Expressway bridge deformation detection and early warning method based on cold region environment

Abstract

The invention provides an expressway bridge deformation detection early warning method based on a cold region environment, which relates to the technical field of bridge structure health monitoring and comprises the steps of monitoring point arrangement and equipment installation, real-time data acquisition and transmission, data processing and analysis, early warning threshold setting and early warning grading, early warning response and disposal, and maintenance and updating. Through the temperature compensation and protection device, the influence of severe environments such as low temperature, wind and snow in the cold region on the performance of the sensor is effectively solved, long-term stable operation of the monitoring system is ensured, and the micro deformation of the bridge in the complex environment of the cold region can be comprehensively and accurately captured by adopting multi-source sensor data fusion and a deep learning algorithm. Through scientific and reasonable early warning threshold setting and a grading early warning mechanism and in combination with the bridge deformation prediction model, potential safety hazards of the bridge can be found in time, early warning signals are accurately sent out, and powerful guarantee is provided for safe operation of the bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure health monitoring, and in particular to a highway bridge deformation detection and early warning method based on a cold region environment. Background Art

[0002] Cold regions are characterized by low temperatures, large temperature swings, frequent freeze-thaw cycles, and complex wind and snow loads. These factors significantly impact highway bridge structures. Low temperatures alter the mechanical properties of bridge construction materials, causing steel to become brittle and concrete shrinkage and creep to intensify. Freeze-thaw cycles can damage concrete structures and corrode steel reinforcement. Frequent wind and snow loads also place greater stress on bridges.

[0003] At present, traditional bridge deformation detection methods have many shortcomings in cold environment. For example, conventional displacement sensors may experience reduced accuracy or even failure at low temperatures. A single detection method is difficult to fully capture the subtle deformation of bridges under the influence of complex environmental factors in cold regions, and cannot provide timely and accurate warning of bridge deformation. This poses a great hidden danger to the safe operation of highway bridges in cold regions and seriously threatens the safety of life and property of passing vehicles and personnel. To this end, we propose a highway bridge deformation detection and early warning method based on cold environment to address the above problems. Summary of the Invention

[0004] The problem to be solved by the present invention is that the existing bridge deformation detection method uses a single detection means, which is difficult to fully capture the subtle deformation of the bridge under the influence of complex environmental factors in cold regions and cannot provide timely and accurate early warning of bridge deformation.

[0005] In order to solve the above technical problems, the present invention provides a highway bridge deformation detection and early warning method based on a cold region environment, the detection and early warning method specifically includes: S1: Monitoring point layout and equipment installation: set up monitoring points at important locations on key parts of the bridge and install monitoring equipment at the monitoring points; S2: Real-time data collection and transmission, using monitoring equipment to continuously record bridge displacement, vibration and other signals at a certain frequency, and transmit the data to a computer or central control system for processing through wireless communication technology; S3: Data processing and analysis: pre-processing the collected raw data and analyzing the processed data to determine the status of the bridge; S4: Warning threshold setting and warning classification, setting warning thresholds based on bridge design parameters and historical data; S5: Early warning response and disposal, comparing the actual measured displacement or acceleration signal with the set threshold. If it exceeds the threshold, it is determined to be in an early warning state; S6: Maintenance and update: regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy; Preferably, the monitoring point arrangement and equipment installation specifically include: S101: Monitoring point selection: Set up monitoring points at key locations on the bridge. Determine the unit length along the longitudinal direction of the bridge. Arrange comprehensive detection and early warning modules at the central divider. The locations of these modules must be marked in the central control system. S102: Sensor placement: Sensors are scientifically arranged at key locations on the bridge. Inclinometers are installed at the bottom and middle of the piers to monitor changes in their inclination angles. Distributed fiber optic sensors and accelerometers are installed at the mid-span and support points of the main beams. Distributed fiber optic sensors can monitor the strain distribution of the bridge structure in real time, while accelerometers can capture the vibration response of the bridge under vehicle loads, wind loads, and other factors. In addition, high-precision total stations and levels are installed in stable areas around the bridge to regularly measure the overall shape of the bridge as auxiliary calibration data. S103: Temperature compensation and protection device. For low temperature environments in cold areas, the sensor is equipped with a dedicated temperature compensation device. The sensor is wrapped with a heating film, and the temperature around the sensor is monitored in real time through a temperature control system. When the temperature is lower than the set threshold, the heating film is automatically started to heat the sensor to maintain the working environment temperature within the normal operating range. At the same time, a cold-proof and waterproof protective shell is installed for the sensor to prevent wind, snow, and frost from causing physical damage and electrical interference to the sensor.

[0006] Preferably, the real-time collection and transmission of the data specifically includes: S201: Multi-source data acquisition: Establish a multi-source data acquisition mechanism. Various sensors collect data at different frequencies. Inclinometers and accelerometers collect dynamic data in real time at a higher frequency to capture the instantaneous deformation and vibration of the bridge. Distributed fiber optic sensors collect static strain data at a relatively low frequency. High-precision total stations and levels measure the overall shape of the bridge once a week to obtain macroscopic deformation data of the bridge. S202: Data transmission network: Build a hybrid data transmission network. For sensor data with short distances and small data volumes, use low-power Bluetooth or Zigbee wireless transmission technology to transmit it to the data aggregation node. For information with large data volumes such as distributed fiber optic sensors, use a dedicated fiber optic network for transmission. High-precision total station and level measurement data are collected manually on-site and uploaded to the cloud server or central control system via the 4G / 5G wireless network.

[0007] Preferably, the data processing and analysis specifically include: S301: Data preprocessing: using wavelet transform algorithm to filter sensor data, remove environmental noise and interference signals, and using normalization method to unify different types of sensor data into the same numerical range to facilitate subsequent analysis; S302: Feature extraction and fusion: Extracting feature parameters related to bridge deformation from pre-processed data; S303: Deformation Analysis and Prediction Model: Establish a bridge deformation analysis and prediction model based on deep learning. Use the long short-term memory network algorithm, combined with cold region environmental parameters and bridge structural mechanics models, to predict bridge deformation trends. Input multi-source fusion data collected in real time into the model. Through model training and optimization, achieve high-precision prediction of bridge deformation.

[0008] Preferably, the warning threshold setting and warning classification specifically include: S401: Threshold setting method, which comprehensively considers bridge design specifications, historical monitoring data and cold region environmental factors, and uses a combination of statistical analysis and mechanical calculation to set the warning threshold; For the serviceability limit state, the allowable deformation range of each part of the bridge is calculated by conducting finite element analysis on the bridge structure and combining it with the load conditions in cold regions. For the ultimate bearing capacity state, corresponding thresholds are set according to the safety reserve requirements of the bridge structure. At the same time, statistical analysis of historical monitoring data is conducted to determine the deformation fluctuation range of the bridge under normal operation, and the warning threshold is adjusted based on this; S402: Warning grading mechanism: Warnings are divided into four levels, namely blue warning, yellow warning, orange warning and red warning; A blue warning indicates that the bridge has experienced minor deformation, possibly caused by environmental factors, and requires increased daily monitoring; A yellow warning indicates that the deformation trend has intensified and a professional on-site inspection is required; Orange warning means that the deformation of the bridge is close to the allowable limit and traffic restrictions such as speed and load restrictions need to be taken; A red alert indicates that the deformation of the bridge exceeds the allowable limit and there is a serious safety hazard. Traffic must be closed immediately for comprehensive inspection and maintenance.

[0009] Preferably, the early warning response and disposal specifically include: S501: Early Warning Notification System: Establish a multi-channel early warning notification system. When the system triggers an early warning, early warning information will be pushed to bridge management departments, maintenance units, traffic management departments, and relevant responsible persons in real time through various means such as SMS, email, WeChat public accounts, and dedicated bridge monitoring apps. The early warning information includes detailed information such as the warning level, bridge location, deformation parameters, and recommended treatment measures; S502: Emergency Response Process: Develop a comprehensive emergency response process and take appropriate measures based on different warning levels; During a blue alert, increase the frequency of bridge inspections and closely monitor the development of deformation; When a yellow alert is issued, bridge structure experts will be organized to conduct on-site inspections and assessments of bridges and develop targeted monitoring and maintenance plans; During an orange alert, traffic control measures such as speed and load limits will be implemented on bridges in accordance with the traffic management plan, and professional teams will be arranged for 24-hour real-time monitoring; When a red alert is issued, the bridge traffic will be immediately closed, vehicles and personnel on the bridge will be evacuated, comprehensive structural inspections and safety assessments will be organized, and an emergency repair and reinforcement plan will be formulated.

[0010] Preferably, the maintenance and updating specifically include: S601: Subsequent monitoring: After the warning is lifted, continue to monitor the bridge to ensure its safety; S602: Subsequent maintenance: Regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy.

[0011] The technical effects and advantages of the present invention are as follows: Through temperature compensation and protection devices, the present invention effectively solves the impact of harsh environments such as low temperatures, wind and snow in cold regions on sensor performance, ensuring the long-term stable operation of the monitoring system. By adopting multi-source sensor data fusion and deep learning algorithms, it can comprehensively and accurately capture the tiny deformations of bridges in complex environments in cold regions, thereby improving the accuracy and reliability of deformation detection.

[0012] The present invention uses a scientific and reasonable warning threshold setting and a graded warning mechanism, combined with a bridge deformation prediction model, to promptly detect potential safety hazards in bridges and accurately issue warning signals, providing strong guarantees for the safe operation of bridges. It also adopts a complete warning notification system and emergency response process to ensure that corresponding measures can be taken quickly at different warning levels, minimize the occurrence of bridge safety accidents, and protect people's lives and property and smooth traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0014] The present invention provides a highway bridge deformation detection and early warning method based on cold region environment, such as Figure 1 As shown, the detection and early warning method specifically includes: S1: Monitoring point layout and equipment installation: set up monitoring points at important locations on key parts of the bridge and install monitoring equipment at the monitoring points; Furthermore, the monitoring point arrangement and equipment installation specifically include: S101: Monitoring point selection: Set up monitoring points at key locations on the bridge. Determine the unit length along the longitudinal direction of the bridge. Arrange comprehensive detection and early warning modules at the central divider. The locations of these modules must be marked in the central control system. S102: Sensor placement: Sensors are scientifically arranged at key locations on the bridge. Inclinometers are installed at the bottom and middle of the piers to monitor changes in their inclination angles. Distributed fiber optic sensors and accelerometers are installed at the mid-span and support points of the main beams. Distributed fiber optic sensors can monitor the strain distribution of the bridge structure in real time, while accelerometers can capture the vibration response of the bridge under vehicle loads, wind loads, and other factors. In addition, high-precision total stations and levels are installed in stable areas around the bridge to regularly measure the overall shape of the bridge as auxiliary calibration data. S103: Temperature compensation and protection device. For low-temperature environments in cold regions, the sensor is equipped with a dedicated temperature compensation device. The sensor is wrapped with a heating film, and the temperature around the sensor is monitored in real time through a temperature control system. When the temperature falls below the set threshold, the heating film is automatically activated to heat the sensor to maintain the working environment temperature within the normal operating range (e.g., -10°C to 40°C). At the same time, the sensor is installed with a cold-proof and waterproof protective shell to prevent wind, snow, and frost from causing physical damage to the sensor and electrical interference.

[0015] S2: Real-time data collection and transmission, using monitoring equipment to continuously record bridge displacement, vibration and other signals at a certain frequency, and transmit the data to a computer or central control system for processing through wireless communication technology; Furthermore, the real-time collection and transmission of the data specifically includes: S201: Multi-source data acquisition: Establish a multi-source data acquisition mechanism. Various sensors collect data at different frequencies. Inclinometers and accelerometers collect dynamic data in real time at a higher frequency (e.g., 100 Hz) to capture the instantaneous deformation and vibration of the bridge. Distributed fiber optic sensors collect static strain data at a relatively low frequency (e.g., 1 Hz). High-precision total stations and levels measure the overall shape of the bridge once a week to obtain macroscopic deformation data of the bridge. S202: Data transmission network: Build a hybrid data transmission network. For sensor data with short distances and small data volumes (such as inclinometers and accelerometers), use low-power Bluetooth or Zigbee wireless transmission technology to transmit it to the data aggregation node; for information with large data volumes such as distributed fiber optic sensors, use a dedicated fiber optic network to transmit it; and for high-precision total station and level measurement data, after manual on-site collection, upload it to the cloud server or central control system via the 4G / 5G wireless network.

[0016] S3: Data processing and analysis: pre-processing the collected raw data and analyzing the processed data to determine the status of the bridge; Furthermore, the data processing and analysis specifically include: S301: Data preprocessing: using wavelet transform algorithm to filter sensor data, remove environmental noise and interference signals, and using normalization method to unify different types of sensor data into the same numerical range to facilitate subsequent analysis; S302: Feature Extraction and Fusion: Extract characteristic parameters related to bridge deformation from preprocessed data. For example, the inclination angle and rate of change of bridge piers are extracted from inclinometer data; bridge vibration frequency and amplitude are extracted from accelerometer data; and strain distribution characteristics are extracted from distributed fiber optic sensor data. The multi-source feature data is then fused using the Deutsche Störgau evidence theory to improve data reliability and accuracy. S303: Deformation Analysis and Prediction Model: Establish a bridge deformation analysis and prediction model based on deep learning. Using the long short-term memory network (LSTM) algorithm, combined with cold region environmental parameters (such as temperature, snowfall, number of freeze-thaw cycles, etc.) and bridge structural mechanics models, predict bridge deformation trends. Input multi-source fusion data collected in real time into the model. Through model training and optimization, achieve high-precision prediction of bridge deformation.

[0017] S4: Warning threshold setting and warning classification, setting warning thresholds based on bridge design parameters and historical data; Furthermore, the warning threshold setting and warning classification specifically include: S401: Threshold setting method: This method comprehensively considers bridge design specifications, historical monitoring data, and cold-region environmental factors, and uses a combination of statistical analysis and mechanical calculations to set warning thresholds. For the normal service limit state, finite element analysis of the bridge structure is performed, combined with cold-region environmental load conditions, to calculate the allowable deformation range of each bridge part. For the bearing capacity limit state, the corresponding threshold is set based on the safety reserve requirements of the bridge structure. At the same time, statistical analysis of historical monitoring data is performed to determine the deformation fluctuation range of the bridge under normal operation, and the warning threshold is adjusted based on this. S402: Warning grading mechanism: Warnings are divided into four levels, namely blue warning, yellow warning, orange warning and red warning; A blue warning indicates that the bridge has experienced minor deformation, possibly caused by environmental factors, and requires increased daily monitoring; A yellow warning indicates that the deformation trend has intensified and a professional on-site inspection is required; Orange warning means that the deformation of the bridge is close to the allowable limit and traffic restrictions such as speed and load restrictions need to be taken; A red alert indicates that the deformation of the bridge exceeds the allowable limit and there is a serious safety hazard. Traffic must be closed immediately for comprehensive inspection and maintenance.

[0018] S5: Early warning response and disposal, comparing the actual measured displacement or acceleration signal with the set threshold. If it exceeds the threshold, it is determined to be in an early warning state; Furthermore, the early warning response and disposal specifically include: S501: Early Warning Notification System: Establish a multi-channel early warning notification system. When the system triggers an early warning, early warning information will be pushed to bridge management departments, maintenance units, traffic management departments, and relevant responsible persons in real time through various means such as SMS, email, WeChat public accounts, and dedicated bridge monitoring apps. The early warning information includes detailed information such as the warning level, bridge location, deformation parameters, and recommended treatment measures; S502: Emergency Response Process: Develop a comprehensive emergency response process and take appropriate measures based on different warning levels; During a blue alert, increase the frequency of bridge inspections and closely monitor the development of deformation; When a yellow alert is issued, bridge structure experts will be organized to conduct on-site inspections and assessments of bridges and develop targeted monitoring and maintenance plans; During an orange alert, traffic control measures such as speed and load limits will be implemented on bridges in accordance with the traffic management plan, and professional teams will be arranged for 24-hour real-time monitoring; When a red alert is issued, the bridge traffic will be immediately closed, vehicles and personnel on the bridge will be evacuated, comprehensive structural inspections and safety assessments will be organized, and an emergency repair and reinforcement plan will be formulated.

[0019] S6: Maintenance and update: regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy.

[0020] Furthermore, the maintenance and update specifically include: S601: Subsequent monitoring: After the warning is lifted, continue to monitor the bridge to ensure its safety; S602: Subsequent maintenance: Regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy.

[0021] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A highway bridge deformation detection and early warning method based on a cold region environment, characterized by: The detection and early warning method specifically includes: S1: Monitoring point layout and equipment installation: set up monitoring points at important locations on key parts of the bridge and install monitoring equipment at the monitoring points; S2: Real-time data collection and transmission, using monitoring equipment to continuously record bridge displacement, vibration and other signals at a certain frequency, and transmit the data to a computer or central control system for processing through wireless communication technology; S3: Data processing and analysis: pre-processing the collected raw data and analyzing the processed data to determine the status of the bridge; S4: Warning threshold setting and warning classification, setting warning thresholds based on bridge design parameters and historical data; S5: Early warning response and disposal, comparing the actual measured displacement or acceleration signal with the set threshold. If it exceeds the threshold, it is determined to be in an early warning state; S6: Maintenance and update: regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy.

2. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The monitoring point arrangement and equipment installation specifically include: S101: Monitoring point selection: Set up monitoring points at key locations on the bridge. Determine the unit length along the longitudinal direction of the bridge. Arrange comprehensive detection and early warning modules at the central divider. The locations of these modules must be marked in the central control system. S102: Sensor placement: Sensors are scientifically arranged at key locations on the bridge. Inclinometers are installed at the bottom and middle of the piers to monitor changes in their inclination angles. Distributed fiber optic sensors and accelerometers are installed at the mid-span and support points of the main beams. Distributed fiber optic sensors can monitor the strain distribution of the bridge structure in real time, while accelerometers can capture the vibration response of the bridge under vehicle loads, wind loads, and other factors. In addition, high-precision total stations and levels are installed in stable areas around the bridge to regularly measure the overall shape of the bridge as auxiliary calibration data. S103: Temperature compensation and protection device. For low temperature environments in cold areas, the sensor is equipped with a dedicated temperature compensation device. The sensor is wrapped with a heating film, and the temperature around the sensor is monitored in real time through a temperature control system. When the temperature is lower than the set threshold, the heating film is automatically started to heat the sensor to maintain the working environment temperature within the normal operating range. At the same time, a cold-proof and waterproof protective shell is installed for the sensor to prevent wind, snow, and frost from causing physical damage and electrical interference to the sensor.

3. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The real-time collection and transmission of the data specifically includes: S201: Multi-source data acquisition: Establish a multi-source data acquisition mechanism. Various sensors collect data at different frequencies. Inclinometers and accelerometers collect dynamic data in real time at a higher frequency to capture the instantaneous deformation and vibration of the bridge. Distributed fiber optic sensors collect static strain data at a relatively low frequency. High-precision total stations and levels measure the overall shape of the bridge once a week to obtain macroscopic deformation data of the bridge. S202: Data transmission network: Build a hybrid data transmission network. For sensor data with short distances and small data volumes, use low-power Bluetooth or Zigbee wireless transmission technology to transmit it to the data aggregation node. For information with large data volumes such as distributed fiber optic sensors, use a dedicated fiber optic network for transmission. High-precision total station and level measurement data are collected manually on-site and uploaded to the cloud server or central control system via the 4G / 5G wireless network.

4. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The data processing and analysis specifically include: S301: Data preprocessing: using wavelet transform algorithm to filter sensor data, remove environmental noise and interference signals, and using normalization method to unify different types of sensor data into the same numerical range to facilitate subsequent analysis; S302: Feature extraction and fusion: Extracting feature parameters related to bridge deformation from pre-processed data; S303: Deformation Analysis and Prediction Model: Establish a bridge deformation analysis and prediction model based on deep learning. Use the long short-term memory network algorithm, combined with cold region environmental parameters and bridge structural mechanics models, to predict bridge deformation trends. Input multi-source fusion data collected in real time into the model. Through model training and optimization, achieve high-precision prediction of bridge deformation.

5. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The warning threshold setting and warning classification specifically include: S401: Threshold setting method, which comprehensively considers bridge design specifications, historical monitoring data and cold region environmental factors, and uses a combination of statistical analysis and mechanical calculation to set the warning threshold; For the serviceability limit state, the allowable deformation range of each part of the bridge is calculated by conducting finite element analysis on the bridge structure and combining it with the load conditions in cold regions. For the ultimate bearing capacity state, corresponding thresholds are set according to the safety reserve requirements of the bridge structure. At the same time, statistical analysis of historical monitoring data is conducted to determine the deformation fluctuation range of the bridge under normal operation, and the warning threshold is adjusted based on this; S402: Warning grading mechanism: Warnings are divided into four levels, namely blue warning, yellow warning, orange warning and red warning; A blue warning indicates that the bridge has experienced minor deformation, possibly caused by environmental factors, and requires increased daily monitoring; A yellow warning indicates that the deformation trend has intensified and a professional on-site inspection is required; Orange warning means that the deformation of the bridge is close to the allowable limit and traffic restrictions such as speed and load restrictions need to be taken; A red alert indicates that the deformation of the bridge exceeds the allowable limit and there is a serious safety hazard. Traffic must be closed immediately for comprehensive inspection and maintenance.

6. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The early warning response and disposal specifically include: S501: Early Warning Notification System: Establish a multi-channel early warning notification system. When the system triggers an early warning, early warning information will be pushed to bridge management departments, maintenance units, traffic management departments, and relevant responsible persons in real time through various means such as SMS, email, WeChat public accounts, and dedicated bridge monitoring apps. The early warning information includes detailed information such as the warning level, bridge location, deformation parameters, and recommended treatment measures; S502: Emergency Response Process: Develop a comprehensive emergency response process and take appropriate measures based on different warning levels; During a blue alert, increase the frequency of bridge inspections and closely monitor the development of deformation; When a yellow alert is issued, bridge structure experts will be organized to conduct on-site inspections and assessments of bridges and develop targeted monitoring and maintenance plans; During an orange alert, traffic control measures such as speed and load limits will be implemented on bridges in accordance with the traffic management plan, and professional teams will be arranged for 24-hour real-time monitoring; When a red alert is issued, the bridge traffic will be immediately closed, vehicles and personnel on the bridge will be evacuated, comprehensive structural inspections and safety assessments will be organized, and an emergency repair and reinforcement plan will be formulated.

7. The method for detecting and warning deformation of highway bridges in cold regions according to claim 1 is characterized by: The maintenance and update specifically include: S601: Subsequent monitoring: After the warning is lifted, continue to monitor the bridge to ensure its safety; S602: Subsequent maintenance: Regularly maintain and update monitoring equipment to ensure its normal operation and data accuracy.

Images