Lightweight bridge structure damage rapid detection method

Abstract

The invention relates to a lightweight bridge structure damage rapid detection method comprising the following steps: applying an impact load to a target bridge, obtaining an acceleration response through contact measurement, and obtaining a displacement response through non-contact measurement; fusing the acceleration response data and the displacement response data, dividing the data into two groups, and recording the two groups as a first measurement set and a second measurement set; establishing a finite element model under the complete state of the bridge, dividing the finite element model into a plurality of units, introducing a damage coefficient to describe the rigidity damage degree of the units, calculating a unit impulse response function, assembling a Hankel matrix and normalizing the Hankel matrix; utilizing the response of the first test set to reconstruct the response of the second test set, and constructing a target function; and optimizing the objective function by using a gradient algorithm to identify the bridge structure damage. Compared with the prior art, convenience of non-contact visual displacement measurement and stability of contact acceleration measurement are integrated, heterogeneous data of acceleration and displacement are fused, and the damage position and degree of the structure under the unknown load can be recognized more accurately.

Description

Technical Field

[0001] The invention relates to the technical field of structural damage identification, and in particular to a lightweight bridge structure damage rapid detection method. Background Art

[0002] With the rapid development of the economy, a large number of infrastructure and complex engineering projects have emerged in recent decades. As the throat of the transportation system, bridges are important lifeline projects to ensure public safety and economic operation. However, during long-term service, bridge structures will be affected by adverse factors such as environmental erosion, overload, material aging and fatigue effects, resulting in damage accumulation and reduced resistance, thus threatening their safety and normal use. In particular, bridge structures are susceptible to environmental factors such as moisture and salt spray, as well as fatigue damage under long-term repeated loads. Therefore, conducting research on bridge structure health monitoring and damage identification is not only of great theoretical significance, but also of significant practical significance. In order to ensure the safety of bridge structures, a large number of bridges that have been built or are under construction have been installed with long-term health monitoring systems, which provide a scientific basis for preventive maintenance and timely repair, extend the service life of the structure and reduce potential risks.

[0003] Displacement, strain, acceleration and other data are the key to bridge structure damage detection, but traditional contact displacement measurement methods, such as dial indicators, pull-rope displacement sensors, linear variable differential transformers (LVDTs), capacitive displacement sensors, etc., have the disadvantages of difficult installation and maintenance, possible impact on the measured structure, large sensor loss and environmental impact. Contact displacement measurement methods are not suitable for monitoring large and complex bridge structures. Non-contact displacement measurement methods such as global positioning system (GPS), total station, laser displacement meter still have low accuracy and high cost. With the development of smart devices and visual measurement technology, non-contact visual measurement is increasingly used for structural damage detection due to its low cost, high efficiency, multi-point perception and continuous monitoring. This method uses portable cameras, consumer smartphones or drones equipped with cameras to record bridge vibration videos, and achieves lightweight displacement measurement at a low cost by tracking the movement of predetermined targets or other identifiable feature points on the surface of the structure. However, the displacement response is insensitive to the higher-order modes of the bridge structure, while the acceleration response is more sensitive to high-frequency dynamic changes, low cost and high accuracy, so it is widely used in bridge damage detection. However, there is currently no effective solution on how to combine the two to improve detection efficiency.

[0004] In addition, external excitations to bridge structures, such as wind loads, earthquake loads, and traffic loads, are often difficult to measure directly and accurately. Previous studies on load identification and structural damage identification often assumed that the structural parameters or external loads were known. However, the complex coupling effect between the structural response and the input load makes this assumption very limited in practical applications. Summary of the invention

[0005] The purpose of the present invention is to provide a lightweight bridge structure damage rapid detection method, which uses existing acceleration sensors and portable visual displacement measurement technology to quickly detect bridge structure damage. Acceleration response is sensitive to high-frequency response, while displacement data is more sensitive to low-frequency and static response. Fusion of these two types of data can provide more comprehensive structural behavior information, thereby improving the accuracy and robustness of damage identification.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A lightweight bridge structure damage rapid detection method comprises the following steps:

[0008] Step 1: Apply impact load to the target bridge, obtain acceleration response through contact measurement, and obtain displacement response through non-contact measurement;

[0009] Step 2: Fusing the acceleration response and displacement response data to obtain heterogeneous responses, dividing the heterogeneous responses into two groups, recorded as a first measurement set and a second measurement set, wherein the number of responses in the first measurement set is equal to or greater than the number of unknown impact loads applied to the bridge structure;

[0010] Step 3: Establish a finite element model of the bridge in its intact state, divide it into multiple units, introduce damage coefficients to describe the degree of unit stiffness damage, calculate the unit impulse response function, assemble the Hankel matrix and normalize it;

[0011] Step 4: Based on the unit impulse response function and the Hankel matrix, the response of the first test set is used to reconstruct the response of the second test set, and the objective function is constructed based on the actually measured response of the second test set and the reconstructed response;

[0012] Step 5: Use the gradient algorithm to continuously optimize the objective function, iterate the unit stiffness coefficient of the structure until the maximum number of iterations is reached or the precision requirement is met, and output the identified bridge structure damage result, where the sum of the unit stiffness coefficient and the damage coefficient is 1.

[0013] The contact measurement specifically includes: obtaining acceleration response through a plurality of accelerometers installed on the target bridge.

[0014] The non-contact measurement specifically includes: performing non-contact measurement by installing multiple targets on the target bridge and a continuous image acquisition device placed at a preset distance from the bridge structure, acquiring video data and processing it using a sub-pixel template matching algorithm to extract the displacement response.

[0015] The continuous image acquisition device is a consumer-grade mobile phone or a portable camera.

[0016] The method uses a sub-pixel template matching algorithm to perform processing and extract a displacement response, specifically comprising: selecting an image block from a reference image as a template, wherein the template includes a feature point or region to be tracked; performing preprocessing including filtering and denoising on the target image and the template; determining a rough matching position of the template in the target image by calculating a similarity measure between the template and each position in the target image; finding a maximum value point of the similarity measure in a similarity graph obtained by the rough matching as a preliminary matching position; performing quadratic surface fitting on pixels near the preliminary matching position to determine a sub-pixel displacement; and, based on a sub-pixel fine matching result, repeatedly matching each frame of the image sequence to calculate the precise displacement of the template between different image frames to extract a displacement response.

[0017] The motion equation of the bridge structure under unit pulse excitation is: Where M, C, K are the mass, damping and stiffness matrices of the bridge structure, δ(t) is the Dirac function, h(t) is the unit pulse displacement response function, is the unit pulse velocity response function, is the unit pulse acceleration response function, where the stiffness matrix is ​​determined by the damage coefficient and is expressed as:

[0018]

[0019] Among them, α i is the damage coefficient of the ith unit, ne is the number of units, is the unit stiffness of the i-th unit, and the unit stiffness coefficient θ of the i-th unit i =1-α i .

[0020] The unit pulse displacement response function h(t) and the unit pulse acceleration response function Calculated by Newmark-β method:

[0021] The displacement u of the bridge structure in the p and s degrees of freedom at time tn p (t n ) and acceleration response ü s (t n ) are respectively expressed as: Where, f is the externally applied impact load, h pand The impulse response functions of displacement on the p degree of freedom and acceleration on the s degree of freedom are represented respectively; then, from t0 to t n At the moment, the displacements in the p and s degrees of freedom and acceleration response Further expressed as and Assume that the number of displacement and acceleration responses are n u and n ü , the total displacement and acceleration response is expressed as and The relationship between the output response and the input load F is Y u =H u F,Y ü =H ü F, where the Hankel matrix H of the displacement impulse response function u and the Hankel matrix H of the acceleration impulse response function ü They are

[0022]

[0023] The above Hankel matrix is ​​simplified as

[0024] The assembling and normalizing of the Hankel matrix is ​​specifically as follows:

[0025] Normalize the displacement and acceleration responses using the scaling factor: Among them, a u and a ü is the scale factor, and are the normalized displacement and acceleration responses respectively;

[0026] Type Y u =H u F, Y ü =H ü F is further simplified to Y = HF, where Normalized Hankel matrix

[0027] In step 4, the response of the second test set is reconstructed by using the response of the first test set:

[0028] The first measurement set divided in step 2 is The second measurement set is From Y = HF we get According to the response reconstruction theory in the time domain, the response of the second measurement set is From the first measurement set and the transmissibility matrix T 12 Reconstructed, in addition, in order to stabilize the solution process, Tikhonov regularization is introduced to obtain Where I represents the identity matrix and λ represents the regularization parameter, which is calculated by the L-curve method.

[0029] In step 4, the objective function is expressed as:

[0030] Establish the objective function based on the actual measured values ​​and reconstructed values ​​of set 2 in, is the actual measured response value of the second measurement set, is the reconstructed response value, θ is the unit stiffness coefficient, and ||·||2 is the L2 norm.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) The present invention integrates non-contact visual displacement measurement and contact acceleration measurement data, utilizes the existing acceleration sensor and portable visual displacement measurement technology on the bridge, and realizes rapid detection of bridge structure damage under unknown loads. This not only improves the accuracy and robustness of damage identification, but also better conforms to the actual situation of various types of sensors in the structural damage detection system, and has good engineering application prospects.

[0033] (2) The present invention combines the convenience of visual measurement and the stability of acceleration measurement, is suitable for damage detection of large bridge structures, reduces the complexity of installation and maintenance, and realizes lightweight detection.

[0034] (3) The present invention uses a finite element model and a unit impulse response function to achieve response reconstruction and damage identification of bridge structures under unknown loads, which can not only improve the accuracy of damage identification, but also better reflect the structural behavior under actual working conditions, thereby improving the accuracy and reliability of structural damage detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of the method of the present invention;

[0036] Figure 2 A schematic diagram of a scaled-down experiment and a finite element model of a three-span bridge structure in an embodiment;

[0037] Figure 3 is a schematic diagram of unit damage occurring on a bridge structure in an embodiment;

[0038] Figure 4 are the measured values ​​and reconstructed values ​​of the displacement response of node 5 in set 2 in one embodiment;

[0039] Figure 5This is a damage identification result of a three-span bridge structure in an embodiment. DETAILED DESCRIPTION

[0040] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0041] This embodiment provides a lightweight bridge structure damage rapid detection method, such as Figure 1 As shown in the figure, it can be roughly divided into two parts: contact acceleration measurement and non-contact displacement measurement, and bridge structure damage identification under unknown loads, including the following steps:

[0042] Step 1: Apply an impact load F to the target bridge, obtain the acceleration response ü(t) through contact measurement, and obtain the displacement response u(t) through non-contact measurement.

[0043] Step 11: Select the target bridge for damage detection, install multiple accelerometers and targets on the bridge, and place a consumer-grade mobile phone or portable camera at an appropriate distance away from the bridge structure.

[0044] Step 12: Contact Measurement: Obtaining n with Multiple Accelerometers ü An acceleration response.

[0045] Step 13: Non-contact measurement: Perform non-contact measurement using multiple targets and a consumer-grade mobile phone or portable camera, acquire video data and process it using a sub-pixel template matching algorithm to extract n u A displacement response.

[0046] In this embodiment, the three-span steel bridge structure model is as follows: Figure 2 As shown in (a), the length, width and height are 4000 mm, 50 mm and 16 mm respectively. The elastic modulus and mass density of the steel are 206 GPa and 7850 kg / m 3 Installation on a three-span bridge u targets or select the number of natural feature points, install n ü An accelerometer is installed and a consumer smartphone or portable camera is placed at an appropriate distance away from the bridge structure so that the camera's field of view covers all target points.

[0047] like Figure 3As shown in the figure, two damages appeared on the bridge structure. An impact load F was applied at node 23 of the three-span bridge structure, and the acceleration response ü(t) of the bridge was collected. The sampling frequency was set to 300 Hz and the sampling duration was 60 s. Continuous image acquisition was performed with a sampling rate of 60 fps and a resolution of 3840 × 2160 pixels. The video was then saved to a computer and processed using a sub-pixel template matching algorithm to extract the displacement u(t).

[0048] A sub-pixel template matching algorithm is used for processing to extract the displacement response, specifically: a small image block is selected from the reference image as a template, and the template contains the feature points or areas to be tracked; the target image and template are preprocessed including filtering and denoising to improve the accuracy and robustness of the matching; the coarse matching position of the template in the target image is determined by calculating the similarity measure between the template and each position in the target image; in the similarity graph obtained by the coarse matching, the maximum value point of the similarity measure is found as the preliminary matching position; quadratic surface fitting is performed on the pixels near the preliminary matching position to determine the sub-pixel displacement; based on the sub-pixel fine matching results, each frame of the image sequence is repeatedly matched to calculate the precise displacement of the template between different image frames, and the displacement response is extracted.

[0049] Step 2: Fuse the acceleration response and displacement response data, that is, arrange the acceleration and displacement responses in order to obtain the heterogeneous response Y, and divide the heterogeneous responses into two groups, recorded as the first measurement set (hereinafter referred to as set ) and the second measurement set (hereinafter referred to as set ), where the set The number of responses in is equal to or greater than the number of unknown impact loads applied to the bridge structure.

[0050] Step 3: Establish the finite element model of the bridge in its complete state, such as Figure 2 As shown in (b), it is divided into ne units with ne+1 nodes and 2*ne degrees of freedom. The damage coefficient α is introduced to describe the degree of unit stiffness damage, the unit impulse response function is calculated, and the Hankel matrix is ​​assembled and normalized.

[0051] Step 31: Calculate the Unit Impulse Response

[0052] The motion equation of the bridge structure under unit pulse excitation is:

[0053]

[0054] Where M, C, K are the mass, damping and stiffness matrices of the bridge structure, δ(t) is the Dirac function, h(t) is the unit pulse displacement response function, is the unit pulse velocity response function, is the unit pulse acceleration response function. Among them, the stiffness matrix is ​​determined by the damage coefficient α and is expressed as:

[0055]

[0056] Among them, α i is the damage coefficient of the ith unit, ne is the number of units, is the unit stiffness of the i-th unit. The sum of the unit stiffness coefficient and the damage coefficient is 1, that is, the unit stiffness coefficient of the i-th unit is expressed as θ i =1-α i .

[0057] Unit pulse displacement response function h(t) and unit pulse acceleration response function Calculated by Newmark-β method.

[0058] Bridge structure in t n The displacement u of the degrees of freedom p and s at the moment p (t n ) and acceleration response ü s (t n ) are respectively expressed as: Where, f is the externally applied impact load, h p and They represent the impulse response functions of displacement on the p degree of freedom and acceleration on the s degree of freedom respectively. n At the moment, the displacements in the p and s degrees of freedom and acceleration response Further expressed as and Assume that the number of displacement and acceleration responses are n u and n ü , the total displacement and acceleration response is expressed as and

[0059] Step 32: Assemble the Hankel matrix and normalize

[0060] The relationship between the output response and the input load F is:

[0061] Y u =H u F,Y ü =H ü F (3)

[0062] Among them, the Hankel matrix H of the displacement impulse response function u and the Hankel matrix H of the acceleration impulse response function ü They are

[0063]

[0064] The above Hankel matrix is ​​simplified as

[0065] It should be noted that the magnitudes of displacement and acceleration responses are very different, so in order to achieve data fusion, they are normalized using a scaling factor: Among them, a u and a ü is the scale factor, and are the normalized displacement and acceleration responses, respectively.

[0066] Formula (3) Y u =H u F, Y ü =H ü F is further simplified to:

[0067] Y=HF (4)

[0068] in, Normalized Hankel matrix

[0069] Step 4: Based on the unit impulse response function and the Hankel matrix, the response of set 1 is used to reconstruct the response of set 2, and the objective function is constructed based on the actual measured response of set 2 and the reconstructed response.

[0070] In step 2, the heterogeneous responses were divided into sets and collection From formula (4), we can know that:

[0071]

[0072] According to the response reconstruction theory in the time domain, the response of set 2 By Collection and the transmissibility matrix T 12 Reconstructed. In addition, in order to stabilize the solution process, Tikhonov regularization is introduced:

[0073]

[0074] Where I represents the identity matrix and λ represents the regularization parameter, which is calculated by the L-curve method.

[0075] Figure 4 The measured displacement and reconstructed response of node 5 in set 2. It can be found that the displacement reconstruction data almost coincides with the actual measurement.

[0076] Damage identification of bridge structures can be transformed into an optimization problem based on the actual measured values ​​of set 2 and reconstruction value Establish the objective function:

[0077]

[0078] Where θ is the element stiffness coefficient and ||·||2 is the L2 norm.

[0079] Step 5: Use the gradient algorithm to continuously optimize the objective function, iterate the unit stiffness coefficient of the structure, until the maximum number of iterations is reached or the precise requirements are met, and output the identified bridge structure damage results.

[0080] like Figure 5 As shown, it can be found that the method proposed in the present invention can accurately identify the damage location and degree at units 6 and 35.

[0081] For this three-span bridge structure example, the displacement response quantity n u =5, the target or feature points are nodes 5, 15, 21, 33, 37, and the number of accelerometers is n ü =5, installed at nodes 2, 14, 24, 29, 40. The three-span bridge structure is divided into 40 units, with a total of 41 nodes and 80 degrees of freedom. Units 6 and 35 suffer 40% damage. The response of set 1 includes the acceleration response at nodes 2, 24, and 40, and the displacement response at nodes 15 and 33. The response of set 2 includes the acceleration response at nodes 14 and 29, and the displacement response at nodes 5, 21, and 37.

[0082] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A lightweight bridge structure damage rapid detection method, characterized in that: The following steps are involved: Step 1: Apply impact load to the target bridge, obtain acceleration response through contact measurement, and obtain displacement response through non-contact measurement; Step 2: Fusing the acceleration response and displacement response data to obtain heterogeneous responses, dividing the heterogeneous responses into two groups, recorded as a first measurement set and a second measurement set, wherein the number of responses in the first measurement set is equal to or greater than the number of unknown impact loads applied to the bridge structure; Step 3: Establish a finite element model of the bridge in its intact state, divide it into multiple units, introduce damage coefficients to describe the degree of unit stiffness damage, calculate the unit impulse response function, assemble the Hankel matrix and normalize it; Step 4: Based on the unit impulse response function and the Hankel matrix, the response of the first test set is used to reconstruct the response of the second test set, and the objective function is constructed based on the actually measured response of the second test set and the reconstructed response; Step 5: Use the gradient algorithm to continuously optimize the objective function, iterate the unit stiffness coefficient of the structure until the maximum number of iterations is reached or the precision requirement is met, and output the identified bridge structure damage result, where the sum of the unit stiffness coefficient and the damage coefficient is 1.

2. A lightweight bridge structure damage rapid detection method according to claim 1, characterized in that: The contact measurement specifically includes: obtaining acceleration response through a plurality of accelerometers installed on the target bridge.

3. A lightweight bridge structure damage rapid detection method according to claim 1, characterized in that: The non-contact measurement specifically includes: performing non-contact measurement by installing multiple targets on the target bridge and a continuous image acquisition device placed at a preset distance from the bridge structure, acquiring video data and processing it using a sub-pixel template matching algorithm to extract the displacement response.

4. A lightweight bridge structure damage rapid detection method according to claim 3, characterized in that: The continuous image acquisition device is a consumer-grade mobile phone or a portable camera.

5. The lightweight bridge structure damage rapid detection method according to claim 1 is characterized in that: The sub-pixel template matching algorithm is used to process and extract the displacement response, specifically: an image block is selected from the reference image as a template, the template contains the feature point or area to be tracked; the target image and the template are pre-processed including filtering and denoising; The rough matching position of the template in the target image is determined by calculating the similarity measure between the template and each position in the target image; in the similarity graph obtained by the rough matching, the maximum value point of the similarity measure is found as the preliminary matching position; Quadratic surface fitting is performed on the pixels near the preliminary matching position to determine the sub-pixel displacement. Based on the sub-pixel fine matching results, the precise displacement of the template between different image frames is repeatedly matched for each frame of the image sequence to extract the displacement response.

6. A lightweight bridge structure damage rapid detection method according to claim 1, characterized in that: The motion equation of the bridge structure under unit pulse excitation is: Where M, C, K are the mass, damping and stiffness matrices of the bridge structure, δ(t) is the Dirac function, h(t) is the unit pulse displacement response function, is the unit pulse velocity response function, is the unit pulse acceleration response function, where the stiffness matrix is ​​determined by the damage coefficient and is expressed as: Among them, α i is the damage coefficient of the ith unit, ne is the number of units, is the unit stiffness of the i-th unit, and the unit stiffness coefficient θ of the i-th unit i =1-α i .

7. A lightweight bridge structure damage rapid detection method according to claim 6, characterized in that: The unit pulse displacement response function h(t) and the unit pulse acceleration response function Calculated by Newmark-β method: Bridge structure in t n The displacement u of the degrees of freedom p and s at the moment p (t n ) and acceleration response Respectively expressed as: Where, f is the externally applied impact load, h p and The impulse response functions of displacement on the p degree of freedom and acceleration on the s degree of freedom are represented respectively; then, from t0 to t n At the moment, the displacements in the p and s degrees of freedom and acceleration response Further expressed as and Assume that the number of displacement and acceleration responses are n u and n ü , the total displacement and acceleration response is expressed as and The relationship between the output response and the input load F is Among them, the Hankel matrix H of the displacement impulse response function u and the Hankel matrix of the acceleration impulse response function They are The above Hankel matrix is ​​simplified as 8. A lightweight bridge structure damage rapid detection method according to claim 7, characterized in that: The assembling and normalizing of the Hankel matrix is ​​specifically as follows: Normalize the displacement and acceleration responses using the scaling factor: Among them, a u and is the scale factor, and are the normalized displacement and acceleration responses respectively; Type Y u =H u F, Further simplified to Y = HF, where Normalized Hankel matrix 9. A lightweight bridge structure damage rapid detection method according to claim 8, characterized in that: In step 4, the response of the second test set is reconstructed by using the response of the first test set: The first measurement set divided in step 2 is The second measurement set is From Y = HF we get According to the response reconstruction theory in the time domain, the response of the second measurement set is From the first measurement set and the transmissibility matrix T 12 Reconstructed, in addition, in order to stabilize the solution process, Tikhonov regularization is introduced to obtain Where I represents the identity matrix and λ represents the regularization parameter, which is calculated by the L-curve method.

10. A lightweight bridge structure damage rapid detection method according to claim 9, characterized in that: In step 4, the objective function is expressed as: Establish the objective function based on the actual measured values ​​and reconstructed values ​​of set 2 in, is the actual measured response value of the second measurement set, is the reconstructed response value, θ is the unit stiffness coefficient, and ||·||2 is the L2 norm.

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