CN109506714A - A kind of artificial intelligence bridge security detection system - Google Patents
A kind of artificial intelligence bridge security detection system Download PDFInfo
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- CN109506714A CN109506714A CN201811575627.4A CN201811575627A CN109506714A CN 109506714 A CN109506714 A CN 109506714A CN 201811575627 A CN201811575627 A CN 201811575627A CN 109506714 A CN109506714 A CN 109506714A
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Abstract
The invention discloses a kind of artificial intelligence bridge security detection systems, including real-time detection subsystem, sensor monitoring subsystem, data acquisition subsystem, analysis evaluation subsystem, Intelligent treatment subsystem and cloud database；The system is using common bridge as main object, implement real-time detection and long-term periodic monitoring bridge health state, pass through various data and signal of the acquisition bridge under operation state, and the arrangement works state and health status of bridge are finally inversed by with signal according to the collected data, identify degree and its position of possible structural damage, and then the security reliability assessment of bridge is provided, technical support is provided for bridge management maintenance, ensures the safe operation of bridge；In addition, being measured in real time operation using trolley and cross testing stand is detected, it can quick, real-time, automatically acquire, storage, transmit and manage data, avoid unnecessary carelessness, reduce security risk.
Description
Technical Field
The invention relates to the technical field of safety detection, in particular to an artificial intelligent bridge safety detection system.
Background
At present, a great number of bridges exist in China, great convenience is brought to the passage of people, but the construction materials, time, processes and the like of the bridges are different, and the bridges are damaged or have quality safety problems more or less along with the passage of time. On the one hand, some bridge safety inspection now are mostly the detection of outward appearance, and can not detect inside substantive problem, and appearance detecting instrument can only the extraction inspection of unscheduled, and negligence appears in the inevitable, and the safety problem easily appears
On the other hand, data obtained in the bridge safety detection process is not fully mined and utilized by people in the prior art, and when the intelligence is realized in the strong artificial intelligence of the prior art by purely adopting a human brain simulation mode, the requirements on software and hardware are very high, the existing data processing technology cannot be adopted, and the advantages of the existing data processing technology cannot be fully exerted.
Disclosure of Invention
The invention aims to provide an artificial intelligent bridge safety detection system which can make up the defects of the existing bridge safety detection technology and processing scheme.
In order to achieve the above purpose, the invention aims to provide an artificial intelligent bridge safety detection system, which comprises a real-time detection subsystem, a sensor monitoring subsystem, a data acquisition subsystem, an analysis and evaluation subsystem, an intelligent processing subsystem and a cloud database; the real-time detection subsystem is electrically connected with the sensor monitoring subsystem or is connected in a wireless mode, the sensor monitoring subsystem is connected with the data acquisition subsystem in a wireless mode, the data acquisition subsystem is connected with the analysis and evaluation subsystem in a wireless mode, the analysis and evaluation subsystem is connected with the intelligent processing subsystem in a wireless mode, and the real-time detection subsystem, the sensor monitoring subsystem, the data acquisition subsystem, the analysis and evaluation subsystem and the intelligent processing subsystem are connected with the cloud database in an electric connection or in a wireless mode.
Furthermore, the real-time detection subsystem comprises a detection trolley and a detection unit, the detection unit comprises a detection trolley displacement detection part, a cross-shaped detection frame and laser sensors, the laser sensors are distributed on a cross beam and a longitudinal beam of the cross-shaped detection frame, the center of gravity of the longitudinal beam is low, and the longitudinal beam can rotate around a longitudinal axis; the detection direction of the laser sensor is downward, and a displacement signal detected by the detection trolley displacement detection part and a bridge deck data signal detected by the laser sensor are transmitted to the cloud database in a wireless mode; and a balance block for reducing the center of gravity is fixedly arranged at the lower part of the longitudinal beam.
Further, the specific operation steps of the real-time detection subsystem to perform detection are as follows:
a. setting a coordinate system, driving the detection trolley to longitudinally run along the bridge deck, acquiring transverse data signals and longitudinal data signals of the bridge deck in real time by the laser sensor respectively, and obtaining real-time coordinate values of each detection point of the bridge deck according to longitudinal displacement data of the detection trolley and test data of the laser sensor on the longitudinal beam and the cross beam;
b. combining the detection trolley displacement signal according to the real-time transverse data signal to obtain digital space curved surfaces of the bridge deck structure along the longitudinal direction and the transverse direction of the bridge;
c. using step ab, acquiring the digital space curved surface of the bridge deck structure for the same bridge according to a set period, acquiring digital space curved surface data of the bridge deck structure in different periods, and storing the digital space curved surface data in the cloud database;
d. comparing the digital space curved surface of the bridge deck structure measured in the step b with the digital space curved surface of the bridge deck structure measured in the step c, and simultaneously comparing the digital space curved surface of the bridge deck structure measured in the step c each time with the digital space curved surface of the bridge deck structure measured in the previous time;
e. and d, according to the comparison result in the step d, establishing a relation between the digital space curved surface change of the bridge deck structure and the safety of the bridge deck structure according to the experimental research result and computer simulation analysis, and judging the safety of the bridge deck structure by analyzing and comparing the digital space curved surface change of the bridge deck structure of the same bridge.
Furthermore, the sensor monitoring subsystem comprises a main beam vibration monitoring unit, a main beam internal force monitoring unit, a main beam temperature monitoring unit, a bridge tower displacement monitoring unit, a main beam line type monitoring unit, a bridge tower vibration monitoring unit, a cable vibration monitoring unit, a structural damage monitoring unit and an environment monitoring unit; the sensor monitoring subsystem is provided with a temperature sensor, an acceleration sensor, a vibrating wire strain gauge, a total station, an anemorumbometer and an ultrasonic flaw detector; wherein,
the main beam vibration monitoring unit is characterized in that main beam vibration monitoring sections are arranged at key positions including a bridge 1/2 node, each section is provided with 3 first acceleration sensors, and the first acceleration sensors are used for monitoring vibration responses in the forward bridge direction and the transverse bridge direction;
the main beam internal force monitoring unit is used for monitoring the internal force of a main beam by selecting a representative section of a bridge, and is provided with the vibrating wire type strain gauge sensor which is used for monitoring the strain condition of important concrete members or steel members of the bridge under the load action of traffic load, wind load and temperature load;
the main beam temperature monitoring unit is used for selecting a representative section of a main beam and monitoring the influence of temperature distribution and temperature on the internal force and deformation of the main beam by using the temperature sensor;
the bridge tower displacement monitoring unit and the main beam line type monitoring unit adopt the total station to monitor the main beam line type and the bridge tower displacement;
the bridge tower vibration monitoring unit is characterized in that monitoring sections are arranged on a bridge tower, each monitoring section is provided with 3 second acceleration sensors, and the second acceleration sensors are used for monitoring acceleration response of the bridge tower under the action of wind and traffic loads;
the cable vibration monitoring unit is characterized in that monitoring points are arranged on a cable, and a third acceleration sensor is selected for monitoring vibration responses of the cable in the bridge direction and the transverse bridge direction;
the structural damage monitoring unit adopts the ultrasonic flaw detector or the magnetic powder flaw detector or the crack width gauge, and is used for detecting concrete crack depth, non-compact areas, honeycomb cavities, joint surface quality, surface damage layer thickness, steel pipe concrete internal defects and pavement diseases;
the environment monitoring unit adopts the anemorumbometer to monitor the environmental wind power and the wind direction at the bridge site in real time, and monitors the environmental temperature at the bridge site, and the temperature gradient of main components of the bridge.
Further, the application range of the ultrasonic flaw detector for detecting the damage of the bridge comprises the following steps:
judging and calculating the size of the defect according to the diffraction phenomenon of the low-frequency ultrasound when the defect is encountered in the concrete and the time interval of the sound and the change of the sound propagation path;
judging the existence and the size of the defect according to the phenomenon that the ultrasonic generates scattering on the defect interface and the energy is obviously attenuated when the ultrasonic reaches a receiving probe;
according to different attenuation degrees of each frequency component of the ultrasonic pulse when encountering the defect, the receiving frequency is obviously reduced, or the difference between the frequency spectrum of the received wave and the frequency spectrum of the reflected wave is generated, the internal defect can also be judged;
and judging the defect according to the waveform conversion and superposition of the ultrasonic wave at the defect position to cause the phenomenon of received waveform distortion.
Furthermore, a camera device used for acquiring bridge deck video data is arranged at the front end of the chassis of the detection trolley, and picture data obtained by shooting through the camera device is transmitted to the cloud database in a wireless mode.
Further, the data acquisition subsystem comprises an acquisition unit and a classification unit, wherein,
the acquisition unit is used for acquiring the digital space curved surface data of the bridge deck structure and the picture data shot by the camera device from the cloud database;
the classification unit is used for classifying the data in the acquisition unit according to the attributes, so that the subsequent processing is facilitated.
Further, the analysis and evaluation subsystem comprises a model establishing unit, a correcting unit and an evaluation and interpretation unit, wherein,
the model establishing unit is used for establishing a bridge structure analysis model adaptive to bridge health conditions and system monitoring;
the correction unit corrects the theoretical model to a certain degree based on deep learning according to the approximation degree of the theoretical model and the system actual measurement, so that the theoretical model has higher universality and accuracy;
and the evaluation and interpretation unit evaluates and interprets the data acquired from the data acquisition subsystem on the basis of the bridge structure analysis model and establishes the relevance between the fluctuating detection value and the bridge structure state evaluation and interpretation result.
Further, the intelligent processing subsystem comprises a processing determination unit and a learning feedback unit, wherein,
the processing and determining unit is used for determining monitoring arrangement, monitoring modes and necessary monitoring technical conditions which are necessary for each state of the bridge structure and each important structural characteristic of the bridge according to the bridge structure analysis model;
the learning feedback unit deeply analyzes data accumulation and data quality in the cloud database based on a machine learning method, feeds back effective subsystems and corresponding detection data processing methods in the system, and improves the subsystems and the corresponding detection data processing methods.
Furthermore, the intelligent processing subsystem further comprises a first management unit, an improvement unit, a second management unit and an execution unit, wherein the first management unit is electrically connected with the improvement unit, the improvement unit is electrically connected with the second management unit, and the second management unit is electrically connected with the execution unit; wherein,
the first management unit is used for collecting the performance information of the artificial intelligent bridge safety detection system;
the improvement unit is used for generating an improvement scheme according to the performance information collected by the first management unit;
the second management unit is used for distributing the improvement scheme generated by the improvement unit to the execution unit;
the execution unit is used for executing according to the improvement scheme distributed by the second management unit so as to improve the performance of the artificial intelligent bridge safety detection system.
Compared with the prior art, the invention has the beneficial effects that:
firstly, the invention takes a common bridge as a main object, and carries out real-time detection and long-term regular monitoring on bridge structure response including internal force, displacement, vibration, temperature and the like and analysis and evaluation on bridge structure health status aiming at the characteristics of multiple structural bridges such as a concrete structure, a steel structure and the like.
Secondly, the detection trolley and the cross-shaped detection frame are adopted for real-time detection operation, data can be rapidly, automatically collected, stored, transmitted and managed in real time, the safety condition of the bridge can be observed at any time, unnecessary negligence is avoided, potential safety hazards are reduced, and risks and economic losses are reduced.
Thirdly, the invention integrates the performance information through the improvement unit, autonomously makes an improvement plan and executes the improvement scheme through the execution unit, can explore the software and hardware potentials and improve the system performance, so that the artificial intelligent bridge safety detection system has the capability of autonomously solving the problems, can realize strong artificial intelligence and can fully exert the advantages of the existing data processing technology.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic structural diagram of an artificial intelligent bridge safety detection system according to the present invention;
FIG. 2 is a schematic structural diagram of a real-time detection subsystem in an artificial intelligent bridge safety detection system according to the present invention;
FIG. 3 is a schematic structural diagram of a sensor monitoring subsystem in the artificial intelligent bridge safety detection system according to the present invention;
FIG. 4 is a schematic structural diagram of a data acquisition subsystem in an artificial intelligent bridge safety detection system according to the present invention;
FIG. 5 is a schematic structural diagram of an analysis and evaluation subsystem in an artificial intelligent bridge safety detection system according to the present invention;
FIG. 6 is a schematic structural diagram of an intelligent processing subsystem in an artificial intelligent bridge safety inspection system according to the present invention;
FIG. 7 is a schematic structural diagram of a detection trolley in the artificial intelligent bridge safety detection system of the invention.
In the figure: 1-a real-time detection subsystem, 2-a sensor monitoring subsystem, 3-a data acquisition subsystem, 4-an analysis and evaluation subsystem, 5-an intelligent processing subsystem, 6-a cloud database,
11-detection trolley, 12-detection unit, 121-detection trolley displacement detection part, 122-cross detection frame, 123-laser sensor, 124-balance block, 21-main beam vibration monitoring unit, 22-main beam internal force monitoring unit, 23-main beam temperature monitoring unit, 24-bridge tower displacement monitoring unit, 25-main beam linear monitoring unit, 26-bridge tower vibration monitoring unit, 27-cable vibration monitoring unit, 28-structure damage monitoring unit, 29-environment monitoring unit, 201-temperature sensor, 202-acceleration sensor, 203-vibrating string strain gauge, 204-total station, 205-anemoscope, 206-ultrasonic flaw detector, 31-acquisition unit, 32-classification unit, 41-modeling unit, 42-correction unit, 43-evaluation interpretation unit, 51-processing determination unit, 52-learning feedback unit, 53-first management unit, 54-improvement unit, 55-second management unit, 56-execution unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first objective of the present invention is to provide an artificial intelligent bridge safety detection system, which includes a real-time detection subsystem 1, a sensor monitoring subsystem 2, a data acquisition subsystem 3, an analysis and evaluation subsystem 4, an intelligent processing subsystem 5, and a cloud database 6; the real-time detection subsystem 1 with sensor monitoring subsystem 2 electric connection perhaps connects through wireless mode, sensor monitoring subsystem 2 with data acquisition subsystem 3 connects through wireless mode, data acquisition subsystem 3 with analysis evaluation subsystem 4 connects through wireless mode, analysis evaluation subsystem 4 with intelligence processing subsystem 5 connects through wireless mode, real-time detection subsystem 1 sensor monitoring subsystem 2 data acquisition subsystem 3 analysis evaluation subsystem 4 and intelligence processing subsystem 5 all with high in the clouds database 6 electric connection or connect through wireless mode.
Referring to fig. 2 and 7, the real-time detection subsystem 1 includes a detection trolley 11 and a detection unit 12, the detection unit 12 includes a detection trolley displacement detection member 121, a cross-shaped detection frame 122 and laser sensors 123, the laser sensors 123 are distributed on a cross beam and a longitudinal beam of the cross-shaped detection frame 122, the center of gravity of the longitudinal beam is low and the longitudinal beam can rotate around a longitudinal axis; the detection direction of the laser sensor 123 is downward, and a displacement signal detected by the detection trolley displacement detection part 121 and a bridge floor data signal detected by the laser sensor 123 are transmitted to the cloud database 6 in a wireless mode; a balance weight 124 for lowering the center of gravity is fixedly arranged at the lower part of the longitudinal beam.
The specific operation steps of the real-time detection subsystem 1 for performing detection are as follows:
a. setting a coordinate system, driving the detection trolley 11 to longitudinally run along the bridge deck, acquiring transverse data signals and longitudinal data signals of the bridge deck in real time by the laser sensor 123, and obtaining real-time coordinate values of each detection point of the bridge deck according to longitudinal displacement data of the detection trolley 11 and test data of the laser sensor 123 on the longitudinal beam and the cross beam;
b. combining the displacement signals of the detection trolley 11 according to the real-time transverse data signals to obtain digital space curved surfaces of the bridge deck structure along the longitudinal direction and the transverse direction of the bridge;
c. using step ab, acquiring the digital space curved surface of the bridge deck structure for the same bridge according to a set period, acquiring digital space curved surface data of the bridge deck structure in different periods, and storing the digital space curved surface data in the cloud database 6;
d. comparing the digital space curved surface of the bridge deck structure measured in the step b with the digital space curved surface of the bridge deck structure measured in the step c, and simultaneously comparing the digital space curved surface of the bridge deck structure measured in the step c each time with the digital space curved surface of the bridge deck structure measured in the previous time;
e. and d, according to the comparison result in the step d, establishing a relation between the digital space curved surface change of the bridge deck structure and the safety of the bridge deck structure according to the experimental research result and computer simulation analysis, and judging the safety of the bridge deck structure by analyzing and comparing the digital space curved surface change of the bridge deck structure of the same bridge.
Further, in one embodiment, another manner may be employed for real-time detection. The detection mode comprises a CCD image sensor, a single chip microcomputer, a ray generator, a refraction lens and a reflecting plate; the refraction lens is arranged at the detection end of the CCD image sensor; the CCD image sensor and the ray generator are both electrically connected with the singlechip, wherein,
the singlechip is used for sending a control instruction to the ray generator; the ray generator is used for receiving the control instruction, sending rays to the reflecting plate according to the control instruction, and then refracting the rays to the CCD image sensor through the refraction lens; the CCD image sensor is used for receiving the rays emitted by the ray generator, converting the rays into electric signals and sending the electric signals to the singlechip. The ray generator is controlled to emit regular rays through an instruction downloaded by the singlechip, the rays pass through the reflecting plate and the refraction lens and are finally received by the CCD image sensor, and the static and dynamic deflection degrees of the bridge are judged by measuring the moving distance of the rays. Under normal weather environment, the propagation approach of light is comparatively stable, and the measuring accuracy is also very high, and entire system also is by singlechip automatic control, guarantees stability when raising the efficiency.
Referring to fig. 3, the sensor monitoring subsystem 2 includes a main beam vibration monitoring unit 21, a main beam internal force monitoring unit 22, a main beam temperature monitoring unit 23, a bridge tower displacement monitoring unit 24, a main beam line type monitoring unit 25, a bridge tower vibration monitoring unit 26, a cable vibration monitoring unit 27, a structural damage monitoring unit 28, and an environment monitoring unit 29; the sensor monitoring subsystem 2 is provided with a temperature sensor 201, an acceleration sensor 202, a vibrating wire strain gauge 203, a total station 204, an anemorumbometer 205 and an ultrasonic flaw detector 206; wherein,
the girder vibration monitoring unit 21 is provided with girder vibration monitoring sections at key positions including a node 1/2, each section is provided with 3 first acceleration sensors 2021, and the first acceleration sensors 2021 are used for monitoring vibration responses in the forward bridge direction and the transverse bridge direction;
the main beam internal force monitoring unit 22 is used for monitoring the internal force of a main beam by selecting a representative section of a bridge, the main beam internal force monitoring unit is provided with the vibrating wire type strain gauge sensor 203, and the vibrating wire type strain gauge sensor 203 is used for monitoring the strain condition of important concrete members or steel members of the bridge under the load action including traffic load, wind load and temperature load;
the main beam temperature monitoring unit 23 is used for monitoring the influence of temperature distribution and temperature on the internal force and deformation of the main beam by selecting a representative section of the main beam and using the temperature sensor 201;
the bridge tower displacement monitoring unit 24 and the main beam linear monitoring unit 25 adopt the total station 204 to monitor the main beam linear and the bridge tower displacement;
the bridge tower vibration monitoring unit 26 is provided with monitoring sections on the bridge tower, each monitoring section is provided with 3 second acceleration sensors 2022, and the second acceleration sensors 2022 are used for monitoring acceleration response of the bridge tower under the action of wind and traffic loads;
the cable vibration monitoring unit 27 is provided with a monitoring point on the cable, and a third acceleration sensor 2023 is selected for monitoring the vibration response of the cable in the bridge direction and the transverse bridge direction;
the structural damage monitoring unit 28 adopts the ultrasonic flaw detector 206 or the magnetic particle flaw detector or the crack width measuring instrument, and is used for detecting concrete crack depth, non-compact areas, honeycomb cavities, joint surface quality, surface damage layer thickness, steel pipe concrete internal defects and pavement diseases;
the environment monitoring unit 29 uses the anemorumbometer 205 to monitor the environmental wind power and the wind direction at the bridge site in real time, and monitors the environmental temperature at the bridge site, the temperature of the main components of the bridge, and the temperature gradient.
The application range of the ultrasonic flaw detector 206 in detecting bridge damage includes:
judging and calculating the size of the defect according to the diffraction phenomenon of the low-frequency ultrasound when the defect is encountered in the concrete and the time interval of the sound and the change of the sound propagation path;
judging the existence and the size of the defect according to the phenomenon that the ultrasonic generates scattering on the defect interface and the energy is obviously attenuated when the ultrasonic reaches a receiving probe;
according to different attenuation degrees of each frequency component of the ultrasonic pulse when encountering the defect, the receiving frequency is obviously reduced, or the difference between the frequency spectrum of the received wave and the frequency spectrum of the reflected wave is generated, the internal defect can also be judged;
and judging the defect according to the waveform conversion and superposition of the ultrasonic wave at the defect position to cause the phenomenon of received waveform distortion.
The front end of the chassis of the detection trolley 11 is provided with a camera device used for acquiring bridge deck video data, and picture data obtained by shooting through the camera device is transmitted to the cloud database 6 in a wireless mode.
Referring to fig. 4, the data acquisition subsystem 3 includes an acquisition unit 31 and a classification unit 32, wherein,
the acquiring unit 31 is configured to acquire digitized space curved surface data of the bridge deck structure and picture data obtained by shooting by the camera from the cloud database 6;
the classifying unit 32 is configured to classify the data in the obtaining unit according to the attribute, so as to facilitate subsequent processing.
Referring to fig. 5, the analysis and evaluation subsystem 4 includes a model building unit 41, a modification unit 42 and an evaluation and interpretation unit 43, wherein,
the model establishing unit 41 is used for establishing a bridge structure analysis model adaptive to bridge health conditions and system monitoring;
the correction unit 42 corrects the theoretical model to a certain extent based on deep learning according to the approximation degree of the theoretical model and the actual measurement of the system, so that the theoretical model has higher universality and accuracy;
the evaluation and interpretation unit 43 evaluates and interprets the data acquired from the data acquisition subsystem 3 based on the bridge structure analysis model, and establishes a correlation between the fluctuating detection value and the bridge structure state evaluation and interpretation result.
Referring to fig. 6, the intelligent processing subsystem 5 includes a processing determination unit 51 and a learning feedback unit 52, wherein,
the processing and determining unit 51 is configured to determine, according to the bridge structure analysis model, monitoring arrangements, monitoring modes and necessary monitoring technical conditions necessary for each state of the bridge structure and each important structural characteristic of the bridge;
the learning feedback unit 52 performs deep analysis on data accumulation and data quality in the cloud database 6 based on a machine learning method, feeds back an effective subsystem and a corresponding detection data processing method in the system, and improves the system.
The intelligent processing subsystem 5 further includes a first management unit 53, an improvement unit 54, a second management unit 55 and an execution unit 56, wherein the first management unit 53 is electrically connected to the improvement unit 54, the improvement unit 54 is electrically connected to the second management unit 55, and the second management unit 55 is electrically connected to the execution unit 56; wherein,
the first management unit 53 is configured to collect performance information of the artificial intelligent bridge safety detection system;
the improvement unit 54 is configured to generate an improvement scheme according to the performance information collected by the first management unit 53;
the second management unit 55 is configured to allocate the improvement scheme generated by the improvement unit 54 to the execution unit 56;
the execution unit 56 is configured to execute according to the improvement scheme allocated by the second management unit 55, so as to improve the performance of the artificial intelligent bridge safety detection system.
The working principle of the invention is as follows:
firstly, the invention takes a common bridge as a main object, and carries out real-time detection and long-term regular monitoring on bridge structure response including internal force, displacement, vibration, temperature and the like and analysis and evaluation on bridge structure health status aiming at the characteristics of multiple structural bridges such as a concrete structure, a steel structure and the like.
Secondly, the detection trolley and the cross-shaped detection frame are adopted for real-time detection operation, data can be rapidly, automatically collected, stored, transmitted and managed in real time, the safety condition of the bridge can be observed at any time, unnecessary negligence is avoided, potential safety hazards are reduced, and risks and economic losses are reduced.
Thirdly, the invention integrates the performance information through the improvement unit, autonomously makes an improvement plan and executes the improvement scheme through the execution unit, can explore the software and hardware potentials and improve the system performance, so that the artificial intelligent bridge safety detection system has the capability of autonomously solving the problems, can realize strong artificial intelligence and can fully exert the advantages of the existing data processing technology.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An artificial intelligent bridge safety detection system is characterized by comprising a real-time detection subsystem (1), a sensor monitoring subsystem (2), a data acquisition subsystem (3), an analysis and evaluation subsystem (4), an intelligent processing subsystem (5) and a cloud database (6); real-time detection subsystem (1) with sensor monitoring subsystem (2) electric connection perhaps connects through wireless mode, sensor monitoring subsystem (2) with data acquisition subsystem (3) connect through wireless mode, data acquisition subsystem (3) with analysis evaluation subsystem (4) connect through wireless mode, analysis evaluation subsystem (4) with intelligent processing subsystem (5) connect through wireless mode, real-time detection subsystem (1) sensor monitoring subsystem (2) data acquisition subsystem (3) analysis evaluation subsystem (4) and intelligent processing subsystem (5) all with high in the clouds database (6) electric connection perhaps connects through wireless mode.
2. The artificial intelligent bridge safety detection system according to claim 1, wherein the real-time detection subsystem (1) comprises a detection trolley (11) and a detection unit (12), the detection unit (12) comprises a detection trolley displacement detection piece (121), a cross-shaped detection frame (122) and laser sensors (123), the laser sensors (123) are distributed on a cross beam and a longitudinal beam of the cross-shaped detection frame (122), the center of gravity of the longitudinal beam is low, and the longitudinal beam can rotate around a longitudinal axis; the detection direction of the laser sensor (123) is downward, and a displacement signal detected by the detection trolley displacement detection piece (121) and a bridge floor data signal detected by the laser sensor (123) are transmitted to the cloud database (6) in a wireless mode; and a balance weight (124) for lowering the center of gravity is fixedly arranged at the lower part of the longitudinal beam.
3. The system for detecting the safety of the artificial intelligent bridge according to the claim 2, wherein the real-time detection subsystem (1) performs the following specific operation steps:
a. setting a coordinate system, driving the detection trolley (11) to longitudinally run along the bridge deck, acquiring transverse data signals and longitudinal data signals of the bridge deck in real time by the laser sensor (123), and obtaining real-time coordinate values of each detection point of the bridge deck according to longitudinal displacement data of the detection trolley (11) and test data of the laser sensor (123) on the longitudinal beam and the cross beam;
b. according to the real-time transverse data signals and the displacement signals of the detection trolley (11), digital space curved surfaces of the bridge deck structure along the longitudinal direction and the transverse direction of the bridge are obtained;
c. using step ab, acquiring the digital space curved surface for the same bridge according to a set period by the program, acquiring the digital space curved surface data at different periods and storing the digital space curved surface data in the cloud database (6);
d. comparing the digital space curved surface measured in the step b with the digital space curved surface measured in the step c, and simultaneously comparing the digital space curved surface measured in the step c each time with the digital space curved surface measured in the previous time;
e. and d, according to the comparison result in the step d, establishing a relation between the digital space curved surface change and the safety of the bridge structure according to the experimental research result and computer simulation analysis, and judging the safety of the bridge structure by analyzing and comparing the digital space curved surface change of the same bridge.
4. The artificial intelligent bridge safety detection system according to claim 1, wherein the sensor monitoring subsystem (2) comprises a girder vibration monitoring unit (21), a girder internal force monitoring unit (22), a girder temperature monitoring unit (23), a bridge tower displacement monitoring unit (24), a girder line type monitoring unit (25), a bridge tower vibration monitoring unit (26), a cable vibration monitoring unit (27), a structural damage monitoring unit (28) and an environment monitoring unit (29); the sensor monitoring subsystem (2) is provided with a temperature sensor (201), an acceleration sensor (202), a vibrating wire strain gauge sensor (203), a total station (204), an anemorumbometer (205) and an ultrasonic flaw detector (206); wherein,
the girder vibration monitoring unit (21) is provided with girder vibration monitoring sections at key positions including a node 1/2 of the bridge, each section is provided with 3 first acceleration sensors (2021), and the first acceleration sensors (2021) are used for monitoring vibration responses in the forward bridge direction and the transverse bridge direction;
the main beam internal force monitoring unit (22) is used for monitoring the internal force of the main beam by selecting a representative section of the bridge, the main beam internal force monitoring unit is provided with the vibrating wire strain gauge sensor (203), and the vibrating wire strain gauge sensor (203) is used for monitoring the strain condition of important concrete members or steel members of the bridge under the action of traffic load, wind load and temperature load;
the main beam temperature monitoring unit (23) is used for monitoring the temperature distribution and the influence of temperature on the internal force and deformation of the main beam by selecting a representative section of the main beam through the temperature sensor (201);
the bridge tower displacement monitoring unit (24) and the main beam linear monitoring unit (25) adopt the total station (204) to monitor the main beam linear and the bridge tower displacement;
the bridge tower vibration monitoring unit (26) is provided with monitoring sections on a bridge tower, each monitoring section is provided with 3 second acceleration sensors (2022), and the second acceleration sensors (2022) are used for monitoring acceleration response of the bridge tower under the action of wind and traffic loads;
the cable vibration monitoring unit (27) is characterized in that a monitoring point is arranged on the cable, and a third acceleration sensor (2023) is selected for monitoring the vibration response of the cable in the bridge direction and the transverse bridge direction;
the structural damage monitoring unit (28) adopts the ultrasonic flaw detector (206) or the magnetic powder flaw detector or the crack width measuring instrument, and is used for detecting the depth of concrete cracks, non-compact areas, honeycomb cavities, quality of a joint surface, thickness of a surface damage layer, internal defects of steel pipe concrete and road surface diseases;
the environment monitoring unit (29) adopts the anemorumbometer (205) to monitor the environmental wind power and the wind direction at the bridge site in real time, and monitors the environmental temperature at the bridge site, and the temperature gradient of the main components of the bridge.
5. The system of claim 4, wherein the ultrasonic flaw detector (206) is used for detecting bridge damages in a range of applications including:
judging and calculating the size of the defect according to the diffraction phenomenon of the low-frequency ultrasound when the defect is encountered in the concrete and the time interval of the sound and the change of the sound propagation path;
judging the existence and the size of the defect according to the phenomenon that the ultrasonic generates scattering on the defect interface and the energy is obviously attenuated when the ultrasonic reaches a receiving probe;
according to different attenuation degrees of each frequency component of the ultrasonic pulse when encountering the defect, the receiving frequency is obviously reduced, or the difference between the frequency spectrum of the received wave and the frequency spectrum of the reflected wave is generated, the internal defect can also be judged;
and judging the defect according to the waveform conversion and superposition of the ultrasonic wave at the defect position to cause the phenomenon of received waveform distortion.
6. The system according to claim 5, wherein a camera device for acquiring video data of a bridge deck is arranged at the front end of the chassis of the detection trolley (11), and picture data obtained by the camera device is wirelessly transmitted to the cloud database (6).
7. The bridge security detection system of claim 1, wherein the data acquisition subsystem (3) comprises an acquisition unit (31) and a classification unit (32), wherein,
the acquisition unit (31) is used for acquiring the digital space curved surface data of the bridge deck structure and the picture data shot by the camera device from the cloud database (6);
the classification unit (32) is used for classifying the data in the acquisition unit according to the attributes, so that the subsequent processing is facilitated.
8. The bridge security detection system of claim 7, wherein the analysis and evaluation subsystem (4) comprises a model building unit (41), a correction unit (42) and an evaluation and interpretation unit (43), wherein,
the model establishing unit (41) is used for establishing a bridge structure analysis model adaptive to bridge health conditions and system monitoring;
the correction unit (42) corrects the theoretical model to a certain degree based on deep learning according to the approximation degree of the theoretical model and the actual measurement of the system, so that the theoretical model has higher universality and accuracy;
the evaluation and interpretation unit (43) evaluates and interprets the data acquired from the data acquisition subsystem (3) on the basis of the bridge structure analysis model, and establishes the relevance between the fluctuating detection value and the bridge structure state evaluation and interpretation result.
9. The bridge security detection system of claim 8, wherein the intelligent processing subsystem (5) comprises a processing determination unit (51) and a learning feedback unit (52), wherein,
the processing and determining unit (51) is used for determining monitoring arrangement, monitoring modes and necessary monitoring technical conditions which are necessary for each state of the bridge structure and each important structural characteristic of the bridge according to the bridge structure analysis model;
the learning feedback unit (52) carries out deep analysis on data accumulation and data quality in the cloud database (6) based on a machine learning method, feeds back effective subsystems and corresponding detection data processing methods in the system, and improves the subsystems and the corresponding detection data processing methods.
10. The system according to claim 9, wherein the intelligent processing subsystem (5) further comprises a first management unit (53), an improvement unit (54), a second management unit (55) and an execution unit (56), the first management unit (53) is electrically connected to the improvement unit (54), the improvement unit (54) is electrically connected to the second management unit (55), and the second management unit (55) is electrically connected to the execution unit (56); wherein,
the first management unit (53) is used for collecting the performance information of the artificial intelligent bridge safety detection system;
the improvement unit (54) is used for generating an improvement scheme according to the performance information collected by the first management unit (53);
the second management unit (55) is used for distributing the improvement scheme generated by the improvement unit (54) to the execution unit (56);
the execution unit (56) is used for executing according to the improvement scheme distributed by the second management unit (55) so as to improve the performance of the artificial intelligent bridge safety detection system.
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