A method for identifying bridge bearing detachment based on frequency offset

Abstract

This invention provides a method for identifying bridge bearing detachment based on frequency shift, comprising the following steps: Step 1: Select a heavy-duty vehicle, and test the free vibration acceleration of the heavy-duty vehicle and the bridge under test respectively. Obtain the approximate vehicle frequency f through Fourier transform and peak picking. v and bridge frequency f b Step 2: Adjust the weight of the heavy-duty vehicle to achieve the desired vehicle frequency f. v Approaching bridge frequency f b Step 3: Based on the heavy-load vehicle from Step 2, make it pass through the bridge under test at a constant speed, and measure the acceleration response a(t) of the heavy-load vehicle using the acceleration sensor; use approximate formulas to express the acceleration response a(t) of the heavy-load vehicle and the vehicle frequency f. v and bridge frequency f b The relationship of change; Step 4: Use the time-frequency analysis module to perform time-frequency analysis on the acceleration response a(t) of the heavy-duty vehicle to obtain the time spectrum, and use the frequency shift of the time spectrum to identify the bridge support detachment.

Description

Technical Field

[0001] This invention relates to the field of bridge structural health monitoring, and in particular to a method for identifying bridge bearing detachment based on frequency offset. Background Technology

[0002] Bridge bearings, as a critical component of bridge structures, bear the vital task of transferring loads from the superstructure to the substructure. Bearings are designed to withstand only compressive forces, not tensile forces. Once a bearing loses its load-bearing capacity, it typically indicates that the bearing has come loose. Bearing loosening alters the structural stress pattern of the bridge, causing changes in the reactions of the main beams, crossbeams, and bearings. This can potentially damage the main beams, bridge deck, and piers, reducing bridge stability and even jeopardizing the safety of the entire bridge structure.

[0003] Currently, the detection techniques for bearing detachment are mainly divided into two categories: direct methods and indirect methods.

[0004] 1. Direct Methods: These include pressure sensor methods, blade methods, and camera methods. The pressure sensor method requires the sensor to be installed simultaneously with the support installation, but most existing bridges do not have such sensors pre-installed. The blade method has low measurement accuracy and exhibits a time lag. While the camera method is intuitive, it has high requirements for equipment and installation conditions.

[0005] 2. Indirect method: This method is based on the difference in static or dynamic response of the bridge structure before and after damage. It does not require direct contact with the bearings and is suitable for non-destructive testing of bridges in service.

[0006] However, for actual bridge structures, given that vehicle loads are one of the main external loads on bridges, researching a bridge bearing disengagement identification method based on the response of moving vehicles has significant practical application value. Existing technologies mainly focus on identifying bridge frequency changes through the response of light moving vehicles crossing the bridge, but have not yet been able to accurately identify bearing disengagement problems. Summary of the Invention

[0007] To overcome the limitations of existing technologies, this invention provides a method for identifying bridge bearing detachment based on frequency offset. By analyzing the dynamic response of the bridge under heavy vehicle loads, the frequency changes of the bridge are detected, thereby identifying whether the bearing has detached.

[0008] The main technical solution is as follows: A method for identifying bridge bearing detachment based on frequency offset, including...

[0009] Heavy-duty vehicles are used to stimulate vibrations in bridges.

[0010] Accelerometers are used to measure the free vibration acceleration of vehicles and bridges, as well as the acceleration response of vehicles in motion.

[0011] The signal acquisition module receives the voltage signal from the accelerometer and converts it from an analog signal to a digital signal for recording.

[0012] The time-frequency analysis module receives the acceleration signal from the signal acquisition module and converts it into a time-frequency spectrum based on Fourier transform.

[0013] The identification method includes the following steps:

[0014] Step 1: Select a heavy-duty vehicle and test the free vibration acceleration of both the heavy-duty vehicle and the bridge under test. Obtain the approximate vehicle frequency f through Fourier transform and peak picking. v and bridge frequency f b ;

[0015] Step 2: Adjust the weight of the heavy-duty vehicle to achieve the desired vehicle frequency f. v Approaching bridge frequency f b And the acceleration sensor was installed on the adjusted heavy-duty vehicle;

[0016] Step 3: Based on the heavy-load vehicle from Step 2, make it pass over the bridge under test at a constant speed, and measure the acceleration response a(t) of the heavy-load vehicle using an accelerometer; use an approximate formula to express the acceleration response a(t) of the heavy-load vehicle and the vehicle frequency f. v and bridge frequency f b The relationship of change;

[0017] Step 4: Use the time-frequency analysis module to perform time-frequency analysis on the acceleration response a(t) of the heavy-duty vehicle to obtain the time spectrum, and use the frequency shift of the time spectrum to identify the bridge support detachment.

[0018] In a preferred embodiment, step 1, measuring the free vibration acceleration of the vehicle includes the following steps:

[0019] Step a: Select a heavy-duty vehicle, install the accelerometer on the vehicle, and use a tapping method to make the vehicle vibrate freely. Measure the free vibration acceleration of the accelerometer using the accelerometer.

[0020] Step b: High-pass filtering is applied to the measured vehicle free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise;

[0021] Step c: Perform a Fourier transform on the filtered vehicle free vibration acceleration to obtain the vehicle's fundamental spectrum. Determine the position of the peak in the vehicle's fundamental spectrum by peak picking to obtain the approximate vehicle frequency f. v .

[0022] In a preferred embodiment, step 1, determining the free vibration acceleration of the bridge under test includes the following steps:

[0023] Step A: Install the accelerometer on the bridge to be tested and use the accelerometer to measure the free vibration acceleration of the bridge under random traffic flow;

[0024] Step B: High-pass filtering is applied to the measured bridge free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise;

[0025] Step C: Perform a Fourier transform on the filtered bridge free vibration acceleration to obtain the bridge's fundamental spectrum. Determine the location of the peaks in the bridge's fundamental spectrum by peak picking to obtain the approximate bridge frequency f. b .

[0026] In a preferred embodiment, in step 2, the vehicle frequency f v Less than the bridge frequency f b This reduces the weight of heavy-duty vehicles;

[0027] Vehicle frequency f v Greater than the bridge frequency f b This increases the weight of heavy-duty vehicles.

[0028] In a preferred embodiment, in step 3, the heavy-duty vehicle passes through the bridge under test at a constant speed, and the vehicle acceleration time history signal is measured and recorded; the vehicle speed v, bridge length L, vehicle weight P, and vehicle acceleration response a(t) are measured and recorded.

[0029] The acceleration a(t) of the vehicle body can be approximated as:

[0030]

[0031] Where A1, A2, and A3 are dimensionless parameters. The frequency offset curve of the bridge. This is the frequency offset curve of the vehicle, where t is time.

[0032] In a preferred embodiment, in step 4, the acceleration response a(t) of the heavy-duty vehicle is analyzed by time-frequency analysis using short-time Fourier transform to obtain the time spectrum.

[0033] In a preferred embodiment, in step 4, the short-time Fourier transform formula for the acceleration response a(t) of the heavy-duty vehicle is:

[0034] In a preferred embodiment, the frequency shift in the time spectrum includes the frequency shift curve of the bridge and the frequency shift curve of the vehicle;

[0035] In step 4, the determination of whether the bridge support has become detached is as follows:

[0036] If the frequency offset curve of the bridge and the frequency offset curve of the vehicle intersect at the support, it is determined that the bridge support is not detached.

[0037] If the frequency offset curve of the bridge and the frequency offset curve of the vehicle diverge at the support, it is determined that the bridge support is detached.

[0038] In a preferred embodiment, the time-frequency analysis module receives the acceleration signal from the signal acquisition module, obtains the bridge fundamental frequency and the vehicle fundamental frequency using Fourier transform, and then converts the vehicle's acceleration response into a time-frequency spectrum using short-time Fourier transform.

[0039] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0040] 1. The method of the present invention can eliminate the influence of vehicle frequency interference and road surface roughness on the detection results, showing good robustness. Even under different vehicle loads and road surface conditions, the method can provide stable and reliable identification results.

[0041] 2. The method of the present invention uses indirect identification means, avoiding direct destructive testing of the bridge structure, which conforms to the non-invasive principle of modern bridge health monitoring.

[0042] 3. Due to its robustness and non-invasiveness, the method of the present invention is very suitable for application in the health monitoring and maintenance of actual bridges.

[0043] 4. Compared with the traditional method of using light vehicles, this invention proposes to use heavy-duty vehicles for testing. The excitation generated by heavy-duty vehicles is closer to the actual working state of the bridge, which helps to more accurately simulate the actual stress conditions of the bridge.

[0044] 5. By using heavy-duty vehicles, the method of the present invention can transform the measurement of mode shape into the measurement of bridge frequency shift curve. This transformation significantly improves the measurement accuracy and makes the identification of bearing detachment more accurate.

[0045] 6. The method of the present invention can effectively identify bridge bearing delamination defects by analyzing the frequency response changes of bridges under heavy vehicle loads, providing an important basis for bridge maintenance and management. Attached Figure Description

[0046] Figure 1 This is a flowchart of the identification method in a preferred embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the bridge elevation of the bridge under test in a preferred embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the support arrangement of a three-span continuous beam bridge under test in a preferred embodiment of the present invention;

[0049] Figure 4 This is a structural diagram of a heavy-duty vehicle in a vehicle-bridge coupled finite element model in a preferred embodiment of the present invention.

[0050] Figure 5 This is a structural diagram of a continuous beam bridge in a vehicle-bridge coupled finite element model according to a preferred embodiment of the present invention.

[0051] Figure 6 This is a structural diagram of the coupled model in the vehicle-bridge coupled finite element model of the preferred embodiment of the present invention;

[0052] Figure 7 This is a time-frequency analysis diagram (without empty space) in a preferred embodiment of the present invention;

[0053] Figure 8 This is a time-frequency analysis diagram (partial enlarged view without voids) in a preferred embodiment of the present invention;

[0054] Figure 9 This is a time-frequency analysis diagram (without voids) in a preferred embodiment of the present invention;

[0055] Figure 10 This is a time-frequency analysis diagram (enlarged view of the voided portion) in a preferred embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed", "equipped", "sleeved / connected", "connected", etc., should be interpreted broadly. For example, "connection" can be a wall-mounted connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0059] refer to Figures 1-10 This embodiment provides a method for identifying bridge bearing detachment based on frequency shift. This method effectively identifies whether bridge bearings have detached by analyzing the frequency shift curve of the bridge under specific working conditions.

[0060] In this embodiment, based on finite element modeling technology and using an actual three-span continuous bridge as a foundation, a finite element model consistent with the actual bridge support layout was established to ensure that the model's frequency characteristics are close to those of the actual bridge. Simulations were then performed for two different operating conditions, and for each condition, the acceleration response a(t) data at the center of gravity of the heavy-load vehicle was extracted. Time-frequency analysis was then performed using short-time Fourier transform (STFT) to obtain the frequency shift curve. The two operating conditions are as follows:

[0061] First, heavy-duty vehicles travel on continuous beam bridges where the supports are not yet detached (such as...). Figure 7-8 When a heavy-duty vehicle passes over a bearing that has not been detached, the time-frequency analysis spectrum shows that the vehicle's fundamental frequency intersects with the bridge's fundamental frequency.

[0062] Second, continuous beam bridges where heavy-load vehicles lose contact with the third support (counted in the direction of vehicle travel) (e.g.) Figure 9-10 When a heavy-duty vehicle passes over a dislodged bearing, in addition to the fundamental frequency convergence, a significant low-order instantaneous frequency appears on the time-frequency analysis spectrum, which is related to the bearing dislodgment phenomenon.

[0063] This embodiment utilizes finite element modeling and time-frequency analysis techniques to avoid damage to the actual bridge. It also provides an efficient and accurate method for identifying detached bearings. By comparing frequency shift curves under different working conditions, the presence of detached bearings can be accurately identified. This method has significant practical application value for bridge maintenance and safety management, helping to promptly identify and address potential safety hazards.

[0064] This embodiment provides a method for identifying bridge bearing detachment based on frequency shift. The method further includes the following equipment: a heavy-duty vehicle to excite vibration in the bridge; an accelerometer to measure the free vibration acceleration of the vehicle and bridge, as well as the vehicle's acceleration response during travel; a signal acquisition module to receive the voltage signal from the accelerometer and convert it from analog to digital for recording; and a time-frequency analysis module to receive the acceleration signal from the signal acquisition module, obtain the bridge's fundamental frequency and the vehicle's fundamental frequency using Fourier transform, and then convert the vehicle's acceleration response into a time-frequency spectrum using short-time Fourier transform.

[0065] When implementing this identification method, dynamic response data of vehicles passing over bridges can be collected by deploying detection equipment such as acceleration sensors, and then the frequency shift curve of the bridge can be obtained by using signal processing techniques such as time-frequency analysis.

[0066] The method for identifying bridge bearing detachment based on frequency offset includes the following steps:

[0067] Step 1: Select a heavy-duty vehicle and test the free vibration acceleration of both the heavy-duty vehicle and the bridge under test. Obtain the approximate vehicle frequency f through Fourier transform and peak picking. v and bridge frequency f b .

[0068] The specific implementation steps of step 1 include measuring the free vibration acceleration of the vehicle and the free vibration acceleration of the bridge to be tested.

[0069] Measuring the free vibration acceleration of a vehicle includes the following steps:

[0070] Step a: Select a heavy-duty vehicle, install the accelerometer on the vehicle, and use a tapping method to make the vehicle vibrate freely. Measure the free vibration acceleration of the accelerometer using the accelerometer.

[0071] Step b: High-pass filtering is applied to the measured vehicle free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise;

[0072] Step c: Perform a Fourier transform on the filtered vehicle free vibration acceleration to obtain the vehicle's fundamental spectrum. Determine the position of the peak in the vehicle's fundamental spectrum by peak picking to obtain the approximate vehicle frequency f. v .

[0073] The free vibration acceleration of the bridge under test includes the following steps:

[0074] Step A: Install the accelerometer on the bridge to be tested and use the accelerometer to measure the free vibration acceleration of the bridge under random traffic flow;

[0075] Step B: High-pass filtering is applied to the measured bridge free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise;

[0076] Step C: Perform a Fourier transform on the filtered bridge free vibration acceleration to obtain the bridge's fundamental spectrum. Determine the location of the peaks in the bridge's fundamental spectrum by peak picking to obtain the approximate bridge frequency f. b .

[0077] Step 2: Adjust the weight of the heavy-duty vehicle to achieve the desired vehicle frequency f. v Approaching bridge frequency f b And the acceleration sensor was installed on the adjusted heavy-duty vehicle.

[0078] The weight of heavy-duty vehicles is adjusted to: vehicle frequency f v Less than the bridge frequency f b This reduces the weight of heavily loaded vehicles. Vehicle frequency f v Greater than the bridge frequency f b This increases the weight of heavy-duty vehicles.

[0079] Step 3: Based on the heavy-load vehicle from Step 2, make it pass over the bridge under test at a constant speed, and measure the acceleration response a(t) of the heavy-load vehicle using an accelerometer; use an approximate formula to express the acceleration response a(t) of the heavy-load vehicle and the vehicle frequency f. v and bridge frequency f b The changing relationship.

[0080] When a heavy-load vehicle passes over the bridge under test at a constant speed, the vehicle acceleration time history signal is measured and recorded; the vehicle speed v, bridge length L, vehicle weight P, and vehicle acceleration response a(t) are measured and recorded.

[0081] The acceleration a(t) of the vehicle body can be approximated as:

[0082]

[0083] Where A1, A2, and A3 are dimensionless parameters. The frequency offset curve of the bridge. This is the frequency offset curve of the vehicle, where t is time.

[0084] Step 4: Use the time-frequency analysis module to perform time-frequency analysis on the acceleration response a(t) of the heavy-duty vehicle to obtain the time spectrum, and use the frequency shift of the time spectrum to identify the bridge support detachment.

[0085] The acceleration response a(t) of a heavy-duty vehicle is analyzed by short-time Fourier transform to obtain the time spectrum. The frequency shift in the time spectrum includes the frequency shift curve of the bridge and the frequency shift curve of the vehicle.

[0086] The short-time Fourier transform formula for the acceleration response a(t) of a heavy-duty vehicle is:

[0087]

[0088] Based on the time spectrum, the determination of whether a bridge bearing is detached is as follows: if the frequency offset curve of the bridge and the frequency offset curve of the vehicle intersect at the bearing, it is determined that the bridge bearing is not detached; if the frequency offset curve of the bridge and the frequency offset curve of the vehicle fork at the bearing, it is determined that the bridge bearing is detached.

[0089] The above description is merely a preferred embodiment of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention by those skilled in the art within the scope of the technology disclosed in the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A method for identifying bridge bearing detachment based on frequency offset, characterized in that: This includes heavy-duty vehicles used to induce vibrations in bridges; Accelerometers are used to measure the free vibration acceleration of vehicles and bridges, as well as the acceleration response of vehicles in motion. The signal acquisition module receives the voltage signal from the accelerometer and converts it from an analog signal to a digital signal for recording. The time-frequency analysis module receives the acceleration signal from the signal acquisition module and converts it into a time-frequency spectrum based on Fourier transform. The identification method includes the following steps: Step 1: Select a heavy-duty vehicle and test the free vibration acceleration of both the heavy-duty vehicle and the bridge under test. Obtain the approximate vehicle frequency f through Fourier transform and peak picking. b and bridge frequency f b ; Step 2: Adjust the weight of the heavy-duty vehicle to achieve the desired vehicle frequency f. v Approaching bridge frequency f b And the acceleration sensor was installed on the adjusted heavy-duty vehicle; Step 3: Based on the heavy-load vehicle from Step 2, make it pass over the bridge under test at a constant speed, and measure the acceleration response a(t) of the heavy-load vehicle using an accelerometer; use an approximate formula to express the acceleration response a(t) of the heavy-load vehicle and the vehicle frequency f. v and bridge frequency f b The changing relationship; Step 4: Use the time-frequency analysis module to perform time-frequency analysis on the acceleration response a(t) of the heavy-duty vehicle to obtain the time spectrum, and use the frequency shift of the time spectrum to identify the bridge support detachment.

2. The method for identifying bridge bearing detachment based on frequency offset according to claim 1, characterized in that: In step 1, measuring the free vibration acceleration of the vehicle includes the following steps: Step a: Select a heavy-duty vehicle, install the accelerometer on the vehicle, and use a tapping method to make the vehicle vibrate freely. Measure the free vibration acceleration of the accelerometer using the accelerometer. Step b: High-pass filtering is applied to the measured vehicle free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise; Step c: Perform a Fourier transform on the filtered vehicle free vibration acceleration to obtain the vehicle's fundamental spectrum. Determine the position of the peak in the vehicle's fundamental spectrum by peak picking to obtain the approximate vehicle frequency f. b .

3. The method for identifying bridge bearing detachment based on frequency offset according to claim 2, characterized in that: In step 1, the free vibration acceleration of the bridge under test includes the following steps: Step A: Install the accelerometer on the bridge to be tested and use the accelerometer to measure the free vibration acceleration of the bridge under random traffic flow; Step B: High-pass filtering is applied to the measured bridge free vibration acceleration to remove the trend term, and then low-pass filtering is applied to remove measurement noise; Step C: Perform a Fourier transform on the filtered bridge free vibration acceleration to obtain the bridge's fundamental spectrum. Determine the location of the peaks in the bridge's fundamental spectrum by peak picking to obtain the approximate bridge frequency f. b .

4. The method for identifying bridge bearing detachment based on frequency offset according to claim 1, characterized in that: In step 2, the vehicle frequency f v Less than the bridge frequency f b This reduces the weight of heavy-duty vehicles; Vehicle frequency f b Greater than the bridge frequency f b This increases the weight of heavy-duty vehicles.

5. The method for identifying bridge bearing detachment based on frequency offset according to claim 1, characterized in that: In step 3, the heavy-load vehicle passes through the bridge under test at a constant speed, and the vehicle acceleration time history signal is measured and recorded; the vehicle speed v, bridge length L, vehicle weight P, and vehicle acceleration response a(t) are measured and recorded. The acceleration a(t) of the vehicle body can be approximated as: Where A1, A2, and A3 are dimensionless parameters. The frequency offset curve of the bridge. This is the frequency offset curve of the vehicle, where t is time.

6. The method for identifying bridge bearing detachment based on frequency offset according to claim 1, characterized in that: In step 4, the acceleration response a(t) of the heavy-duty vehicle is analyzed by time-frequency analysis using short-time Fourier transform to obtain the time spectrum.

7. The method for identifying bridge bearing detachment based on frequency offset according to claim 6, characterized in that: In step 4, the short-time Fourier transform formula for the acceleration response a(t) of the heavy-duty vehicle is as follows:

8. The method for identifying bridge bearing detachment based on frequency offset according to claim 6, characterized in that: The frequency shift in the time spectrum includes the frequency shift curve of the bridge and the frequency shift curve of the vehicle. In step 4, the determination of whether the bridge support has become detached is as follows: If the frequency offset curve of the bridge and the frequency offset curve of the vehicle intersect at the support, it is determined that the bridge support is not detached. If the frequency offset curve of the bridge and the frequency offset curve of the vehicle diverge at the support, it is determined that the bridge support is detached.

9. The method for identifying bridge bearing detachment based on frequency offset according to claim 1, characterized in that: The time-frequency analysis module receives the acceleration signal from the signal acquisition module, uses Fourier transform to obtain the bridge fundamental frequency and the vehicle fundamental frequency, and then uses short-time Fourier transform to convert the vehicle's acceleration response into a time-frequency spectrum.

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