Bridge damage positioning method and device, computer equipment and storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JINAN UNIVERSITY
- Filing Date
- 2023-07-04
- Publication Date
- 2026-06-09
Smart Images
Figure CN116878787B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of structural damage detection technology, specifically to a bridge damage localization method, apparatus, computer equipment, and storage medium based on the reconstruction of wavelet coefficients of the first principal component. Background Technology
[0002] Bridge structures maintain the survival function of cities and are vital urban lifeline projects. However, under the influence of external loads, material aging, and human interference, the structural performance of bridge structures can decline or even be damaged, affecting traffic operations and the safety of people and property. Therefore, structural damage detection, as the core of structural safety monitoring systems, is a challenging research topic in the field of safety monitoring.
[0003] Structural damage detection can currently be divided into two categories: model-based methods and data-driven methods. Model-based methods require modeling, followed by continuous model refinement based on the actual structure. This places high demands on the accuracy of the theoretical model and the quality of the monitoring data. Currently, model-based methods are difficult to implement in practical engineering, especially for complex bridges, where modeling is time-consuming and accuracy requirements are not met. Data-driven methods, such as dynamic fingerprint indicators like curvature mode and coordination mode confidence factors, require a large number of sensors to achieve good damage identification results, significantly increasing costs in practical applications. Furthermore, the large number of measurement points presents challenges in processing massive amounts of data, which is a key issue that needs to be addressed in the field of structural health monitoring.
[0004] To address the aforementioned problems, there is an urgent need to propose a new method for monitoring the safety of bridge structures using a small number of sensors without the need to establish a finite element model. This method would be of great significance for reducing engineering costs, avoiding the difficulty of processing massive amounts of data, and improving detection efficiency. Summary of the Invention
[0005] The purpose of this invention is to overcome the above-mentioned defects in the prior art and provide a bridge damage location method, device, computer equipment and storage medium based on the reconstruction of the first principal component wavelet coefficients, which does not require setting up a large number of sensors or establishing a finite element model.
[0006] The first objective of this invention can be achieved by adopting the following technical solution:
[0007] A bridge damage localization method based on wavelet coefficient reconstruction of the first principal component, the bridge damage localization method comprising the following steps:
[0008] S1. Install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface.
[0009] S2. Measure the vertical displacement response of the vehicle as it passes over the bridge at a constant speed under load, and obtain the displacement response matrix U.n×i n is the number of sampling points for each sensor, and 4000≤n≤6000;
[0010] S3. Calculate the displacement response matrix U n×i covariance matrix K i×i :
[0011]
[0012] in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :
[0013] Eig(K i×i )=[V i×i B i×i ]
[0014] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let B be an i×i diagonal matrix with eigenvalues in B. i×i Arranged diagonally from largest to smallest;
[0015] S4, the displacement response matrix U n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i :
[0016] G n×i =U n×i ×V i×i
[0017] Assume G1 is the principal component matrix G n×i First column of data;
[0018] S5. Select db4 as the wavelet basis function, with a decomposition level of 5. Perform wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m):
[0019]
[0020] Where m is the scaling factor. It is a low-pass filter;
[0021] The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m):
[0022]
[0023] S6. Subtract the reconstructed signals S(n) from the damaged and non-damaged conditions to obtain the damage index DI:
[0024] DI=S w (n)-S y (n)
[0025] Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions;
[0026] S7. Draw the damage index DI curve to locate the damage in the beam bridge structure.
[0027] Furthermore, the process of locating damage to the beam bridge structure using the damage index DI curve in step S7 is as follows:
[0028] S71. Draw the damage index DI curve based on the calculated damage index DI value;
[0029] S72. Determine the location of damage in beam bridges by identifying the peak position of the damage index DI curve.
[0030] Furthermore, the number of displacement sensors i can be in the following range: 3≤i≤5.
[0031] Furthermore, in step S3, the covariance matrix is used to measure the degree of common variation of variables of different dimensions in the population. The larger the value of an element in the covariance matrix, the higher the correlation between the features of the corresponding subscripts.
[0032] Furthermore, in step S4, the eigenvectors of the covariance matrix are the basis vectors that retain the most original information. Therefore, by calculating the eigenvector matrix of the covariance matrix, the dimensionality of the data can be reduced, thereby completing the feature extraction of the original data.
[0033] Furthermore, in step S6, db4 is selected as the wavelet basis function, and the decomposition level is 5 for analysis. The db wavelet has good time and frequency localization characteristics, which can better capture the local features of the signal. Moreover, the frequency band division effect is better as the order increases, but it brings about computational efficiency issues. At the same time, too many decomposition levels can lead to excessive computation or overfitting, while too few levels can lead to incomplete decomposition. Therefore, based on the characteristics of the displacement response, the db4 wavelet basis function and 5 decomposition levels were selected for analysis.
[0034] The second objective of this invention can be achieved by adopting the following technical solution:
[0035] A bridge damage location device based on wavelet coefficient reconstruction of the first principal component, the bridge damage location device comprising:
[0036] The sensor setting module is used to install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface.
[0037] The displacement response measurement module is used to measure the vertical displacement response of a vehicle carrying a uniform load as it passes over a bridge, and to obtain the displacement response matrix U. n×i n is the number of sampling points for each sensor, and 4000≤n≤6000;
[0038] The covariance matrix calculation module is used to calculate the displacement response matrix U. n×i covariance matrix K i×i :
[0039]
[0040] in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :
[0041] Eig(K i×i )=[V i×i B i×i ]
[0042] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let B be an i×i diagonal matrix with eigenvalues in B. i×i Arranged diagonally from largest to smallest;
[0043] The principal component matrix calculation module is used to calculate the displacement response matrix U. n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i :
[0044] G n×i =U n×i ×V i×i
[0045] Assume G1 is the principal component matrix G n×i First column of data;
[0046] The wavelet decomposition module is used to select db4 as the wavelet basis function, with a decomposition level of 5. It performs wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m):
[0047]
[0048] Where m is the scaling factor. It is a low-pass filter;
[0049] The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m):
[0050]
[0051] The damage index calculation module is used to subtract the reconstructed signals S(n) from the damaged and non-damaged operating conditions to obtain the damage index DI.
[0052] DI=S w (n)-S y (n)
[0053] Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions;
[0054] The damage location module is used to draw the damage index DI curve to locate damage in beam bridge structures.
[0055] The third objective of this invention is achieved through the following technical solution:
[0056] A computer device includes a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, it implements the above-described bridge damage localization method based on the reconstruction of wavelet coefficients of the first principal component.
[0057] The fourth objective of this invention is achieved through the following technical solution:
[0058] A storage medium storing a program that, when executed by a processor, implements the aforementioned bridge damage localization method based on the reconstruction of wavelet coefficients of the first principal component.
[0059] The present invention has the following advantages and effects compared with the prior art:
[0060] (1) This invention extracts damage features based on structural displacement data, without the need to establish a structural finite element model, and has fast computational efficiency;
[0061] (2) The present invention uses the characteristic orthogonal decomposition method to construct the principal component matrix and extracts the first principal component as the analysis object, which reduces the impact of environmental noise and is more in line with the application of actual engineering.
[0062] (3) The present invention reconstructs the first principal component, which has a sensitive amplification function for signal mutations, and only requires a small amount of sensor data to accurately identify the location of bridge damage, thereby reducing engineering costs.
[0063] (4) The method proposed in this invention combines the advantages of principal component decomposition and wavelet decomposition, extracts the first principal component containing most of the damage information for decomposition and reconstruction, improves the sensitivity to signal mutations, and has the advantage of obvious damage localization effect. Attached Figure Description
[0064] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:
[0065] Figure 1 This is a flowchart of the bridge damage localization method disclosed in this invention, which uses a small number of sensors to reconstruct the wavelet coefficients of the first principal component.
[0066] Figure 2 This is a simplified diagram of the beam bridge model in Example 1;
[0067] Figure 3 This is a schematic diagram of the time history signal of the first principal component in Example 1;
[0068] Figure 4 This is a DI value curve for single-loss conditions with a damage level of 10%-30% in Example 1;
[0069] Figure 5 This is a graph showing the DI value of the double-loss conditions in Example 1 with damage levels of 20% and 40%.
[0070] Figure 6 This is a simplified diagram of the steel box girder model in Example 2;
[0071] Figure 7 This is a schematic diagram of the steel box girder in Example 2;
[0072] Figure 8 This is a schematic diagram of the mobile trolley in Example 2;
[0073] Figure 9 This is a schematic diagram of the time history signal of the first principal component in Example 2;
[0074] Figure 10 This is a graph showing the DI value of a single-damage condition with damage lengths of 3mm, 5mm, and 7mm in Example 2.
[0075] Figure 11 This is a curve of DI value for the double-damage conditions with damage lengths of 4mm+7mm and 7mm+7mm in Example 2.
[0076] Figure 12 This is a schematic diagram of the time history signal of the first principal component in Example 3;
[0077] Figure 13This is a DI value curve for single-loss conditions with a damage level of 10%-30% in Example 3;
[0078] Figure 14 This is a graph showing the DI value of the double-loss conditions in Example 3 with damage levels of 20% and 40%.
[0079] Figure 15 This is a schematic diagram of the time history signal of the first principal component in Example 4;
[0080] Figure 16 This is a graph showing the DI value of a single-damage condition with damage lengths of 3mm, 5mm, and 7mm in Example 4.
[0081] Figure 17 This is a graph showing the DI value curves for the double-damage conditions with damage lengths of 4mm+7mm and 7mm+7mm in Example 4.
[0082] Figure 18 This is a structural block diagram of the bridge damage location device in Embodiment 5 of the present invention;
[0083] Figure 19 This is a structural block diagram of the computer device in Embodiment 6 of the present invention. Detailed Implementation
[0084] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0085] Example 1
[0086] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the bridge damage localization method using a small number of sensors to reconstruct the first principal component wavelet coefficients. A schematic diagram of the steel beam bridge model used in this embodiment is shown below. Figure 2 The model beam is 20m long (L), 0.1m wide, and 0.2m high, with a sampling frequency (f). s The frequency is 200Hz, the vehicle speed is 1m / s, the single-damage location is at 0.4L, and the double-damage locations are at 0.4L and 0.7L. Triangular markers indicate the damage locations. The specific implementation process is as follows:
[0087] S1. Five displacement sensors are installed on the beam bridge, with the installation direction perpendicular to the bridge deck. Figure 2 As shown.
[0088] S2. Measure the vertical displacement response of the vehicle as it passes the beam bridge at a constant speed under load, and obtain the displacement signal matrix U. n×i n is the length of the signal sampling points, and i is the number of sensors. In this embodiment, n is 4000 and i is 5.
[0089] S3. Calculate the displacement response matrix U n×i covariance matrix K i×i :
[0090]
[0091] in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :
[0092] Eig(K i×i )=[V i×i B i×i (2)
[0093] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let i be an i×i diagonal matrix, and let the eigenvalues be arranged in descending order along the diagonal.
[0094] S4, the displacement response matrix U n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i :
[0095] G n×i =U n×i ×Vi ×i (3)
[0096] Assume G1 is the principal component matrix G n×i The first column of data, such as Figure 3 As shown;
[0097] S5. Select db4 as the wavelet basis function, with a decomposition level of 5. Perform wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m):
[0098]
[0099] Where m is the scaling factor. It is a low-pass filter;
[0100] The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m):
[0101]
[0102] S6. Subtract the reconstructed signals S(n) from the damaged and non-damaged conditions to obtain the damage index DI:
[0103] DI=S w (n)-S y (n) (6)
[0104] Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions;
[0105] S7. Draw the damage index DI curve to locate the damage in the beam bridge structure.
[0106] The specific process for this step is as follows:
[0107] S71. Draw the damage index DI curve based on the calculated damage index DI value;
[0108] S72. Determine the location of damage in beam bridges by identifying the peak position of the damage index DI curve.
[0109] like Figure 4 The graphs show the DI value curves for three single-loss conditions with damage levels ranging from 10% to 30%. The peak of the curve indicates the bridge damage location is at 0.4L, accurately pinpointing the damage location. Figure 5 The DI value curves for two double-loss conditions with damage levels of 20% and 40% are shown. The peaks of the curves indicate that the bridge damage locations are 0.4L and 0.7L, accurately locating the bridge damage.
[0110] Example 2
[0111] To further illustrate the effectiveness of the bridge damage identification method proposed in this invention, an experimental steel box girder was used for verification. A simplified diagram of the steel box girder model used in Example 2 is shown below. Figure 6 As shown. The steel box girder is 6m long (L), 0.2m wide, 0.1m high, and 3mm thick. The sampling frequency is f. s The frequency is 500Hz, and the vehicle speed is 0.5m / s. Single-damage damage is located at 0.68L, and double-damage damage is located at 0.38L and 0.68L. Triangular markers indicate the damage locations. The specific implementation process is as follows:
[0112] S1-S7: Refer to Example 1; in this example, n is 6000.
[0113] like Figure 10The graphs show the DI value curves for three single-loss conditions with damage lengths of 3mm, 5mm, and 7mm. The peak of the curve indicates the bridge damage location is at 0.68L, accurately pinpointing the bridge damage. Figure 11 The DI value curves for two double-loss conditions with damage lengths of 4mm+7mm and 7mm+7mm are shown. The peaks of the curves indicate that the bridge damage locations are 0.38L and 0.68L, accurately locating the bridge damage.
[0114] Example 3
[0115] To further illustrate the effectiveness of the bridge damage identification method proposed in this invention when reducing the number of sensors, [the following was selected] Figure 2 Three sensors, numbered 1, 4, and 7, were used for verification; other parameters were as described in Example 1. Triangular markers indicate the location of the damage. The specific implementation process is as follows:
[0116] S1-S7: Refer to Example 1.
[0117] like Figure 13 The graphs show the DI value curves for three single-loss conditions with damage levels ranging from 10% to 30%. The peak of the curve indicates the bridge damage location is at 0.4L, accurately pinpointing the damage location. Figure 14 The DI value curves for two double-loss conditions with damage levels of 20% and 40% are shown. The peaks of the curves indicate that the bridge damage locations are 0.4L and 0.7L, accurately locating the bridge damage.
[0118] Example 4
[0119] To further illustrate the effectiveness of the bridge damage identification method proposed in this invention when reducing the number of sensors, [the following was selected] Figure 6 Three sensors, numbered 1, 3, and 5, were used for verification; other parameters were as described in Example 2. Triangular markers indicate the location of the damage. The specific implementation process is as follows:
[0120] S1-S7: Refer to Example 2.
[0121] like Figure 16 The graphs show the DI value curves for three single-loss conditions with damage lengths of 3mm, 5mm, and 7mm. The peak of the curve indicates the bridge damage location is at 0.68L, accurately pinpointing the bridge damage. Figure 17 The DI value curves for two double-loss conditions with damage lengths of 4mm+7mm and 7mm+7mm are shown. The peaks of the curves indicate that the bridge damage locations are 0.38L and 0.68L, accurately locating the bridge damage.
[0122] Example 5
[0123] like Figure 18As shown, this embodiment provides a bridge damage location device based on the reconstruction of wavelet coefficients of the first principal component. The device includes a sensor setting module 501, a displacement response measurement module 502, a covariance matrix calculation module 503, a principal component matrix calculation module 504, a wavelet decomposition module 505, a damage index calculation module 506, and a damage location module 507. The specific functions of each module are as follows:
[0124] The sensor setting module 501 is used to install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface.
[0125] The displacement response measurement module 502 is used to measure the vertical displacement response of a vehicle carrying a uniform load as it passes over a bridge, and to obtain the displacement response matrix U. n×i n is the number of sampling points for each sensor, and 4000≤n≤6000;
[0126] Covariance matrix calculation module 503 is used to calculate the displacement response matrix U. n×i covariance matrix K i×i :
[0127]
[0128] in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :
[0129] Eig(K i×i )=[V i×i B i×i ]
[0130] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let B be an i×i diagonal matrix with eigenvalues in B. i×i Arranged diagonally from largest to smallest;
[0131] Principal component matrix calculation module 504 is used to calculate the displacement response matrix U. n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i :
[0132] G n×i =U n×i ×V i×i
[0133] Assume G1 is the principal component matrix G n×iFirst column of data;
[0134] Wavelet decomposition module 505 is used to select db4 as the wavelet basis function, with a decomposition level of 5, and performs wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m):
[0135]
[0136] Where m is the scaling factor. It is a low-pass filter;
[0137] The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m):
[0138]
[0139] Damage index calculation module 506 is used to subtract the reconstructed signals S(n) of damaged and non-damaged conditions to obtain the damage index DI:
[0140] DI=S w (n)-S y (n)
[0141] Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions;
[0142] Damage location module 507 is used to draw the damage index DI curve to locate damage in beam bridge structures.
[0143] Example 6
[0144] This embodiment provides a computer device, which can be a computer, such as... Figure 19 As shown, the system bus 601 connects a processor 602, a memory, an input device 603, a display 604, and a network interface 605. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 606 and internal memory 607. The non-volatile storage medium 606 stores the operating system, computer programs, and a database. The internal memory 607 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 602 executes the computer program stored in the memory, it implements the bridge damage localization method based on the reconstruction of the first principal component wavelet coefficients proposed in this invention, including the following steps:
[0145] S1. Install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface.
[0146] S2. Measure the vertical displacement response of the vehicle as it passes over the bridge at a constant speed under load, and obtain the displacement response matrix U.n×i n is the number of sampling points for each sensor, and 4000≤n≤6000;
[0147] S3. Calculate the displacement response matrix U n×i covariance matrix K i×i : in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :Eig(K i×i )=[V i×i B i×i ]
[0148] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let B be an i×i diagonal matrix with eigenvalues in B. i×i Arranged diagonally from largest to smallest;
[0149] S4, the displacement response matrix U n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix, G. n×i Assume G1 is the principal component matrix G n×i First column of data;
[0150] S5. Select db4 as the wavelet basis function, decompose the number of layers to 5, perform wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m), and reconstruct the low-frequency wavelet coefficients A(m) to obtain the reconstructed signal S(n).
[0151] S6. Subtract the reconstructed signals S(n) of the damaged and non-damaged conditions to obtain the damage index DI;
[0152] S7. Draw the damage index DI curve to locate the damage in the beam bridge structure.
[0153] Example 7
[0154] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements a bridge damage localization method based on the reconstruction of wavelet coefficients of the first principal component proposed in this invention. The bridge damage localization method includes the following steps:
[0155] S1. Install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface.
[0156] S2. Measure the vertical displacement response of the vehicle as it passes over the bridge at a constant speed under load, and obtain the displacement response matrix U. n×i n is the number of sampling points for each sensor, and 4000≤n≤6000;
[0157] S3. Calculate the displacement response matrix U n×i covariance matrix K i×i : in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i :Eig(K i×i )=[V i×i B i×i ]
[0158] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let B be an i×i diagonal matrix with eigenvalues in B. i×i Arranged diagonally from largest to smallest;
[0159] S4, the displacement response matrix U n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix, G. n×i Assume G1 is the principal component matrix G n×i First column of data;
[0160] S5. Select db4 as the wavelet basis function, decompose the number of layers to 5, perform wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m), and reconstruct the low-frequency wavelet coefficients A(m) to obtain the reconstructed signal S(n).
[0161] S6. Subtract the reconstructed signals S(n) of the damaged and non-damaged conditions to obtain the damage index DI;
[0162] S7. Draw the damage index DI curve to locate the damage in the beam bridge structure.
[0163] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.
Claims
1. A bridge damage localization method based on wavelet coefficient reconstruction of the first principal component, characterized in that, The bridge damage location method includes the following steps: S1. Install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface. S2. Measure the vertical displacement response of the vehicle as it passes over the bridge at a constant speed under load, and obtain the displacement response matrix U. n×i n is the number of sampling points for each sensor; S3. Calculate the displacement response matrix U n×i covariance matrix K i×i : in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i : Eig(K i×i )=[V i×i ,B i×i ] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let i be an i×i diagonal matrix, and let its eigenvalues be in B. i×i Arranged diagonally from largest to smallest; S4, the displacement response matrix U n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i : G n×i =U n×i ×V i×i Assume G1 is the principal component matrix G n×i First column of data; S5. Select db4 as the wavelet basis function, with a decomposition level of 5. Perform wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m): Where m is the scaling factor. It is a low-pass filter; The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m): S6. Subtract the reconstructed signals S(n) from the damaged and non-damaged conditions to obtain the damage index DI: DI=S w (n)-S y (n) Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions; S7. Draw the damage index DI curve to locate the damage in the beam bridge structure.
2. The bridge damage localization method based on wavelet coefficient reconstruction of the first principal component according to claim 1, characterized in that, The process of locating damage to the beam bridge structure using the damage index DI curve in step S7 is as follows: S71. Draw the damage index DI curve based on the calculated damage index DI value; S72. Determine the location of damage in beam bridges by identifying the peak position of the damage index DI curve.
3. The bridge damage localization method based on wavelet coefficient reconstruction of the first principal component according to claim 1, characterized in that, The number of displacement sensors i can be in the following range: 3≤i≤5.
4. The bridge damage localization method based on wavelet coefficient reconstruction of the first principal component according to claim 1, characterized in that, The range of the number of sampling points n of the displacement sensor is as follows: 4000≤n≤6000.
5. A bridge damage location device based on the bridge damage location method based on the first principal component wavelet coefficient reconstruction as described in any one of claims 1 to 4, characterized in that, The bridge damage location device includes: The sensor setting module is used to install i displacement sensors at different locations on the bridge, with the installation direction perpendicular to the bridge surface. The displacement response measurement module is used to measure the vertical displacement response of a vehicle carrying a uniform load as it passes over a bridge, and to obtain the displacement response matrix U. n×i n is the number of sampling points for each sensor, and 4000≤n≤6000; The covariance matrix calculation module is used to calculate the displacement response matrix U. n×i covariance matrix K i×i : in For U n×i The transpose of the matrix; for the covariance matrix K i×i Perform eigenvalue orthogonal decomposition Eig(K) i×i ), thus obtaining the eigenvector matrix V i×i : Eig(K i×i )=[V i×i ,B i×i ] Where V i×i B is the eigenvector matrix. i×i Let B be the eigenvalue matrix. i×i Let i be an i×i diagonal matrix, and let its eigenvalues be in B. i×i Arranged diagonally from largest to smallest; The principal component matrix calculation module is used to calculate the displacement response matrix U. n×i With the eigenvector matrix V i×i Multiplying them together yields the principal component matrix G. n×i : G n×i =U n×i ×V i×i Assume G1 is the principal component matrix G n×i First column of data; The wavelet decomposition module is used to select db4 as the wavelet basis function, with a decomposition level of 5. It performs wavelet decomposition on the first principal component G1 in the principal component matrix to obtain the low-frequency wavelet coefficients A(m): Where m is the scaling factor. It is a low-pass filter; The reconstructed signal S(n) is obtained by reconstructing the low-frequency wavelet coefficients A(m): The damage index calculation module is used to subtract the reconstructed signals S(m) from those under damaged and those under non-damaged conditions to obtain the damage index DI. DI=S w (n)-S y (n) Where S w (n) represents the reconstructed signal obtained under the lossless operating condition, S y (n) represents the reconstructed signal obtained under lossy operating conditions; The damage location module is used to draw the damage index DI curve to locate damage in beam bridge structures.
6. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the bridge damage localization method based on the reconstruction of the first principal component wavelet coefficients as described in any one of claims 1-5.
7. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the bridge damage localization method based on the reconstruction of wavelet coefficients of the first principal component as described in any one of claims 1-5.