Bridge structure health monitoring system and bridge structure evaluation method

Abstract

The invention relates to bridge monitoring, in particular to a bridge structure health monitoring system and a bridge structure assessment method.The sensor layer serves as the sensing basis of the system and is used for collecting key physical signals of a bridge structure; the data acquisition layer is used for converting an analog signal acquired by the sensor layer into a digital signal and carrying out preliminary data arrangement and synchronization; the data transmission layer is used for transmitting the data processed by the data acquisition layer to the data processing layer; the data processing layer is used for carrying out preprocessing, feature extraction and feature fusion on the data sent by the data transmission layer and providing data support for subsequent bridge structure evaluation; the evaluation and early warning layer is used for carrying out state evaluation, damage identification and safety early warning on the bridge structure according to the data provided by the data processing layer; according to the technical scheme provided by the invention, the defect that the state of the bridge structure is difficult to comprehensively and accurately assess and early warn in the prior art can be effectively overcome.

Description

Technical Field

[0001] This invention relates to bridge monitoring, specifically to a bridge structural health monitoring system and a bridge structural assessment method. Background Technology

[0002] As a key component of transportation infrastructure, bridges play an irreplaceable role in economic development and social life. However, with the increase in bridge service life and the continuous increase in traffic flow and vehicle load, bridge structures are inevitably affected by various factors, such as environmental erosion, material aging, and vehicle impact. These factors can lead to varying degrees of damage and performance degradation in bridge structures.

[0003] Traditional bridge inspection methods mainly rely on regular manual inspections. This approach is not only costly in terms of manpower, resources, and time, but also has a long inspection cycle, making it difficult to monitor the health status of the bridge structure in real time. Furthermore, manual inspections are often limited by factors such as the experience and skill level of the inspectors and the accuracy of the inspection equipment, making it difficult to effectively detect damage in hidden areas and early, minor damage, which can easily leave hidden safety hazards.

[0004] With the rapid development of sensor technology, communication technology, and computer technology, bridge structural health monitoring systems have emerged. These systems utilize various sensors deployed on the bridge structure to collect key physical signals (such as strain, displacement, and vibration) in real time. Through data acquisition, transmission, and processing, they assess and provide early warnings about the bridge structure's condition. However, existing bridge structural health monitoring systems still have certain shortcomings in terms of functional integration, data processing capabilities, and the accuracy of assessment and early warning, making it difficult to meet the higher requirements of modern bridge structural health monitoring. Therefore, developing a fully functional and reliable bridge structural health monitoring system has significant practical importance and application value. Summary of the Invention

[0005] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides a bridge structure health monitoring system and a bridge structure assessment method, which can effectively overcome the defects of the existing technology in that it is difficult to comprehensively and accurately assess and warn of the state of bridge structure.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A bridge structural health monitoring system includes a sensor layer, a data acquisition layer, a data transmission layer, a data processing layer, and an assessment and early warning layer. The sensor layer forms the basis of the system's perception and is used to collect key physical signals of the bridge structure. The data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization. The data transmission layer transmits the data processed by the data acquisition layer to the data processing layer. The data processing layer preprocesses, extracts, and fuses features from the data transmitted from the data transmission layer, providing data support for subsequent bridge structure evaluation. The assessment and early warning layer, based on data provided by the data processing layer, conducts status assessments, damage identification, and safety warnings regarding the bridge structure.

[0007] Preferably, the sensor layer forms the sensing foundation of the system and is used to collect key physical signals of the bridge structure, including: Configure appropriate sensor combinations based on bridge type: 1) For suspension bridges: Wind speed and temperature sensors were installed at the top of the tower, the bridge deck, and the anchorage. Acceleration, strain, and displacement sensors were installed at key sections of each main beam. Inclinometers were installed at different heights of the tower. Corrosion sensors were installed at moisture-prone areas, including the tower foundation. 2) For cable-stayed bridges: Wind speed and temperature sensors were installed at the top of the tower and on the bridge deck, acceleration sensors were installed at key sections of each main beam and on the cables, and GPS displacement sensors were installed on the main beam to monitor changes in the beam's alignment.

[0008] Preferably, the sensor layer optimizes sensor arrangement based on the Modal Confidence Criterion (MAC), including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ; Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ; in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method.

[0009] Preferably, the sensors used in the sensor layer employ high-precision fiber optic sensing technology, and the strain displacement sensor adopts a fiber-encapsulated FBG strain sensor with high precision, good linearity and durability, and the sensitivity coefficient is calibrated by a long gauge length strain sensor high-precision calibration frame.

[0010] Preferably, the data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization, including: Multi-channel synchronous acquisition is achieved through distributed acquisition stations, ensuring the timing consistency of analog signals from each sensor and converting the analog signals from each sensor into digital signals. The obtained data is initially validated to remove erroneous data, and the remaining data is timestamped to ensure data continuity and traceability.

[0011] Preferably, the data transmission layer transmits the data processed by the data acquisition layer to the data processing layer, including: Select an appropriate data transmission method based on the site environment, and encrypt the data during the data transmission process to prevent data leakage or tampering and ensure the security of data transmission.

[0012] Preferably, the data processing layer preprocesses, extracts features, and fuses features from the data transmission layer to provide data support for subsequent bridge structure evaluation, including: The data is preprocessed, and frequency domain analysis and wavelet analysis are used for signal denoising and feature extraction. The extracted features are fused to improve the completeness and accuracy of the data, providing data support for subsequent bridge structure assessment. Among them, frequency domain analysis methods include power spectral density method and peak point ratio fluctuation method. Power spectral density method is used for random vibration analysis, peak point ratio fluctuation method is used to identify frequency domain data anomalies, and wavelet analysis method uses Db8 wavelet basis function for signal denoising and feature extraction. For cable force monitoring, the broadband frequency difference method is used to improve the traditional narrowband peak search method. The vibration spectrum is obtained by Fourier transform (FFT), the fundamental frequency is identified, and the cable force is calculated, thereby improving the accuracy of cable force identification.

[0013] Preferably, the data preprocessing includes: The system identifies data anomaly types, including missing data, drift, jump points, and noise interference, and uses an anomaly detection method based on logical group correlation analysis for data cleaning. Simultaneously, LabVIEW and Matlab are used for online data processing to achieve real-time data correction and filtering.

[0014] Preferably, the assessment and early warning layer performs a status assessment, damage identification, and safety warning of the bridge structure based on the data provided by the data processing layer, including: Condition assessment: A hierarchical assessment system is constructed based on the assessment criteria, including bridge components, parts, bridge deck system, superstructure and substructure. The assessment model of first assessing each part and then integrating them is adopted, taking into account the weight of each component, conducting quantitative scoring, and deducting points based on the most severe level of different defects. Damage identification: Combining time-varying reliability analysis, incremental static analysis and stochastic finite element method, the reliability of bridge structures under different working conditions is evaluated and potential damage is identified; Safety warning: Establish a three-level alarm system of yellow, orange and red. Yellow alarm indicates that the data is different from the normal level. Orange alarm indicates that the worst combination of operating conditions is exceeded. Red alarm indicates that the design is exceeded or the specifications are exceeded. Based on the condition assessment and damage identification results, the corresponding warning signals will be issued in a timely manner to guide maintenance decisions.

[0015] A bridge structure evaluation method includes the following steps: S11. Optimize sensor placement based on the Modal Confidence Criterion (MAC) and utilize sensor combinations to acquire key physical signals of the bridge structure, including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ; Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ; in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method; S4. Use a combination of sensors to collect key physical signals of the bridge structure; S12. Convert the analog signals collected by the sensor combination into digital signals, and perform preliminary data processing and synchronization; S13. Perform preprocessing, feature extraction, and feature fusion on the data to provide data support for subsequent bridge structure evaluation; S14. Based on the fusion characteristics, conduct condition assessment, damage identification, and safety early warning of the bridge structure.

[0016] (III) Beneficial Effects Compared with the prior art, the bridge structural health monitoring system and bridge structural assessment method provided by the present invention have the following beneficial effects: 1) Accurate data collection and processing to improve data quality. The sensor layer rationally configures sensor combinations for different types of bridges and optimizes sensor layout based on the Modal Confidence Ratio (MAC) criterion. High-precision fiber optic sensing technology is employed to ensure the accuracy and reliability of the collected key physical signals of the bridge structure. The data acquisition layer achieves multi-channel synchronous acquisition through distributed acquisition stations and performs preliminary verification and timestamping of the data to ensure continuity and traceability. The data processing layer employs various methods for signal denoising, feature extraction, and fusion. Furthermore, algorithms for cable tension monitoring are improved to further enhance cable tension identification accuracy. These measures comprehensively guarantee data quality from the source to the processing stage, providing a solid foundation for subsequent analysis and evaluation. 2) Conduct comprehensive monitoring of the bridge structure to understand its health status. Based on the characteristics of different bridge types such as suspension bridges and cable-stayed bridges, the sensor layer deploys various types of sensors at key locations such as the tower tops, bridge decks, anchorages, key sections of the main beams, pylons, and cables. This comprehensively collects key physical signals of the bridge structure, such as wind speed, temperature, acceleration, strain, displacement, tilt, and corrosion. This all-round monitoring layout can cover all important aspects of the bridge structure, obtain rich structural information, and enable the system to fully understand the health status of the bridge under different working conditions and environments, and promptly detect potential problems. 3) Timely and accurate assessment and early warning to ensure safe operation. The assessment and early warning layer has established a comprehensive assessment and early warning system. In terms of condition assessment, a hierarchical assessment system is constructed based on the evaluation standards, and a quantitative scoring method is adopted, which first assesses the components and then integrates them. Damage identification combines multiple methods to assess the reliability of the bridge structure and identify potential damage. Safety early warning establishes a three-level alarm system of yellow, orange, and red, and issues corresponding early warning signals in a timely manner based on the condition assessment and damage identification results. This comprehensive and detailed assessment and early warning mechanism can promptly detect potential safety hazards in the bridge structure, provide a scientific basis for maintenance decisions, and effectively ensure the safe operation of the bridge. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram illustrating the sensor arrangement optimization based on the Modal Confidence Criterion (MAC) in the sensor layer of this invention. Figure 3 This is a graph showing the real-time data fluctuations during online data processing in this invention; Figure 4 This is a diagram showing the cable force monitoring results based on vibration spectrum analysis in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0020] The following describes the specific functional modules of a bridge structural health monitoring system provided by the present invention, using concrete examples (such as...). Figure 1 (As shown). The system's functional modules include: sensor layer, data acquisition layer, data transmission layer, data processing layer, and evaluation and early warning layer; The sensor layer forms the basis of the system's perception and is used to collect key physical signals of the bridge structure. The data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization. The data transmission layer transmits the data processed by the data acquisition layer to the data processing layer. The data processing layer preprocesses, extracts, and fuses features from the data transmitted from the data transmission layer, providing data support for subsequent bridge structure evaluation. The assessment and early warning layer, based on data provided by the data processing layer, conducts status assessments, damage identification, and safety warnings regarding the bridge structure.

[0021] I. Sensor Layer The sensor layer forms the foundation of the system's perception, used to acquire key physical signals of the bridge structure, including: Configure appropriate sensor combinations based on bridge type: 1) For suspension bridges: Wind speed and temperature sensors were installed at the top of the tower, the bridge deck, and the anchorage. Acceleration, strain, and displacement sensors were installed at key sections of each main beam. Inclinometers were installed at different heights of the tower. Corrosion sensors were installed at moisture-prone areas, including the tower foundation. 2) For cable-stayed bridges: Wind speed and temperature sensors were installed at the top of the tower and on the bridge deck, acceleration sensors were installed at key sections of each main beam and on the cables, and GPS displacement sensors were installed on the main beam to monitor changes in the beam's alignment.

[0022] The sensor layer optimizes sensor placement based on the modal confidence criterion (MAC), including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ; Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ; in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method (e.g., ... Figure 2 (As shown).

[0023] The sensors used in the sensor layer employ high-precision fiber optic sensing technology. The strain displacement sensor uses a fiber-encapsulated FBG strain sensor with high precision, good linearity, and durability (its durability has been verified through acid, alkali, and salt corrosion tests, freeze-thaw cycle tests, and damp heat aging tests). The sensitivity coefficient is calibrated using a high-precision calibration frame for long gauge-length strain sensors.

[0024] II. Data Acquisition Layer The data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization, including: Multi-channel synchronous acquisition is achieved through distributed acquisition stations, ensuring the timing consistency of analog signals from each sensor and converting the analog signals from each sensor into digital signals. The obtained data is initially validated to remove erroneous data, and the remaining data is timestamped to ensure data continuity and traceability.

[0025] III. Data Transmission Layer The data transmission layer transmits the data processed by the data acquisition layer to the data processing layer, including: Select an appropriate data transmission method based on the site environment, and encrypt the data during the data transmission process to prevent data leakage or tampering and ensure the security of data transmission.

[0026] IV. Data Processing Layer The data processing layer preprocesses, extracts, and fuses features from the data transmission layer, providing data support for subsequent bridge structure evaluation, including: The data is preprocessed, and frequency domain analysis and wavelet analysis are used for signal denoising and feature extraction. The extracted features are fused to improve the completeness and accuracy of the data, providing data support for subsequent bridge structure assessment. Among them, frequency domain analysis methods include power spectral density method and peak point ratio fluctuation method. Power spectral density method is used for random vibration analysis, peak point ratio fluctuation method is used to identify frequency domain data anomalies, and wavelet analysis method uses Db8 wavelet basis function for signal denoising and feature extraction. like Figure 4 As shown, for cable force monitoring, the broadband frequency difference method is used to improve the traditional narrowband peak search method. The vibration spectrum is obtained by Fourier transform (FFT), the fundamental frequency is identified, and the cable force is calculated, thereby improving the cable force identification accuracy.

[0027] Specifically, data preprocessing includes: Data anomaly types, including missing data, drift, jump points, and noise interference, are identified. An anomaly detection method based on logical group correlation analysis is used for data cleaning. Simultaneously, online data processing is performed using LabVIEW and Matlab (e.g.,...). Figure 3 As shown in the figure, real-time data correction and filtering are achieved.

[0028] V. Assessment and Early Warning Layer The assessment and early warning layer, based on data provided by the data processing layer, performs status assessments, damage identification, and safety warnings regarding the bridge structure, including: Condition assessment: A hierarchical assessment system is constructed based on the assessment standard (JTG / T H21-2011 "Technical Condition Assessment Standard for Highway Bridges"). The system includes bridge components, parts, bridge deck system, superstructure and substructure. The assessment mode of first assessing each part and then integrating them is adopted. The weight of each component is considered, quantitative scoring is carried out, and points are deducted in combination with the most severe level of different defects. Damage identification: By combining time-varying reliability analysis (reliability index calculation considering material degradation and load changes), incremental static analysis, and stochastic finite element method, the reliability of bridge structures under different working conditions is evaluated and potential damage is identified. Safety warning: Establish a three-level alarm system of yellow, orange and red. Yellow alarm indicates that the data is different from the normal level. Orange alarm indicates that the worst combination of operating conditions is exceeded. Red alarm indicates that the design is exceeded or the specifications are exceeded. Based on the condition assessment and damage identification results, the corresponding warning signals will be issued in a timely manner to guide maintenance decisions.

[0029] Based on the aforementioned bridge structural health monitoring system, this application also discloses a bridge structural assessment method, comprising the following steps: S11. Optimize sensor placement based on the Modal Confidence Criterion (MAC) and utilize sensor combinations to acquire key physical signals of the bridge structure, including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ; Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ; in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method; S4. Use a combination of sensors to collect key physical signals of the bridge structure; S12. Convert the analog signals collected by the sensor combination into digital signals, and perform preliminary data processing and synchronization; S13. Perform preprocessing, feature extraction, and feature fusion on the data to provide data support for subsequent bridge structure evaluation; S14. Based on the fusion characteristics, conduct condition assessment, damage identification, and safety early warning of the bridge structure.

[0030] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge structural health monitoring system, characterized in that: It includes a sensor layer, a data acquisition layer, a data transmission layer, a data processing layer, and an assessment and early warning layer; The sensor layer forms the basis of the system's perception and is used to collect key physical signals of the bridge structure. The data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization. The data transmission layer transmits the data processed by the data acquisition layer to the data processing layer. The data processing layer preprocesses, extracts, and fuses features from the data transmitted from the data transmission layer, providing data support for subsequent bridge structure evaluation. The assessment and early warning layer, based on data provided by the data processing layer, conducts status assessments, damage identification, and safety warnings regarding the bridge structure.

2. The bridge structural health monitoring system according to claim 1, characterized in that: The sensor layer forms the basis of the system's perception, used to collect key physical signals of the bridge structure, including: Configure appropriate sensor combinations based on bridge type: 1) For suspension bridges: Wind speed and temperature sensors were installed at the top of the tower, the bridge deck, and the anchorage. Acceleration, strain, and displacement sensors were installed at key sections of each main beam. Inclinometers were installed at different heights of the tower. Corrosion sensors were installed at moisture-prone areas, including the tower foundation. 2) For cable-stayed bridges: Wind speed and temperature sensors were installed at the top of the tower and on the bridge deck, acceleration sensors were installed at key sections of each main beam and on the cables, and GPS displacement sensors were installed on the main beam to monitor changes in the beam's alignment.

3. The bridge structural health monitoring system according to claim 2, characterized in that: The sensor layer optimizes sensor placement based on the Modal Confidence Criterion (MAC), including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ； Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ； in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method.

4. The bridge structural health monitoring system according to claim 3, characterized in that: The sensors used in the sensor layer employ high-precision fiber optic sensing technology. The strain displacement sensor uses a fiber-encapsulated FBG strain sensor with high precision, good linearity, and durability, and its sensitivity coefficient is calibrated using a long gauge length strain sensor high-precision calibration frame.

5. The bridge structural health monitoring system according to claim 1, characterized in that: The data acquisition layer converts the analog signals acquired by the sensor layer into digital signals and performs preliminary data processing and synchronization, including: Multi-channel synchronous acquisition is achieved through distributed acquisition stations, ensuring the timing consistency of analog signals from each sensor and converting the analog signals from each sensor into digital signals. The obtained data is initially validated to remove erroneous data, and the remaining data is timestamped to ensure data continuity and traceability.

6. The bridge structural health monitoring system according to claim 1, characterized in that: The data transmission layer transmits the data processed by the data acquisition layer to the data processing layer, including: Select an appropriate data transmission method based on the site environment, and encrypt the data during the data transmission process to prevent data leakage or tampering and ensure the security of data transmission.

7. The bridge structural health monitoring system according to claim 1, characterized in that: The data processing layer preprocesses, extracts, and fuses features from the data transmitted by the data transmission layer, providing data support for subsequent bridge structure evaluation, including: The data is preprocessed, and frequency domain analysis and wavelet analysis are used for signal denoising and feature extraction. The extracted features are fused to improve the completeness and accuracy of the data, providing data support for subsequent bridge structure assessment. Among them, frequency domain analysis methods include power spectral density method and peak point ratio fluctuation method. Power spectral density method is used for random vibration analysis, peak point ratio fluctuation method is used to identify frequency domain data anomalies, and wavelet analysis method uses Db8 wavelet basis function for signal denoising and feature extraction. For cable force monitoring, the broadband frequency difference method is used to improve the traditional narrowband peak search method. The vibration spectrum is obtained by Fourier transform (FFT), the fundamental frequency is identified, and the cable force is calculated, thereby improving the accuracy of cable force identification.

8. The bridge structural health monitoring system according to claim 7, characterized in that: The data preprocessing includes: The system identifies data anomaly types, including missing data, drift, jump points, and noise interference, and uses an anomaly detection method based on logical group correlation analysis for data cleaning. Simultaneously, LabVIEW and Matlab are used for online data processing to achieve real-time data correction and filtering.

9. The bridge structural health monitoring system according to claim 1, characterized in that: The data preprocessing includes: the assessment and early warning layer performs a status assessment, damage identification, and safety early warning of the bridge structure based on the data provided by the data processing layer, including: Condition assessment: A hierarchical assessment system is constructed based on the assessment criteria, including bridge components, parts, bridge deck system, superstructure and substructure. The assessment model of first assessing each part and then integrating them is adopted, taking into account the weight of each component, conducting quantitative scoring, and deducting points based on the most severe level of different defects. Damage identification: Combining time-varying reliability analysis, incremental static analysis and stochastic finite element method, the reliability of bridge structures under different working conditions is evaluated and potential damage is identified; Safety warning: Establish a three-level alarm system of yellow, orange and red. Yellow alarm indicates that the data is different from the normal level. Orange alarm indicates that the worst combination of operating conditions is exceeded. Red alarm indicates that the design is exceeded or the specifications are exceeded. Based on the condition assessment and damage identification results, the corresponding warning signals will be issued in a timely manner to guide maintenance decisions.

10. A bridge structure assessment method, using the bridge structure health monitoring system of claim 1, characterized in that: Includes the following steps: S11. Optimize sensor placement based on the Modal Confidence Criterion (MAC) and utilize sensor combinations to acquire key physical signals of the bridge structure, including: S1. The objective function F is to minimize the off-diagonal elements of the MAC matrix: ； Where m is the current number of candidate measurement points, and MAC(i,j) is the MAC value between the i-th measurement point and the j-th measurement point. , , These are the modal shape vectors of the i-th and j-th measurement points, respectively. S2. Make the MAC matrix tend to be orthogonal by increasing or decreasing the degrees of freedom of the measuring points: ； in, Kronek function, ; S3. Determine the optimal measurement point location using the step-by-step addition method; S4. Use a combination of sensors to collect key physical signals of the bridge structure; S12. Convert the analog signals collected by the sensor combination into digital signals, and perform preliminary data processing and synchronization; S13. Perform preprocessing, feature extraction, and feature fusion on the data to provide data support for subsequent bridge structure evaluation; S14. Based on the fusion characteristics, conduct condition assessment, damage identification, and safety early warning of the bridge structure.

Images