculate the linear regression slope vector and the linear regression intercept matrix based on the normalized time matrix, the peak mean matrix, and the peak matrix. The residual matrix is ​​obtained by removing the trend term of the peak from the linear regression slope vector and the linear regression intercept matrix. The standard residual vector determination module is used to calculate the standard deviation of each column of the residual matrix to obtain the standard residual vector. The vibration identification module is used to compare each column element of the standard residual vector and each column element of the linear regression slope vector with the corresponding threshold to obtain the identification result of the vortex-induced vibration of each cable.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the batch automatic identification method for vortex-induced vibration of cable-stayed bridges as described in any one of claims 1-5.

8. A computer program product, characterized in that, It includes a computer program / instruction, which, when executed by a processor, implements the batch automatic identification method for vortex-induced vibration of cable-stayed bridges as described in any one of claims 1-5.

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