Bridge modal parameter automatic identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a bridge modal parameter automatic identification method and device, electronic equipment and a storage medium. The method comprises the steps that firstly, vibration response of a bridge under environmental excitation is obtained, then modal analysis is conducted on response signals, bridge modal parameters and uncertainty of the modal parameters are obtained, most false modals are removed based on the uncertainty of the modal parameters, and finally the vibration response of the bridge under environmental excitation is obtained. A density clustering algorithm is adopted to detect residual false modals and cluster physical modals, and finally modal parameters corresponding to damping median values in the clustered physical modals serve as recognition results of bridge modal parameters. According to the method and the device, automation of bridge modal parameter identification can be realized, and relatively high robustness is realized.

Description

Technical Field

[0001] The present application relates to the technical field of bridge maintenance management, and particularly to a method, device, electronic device and storage medium for automatically identifying bridge modal parameters. Background Art

[0002] A large amount of bridge vibration data is generated every day by the bridge online monitoring system, which is difficult to analyze manually. Therefore, automatic identification of modal parameters is a key technology for real-time monitoring of bridge conditions. In actual tests, due to the influence of various factors, the identified modal parameters include not only physical modes but also spurious modes. Removing spurious modes is one of the key steps in bridge modal parameter identification. Physical modes are inherent parameters reflecting the dynamic characteristics of the bridge structure, while spurious modes are generated by accidental interference factors in the test data. Therefore, physical modes will appear stably (or in clusters), while spurious modes will appear discretely. According to the above principle, the physical and spurious modes can be screened by plotting the relationship diagram (i.e., the stability diagram) between the stable modes and the model order, and the automatic identification of modal parameters is reduced to the problem of finding the modes that appear in clusters from the stability diagram (this problem is essentially a data clustering problem). The current research has the following deficiencies:

[0003] 1. There are many methods for removing spurious modes, but there is a lack of a method for incorporating the uncertainty of modal parameters into the screening criterion.

[0004] 2. Existing clustering algorithms require manual parameter specification. For example, the K-means clustering algorithm requires specifying the number of clusters (i.e., the order of physical modes), and the hierarchical clustering algorithm requires specifying the truncation distance. These methods cannot achieve true automatic identification of bridge modal parameters.

[0005] 3. Spurious modes cannot be ensured to be completely removed. Existing clustering methods will incorporate a small amount of remaining spurious modes (outliers) into the clustering, resulting in errors in the finally identified modal parameters. Summary of the Invention

[0006] To solve the above technical problems, the present application provides a method, device, electronic device and storage medium for automatically identifying bridge modal parameters, which can effectively improve the accuracy of the finally identified modal parameters.

[0007] The first object of the present application is to provide a method for automatically identifying bridge modal parameters.

[0008] The above object one of the present application is achieved by the following technical solutions:

[0009] A method for automatically identifying bridge modal parameters, the method comprising the following steps:

[0010] S1, obtaining the vibration response signal of the target bridge structure under ambient excitation;

[0011] S2. Perform modal analysis on the vibration response signal to obtain the uncertainty of the bridge modal parameters;

[0012] S3. Eliminate the spurious modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after eliminating the spurious modes;

[0013] S4. Use the density clustering algorithm to cluster the physical modes of the bridge modal parameters after eliminating the spurious modes and detect the outliers to obtain the final identification result of the bridge modal parameters.

[0014] Preferably, in step S2, the performing modal analysis on the vibration response signal to obtain the uncertainty of the bridge modal parameters includes:

[0015] S21. Preprocess the vibration response signal;

[0016] S22. Use the stochastic subspace algorithm to extract the bridge modal parameters from the preprocessed vibration response signal;

[0017] S23. Quantify the uncertainty of the extracted bridge modal parameters.

[0018] Preferably, in step S21, the preprocessing the vibration response signal includes:

[0019] Perform pre-filtering and resampling on the vibration signal to remove the trend term and outliers in the vibration signal.

[0020] Preferably, in step S23, the quantifying the uncertainty of the extracted bridge modal parameters includes:

[0021] According to the first-order perturbation theory, calculate the covariance matrix of the modal parameter identification error, and use the covariance matrix of frequency and damping ratio to represent the uncertainty of the bridge modal parameters.

[0022] Preferably, in step S3, the eliminating the spurious modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after eliminating the spurious modes includes:

[0023] S31. Eliminate the spurious modes with the coefficient of variation exceeding the preset threshold based on the uncertainty of the bridge modal parameters, where the coefficient of variation is the ratio of the standard deviation of frequency to frequency;

[0024] S32. Further use the preset spurious mode elimination criterion to eliminate the remaining spurious modes for the modes with the coefficient of variation not exceeding the preset threshold.

[0025] Preferably, the preset threshold is 2%.

[0026] Preferably, in step S4, using the density clustering algorithm to perform physical modal clustering on the bridge modal parameters after removing false modes and detecting outliers, the final bridge modal parameter identification result includes:

[0027] S41, using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to perform clustering analysis on the physical modes and detect outliers;

[0028] S42, selecting the modal parameters corresponding to the median value of the damping ratio in the clustering modes as the final bridge modal parameter identification result.

[0029] Preferably, the vibration response signal includes at least one of velocity, acceleration, and displacement.

[0030] The second object of the present application is to provide a device for automatically identifying bridge modal parameters.

[0031] The above object two of the present application is achieved by the following technical solutions:

[0032] A device for automatically identifying bridge modal parameters, the device includes:

[0033] A vibration response signal acquisition module, configured to acquire a vibration response signal of a target bridge structure under ambient excitation;

[0034] A modal analysis module, configured to perform modal analysis on the vibration response signal to obtain the uncertainty of bridge modal parameters;

[0035] A false mode removal module, configured to remove false modes based on the uncertainty of the bridge modal parameters to obtain bridge modal parameters after removing false modes;

[0036] A modal clustering module, configured to use the density clustering algorithm to perform physical modal clustering on the bridge modal parameters after removing false modes and detect outliers to obtain the final bridge modal parameter identification result.

[0037] Preferably, the performing modal analysis on the vibration response signal to obtain the uncertainty of bridge modal parameters includes:

[0038] Performing preprocessing on the vibration response signal;

[0039] Using the stochastic subspace algorithm to extract bridge modal parameters from the preprocessed vibration response signal;

[0040] Performing uncertainty quantification on the extracted bridge modal parameters.

[0041] Preferably, the performing preprocessing on the vibration response signal includes:

[0042] Perform pre-filtering and resampling on the vibration signal to remove the trend term and outliers in the vibration signal.

[0043] Preferably, the uncertainty quantification of the extracted bridge modal parameters includes:

[0044] According to the first-order perturbation theory, calculate the covariance matrix of the modal parameter identification error, and use the covariance matrix of frequency and damping ratio to represent the uncertainty of the bridge modal parameters.

[0045] Preferably, the removal of spurious modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after removing spurious modes includes:

[0046] Remove spurious modes with a coefficient of variation exceeding a preset threshold based on the uncertainty of the bridge modal parameters, where the coefficient of variation is the ratio of the standard deviation of frequency to frequency;

[0047] Further use a preset spurious mode removal criterion to remove the remaining spurious modes from the modes with a coefficient of variation not exceeding the preset threshold.

[0048] Preferably, the preset threshold is 2%.

[0049] Preferably, the physical modal clustering and outlier detection of the bridge modal parameters after removing spurious modes using the density clustering algorithm to obtain the final bridge modal parameter identification result includes:

[0050] Use the density-based clustering algorithm with noise space to perform clustering analysis on the physical modes and detect outliers;

[0051] Select the modal parameters corresponding to the median value of the damping ratio in the clustering modes as the final bridge modal parameter identification result.

[0052] Preferably, the vibration response signal includes at least one of velocity, acceleration, and displacement.

[0053] The third object of the present application is to provide an electronic device.

[0054] The above object three of the present application is achieved by the following technical solutions:

[0055] An electronic device, comprising:

[0056] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the bridge modal parameter automatic identification method described in any one of the first objects of the present application.

[0057] The fourth object of the present application is to provide a computer-readable storage medium.

[0058] The above-mentioned fourth application object of the present application is achieved by the following technical solutions:

[0059] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the bridge modal parameter automatic recognition method described in any one of the first objects of the present application above.

[0060] In summary, the present application discloses a method, device, electronic device and storage medium for automatic recognition of bridge modal parameters. By acquiring the vibration response signal of the target bridge structure under ambient excitation, performing modal analysis on the vibration response signal to obtain the uncertainty of the bridge modal parameters, eliminating false modes based on the uncertainty of the bridge modal parameters, obtaining the bridge modal parameters after eliminating false modes, using the density clustering algorithm to perform physical modal clustering on the bridge modal parameters after eliminating false modes and detecting outliers, and obtaining the final bridge modal parameter recognition result.

[0061] The present application incorporates frequency uncertainty into the elimination of false modes, making the finally recognized modal parameters have a certain probability reliability. In addition, the present application uses the density clustering algorithm to perform physical modal clustering analysis on the bridge modal parameters after eliminating false modes, requires fewer parameters to be specified, can achieve the highest degree of automation, and the density clustering algorithm can detect the remaining false modes, making the final bridge modal parameter recognition result robust to noise. Therefore, the present application can effectively improve the accuracy of the finally recognized modal parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is a schematic flow chart of the method for automatic recognition of bridge modal parameters in an embodiment of the present application;

[0064] Figure 2 It is a schematic structural diagram of the device for automatic recognition of bridge modal parameters in an embodiment of the present application;

[0065] Figure 3 It is a schematic structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0067] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described below are only illustrative. For example, the division of units and modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or modules can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0068] In addition, in each embodiment of this application, each functional unit can be fully integrated in one processor, or each unit can be separately used as a device, or two or more units can be integrated in one device; each functional unit in each embodiment of this application can be implemented in the form of hardware, or can be implemented in the form of hardware plus software functional units.

[0069] Those of ordinary skill in the art can understand that all or part of the steps of implementing the following method embodiments can be completed through program instructions and related hardware. The foregoing program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, the steps of the following method embodiments are executed; and the foregoing storage medium includes: various media such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks that can store program codes.

[0070] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, the meanings of "multiple" and "several" are two or more, unless otherwise specifically defined.

[0071] As Figure 1 shown, the embodiments of this application provide a method for automatically identifying bridge modal parameters. The method may include the following steps:

[0072] S1. Obtain the vibration response signal of the target bridge structure under environmental excitation;

[0073] In order to identify the bridge modal parameters, it is first necessary to obtain the vibration response signal of the target bridge structure. Specifically, the vibration response signal of the target bridge structure under environmental excitation collected by sensors at multiple measurement points arranged on the target bridge structure can be obtained from the bridge health monitoring system.

[0074] Specifically, in this embodiment, the types and arrangement positions of the sensors on the target bridge structure can be arranged according to the actual situation of the project and the structural vibration mode. According to the different sensors, the collected vibration response signal can be at least one of an acceleration signal, a velocity signal, or a displacement signal, or a combination of multiple functional signals.

[0075] S2. Perform modal analysis on the vibration response signal to obtain the uncertainty of the bridge modal parameters;

[0076] After obtaining the vibration response signal of the target bridge structure under environmental excitation, in order to obtain the bridge modal parameters of the target bridge structure, in this embodiment, modal analysis is first performed on the vibration response signal to obtain the uncertainty of the bridge modal parameters, so as to facilitate incorporating the uncertainty of the bridge modal parameters into the strategy for eliminating false modes in the subsequent process of identifying bridge modal parameters, so that the finally identified modal parameters have a certain probability reliability.

[0077] Specifically, in this embodiment, performing modal analysis on the vibration response signal may include the following steps:

[0078] S21. Preprocess the vibration response signal;

[0079] In this embodiment, before performing modal analysis on the vibration response signal, preprocessing the vibration response signal first can reduce the noise influence of the vibration response signal.

[0080] Specifically, in this embodiment, preprocessing the vibration response signal includes:

[0081] Perform pre-filtering and resampling on the vibration signal to remove the trend term and outliers in the vibration signal.

[0082] Through the above preprocessing operations, the noise influence of the vibration response signal can be effectively reduced, ensuring the accuracy of modal identification.

[0083] S22. Use the stochastic subspace algorithm to extract the bridge modal parameters from the preprocessed vibration response signal;

[0084] The core idea of the random subspace algorithm is to construct an extended observation matrix using preprocessed data, and then extract the bridge modal parameters of the system through singular value decomposition or eigenvalue decomposition techniques.

[0085] S23, quantify the uncertainty of the extracted bridge modal parameters.

[0086] After the bridge modal parameters are extracted by the random subspace algorithm, in order to introduce frequency uncertainty to identify the bridge modal parameters and reduce the influence of frequency uncertainty on the bridge modal parameters, the uncertainty of the extracted bridge modal parameters is further quantified.

[0087] Specifically, in this embodiment, when quantifying the uncertainty of the bridge modal parameters, according to the first-order perturbation theory, the covariance matrix of the modal parameter identification error is calculated, and the covariance matrix of frequency and damping ratio is used to represent the uncertainty of the bridge modal parameters.

[0088] Specifically, in this embodiment, to calculate the uncertainty of the bridge modal parameters, the test data is first organized as:

[0089]

[0090] where y + and y - are the test data composed of all test channels and reference channels respectively; q and p are constants to be specified, and N represents the number of columns of y + and y - , and N + p + q is equal to the length of the test signal;

[0091] Then y + and y - are divided into n b column blocks:

[0092]

[0093] where N b represents the number of columns of each sub-column block;

[0094] The estimated value of the subspace matrix is calculated from the corresponding column blocks:

[0095]

[0096] where (■) T represents the transpose of the matrix, The average value of is:

[0097]

[0098] Vectorize The covariance matrix of is:

[0099]

[0100] vet(■) represents the vectorization operation, that is, stacking the matrix by columns into a vector;

[0101] According to the first-order perturbation theory, the frequency f j and the damping ratio ξ j have the following covariance matrix:

[0102]

[0103] where represents the sensitivity matrix of the frequency to the vectorized state transition matrix, represents the sensitivity matrix of the damping ratio to the vectorized state transition matrix; is the sensitivity matrix of the vectorized state transition matrix to the vectorized observable matrix; is the sensitivity matrix of the vectorized observable matrix to the vectorized subspace matrix. Their calculation methods are as follows:

[0104]

[0105] s1 = [I pr 0 pr,r , S2 = [0 pr,r I pr ,

[0106]

[0107] where: represents the Kronecker product; 0 and I represent the zero and identity matrices respectively, and their sizes are determined by the subscripts; and (■) * represent the pseudo-inverse of the matrix and the complex conjugate of the complex variable; and represent the real and imaginary parts of a certain complex variable; represents a matrix of size a×b, which is 1 only at the (k, l) position and 0 at other positions; A and C are the state transition matrix and the observation matrix of the bridge system respectively. The former is determined by the stochastic subspace algorithm, and the latter is determined by the number and installation positions of the sensors; u j , v j , σ j are respectively 's left and right eigenvectors and eigenvalues; χ i , φ i , λ i represent the left and right eigenvectors and eigenvalues of the system transition matrix A.

[0108] S3. Eliminate the spurious modes based on the uncertainties of the bridge modal parameters to obtain the bridge modal parameters after eliminating the spurious modes.

[0109] After obtaining the uncertainties of the bridge modal parameters through modal analysis, the frequency uncertainties are incorporated into the elimination of spurious modes, that is, eliminate the spurious modes based on the uncertainties of the bridge modal parameters to obtain the bridge modal parameters after eliminating the spurious modes. The specific process is as follows:

[0110] S31. Eliminate the spurious modes with a coefficient of variation exceeding the preset threshold based on the uncertainties of the bridge modal parameters, where the coefficient of variation is the ratio of the standard deviation of the frequency to the frequency. Specifically, in this embodiment, the preset threshold of the coefficient of variation is 2%, that is, the modes with the ratio of the standard deviation of the frequency to the frequency exceeding 2% are eliminated as spurious modes.

[0111] S32. Further use the preset spurious mode elimination criterion to eliminate the remaining spurious modes from the modes with a coefficient of variation not exceeding the preset threshold.

[0112] Since there may still be some residual spurious modes in the bridge modal parameters after eliminating the spurious modes based on the uncertainties of the bridge modal parameters, in this embodiment, after eliminating the spurious modes with a coefficient of variation exceeding the preset threshold based on the uncertainties of the bridge modal parameters, further use the preset spurious mode elimination criterion to eliminate the remaining spurious modes from the modes with a coefficient of variation not exceeding the preset threshold, so as to remove as many spurious modes as possible.

[0113] Specifically, the preset spurious mode elimination criterion can be a commonly used spurious mode elimination criterion in the art. For example, relevant criteria for measuring the differences in frequency, damping ratio, and mode shape between modes can be used to further eliminate spurious modes.

[0114] Specifically, in this embodiment, the relevant criteria for measuring the differences in frequency, damping ratio, and mode shape between modes are as follows:

[0115]

[0116] where δ f , δ ξ and δ ψ are the acceptable error thresholds respectively, and their values are relatively small, such as 0.01 - 0.05.

[0117] S4. Use the density clustering algorithm to perform physical modal clustering on the bridge modal parameters after eliminating the spurious modes and detect outliers to obtain the final bridge modal parameter identification result.

[0118] After removing as many false modes as possible, the density clustering algorithm is used to cluster the physical modes of the bridge modal parameters after removing the false modes and detect outliers, and the final identification result of the bridge modal parameters is obtained. The specific process is as follows:

[0119] S41, Use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the physical modes and detect outliers;

[0120] S42, Select the modal parameters corresponding to the median value of the damping ratio in the clustered modes as the final identification result of the bridge modal parameters.

[0121] In summary, in the automatic identification method of bridge modal parameters in the above embodiments, first, the vibration response signal of the target bridge structure under environmental excitation is obtained, then the modal analysis is performed on the vibration response signal to obtain the uncertainty of the bridge modal parameters, and then the false modes are removed based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after removing the false modes. Finally, the density clustering algorithm is used to cluster the physical modes of the bridge modal parameters after removing the false modes and detect outliers to obtain the final identification result of the bridge modal parameters. The automatic identification method of bridge modal parameters in the embodiments of the present application incorporates frequency uncertainty into the removal of false modes, making the finally identified modal parameters have a certain probability reliability. In addition, the embodiments of the present application use the density clustering algorithm to perform physical mode clustering analysis on the bridge modal parameters after removing the false modes, which requires fewer parameters to be specified, can achieve the maximum degree of automation, and the density clustering algorithm can detect the remaining false modes, making the final identification result of the bridge modal parameters more robust to noise. Therefore, the automatic identification method of bridge modal parameters in the embodiments of the present application can effectively improve the accuracy of the finally identified modal parameters.

[0122] As Figure 2 shown, the embodiments of the present application provide an automatic identification device for bridge modal parameters, and the device may include:

[0123] A vibration response signal acquisition module 201, configured to acquire a vibration response signal of a target bridge structure under environmental excitation;

[0124] A modal analysis module 202, configured to perform modal analysis on the vibration response signal to obtain the uncertainty of the bridge modal parameters;

[0125] A false mode removal module 203, configured to remove false modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after removing the false modes;

[0126] The modal clustering module 204 is used to perform physical modal clustering on the bridge modal parameters after removing false modes by using a density clustering algorithm and detect outliers, so as to obtain the final identification result of the bridge modal parameters.

[0127] In one embodiment, the uncertainties of the bridge modal parameters obtained by performing modal analysis on the vibration response signal include:

[0128] Preprocess the vibration response signal;

[0129] Use the stochastic subspace algorithm to extract the bridge modal parameters from the preprocessed vibration response signal;

[0130] Quantify the uncertainties of the extracted bridge modal parameters.

[0131] In one embodiment, preprocessing the vibration response signal includes:

[0132] Perform pre-filtering and resampling on the vibration signal to remove the trend terms and outliers in the vibration signal.

[0133] In one embodiment, quantifying the uncertainties of the extracted bridge modal parameters includes:

[0134] According to the first-order perturbation theory, calculate the covariance matrix of the modal parameter identification error, and use the covariance matrix of frequency and damping ratio to represent the uncertainties of the bridge modal parameters.

[0135] In one embodiment, removing false modes based on the uncertainties of the bridge modal parameters, and the bridge modal parameters after removing false modes include:

[0136] Remove false modes with a coefficient of variation exceeding a preset threshold based on the uncertainties of the bridge modal parameters, where the coefficient of variation is the ratio of the standard deviation of frequency to frequency;

[0137] Further use a preset false mode removal criterion to remove the remaining false modes for the modes with a coefficient of variation not exceeding the preset threshold.

[0138] In one embodiment, the preset threshold is 2%.

[0139] In one embodiment, using a density clustering algorithm to perform physical modal clustering on the bridge modal parameters after removing false modes and detect outliers, and the final identification result of the bridge modal parameters includes:

[0140] Use the density-based spatial clustering of applications with noise (DBSCAN) algorithm to perform clustering analysis on the physical modes and detect outliers;

[0141] Select the modal parameters corresponding to the median value of the damping ratio in the clustering modes as the final identification result of the bridge modal parameters.

[0142] In one embodiment, the vibration response signal includes at least one of velocity, acceleration, and displacement.

[0143] The automatic bridge modal parameter identification device in the above embodiment has the same working principle and technical effect as the automatic bridge modal parameter identification method in the above embodiment, which will not be elaborated here.

[0144] As Figure 3 shown, an embodiment of the present application provides an electronic device 3, which includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and executable on the processor 302. Among them, the memory 301 and the processor 302 complete communication with each other through a bus 304. When the processor 302 executes the computer program 303, it implements the steps of the automatic bridge modal parameter identification method in the method embodiment of the present application as described above.

[0145] Specifically, the electronic device 3 can be an intelligent device with a memory and a processor such as an industrial control computer, a PC, or a smart mobile terminal, or a computer component such as a CPU or a GPU with a memory and a processor.

[0146] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the automatic bridge modal parameter identification method in the method embodiment of the present application as described above.

[0147] In this specification, the various embodiments are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0148] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0149] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented directly in hardware, in software modules executed by a processor, or in a combination thereof. The software modules may be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art of technology

[0150] The foregoing description of the disclosed embodiments enables those skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein

Claims

1. An automatic identification method for bridge modal parameters, characterized in that, The method includes the following steps: S1. Obtain the vibration response signal of the target bridge structure under environmental excitation; S2. Conduct modal analysis on the vibration response signal to obtain the uncertainty of bridge modal parameters; S3. Eliminate false modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after eliminating false modes; S4. Use the density clustering algorithm to cluster the physical modes of the bridge modal parameters after eliminating false modes and detect outliers to obtain the final identification result of the bridge modal parameters.

2. The automatic identification method of bridge modal parameters according to claim 1, characterized in that In step S2, the conduct of modal analysis on the vibration response signal to obtain the uncertainty of bridge modal parameters includes: S21. Preprocess the vibration response signal; S22. Use the stochastic subspace algorithm to extract bridge modal parameters from the preprocessed vibration response signal; S23. Quantify the uncertainty of the extracted bridge modal parameters.

3. The automatic identification method of bridge modal parameters according to claim 2, characterized in that In step S21, the preprocessing of the vibration response signal includes: Perform pre-filtering and resampling on the vibration signal to remove the trend term and outliers in the vibration signal.

4. The automatic identification method of bridge modal parameters according to claim 2, wherein In step S23, the quantification of the uncertainty of the extracted bridge modal parameters includes: According to the first-order perturbation theory, calculate the covariance matrix of the modal parameter identification error, and use the covariance matrix of frequency and damping ratio to represent the uncertainty of the bridge modal parameters.

5. The automatic identification method for bridge modal parameters according to claim 1, characterized in that In step S3, the elimination of false modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after eliminating false modes includes: S31. Eliminate false modes with a coefficient of variation exceeding a preset threshold based on the uncertainty of the bridge modal parameters, where the coefficient of variation is the ratio of the standard deviation of frequency to frequency; S32. Further use a preset false mode elimination criterion to eliminate the remaining false modes for modes with a coefficient of variation not exceeding the preset threshold.

6. The automatic identification method of bridge modal parameters according to claim 1, characterized in that In step S4, the use of the density clustering algorithm to cluster the physical modes of the bridge modal parameters after eliminating false modes and detect outliers to obtain the final identification result of the bridge modal parameters includes: S41. Use the density-based clustering algorithm with noise space to cluster analyze the physical modes and detect outliers; S42. Select the modal parameters corresponding to the median value of the damping ratio in the clustering modes as the final identification result of the bridge modal parameters.

7. The automatic identification method for bridge modal parameters according to any one of claims 1-6, characterized in that, The vibration response signal includes at least one of velocity, acceleration, and displacement.

8. An automatic bridge modal parameter identification device, characterized in that, The device includes: A vibration response signal acquisition module for obtaining the vibration response signal of the target bridge structure under environmental excitation; A modal analysis module for conducting modal analysis on the vibration response signal to obtain the uncertainty of bridge modal parameters; A false mode elimination module for eliminating false modes based on the uncertainty of the bridge modal parameters to obtain the bridge modal parameters after eliminating false modes; A modal clustering module for using the density clustering algorithm to cluster the physical modes of the bridge modal parameters after eliminating false modes and detect outliers to obtain the final identification result of the bridge modal parameters.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for automatically identifying bridge modal parameters as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method for automatically identifying bridge modal parameters as described in any one of claims 1-7.

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