An automatic monitoring system and method for bridge bearing capacity

Abstract

The present invention discloses an automatic monitoring system and method for bridge bearing capacity, belonging to the technical field of intelligent management and monitoring. 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 advantages of the present invention are as follows: it 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 do not depend on manual work and have high credibility.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring, and particularly 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, promote economic development, but also carry rich cultural connotations and historical values. With the continuous progress of technology and the increasing traffic demand, bridges will continue to play an important role in the transportation system. Regularly detecting the bearing capacity and health condition of bridges can timely discover potential structural problems, take corresponding maintenance measures, thereby extending the service life of bridges, reducing maintenance costs, ensuring the safety of bridges, and preventing accidents. However, at present, the detection of bridge bearing capacity mostly relies on manual inspections, which cannot achieve real-time monitoring. Even traffic needs to be interrupted for detection, resulting in low detection efficiency and inaccurate detection results. Therefore, researching and developing 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 achieve real-time online monitoring of existing bridges, and can evaluate the bearing capacity of bridges in real time, without interrupting traffic, saving manpower, having high monitoring efficiency, and the monitoring results not relying on manual work and having high credibility.

[0004] To solve the above technical problem, 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;

[0005] The data storage module is used to store the real-time monitoring data of this bridge, and through the wireless communication module, obtain and store the real-time monitoring data of other bridges; the load sensor, strain sensor, and vibration sensor are respectively used to monitor the load, strain, and vibration borne by the bridge in real time, and all send their respective 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, uses the data stored in the data storage module as the 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; the finite element analysis module is used to establish the structural model of the bridge, simulate and obtain the results of each dependent variable according to the real-time monitored results of the independent variables of the bridge, combine the corresponding relationship stored in the bearing capacity definition module, calculate the bearing capacity R2 of the bridge, and send the bearing capacity result to the central control module;

[0006] The central control module is used to first compare the received R1 and R2, and take the relatively smaller data to compare with the preset maximum bearing capacity threshold R saf If it exceeds the bearing capacity threshold R saf , it is determined that there is a potential 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;

[0007] The finite element analysis module is also used to calculate the ultimate load Plim of the bridge according to the specified time period T; the central control module is also used to synchronously update the preset maximum bearing capacity threshold Rsaf, Rsaf = k * Plim, where K is the safety factor and 0 < K < 1; the central control module is also connected to the traffic flow monitoring system and the traffic flow prediction system through the wireless communication module; when the monitored traffic flow or the predicted traffic flow exceeds the predetermined value, the central control module will decrease the value of K.

[0008] Furthermore, the independent variable data acquisition unit further includes a temperature and humidity sensor and a wind speed sensor; the dependent variable data acquisition unit further includes an inclination sensor and a displacement sensor; the temperature and humidity sensor and the wind speed sensor are respectively used to monitor the environmental temperature, humidity, and wind speed in 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.

[0009] Furthermore, both the independent variable data acquisition unit and the dependent variable data acquisition unit include a crack monitoring sensor; the crack monitoring sensor is used to monitor the cracks on the surface of the bridge in real time.

[0010] Further, it further includes a data processing module, which is used to preprocess the extracted feature data; the preprocessing includes normalization processing.

[0011] Further, the finite element analysis module is also used to adjust the geometric shape, material properties, and boundary conditions of the model 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.

[0012] Further, it further includes an audible and visual alarm, which is used to give an audible and visual alarm in the area where the bridge is located.

[0013] The present invention also provides an automatic monitoring method for bridge bearing capacity, including the following steps:

[0014] S1. Define the bearing capacity: Set the corresponding relationship between each factor in the dependent variable and the bridge bearing capacity in the bearing capacity definition module;

[0015] S2. Data acquisition: The independent variable data acquisition unit and the dependent variable data acquisition unit in the data acquisition module respectively acquire the corresponding data and send them to the data storage module, the bearing capacity evaluation 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;

[0016] S3. Calculate the bearing capacity R1: The bearing capacity evaluation module is based on a convolutional neural network model, uses the data stored in the data storage module as the 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;

[0017] S4. Calculate the bearing capacity R2: Establish a structural model of the bridge in the finite element analysis module, simulate the dependent variable results according to the independent variable results of the bridge monitored in real time, and combine the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R2 of the bridge;

[0018] S5. Determine whether there is a potential safety hazard: The central control module compares the received R1 and R2, takes the relatively smaller data and compares it with the preset maximum bearing capacity threshold R saf for comparison. If it exceeds the bearing capacity threshold R saf , it is determined that there is a potential 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;

[0019] S6. Update the bearing capacity threshold R saf : The finite element analysis module calculates the ultimate load P of the bridge according to the specified time period Tlim ; The central control module synchronously updates the preset maximum bearing capacity threshold R saf , R saf =k*P lim , where K is a safety factor, and 0 < K < 1;

[0020] S7. Determine whether to update the value of K: The central control module is also connected to the traffic flow monitoring system and / or the traffic flow prediction system through the wireless communication module; when the monitored traffic flow and / or the predicted traffic flow exceeds a predetermined value, the central control module reduces the value of K.

[0021] Advantages of the present invention:

[0022] The present invention can achieve 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, the monitoring results do not depend on manual work, and have high credibility.

[0023] The present invention conducts online monitoring of the bridge state based on deep learning algorithms and finite element analysis methods, comprehensively considers the impacts of load, wind force, temperature and humidity, cracks, etc. on the bridge bearing capacity, and can dynamically adjust the preset maximum bearing capacity threshold R according to the real-time state of the bridge saf , which greatly ensures the safety of the bridge.

[0024] The present invention also considers the change of traffic flow, so as to realize the dynamic adjustment of the safety factor. Description of the Drawings

[0025] Figure 1 It is the overall structural block diagram of the embodiment. Detailed Embodiments

[0026] The following further describes the detailed embodiments of the present invention with reference to the drawings. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation to 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. Embodiment

[0027] An automatic bridge bearing capacity monitoring system, as Figure 1 shown, 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;

[0028] The data storage module is used to store the real-time monitoring data of this bridge, and through the wireless communication module, obtain and store the real-time monitoring data of other bridges; the load sensor, strain sensor and vibration sensor are respectively used to monitor the load, strain and vibration borne by the bridge in real time, and all send their respective 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, uses the data stored in the data storage module as the 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; the finite element analysis module is used to establish the structural model of the bridge, simulate the results of each dependent variable according to the real-time monitored independent variable results of the bridge, combine the corresponding relationship stored in the bearing capacity definition module, calculate the bearing capacity R2 of the bridge, and send the bearing capacity result to the central control module;

[0029] The central control module is used to first compare the received R1 and R2, and take the relatively smaller data to compare with the preset maximum bearing capacity threshold R saf for comparison. If it exceeds the bearing capacity threshold R saf , it is determined that there is a potential 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;

[0030] The finite element analysis module is also used to calculate the ultimate load Plim of the bridge according to the specified time period T; the central control module is also used to synchronously update the preset maximum bearing capacity threshold Rsaf, Rsaf = k * Plim, where K is a safety factor and 0 < K < 1; the central control module is also connected to the traffic flow monitoring system and the traffic flow prediction system through the wireless communication module; when the monitored traffic flow or the predicted traffic flow exceeds a predetermined value, the central control module will reduce the value of K.

[0031] Furthermore, the independent variable data acquisition unit further includes a temperature and humidity sensor and a wind speed sensor; the dependent variable data acquisition unit further includes an inclination sensor and a displacement sensor; the temperature and humidity sensor and the wind speed sensor are respectively used to monitor the environmental temperature, humidity and wind speed in 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.

[0032] Furthermore, both the independent variable data acquisition unit and the dependent variable data acquisition unit include a crack monitoring sensor; the crack monitoring sensor is used to monitor the cracks on the surface of the bridge in real time.

[0033] Further, it further includes a data processing module, which is used to preprocess the extracted feature data; the preprocessing includes normalization processing.

[0034] Further, the finite element analysis module is further used to adjust the geometric shape, material properties, and boundary conditions of the model 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.

[0035] Further, it further includes an audible and visual alarm, which is used to perform audible and visual alarms in the area where the bridge is located. Embodiment

[0036] A method for automatically monitoring the bearing capacity of a bridge includes the following steps:

[0037] S1. Define the bearing capacity: Set the corresponding relationship between each factor in the dependent variable and the bearing capacity of the bridge in the bearing capacity definition module;

[0038] S2. Data collection: The independent variable data collection unit and the dependent variable data collection unit in the data collection module respectively collect the corresponding data and send them to the data storage module, the bearing capacity evaluation module, and the finite element analysis module; the independent variable data collection unit includes a load sensor, a temperature and humidity sensor, a wind speed sensor, and a crack monitoring sensor; the dependent variable data collection unit includes a strain sensor, a vibration sensor, an inclination sensor, a displacement sensor, and a crack monitoring sensor;

[0039] S3. Calculate the bearing capacity R1: The bearing capacity evaluation module is based on the convolutional neural network model, uses the data stored in the data storage module as the 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;

[0040] S4. Calculate the bearing capacity R2: Establish a structural model of the bridge in the finite element analysis module, simulate the dependent variable results according to the independent variable results of the bridge monitored in real time, and combine the corresponding relationship stored in the bearing capacity definition module to calculate the bearing capacity R2 of the bridge;

[0041] S5. Determine whether there is a potential safety hazard: The central control module compares the received R1 and R2, takes the relatively smaller data and compares it with the preset maximum bearing capacity threshold R saf for comparison. If it exceeds the bearing capacity threshold R saf , it is determined that there is a potential 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;

[0042] S6. Update the bearing 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 bearing capacity threshold R saf , R saf =k*P lim , where K is a safety factor and 0 < K < 1;

[0043] S7. Determine whether to update the value of K: The central control module is also connected to the traffic flow monitoring system and / or the traffic flow prediction system through the wireless communication module; when the monitored traffic flow and / or the predicted traffic flow exceeds a predetermined value, the central control module decreases the value of K.

[0044] The above has described the embodiments of the present invention in detail in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principle and spirit of the present invention, various changes, modifications, substitutions, and variations made to these embodiments still fall within the protection scope 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, a temperature and humidity sensor, and a wind speed sensor; the dependent variable data acquisition unit includes a strain sensor, a vibration sensor, an inclination sensor, and a displacement sensor; 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 data storage module is used to store the real-time monitoring data of this bridge, and through the wireless communication module, obtain and store the real-time monitoring data of other bridges; the load sensor, the strain sensor, and the vibration sensor are respectively used to monitor the load, strain, and vibration borne by the bridge in real time, and send their respective 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 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; The finite element analysis module is used to establish a structural model of the bridge, simulate the results of each dependent variable according to the real-time monitored results of the independent variables of the bridge, combine the corresponding relationship stored in the bearing capacity definition module, calculate the bearing capacity R2 of the bridge, and send the bearing capacity result to the central control module; The central control module is used to compare the received R1 and R2 first, and compare the relatively small data with the preset maximum bearing capacity threshold R saf For comparison, if it 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; The finite element analysis module is also used to calculate the ultimate load Plim of the bridge according to the specified time period T; the central control module is also used to synchronously update the preset maximum bearing capacity threshold Rsaf, Rsaf = k * Plim, where K is a safety factor and 0 < K < 1; the central control module is also connected to the traffic flow monitoring system and the traffic flow prediction system through the wireless communication module; when the monitored traffic flow or the predicted traffic flow exceeds a predetermined value, the central control module will reduce the value of K.

2. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: The temperature and humidity sensor and the wind speed sensor are respectively used to monitor the environmental temperature, humidity, and wind speed in the area where the bridge is located; the inclination sensor and the displacement sensor are respectively used to monitor the inclination and displacement of the bridge.

3. The bridge bearing capacity automatic monitoring system according to claim 1 is characterized in that: Both the independent variable data acquisition unit and the dependent variable data acquisition unit include a crack monitoring sensor; the crack monitoring sensor is used to monitor the cracks on the surface of the bridge 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, and the data processing module is used to preprocess 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: The finite element analysis module is also used to adjust the geometric shape, material properties, and boundary conditions of the model according to the real-time monitored results of the independent variables and dependent variables of the bridge, and perform real-time calibration on the established structural model of the bridge.

6. The monitoring method of the bridge bearing capacity automatic monitoring system according to any one of claims 1 to 5, characterized in that: It includes the following steps: S1. Define the bearing capacity: Set the corresponding relationship between each factor in the dependent variable and the bearing capacity of the bridge 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 it 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.

7. The monitoring method according to claim 6, 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 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.

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