Bridge support detection system and multi-load joint detection method thereof

Abstract

The invention discloses a bridge support detection system and a multi-load joint detection method thereof, and relates to the technical field of bridge structure health monitoring, the detection system comprises a camera, an eddy current thickness measurement sensor, an acceleration sensor, a laser Doppler vibration meter, a strain gauge, a data acquisition module, a data processing module and a data sending module, at least one camera is fixed on the side surface of the bridge support or right above the bridge support through an adjustable bracket, and the optical axis of the camera faces the surface of the bridge support, so that the comprehensiveness and definition of image acquisition are ensured. The system utilizes an image processing algorithm to detect cracks on the surface of the support, combines dynamic response analysis and a model to predict rigidity degradation and void states, and evaluates the corrosion degree through thickness monitoring, thereby realizing full life cycle management of the support.

Description

Technical Field

[0001] The present application relates to the technical field of bridge structure health monitoring, specifically a bridge bearing detection system and a multi-load joint detection method that integrates multiple sensors, image processing algorithms, dynamic response analysis and optimization models, which can monitor in real time the health status of bridge bearings under multi-dimensional disease characteristics such as cracking, stiffness degradation, voiding and thickness changes. Background Art

[0002] As an important part of the bridge structure, bridge bearings bear the key functions of transferring loads, adjusting deformation and reducing shock. Their health directly affects the overall safety and service life of the bridge. During long-term service, bridge bearings will be affected by a variety of complex factors, including repeated impacts of traffic loads, environmental erosion (such as rain, salt spray, chemical corrosion, etc.) and material fatigue aging. These factors may cause cracking, hollowing, corrosion and stiffness degradation in the bearings. For example, the long-term effect of traffic loads will cause fatigue cracks in the bearings, environmental erosion will accelerate the corrosion of metal parts and the deterioration of concrete, and material aging will cause the gradual degradation of bearing stiffness. These problems will not only reduce the bearing capacity of the bearing, but may also cause partial or even overall failure of the bridge structure, seriously threatening the safe operation of the bridge.

[0003] However, traditional bridge bearing detection methods have obvious limitations. At present, the detection of bridge bearings mainly relies on manual inspection or single parameter monitoring. Manual inspection is usually carried out by visual observation, tapping and listening, or simple measuring tools. Although this method is intuitive, it is limited by the subjective experience and skill level of the inspectors, making it difficult to detect hidden defects, and the detection efficiency is low and the coverage is limited. Single parameter monitoring usually uses displacement sensors, stress sensors, or acceleration sensors, which can only monitor a certain parameter of the bearing (such as displacement, stress, or vibration) and cannot fully reflect the overall status of the bearing. For example, monitoring only displacement changes may ignore stress concentration or corrosion problems, while monitoring only stress may not capture the stiffness degradation or hollowing of the bearing. In addition, traditional methods usually lack real-time and continuity, and cannot promptly detect potential problems of the bearing during bridge operation, resulting in the gradual deterioration of defects without being detected, increasing the risk of bridge operation.

[0004] The main reasons for these deficiencies include the limitations of technical means and the complexity of the detection system. Traditional manual inspection methods rely on manual operations and are difficult to automate; and due to technical limitations, single parameter monitoring systems cannot integrate multiple sensors at the same time, resulting in a single detection parameter. In addition, existing data processing algorithms are often relatively simple and cannot effectively process multi-source heterogeneous data, resulting in insufficient accuracy and reliability of detection results. For example, some systems may only use a simple threshold judgment method and cannot perform in-depth analysis and prediction of much more complex parameter data. At the same time, existing detection systems also have certain difficulties in installation and maintenance. The installation location and number of sensors are often limited by the structure of the bridge, making it difficult to cover all key areas; and the long-term stability and anti-interference ability of sensors are often insufficient, resulting in reduced reliability of detection data.

[0005] In order to solve the above problems, it is of great engineering significance to develop a system that can monitor the health status of bridge bearings in real time during bridge operation and realize multi-dimensional parameter comprehensive analysis and intelligent early warning. Summary of the invention

[0006] The purpose of this application is to overcome at least one of the shortcomings of the prior art, and to provide a bridge bearing detection system and a multi-load joint detection method thereof, which can monitor and diagnose key bridge bearing diseases with high precision through multi-sensor data acquisition, real-time analysis, health assessment and early warning functions. The system uses image processing algorithms to detect cracks on the bearing surface, combines dynamic response analysis with model prediction of stiffness degradation and voiding, and evaluates the degree of corrosion through thickness monitoring, thereby achieving full life cycle management of the bearing.

[0007] To achieve the above objectives, in the first aspect, the present application discloses a bridge bearing detection system, which includes a camera, an eddy current thickness sensor, an acceleration sensor, a laser Doppler vibrometer, a strain gauge, a data acquisition module, a data processing module, and a data transmission module, wherein at least one camera is fixed to the side or directly above the bridge bearing through an adjustable bracket, and its optical axis faces the bridge bearing surface to ensure the comprehensiveness and clarity of image acquisition; the eddy current thickness sensor is installed on the metal surface of the bridge bearing at a position closest to the slip zone, the eddy current thickness sensor faces the bridge bearing surface, and its detection head remains parallel to the metal surface being measured. The acceleration sensor is installed in the middle of the top of the bridge bearing. The acceleration sensor is connected to the data acquisition module through a cable to output a real-time dynamic response signal; the laser Doppler vibrometer is installed on the top of the pier, facing the connection between the bridge support and the beam body, and the measuring beam is parallel to the sliding direction of the support. The laser Doppler vibrometer emits a beam perpendicular to the support surface through a laser transmitter. The beam reflection signal is captured by the photoelectric receiver in the laser Doppler vibrometer, and the laser Doppler vibrometer calculates the displacement response of the bridge support under the dynamic load; the laser Doppler vibrometer is connected to the data acquisition module through an optical fiber cable; the strain gauges are respectively attached to the bridge support by glue The upper and lower contact surfaces and edge areas are covered with heat-shrinkable insulating material to measure the compressive stress in the vertical direction and the shear stress in the horizontal direction. Each strain gauge is connected to the data acquisition module through a cable; the data acquisition module is connected to the data processing module through an interface, and the signal of the data acquisition module interacts with the data processing module through a two-way communication protocol; the data processing module is installed in the bridge management facility, and processes the real-time data sent by each sensor for filtering, compensation and feature extraction; the data processing module is connected to the data sending module, and the detection data is transmitted to the outside through the data sending module to realize the output of the detection data.

[0008] Furthermore, the camera is directly connected to the data acquisition module via a Gigabit Ethernet interface to transmit high-resolution image data in real time.

[0009] Furthermore, the eddy current thickness sensor is fixed by a fixture with a fixing force of 20 N·m, ensuring that the distance between the sensor and the support surface is 1 mm, and the measurement accuracy is ±0.01 mm.

[0010] Furthermore, the bridge bearing detection system also includes a temperature and humidity sensor installed next to the eddy current thickness sensor, and the temperature and humidity sensor is connected to the data acquisition module to provide temperature and humidity information to the data acquisition module.

[0011] Furthermore, the eddy current thickness sensor is connected to the data acquisition module via a shielded twisted pair cable, and the shielding layer of the shielded twisted pair cable is grounded to eliminate electromagnetic interference.

[0012] Furthermore, the acceleration sensor is installed by using M8 stainless steel bolts to fix the sensor base on the beam body with a torque of 40 Nm, with its sensing axis aligned with the sliding direction of the support and the vertical direction.

[0013] Furthermore, the strain gauge is attached at a position 50 mm from the center and 20 mm from the bottom of the support edge to ensure that the measurement covers the core area of ​​the support where stress is applied.

[0014] Furthermore, the data acquisition module is installed in a hidden area on the top of the bridge pier. Furthermore, the data acquisition module is connected to the data processing module via an RS485 interface.

[0015] The components of this application have a close connection relationship and clear functional division in the system. All sensors directly collect data from key positions of the bearings and transmit them to the data acquisition module through physical connections and communication networks. After the acquisition module is connected and processed with the data processing module, the final result is output. The overall design of the system realizes high-precision monitoring of bridge bearings, reliable data transmission and timely status evaluation, providing technical guarantee for the safe operation of bridges.

[0016] In a second aspect, the present application discloses a multi-load joint detection method based on a bridge bearing detection system, the detection method comprising the following steps: Step 1: Collect images of the bridge bearing surface and perform crack detection. Use a high-resolution camera to collect images of the bearing surface, ensure that the image resolution is accurate to within 0.1 mm of the actual size of the bearing, and capture the fine features of the crack; input the collected images into an improved deep learning model, which uses a dynamic weighted loss function to assign higher priority to cracks with larger areas, while increasing edge sharpness constraints to improve the recognition accuracy of complex crack shapes; the model outputs crack length, width, and area features based on the pixel area of ​​the crack. These pixel features are converted into actual physical sizes through the camera's calibration ratio, and the crack growth trend is determined by calculating the ratio of the current crack length to the historical crack length.

[0017] Step 2: Evaluate the stiffness change of the bridge bearing. Under normal operating conditions of the bridge, use acceleration sensors and laser Doppler vibrometers to synchronously collect dynamic response signals of the bridge bearing and the beam. The change in the stiffness of the bridge bearing is calculated comprehensively by the dynamic displacement ratio and the vibration main frequency reduction ratio. The dynamic displacement ratio represents the ratio of the dynamic displacement of the bearing to the dynamic displacement of the beam. The vibration main frequency reduction ratio quantifies the stiffness degradation by comparing the change between the current vibration main frequency and the initial main frequency. If the dynamic displacement ratio exceeds 2 times or the main frequency reduction ratio exceeds 20%, a stiffness degradation warning signal is generated.

[0018] Step 3: Detect the bridge bearing debonding situation and analyze the bridge bearing debonding phenomenon through the flexibility matrix method. The flexibility matrix describes the deformation characteristics of the bridge bearing after being subjected to forces in different directions. The diagonal elements reflect the direct deformation of the bridge bearing, and the non-diagonal elements reflect the coupled deformation between adjacent components. Calculate the standardized diagonal difference ratio of the flexibility matrix, that is, the ratio of the difference between the diagonal elements and the non-diagonal elements to the average value of the initial diagonal elements, quantify the degree of debonding, and combine the dynamic response data of the vehicle impact load and the temperature fluctuation parameters to predict the expansion trend of the debonding through the machine learning model, and output the percentage value of the degree of debonding.

[0019] Step 4: Monitor the thickness change of bridge bearings and evaluate the corrosion situation. The eddy current thickness sensor monitors the thickness change of bridge bearings in real time by detecting the relationship between the sensing signal and the distance from the metal surface, and uses a nonlinear compensation model to adjust the thickness. The actual thickness value is calculated based on the quadratic relationship between temperature change and thickness error. Humidity compensation is based on the interference of humidity changes on the sensor signal, and corrections are made using experimental fitting data. When the corrosion rate exceeds 0.1 mm per year or the thickness is reduced to less than 10% of the design value, a corrosion warning is generated.

[0020] Step 5: Fusion of multi-parameter data and comprehensive evaluation. Integrate the test results of crack characteristics, stiffness changes, voiding degree and thickness changes obtained in steps 1 to 4 to generate a comprehensive health score. The scoring model dynamically assigns weights based on the impact of each parameter on bearing performance.

[0021] Step 6: Generate early warning signals and provide maintenance recommendations. Generate graded early warning signals based on the health scores and test results obtained in step 5.

[0022] The bridge bearing detection method of this application has significant technical advantages, which are mainly reflected in the improvement of detection accuracy, comprehensive evaluation capabilities and intelligent management level. By introducing a dynamic correction model, nonlinear compensation is performed on thickness measurement and stiffness evaluation, effectively eliminating the influence of environmental factors (such as temperature and humidity) on the detection results, and ensuring the accuracy of the data. By adopting an improved target detection algorithm and flexibility matrix analysis method, the recognition accuracy of cracks and voids is greatly improved, especially in the early detection of complex shape cracks and slight voids. The comprehensive health scoring model integrates multi-dimensional parameters of crack propagation, stiffness degradation, void degree and thickness thinning, dynamically adjusts the weights to be closer to the actual service status of the bearing, and provides a comprehensive and quantitative health assessment. At the same time, combined with real-time data and historical trends, clear and unambiguous risk warnings and maintenance recommendations are generated, which greatly improves the scientificity and efficiency of bridge operation and maintenance management, and provides reliable technical support for health monitoring of bridges throughout their life cycle.

[0023] The above-listed beneficial effects are not exhaustive of all advantages. Other potential beneficial effects and detailed technical implementations will be further disclosed in the examples or other description parts of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] After reading the following detailed description in conjunction with the accompanying drawings, you will have a better understanding of various aspects of the present disclosure. The positions, sizes, and ranges of various structures shown in the accompanying drawings sometimes do not represent the actual positions, sizes, and ranges. In the accompanying drawings: Figure 1 It is a system block diagram of an embodiment disclosed in this application. DETAILED DESCRIPTION

[0025] The present disclosure will be described below with reference to the accompanying drawings, wherein the accompanying drawings illustrate several embodiments of the present disclosure. However, it should be understood that the present disclosure can be presented in a variety of different ways and is not limited to the embodiments described below; in fact, the embodiments described below are intended to make the disclosure of the present disclosure more complete and fully illustrate the scope of protection of the present disclosure to those skilled in the art. It should also be understood that the embodiments disclosed herein can be combined in various ways to provide more additional embodiments.

[0026] It should be understood that the same reference numerals represent the same elements throughout the drawings. In the drawings, the dimensions of certain features may be distorted for clarity.

[0027] It should be understood that the terms used in the specification are only used to describe specific embodiments and are not intended to limit the present disclosure. All terms (including technical terms and scientific terms) used in the specification have the meanings commonly understood by those skilled in the art unless otherwise defined. For the sake of brevity and / or clarity, the techniques, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorized specification.

[0028] The singular forms "a", "said" and "the" used in the specification include plural forms unless clearly indicated. The terms "include", "comprise" and "contain" used in the specification indicate the presence of the claimed features, but do not exclude the presence of one or more other features. The term "and / or" used in the specification includes any and all combinations of one or more of the relevant listed items.

[0029] In this embodiment, the bridge bearing detection system is a high-precision detection device for real-time monitoring of the health status of bridge bearings. Its overall structure consists of multiple high-precision sensors, data acquisition modules, data processing modules and data transmission modules. Each component is closely connected through physical connections and communication networks to form a complete monitoring system.

[0030] The camera is fixed to the side or directly above the bridge support through an adjustable bracket, with its optical axis facing the bridge support surface to ensure comprehensive and clear image acquisition. The camera's optical system uses multi-layer coating technology, which can capture clear images of the support surface under complex lighting conditions with a resolution of less than 0.1 mm. The camera is directly connected to the data acquisition module through a Gigabit Ethernet interface to transmit high-resolution image data in real time. The adjustable bracket is made of high-strength aluminum alloy material with corrosion and vibration resistance to ensure that the camera remains stable during long-term use. The bracket's adjustment mechanism uses a precision thread design to achieve fine-tuning of the camera position and angle, ensuring that the optical axis is perpendicular to the support surface to avoid angular deviation.

[0031] The eddy current thickness sensor is installed on the metal surface of the bridge bearing at the position closest to the slip area. It is fixed with a special clamp with a fixing force of 20 Nm to ensure that the distance between the sensor and the bearing surface is 1 mm and the measurement accuracy is ±0.01 mm. The eddy current thickness sensor faces the surface of the bridge bearing, and its detection head remains parallel to the metal surface being measured. The sensor is connected to the data acquisition module via a shielded twisted pair cable, and the shielding layer is grounded to eliminate electromagnetic interference. The sensor's sensitive element is made of highly magnetic permeable material and can accurately measure small changes in the thickness of the bearing. The signal processing unit has a built-in temperature compensation algorithm, which corrects the measurement results based on the thermal expansion effect of the metal and humidity changes to ensure that the measurement accuracy reaches ±0.01 mm. Thickness measurement signal Thickness of support The relationship is:

[0032] in, and is the sensor calibration factor.

[0033] Temperature compensation based on temperature changes Thickness measurement error To make corrections:

[0034] in, and are experimental fitting coefficients.

[0035] Humidity compensation based on humidity changes Thickness measurement error To make corrections:

[0036] in, are experimental fitting coefficients.

[0037] Corrected actual thickness for:

[0038] like ( is the designed thickness), the system triggers a corrosion warning.

[0039] The accelerometer is installed in the center of the top of the bridge support and fixed in the prefabricated mounting hole at the bottom of the beam. M8 stainless steel bolts are used during installation to fix the sensor base to the beam with a torque of 40 Nm, with its sensing axis aligned with the sliding direction and vertical direction of the support. The accelerometer is connected to the data acquisition module via a cable and outputs a real-time dynamic response signal. The sensitive unit of the sensor is made of piezoelectric ceramic material, which can capture the vibration signal of the support within a wide frequency range. The sealed housing has an IP67 protection grade and can withstand the influence of harsh environments. Stiffness evaluation is performed through the dynamic displacement ratio And the vibration frequency reduction ratio Comprehensive calculation:

[0040] in, and are the dynamic displacements of the supports and beam, respectively. and They are the initial main frequency and the current main frequency respectively.

[0041] Comprehensive stiffness index for:

[0042] in, and is the weight coefficient.

[0043] like or , the system generates a stiffness degradation warning.

[0044] The laser Doppler vibrometer is installed on the top of the pier, facing the connection between the bridge support and the beam, and the measuring beam is parallel to the sliding direction of the support. The laser Doppler vibrometer emits a beam perpendicular to the surface of the support through a laser transmitter. The reflected signal of the beam is captured by a photoelectric receiver to calculate the displacement response of the support under dynamic load. The laser Doppler vibrometer is connected to the data acquisition module via a fiber optic cable. Its control unit is fixed to the side wall of the pier, and all components are protected by a steel casing to prevent mechanical damage and environmental erosion. The optical system of the laser Doppler vibrometer uses a high-precision lens group, which can achieve micron-level displacement measurement under long-distance conditions.

[0045] The strain gauges are attached to the upper and lower contact surfaces and edge areas of the bridge support by gluing, and covered with heat shrink insulation material to measure the vertical compressive stress and horizontal shear stress. The attachment position is 50 mm from the center of the support edge and 20 mm from the bottom to ensure that the measurement covers the core area of ​​the support. The strain gauge is connected to the data acquisition module via a cable, and all connection points are welded and packaged to ensure signal integrity. The sensitive grid of the strain gauge is made of high-elastic alloy material, which can maintain stable output characteristics under high stress conditions.

[0046] The data acquisition module is installed in a hidden area on top of the bridge pier, with a maximum distance of no more than 5 meters from the sensor to ensure signal strength and stability. The data acquisition module is connected to the data processing module via the RS485 interface, supporting 1000 times per second of sensor data reading and analysis. The module uses a multi-channel signal conditioning circuit to amplify, filter and digitize the sensor signal. The housing of the data acquisition module is waterproof and dustproof, with a protection level of IP65, ensuring stable operation in harsh environments.

[0047] The data processing module is installed in the bridge management facility, and its processing capabilities support filtering, compensation and feature extraction of real-time data. The data processing module interacts with the data acquisition module through a two-way communication protocol to ensure the real-time and reliability of data transmission. The hardware platform of the data processing module uses a high-performance embedded processor, supports multi-threaded parallel computing, and can quickly process a large amount of sensor data.

[0048] The data transmission module is connected to the data processing module, and the detection data is transmitted to the outside through the data transmission module to realize the detection data output. The data transmission module transmits the detection data to the cloud analysis platform through the 5G high-speed network. The platform saves the detection data through the distributed storage system, uses the preset algorithm to analyze the trend changes and generate a health assessment report. The communication interface of the data transmission module supports multiple protocols, including TCP / IP, MQTT and HTTP, to ensure compatibility with external systems.

[0049] In the specific implementation process, the system first collects images of the bridge bearing surface through a camera, uses a deep learning model to identify the length, width and area characteristics of the crack, and calculates the crack growth rate. When the crack growth rate exceeds 2 mm per month or the crack area exceeds 10% of the total bearing area, the system generates a crack growth warning signal. Then, the system uses an accelerometer and a laser Doppler vibrometer to synchronously collect the dynamic response signals of the bearing and the beam body, and comprehensively evaluates the stiffness changes of the bearing through the dynamic displacement ratio and the vibration main frequency reduction ratio. If the dynamic displacement ratio exceeds 2 times or the main frequency reduction ratio exceeds 20%, the system generates a stiffness degradation warning signal.

[0050] The system also analyzes the bearing debonding phenomenon through the flexibility matrix method, calculates the standardized diagonal difference ratio of the flexibility matrix, and quantifies the degree of debonding. When the standardized diagonal difference ratio exceeds 30%, the system determines that the bearing may be debonded, and predicts the expansion trend of debonding by combining the vehicle impact load and temperature fluctuation parameters. The eddy current thickness sensor monitors the change of bearing thickness in real time, and corrects the measurement error caused by environmental changes through the nonlinear compensation model. When the corrosion rate exceeds 0.1 mm per year or the thickness is reduced to less than 10% of the design value, the system triggers a corrosion warning.

[0051] Finally, the system integrates the test results of crack characteristics, stiffness changes, voiding degree and thickness changes to generate a comprehensive health score. The scoring model dynamically assigns weights based on the impact of each parameter on the bearing performance, and the health score range is set from 0 to 100 points. When the score is less than 30 points, the system indicates a high-risk state and recommends immediate repair or replacement of the bearing. Early warning signals and maintenance recommendations are transmitted to the operation and maintenance management center through display screens and remote terminals, with detailed inspection areas and priority instructions. The system automatically generates inspection reports and maintenance plans for management personnel to execute.

[0052] The contents not disclosed in detail in this embodiment, such as the specific circuit design of the sensor, the software algorithm implementation of the data processing module, and the specific configuration of the communication protocol, are all well-known technologies or existing technologies for those skilled in the art and do not need to be described in detail. Through the above implementation, the bridge bearing detection system achieves high-precision monitoring, reliable data transmission and timely status evaluation, providing technical guarantee for the safe operation of the bridge. The system's dynamic correction, improved detection, comprehensive evaluation and intelligent early warning functions significantly improve the detection accuracy and operation and maintenance efficiency of bridge bearings.

[0053] Although the exemplary embodiments of the present disclosure have been described, it should be understood by those skilled in the art that various changes and modifications can be made to the exemplary embodiments of the present disclosure without departing from the spirit and scope of the present disclosure in essence. Therefore, all changes and modifications are included in the scope of protection of the present disclosure as defined by the claims. The present disclosure is defined by the appended claims, and the equivalents of these claims are also included.

Claims

1. A bridge bearing detection system, characterized in that: The detection system includes a camera, an eddy current thickness sensor, an acceleration sensor, a laser Doppler vibrometer, a strain gauge, a data acquisition module, a data processing module, and a data sending module, wherein at least one camera is fixed on the side or directly above the bridge support through an adjustable bracket, and its optical axis faces the surface of the bridge support to ensure the comprehensiveness and clarity of image acquisition; the eddy current thickness sensor is installed on the metal surface of the bridge support at a position closest to the slip area, the eddy current thickness sensor faces the surface of the bridge support, and its detection head remains parallel to the metal surface being measured; the acceleration sensor is installed at the center of the top of the bridge support; the acceleration sensor is connected to the data acquisition module through a cable to output a real-time dynamic response signal; the laser Doppler vibrometer is installed on the top of the pier, facing the connection between the bridge support and the beam body, and the measuring beam is parallel to the sliding direction of the support. The laser Doppler vibrometer emits a beam perpendicular to the surface of the support through a laser transmitter, and the beam reflection signal is captured by the photoelectric receiver in the laser Doppler vibrometer and is captured by the laser The optical Doppler vibrometer calculates the displacement response of the bridge bearing under the action of dynamic load; the laser Doppler vibrometer is connected to the data acquisition module through an optical fiber cable; the strain gauges are respectively attached to the upper and lower contact surfaces and edge areas of the bridge bearing by glue and covered with heat shrink insulation material, which are used to measure the compressive stress in the vertical direction and the shear stress in the horizontal direction. Each strain gauge is connected to the data acquisition module through a cable; the data acquisition module is connected to the data processing module through an interface, and the signal of the data acquisition module interacts with the data processing module through a two-way communication protocol; the data processing module is installed in the bridge management facility, and processes the real-time data sent by each sensor for filtering, compensation and feature extraction; the data processing module is connected to the data sending module, and the detection data is transmitted to the outside through the data sending module to realize the output of the detection data; the bridge bearing detection system also includes a temperature and humidity sensor installed next to the eddy current thickness sensor, and the temperature and humidity sensor is connected to the data acquisition module to provide temperature and humidity information to the data acquisition module.

2. A bridge bearing detection system as claimed in claim 1, characterized in that: The camera is directly connected to the data acquisition module via a Gigabit Ethernet interface.

3. A bridge bearing detection system as claimed in claim 1, characterized in that: The eddy current thickness sensor is fixed by a fixture with a fixing force of 20 N·m, the distance between the sensor and the support surface is 1 mm, and the measurement accuracy is ±0.01 mm.

4. A bridge bearing detection system as claimed in claim 1, characterized in that: The eddy current thickness sensor is connected to the data acquisition module via a shielded twisted pair cable, and the shielding layer of the shielded twisted pair cable is grounded to eliminate electromagnetic interference.

5. A bridge bearing detection system as claimed in claim 1, characterized in that: The acceleration sensor is installed by using M8 stainless steel bolts to fix the sensor base on the beam body with a torque of 40 Nm, with its sensing axis aligned with the sliding direction of the support and the vertical direction.

6. A bridge bearing detection system as claimed in claim 1, characterized in that: The strain gauge is attached at a position where the edge of the support is 50 mm from the center and 20 mm from the bottom.

7. A bridge bearing detection system as claimed in claim 1, characterized in that: The data acquisition module is connected to the data processing module via an RS485 interface.

8. A multi-load joint detection method, characterized in that: Applied to the bridge bearing detection system according to any one of claims 1 to 7, the detection method comprises the following steps: Step 1: Collect images of the bridge bearing surface and perform crack detection. Use a high-resolution camera to collect images of the bearing surface, ensure that the image resolution is accurate to within 0.1 mm of the actual size of the bearing, and capture the fine features of the crack; input the collected images into the improved deep learning model, which uses a dynamic weighted loss function to assign higher priority to cracks with larger areas, while increasing edge sharpness constraints to improve the recognition accuracy of complex crack shapes; the model outputs crack length, width, and area features based on the pixel area of ​​the crack. These pixel features are converted to actual physical sizes through the camera's calibration ratio, and the crack growth trend is determined by calculating the ratio of the current crack length to the historical crack length; Step 2: Evaluate the stiffness change of the bridge bearing. Under normal operating conditions of the bridge, use acceleration sensors and laser Doppler vibrometers to synchronously collect dynamic response signals of the bridge bearing and the beam body. The change in the stiffness of the bridge bearing is comprehensively calculated by the dynamic displacement ratio and the vibration main frequency reduction ratio. The dynamic displacement ratio represents the ratio of the dynamic displacement of the bearing to the dynamic displacement of the beam body. The vibration main frequency reduction ratio quantifies the stiffness degradation by comparing the change between the current vibration main frequency and the initial main frequency. If the dynamic displacement ratio exceeds 2 times or the main frequency reduction ratio exceeds 20%, a stiffness degradation warning signal is generated. Step 3: Detect the debonding of bridge bearings and analyze the debonding phenomenon of bridge bearings through the flexibility matrix method. The flexibility matrix describes the deformation characteristics of bridge bearings after being subjected to forces in different directions. The diagonal elements reflect the direct deformation of the bridge bearings, and the non-diagonal elements reflect the coupled deformation between adjacent components. The standardized diagonal difference ratio of the flexibility matrix is ​​calculated, that is, the ratio of the difference between the diagonal elements and the non-diagonal elements to the average value of the initial diagonal elements, to quantify the degree of debonding. Combined with the dynamic response data of the vehicle impact load and the temperature fluctuation parameters, the debonding expansion trend is predicted through the machine learning model, and the percentage value of the degree of debonding is output. Step 4: Monitor the thickness change of bridge bearings and evaluate the corrosion situation. The eddy current thickness sensor monitors the thickness change of bridge bearings in real time by detecting the relationship between the sensing signal and the distance from the metal surface, and uses a nonlinear compensation model to adjust the thickness. The actual thickness value is calculated based on the quadratic relationship between temperature change and thickness error. Humidity compensation is based on the interference of humidity changes on the sensor signal, and corrections are made using experimental fitting data. When the corrosion rate exceeds 0.1 mm per year or the thickness is reduced to less than 10% of the design value, a corrosion warning is generated. Step 5: Fusion of multi-parameter data and comprehensive evaluation, integrating the test results of crack characteristics, stiffness change, voiding degree and thickness change obtained in steps 1 to 4 to generate a comprehensive health score; the scoring model dynamically assigns weights based on the impact of each parameter on bearing performance; Step 6: Generate early warning signals and provide maintenance recommendations. Generate graded early warning signals based on the health scores and test results obtained in step 5.

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