Automatic monitoring system and method for bridge bearing capacity

Abstract

The invention discloses a bridge bearing capacity automatic monitoring system and method, and belongs to the technical field of intelligent management monitoring, and the system comprises a central control module, and a wireless communication module, a data storage module, a data acquisition module, a bearing capacity definition module and a bearing capacity evaluation module which are connected with the central control module; the data acquisition module comprises an independent variable data acquisition unit and a dependent variable data acquisition unit; the independent variable data acquisition unit comprises a load sensor; the dependent variable data acquisition unit comprises a strain sensor and a vibration sensor. The method has the advantages that the existing bridge can be monitored online in real time, the bearing capacity of the bridge can be evaluated in real time, traffic interruption is not needed, manpower can be saved, the monitoring efficiency is high, the monitoring result does not depend on manpower, and the reliability is high.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an automatic monitoring system and method for bridge bearing capacity. Background Art

[0002] Bridges are of irreplaceable importance in the transportation system. They not only improve traffic efficiency and promote economic development, but also carry rich cultural connotations and historical values. With the continuous advancement of technology and the continuous growth of transportation demand, bridges will continue to play an important role in the transportation system. Regular inspection of the bearing capacity and health of bridges can timely discover potential structural problems and take corresponding maintenance measures, thereby extending the service life of bridges, reducing maintenance costs, ensuring the safety of bridges, and preventing accidents. However, the current detection of bridge bearing capacity mostly relies on manual inspections, which cannot achieve real-time monitoring and even requires traffic interruption for detection. The detection efficiency is low and the test results are inaccurate. Therefore, the development of an automatic monitoring system and method for bridge bearing capacity has application prospects. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an automatic monitoring system for bridge bearing capacity, which can realize real-time online monitoring of existing bridges and evaluate the bearing capacity of bridges in real time without interrupting traffic, saving manpower, having high monitoring efficiency, and the monitoring results are not dependent on manual labor and have high credibility.

[0004] In order to solve the above technical problems, the present invention provides an automatic monitoring system for bridge bearing capacity, including a central control module and a wireless communication module, a data storage module, a data acquisition module, a bearing capacity definition module and a bearing capacity evaluation module connected thereto; the data acquisition module includes an independent variable data acquisition unit and a dependent variable data acquisition unit; the independent variable data acquisition unit includes a load sensor; the dependent variable data acquisition unit includes a strain sensor and a vibration sensor; The data storage module is used to store the real-time monitoring data of the bridge, and obtain and store the real-time monitoring data of other bridges through the wireless communication module; the load sensor, strain sensor and vibration sensor are used to monitor the load, strain and vibration of the bridge in real time, and send their monitoring results to the data storage module and the bearing capacity evaluation module; the bearing capacity definition module is used to set the corresponding relationship between each factor in the dependent variable and the bearing capacity of the bridge; the bearing capacity evaluation module is based on the convolutional neural network model, using the data stored in the data storage module as the training sample set, load, strain and vibration as input features, and combining the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; the central control module is used to compare the bearing capacity R1 calculated by the bearing capacity evaluation module with the preset maximum bearing capacity threshold Rsaf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is judged that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module.

[0005] Furthermore, the independent variable data acquisition unit also includes a temperature and humidity sensor and a wind speed sensor; the dependent variable data acquisition unit also includes an inclination sensor and a displacement sensor; the temperature and humidity sensor and the wind speed sensor are respectively used to monitor the ambient temperature, humidity and wind speed of the area where the bridge is located in real time; the inclination sensor and the displacement sensor are respectively used to monitor the inclination and displacement of the bridge in real time.

[0006] Furthermore, the independent variable data acquisition unit and the dependent variable data acquisition unit both include crack monitoring sensors; the crack monitoring sensors are used to monitor cracks on the bridge surface in real time.

[0007] Furthermore, it also includes a data processing module, which is used to pre-process the extracted feature data; the pre-processing includes normalization processing.

[0008] Furthermore, it also includes a finite element analysis module; the finite element analysis module is connected to the data acquisition module, the bearing capacity definition module and the central control module; the finite element analysis module is used to establish a structural model of the bridge, simulate the dependent variable results according to the independent variable results of the bridge monitored in real time, calculate the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module, and send the bearing capacity result to the central control module; the finite element analysis module is also used to adjust the model's geometry, material properties, and boundary conditions according to the independent variable results and dependent variable results of the bridge monitored in real time, and perform real-time calibration on the established structural model of the bridge.

[0009] Furthermore, when the central control module determines whether there is a potential safety hazard, it first compares the received R1 and R2, and takes the relatively smaller data and the preset maximum bearing capacity threshold R saf for comparison.

[0010] Furthermore, the finite element analysis module is also used to calculate the ultimate load P of the bridge according to the specified time period T. lim ; Ultimate load of the bridge P lim The calculation method is: using the progressive loading method, gradually increasing the external load until the bridge becomes unstable or damaged. The maximum load at this time is the ultimate load P lim The central control module is also used to synchronously update the preset maximum bearing capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1。

[0011] Furthermore, the central control module is also connected to the vehicle flow monitoring system and the vehicle flow prediction system through the wireless communication module; when the monitored vehicle flow or the predicted vehicle flow exceeds a predetermined value, the central control module reduces the value of K.

[0012] Furthermore, it also includes an audible and visual alarm, which is used to make audible and visual alarms in the area where the bridge is located.

[0013] The present invention also provides a method for automatically monitoring the bearing capacity of a bridge, comprising the following steps: S1. Define bearing capacity: set the corresponding relationship between each factor in the dependent variable and the bridge bearing capacity in the bearing capacity definition module; S2, data acquisition: the independent variable data acquisition unit and the dependent variable data acquisition unit in the data acquisition module respectively collect corresponding data and send them to the data storage module, the bearing capacity assessment module and the finite element analysis module; the independent variable data acquisition unit includes a load sensor, a temperature and humidity sensor, a wind speed sensor, and a crack monitoring sensor; the dependent variable data acquisition unit includes a strain sensor, a vibration sensor, an inclination sensor, a displacement sensor, and a crack monitoring sensor; S3. Calculate bearing capacity R1: The bearing capacity assessment module is based on a convolutional neural network model, uses the data stored in the data storage module as a training sample set, uses load, strain and vibration as input features, and combines the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; S4, calculation of bearing capacity R2: establishing a structural model of the bridge in the finite element analysis module, simulating the results of each dependent variable according to the independent variable results of the bridge monitored in real time, and calculating the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module; S5. Determine whether there is a safety hazard: The central control module compares the received R1 and R2, and takes the relatively small data and compares it with the preset maximum load capacity threshold R saf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is determined that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module; S6. Update the carrying capacity threshold R saf : The finite element analysis module calculates the ultimate load P of the bridge according to the specified time period T lim ; The central control module synchronously updates the preset maximum load capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1; S7. Determine whether to update the K value: the central control module is also connected to the vehicle flow monitoring system and / or the vehicle flow prediction system through the wireless communication module; when the monitored vehicle flow and / or the predicted vehicle flow exceeds a predetermined value, the central control module reduces the value of K.

[0014] Beneficial effects of the present invention: The present invention can realize real-time online monitoring of existing bridges and can evaluate the bearing capacity of bridges in real time without interrupting traffic, which can save manpower, has high monitoring efficiency, and the monitoring results are not dependent on manual labor and have high credibility.

[0015] The present invention monitors the state of the bridge online based on deep learning algorithm and finite element analysis method, comprehensively considers the influence of load, wind force, temperature and humidity, cracks, etc. on the bearing capacity of the bridge, and can dynamically adjust the preset maximum bearing capacity threshold R according to the real-time state of the bridge. saf , ensuring the safety of the bridge to a greater extent.

[0016] The present invention also takes into account the change of vehicle flow, thereby realizing dynamic adjustment of the safety factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is an overall structural block diagram of the embodiment. DETAILED DESCRIPTION

[0018] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. Example

[0019] An automatic monitoring system for bridge bearing capacity, such as Figure 1 As shown, it includes a central control module and a wireless communication module, a data storage module, a data acquisition module, a bearing capacity definition module and a bearing capacity evaluation module connected thereto; the data acquisition module includes an independent variable data acquisition unit and a dependent variable data acquisition unit; the independent variable data acquisition unit includes a load sensor; the dependent variable data acquisition unit includes a strain sensor and a vibration sensor; The data storage module is used to store the real-time monitoring data of the bridge, and obtain and store the real-time monitoring data of other bridges through the wireless communication module; the load sensor, strain sensor and vibration sensor are used to monitor the load, strain and vibration of the bridge in real time, and send their monitoring results to the data storage module and the bearing capacity evaluation module; the bearing capacity definition module is used to set the corresponding relationship between each factor in the dependent variable and the bearing capacity of the bridge; the bearing capacity evaluation module is based on the convolutional neural network model, using the data stored in the data storage module as the training sample set, load, strain and vibration as input features, and combining the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; the central control module is used to compare the bearing capacity R1 calculated by the bearing capacity evaluation module with the preset maximum bearing capacity threshold R saf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is judged that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module.

[0020] Furthermore, the independent variable data acquisition unit also includes a temperature and humidity sensor and a wind speed sensor; the dependent variable data acquisition unit also includes an inclination sensor and a displacement sensor; the temperature and humidity sensor and the wind speed sensor are respectively used to monitor the ambient temperature, humidity and wind speed of the area where the bridge is located in real time; the inclination sensor and the displacement sensor are respectively used to monitor the inclination and displacement of the bridge in real time.

[0021] Furthermore, the independent variable data acquisition unit and the dependent variable data acquisition unit both include crack monitoring sensors; the crack monitoring sensors are used to monitor cracks on the bridge surface in real time.

[0022] Furthermore, it also includes a data processing module, which is used to pre-process the extracted feature data; the pre-processing includes normalization processing.

[0023] Furthermore, it also includes a finite element analysis module; the finite element analysis module is connected to the data acquisition module, the bearing capacity definition module and the central control module; the finite element analysis module is used to establish a structural model of the bridge, simulate the dependent variable results according to the independent variable results of the bridge monitored in real time, calculate the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module, and send the bearing capacity result to the central control module; the finite element analysis module is also used to adjust the model's geometry, material properties, and boundary conditions according to the independent variable results and dependent variable results of the bridge monitored in real time, and perform real-time calibration on the established structural model of the bridge.

[0024] Furthermore, when the central control module determines whether there is a potential safety hazard, it first compares the received R1 and R2, and takes the relatively smaller data and the preset maximum bearing capacity threshold R saf for comparison.

[0025] Furthermore, the finite element analysis module is also used to calculate the ultimate load P of the bridge according to the specified time period T. lim ; Ultimate load of the bridge P lim The calculation method is: using the progressive loading method, gradually increasing the external load until the bridge becomes unstable or damaged. The maximum load at this time is the ultimate load P lim The central control module is also used to synchronously update the preset maximum bearing capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1。

[0026] Furthermore, the central control module is also connected to the vehicle flow monitoring system and the vehicle flow prediction system through the wireless communication module; when the monitored vehicle flow or the predicted vehicle flow exceeds a predetermined value, the central control module reduces the value of K.

[0027] Furthermore, it also includes an audible and visual alarm, which is used to make audible and visual alarms in the area where the bridge is located. Example

[0028] A method for automatically monitoring bridge bearing capacity comprises the following steps: S1. Define bearing capacity: set the corresponding relationship between each factor in the dependent variable and the bridge bearing capacity in the bearing capacity definition module; S2, data acquisition: the independent variable data acquisition unit and the dependent variable data acquisition unit in the data acquisition module respectively collect corresponding data and send them to the data storage module, the bearing capacity assessment module and the finite element analysis module; the independent variable data acquisition unit includes a load sensor, a temperature and humidity sensor, a wind speed sensor, and a crack monitoring sensor; the dependent variable data acquisition unit includes a strain sensor, a vibration sensor, an inclination sensor, a displacement sensor, and a crack monitoring sensor; S3. Calculate bearing capacity R1: The bearing capacity assessment module is based on a convolutional neural network model, uses the data stored in the data storage module as a training sample set, uses load, strain and vibration as input features, and combines the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; S4, calculation of bearing capacity R2: establishing a structural model of the bridge in the finite element analysis module, simulating the results of each dependent variable according to the independent variable results of the bridge monitored in real time, and calculating the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module; S5. Determine whether there is a safety hazard: The central control module compares the received R1 and R2, and takes the relatively small data and compares it with the preset maximum load capacity threshold R saf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is determined that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module; S6. Update the carrying capacity threshold R saf : The finite element analysis module calculates the ultimate load P of the bridge according to the specified time period T lim ; The central control module synchronously updates the preset maximum load capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1; S7. Determine whether to update the K value: the central control module is also connected to the vehicle flow monitoring system and / or the vehicle flow prediction system through the wireless communication module; when the monitored vehicle flow and / or the predicted vehicle flow exceeds a predetermined value, the central control module reduces the value of K.

[0029] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. An automatic monitoring system for bridge bearing capacity, characterized in that: It includes a central control module and a wireless communication module, a data storage module, a data acquisition module, a bearing capacity definition module and a bearing capacity evaluation module connected thereto; the data acquisition module includes an independent variable data acquisition unit and a dependent variable data acquisition unit; the independent variable data acquisition unit includes a load sensor; the dependent variable data acquisition unit includes a strain sensor and a vibration sensor; The data storage module is used to store the real-time monitoring data of the bridge, and obtain and store the real-time monitoring data of other bridges through the wireless communication module; the load sensor, strain sensor and vibration sensor are used to monitor the load, strain and vibration of the bridge in real time, and send their monitoring results to the data storage module and the bearing capacity evaluation module; the bearing capacity definition module is used to set the corresponding relationship between each factor in the dependent variable and the bearing capacity of the bridge; the bearing capacity evaluation module is based on the convolutional neural network model, using the data stored in the data storage module as the training sample set, load, strain and vibration as input features, and combining the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; the central control module is used to compare the bearing capacity R1 calculated by the bearing capacity evaluation module with the preset maximum bearing capacity threshold R saf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is judged that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module.

2. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: The independent variable data acquisition unit also includes a temperature and humidity sensor and a wind speed sensor; the dependent variable data acquisition unit also includes an inclination sensor and a displacement sensor; the temperature and humidity sensor and the wind speed sensor are respectively used to monitor the ambient temperature and humidity and wind speed of the area where the bridge is located in real time; the inclination sensor and the displacement sensor are respectively used to monitor the inclination and displacement of the bridge in real time.

3. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: The independent variable data acquisition unit and the dependent variable data acquisition unit both include crack monitoring sensors; the crack monitoring sensors are used to monitor cracks on the bridge surface in real time.

4. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: It also includes a data processing module, which is used to pre-process the extracted feature data; The preprocessing includes normalization processing.

5. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: It also includes a finite element analysis module; the finite element analysis module is connected to the data acquisition module, the bearing capacity definition module and the central control module; the finite element analysis module is used to establish a structural model of the bridge, simulate and obtain the results of each dependent variable according to the independent variable results of the bridge monitored in real time, calculate the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module, and send the bearing capacity result to the central control module; The finite element analysis module is also used to adjust the model's geometry, material properties, and boundary conditions based on the independent variable results and dependent variable results of the bridge monitored in real time, and to perform real-time calibration on the established structural model of the bridge.

6. The bridge bearing capacity automatic monitoring system according to claim 5 is characterized in that: When the central control module determines whether there is a potential safety hazard, it first compares the received R1 and R2, and takes the relatively small data and the preset maximum load capacity threshold R saf for comparison.

7. The bridge bearing capacity automatic monitoring system according to claim 6 is characterized in that: The finite element analysis module is also used to calculate the ultimate load P of the bridge according to the specified time period T lim The central control module is also used to synchronously update the preset maximum bearing capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1。 8. The automatic monitoring system for bridge bearing capacity according to claim 7 is characterized in that: The central control module is also connected to the vehicle flow monitoring system and the vehicle flow prediction system through the wireless communication module; when the monitored vehicle flow or the predicted vehicle flow exceeds a predetermined value, the central control module reduces the value of K.

9. The monitoring method of the bridge bearing capacity automatic monitoring system according to any one of claims 1 to 8, characterized in that: The steps include: S1. Define bearing capacity: set the corresponding relationship between each factor in the dependent variable and the bridge bearing capacity in the bearing capacity definition module; S2, data acquisition: the independent variable data acquisition unit and the dependent variable data acquisition unit in the data acquisition module respectively collect corresponding data and send them to the data storage module, the bearing capacity assessment module and the finite element analysis module; the independent variable data acquisition unit includes a load sensor, a temperature and humidity sensor, a wind speed sensor, and a crack monitoring sensor; the dependent variable data acquisition unit includes a strain sensor, a vibration sensor, an inclination sensor, a displacement sensor, and a crack monitoring sensor; S3. Calculate bearing capacity R1: The bearing capacity assessment module is based on a convolutional neural network model, uses the data stored in the data storage module as a training sample set, uses load, strain and vibration as input features, and combines the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R1 of the bridge; S4, calculation of bearing capacity R2: establishing a structural model of the bridge in the finite element analysis module, simulating the results of each dependent variable according to the independent variable results of the bridge monitored in real time, and calculating the bearing capacity R2 of the bridge in combination with the corresponding relationship stored in the bearing capacity definition module; S5. Determine whether there is a safety hazard: The central control module compares the received R1 and R2, and takes the relatively small data and compares it with the preset maximum load capacity threshold R saf For comparison, if R1 exceeds the carrying capacity threshold R saf , it is judged that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module.

10. The monitoring method according to claim 9, characterized in that: Also includes S6, updating the carrying capacity threshold R saf and S7, the step of determining whether to update the K value: S6. Update the carrying capacity threshold R saf :The central control module compares the received R1 and R2, and takes the relatively small data and the preset maximum load capacity threshold R saf A comparison is made. If R1 exceeds the bearing capacity threshold Rsaf, it is determined that there is a safety hazard, and the judgment result and the location information of the bridge are sent to the cloud management platform through the wireless communication module; S7. Determine whether to update the K value: the finite element analysis module calculates the ultimate load P of the bridge according to the specified time period T. lim ; The central control module synchronously updates the preset maximum load capacity threshold R saf , R saf =k*P lim , where K is the safety factor, and 0 <K<1。

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