CN116611705A - Safety toughness assessment method for highway bridge after disaster - Google Patents

Safety toughness assessment method for highway bridge after disaster Download PDF

Info

Publication number
CN116611705A
CN116611705A CN202310539432.9A CN202310539432A CN116611705A CN 116611705 A CN116611705 A CN 116611705A CN 202310539432 A CN202310539432 A CN 202310539432A CN 116611705 A CN116611705 A CN 116611705A
Authority
CN
China
Prior art keywords
bridge
toughness
safety
disaster
calculating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310539432.9A
Other languages
Chinese (zh)
Inventor
刘成龙
杜豫川
梁文耀
高倩
吴荻非
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tongji University
Original Assignee
Tongji University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tongji University filed Critical Tongji University
Priority to CN202310539432.9A priority Critical patent/CN116611705A/en
Publication of CN116611705A publication Critical patent/CN116611705A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/02—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
    • G01C11/025—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures by scanning the object
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/36—Videogrammetry, i.e. electronic processing of video signals from a single source or from different sources to give parallax or range information
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0041—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress
    • G01M5/005—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress by means of external apparatus, e.g. test benches or portable test systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Multimedia (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Data Mining & Analysis (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Mathematical Physics (AREA)
  • Tourism & Hospitality (AREA)
  • Computational Mathematics (AREA)
  • Operations Research (AREA)
  • Mathematical Optimization (AREA)
  • Algebra (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Mathematical Analysis (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention relates to a method for evaluating safety toughness of a highway bridge after disaster, which comprises the following steps: s1, measuring the length and the vertical displacement of a bridge, and calculating the vibration toughness according to the modal shape and the rigidity contribution ratio of the bridge; s2, obtaining settlement data of the bridge after disasters, and calculating settlement toughness; s3, obtaining a high-resolution image of the bridge through unmanned aerial vehicle close-range photography, performing feature analysis on the image to obtain bridge structure disease features after disasters, and calculating the structure toughness; s4, calculating the traffic toughness through the topological structure of the traffic network where the bridge with traffic demand and travel time is located; s5, recording repair time from when the bridge is subjected to disasters to when the bridge is repaired to an original state, and calculating the safety toughness of the bridge by combining the vibration toughness, the settlement property, the structural toughness and the passing toughness. Compared with the prior art, the bridge safety monitoring system can be used for widely and comprehensively monitoring and evaluating the bridge safety and efficiently judging the key fragile parts of the bridge so as to repair the bridge in time.

Description

Safety toughness assessment method for highway bridge after disaster
Technical Field
The invention relates to the field of bridge infrastructure safety monitoring, in particular to a safety toughness assessment method after highway bridge disaster.
Background
Bridges are an important component of important road infrastructure and traffic networks. However, bridges are often exposed to natural disasters such as floods, earthquakes, typhoons, and the like. As an important part in a traffic network, bridges can reduce self-safety and seriously affect the traffic efficiency of the traffic road network after being affected by disasters. Because bridge repair needs to consume a large amount of time and cost, data such as vibration, displacement, deformation, diseases and the like of the bridge need to be collected and analyzed through different monitoring modes, and the safety of the bridge is ensured.
In bridge safety inspection, visual inspection is a traditional safety assessment method, and personnel evaluate bridge conditions according to inspection data. This method, which is largely dependent on the expertise of the inspector, is a subjective and often inefficient way of inspection. During bridge construction, sensors are usually installed at some key structural parts of the bridge to acquire data of the bridge in real time. However, the sensor is relatively easy to damage, difficult to reinstall, and the data acquired from the sensor only includes some critical structural parts, so that the safety of the bridge is difficult to comprehensively monitor.
The intent of toughness is to resist and rebound, which many scholars have interpreted and applied in traffic road network systems since the introduction of the concept of toughness into the field of transportation. The existing toughness is mainly developed around a traffic road network system, the toughness is explained through the topology structure, the road network efficiency and the road network attribute of the road network, the road network is simplified into edges and nodes, and the characteristics of different road infrastructure are ignored.
In the field of bridge safety, toughness can be defined as the ability of a bridge to resist damage caused by a disaster, and to recover to original safety after a disaster.
Therefore, the bridge safety toughness assessment method is continuously designed, the safety of the bridge can be monitored and assessed widely and comprehensively, and key fragile parts of the bridge can be judged efficiently, so that the bridge can be repaired.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a safety toughness assessment method after highway bridge disaster, which can be used for widely and comprehensively monitoring and assessing the safety of the bridge and efficiently judging the key fragile parts of the bridge so as to repair the bridge in time.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the present invention, there is provided a method for evaluating safety toughness after a highway bridge disaster, the method comprising the steps of:
s1, measuring the length and the vertical displacement of the bridge, and calculating the vibration toughness R of the bridge according to the modal shape and the rigidity contribution ratio of the bridge vir ；
S2, acquiring settlement data of the bridge after the bridge is subjected to disasters, and calculating settlement toughness R of the bridge set ；
S3, obtaining a high-resolution image of the bridge through unmanned aerial vehicle close-range photography, performing feature analysis on the image to obtain bridge structural disease features after disasters, and calculating structural toughness R of the bridge str ；
S4, calculating the passing toughness V of the bridge through the topological structure of the traffic network where the bridge with traffic demand and travel time is located tra ；
Step S5,Recording repair time t from when the bridge is subjected to disaster to when the bridge is repaired to an original state, and combining vibration toughness R vir Subsidence property R set Structural toughness R str Toughness V of passing through tra And calculating the safety toughness R of the bridge.
Preferably, the step S1 comprises the following sub-steps:
s11, measuring the length L of the bridge, and detecting the vertical displacement y of the bridge by using a bridge strain sensor;
s12, calculating the stiffness contribution ratio R of the jth unit of the ith-order mode shape of the bridge before damage according to the mode shape of the bridge before disaster ij The expression is:
where k (x) is the initial bending stiffness of the bridge, k j Is the initial bending stiffness of the jth cell, Φ i (x) Is the i-th order mode shape;
s13, calculating the stiffness contribution ratio of the ith-order mode shape and the jth unit of the damaged bridge according to the mode shape of the damaged bridgeThe expression is:
wherein k is * (x) Is the bending rigidity of the bridge after being damaged by disasters,is the bending stiffness of the jth cell, which is disaster damaged,/->Is the i-th order mode shape;
step S14, go throughBridge vibration rigidity ratio before and after disaster damage, and vibration toughness R of the bridge is calculated vir ：
Where n is the number of units.
Preferably, said step S2 comprises the following sub-steps:
s21, revisiting a target area through a PS-InSAR satellite, imaging at multiple angles, repeatedly shooting a bridge body and a road surface to obtain elevation information, and extracting sedimentation data by using amplitude dispersion, phase and coherence coefficient of an image after correction registration and radiometric calibration treatment;
s22, extracting pixels with set coherence after a set time interval as PS points by analyzing amplitude stability coefficients of each pixel, removing offset values of an atmospheric phase, a residual phase and a sight direction target object from an interference phase according to phase change of the PS points, analyzing to obtain deformation measurement values, and finally generating an average offset rate graph of surface deformation so as to monitor tiny foundation settlement;
s23, analyzing the settlement amount by an expert analysis methodAnd bridge characteristics, giving the settlement toughness R of the bridge after disasters set 。
Preferably, said step S3 comprises the following sub-steps:
s31, collecting images of bridge piers, bridge supports and beam bodies of bridges after disasters through unmanned aerial vehicles;
s32, identifying the bridge pier disease area, the bridge bearing disease area and the beam body disease area through an artificial intelligent algorithm;
step S33, determining the severity of different structural diseases by combining the type, the length characteristics and the severity and the type of disasters of the bridge through an expert analysis method, and determining the weights w of the different diseases;
s34, calculating structural toughness R of the bridge through the disease areas and weights of different bridge structures str 。
Preferably, the bridge pier diseases in the step S32 include corrosion damage, erosion cracking and cracking disease phenomena of bridge pier concrete; bridge bearing diseases include aging, fracture, dislocation and void phenomena; beam body diseases include corrosion damage, scour cracking and cracking.
Preferably, the structural toughness R of the bridge str The computational expression is:
R str ＝S 1 ·w 1 +S 2 ·w 2 +S 3 ·w 3
wherein S is 1 、S 2 、S 3 Respectively the disease area of the bridge pier structure, the disease area of the support structure and the concrete disease area of the beam body, w 1 、w 2 、w 3 Is the corresponding weight.
Preferably, the passing toughness V of the bridge in the step S4 tra The computational expression is:
wherein q is rs In order to pass the traffic demand through the bridge,is the shortest travel time before and after the occurrence of the disaster.
Preferably, the safety toughness R of the bridge in step S5 is expressed as follows:
according to a second aspect of the present invention there is provided an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method of any one of the above when executing the program.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any one of the above.
Compared with the prior art, the invention has the following advantages:
1) The invention is triggered from the angles of vibration toughness, settlement toughness, structural toughness and traffic toughness of the bridge respectively, can monitor and evaluate the safety of the bridge widely and comprehensively, and efficiently judges the key fragile parts of the bridge so as to repair the bridge.
2) The invention adopts a multisource perception inspection method, comprises the detection technologies of InSAR, unmanned aerial vehicle and the like, and can comprehensively detect the safety of the bridge.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a general frame diagram of the present invention;
FIG. 3 is an acquired vertical displacement diagram of a bridge section;
fig. 4 is a diagram for detecting bridge structural damage of an unmanned aerial vehicle.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
The embodiment provides a disaster-oriented bridge safety toughness assessment method, which comprises the following steps:
step 1: the vibration toughness of the bridge is calculated, and the concrete steps are as follows:
step 11: the length L of the bridge is measured, and the bridge strain sensor is used for detecting the vertical displacement y of the bridge in the longitudinal direction x.
Step 12: the mode shape of the bridge before the disaster is calculated, and the calculation formula is as follows:
by differentiating the formula (1), the initial i-th order mode shape of the bridge can be obtained.
Step 13: the stiffness contribution ratio of the ith-order mode shape jth unit of the bridge before damage is calculated, and the calculation formula is as follows:
wherein: k (x) in formula (1) is the initial bending stiffness of the bridge, k j Is the initial bending stiffness of the jth cell, Φ i (x) Is the i-th order mode shape.
Step 14: and calculating the mode shape of the bridge after being damaged by disasters.
And (3) differentiating the formula to obtain the ith-order mode shape after the bridge is damaged.
Step 15: the stiffness contribution ratio of the jth unit of the ith-order mode shape after the bridge is damaged is calculated, and the calculation formula is as follows:
wherein: k in formula (1) * (x) Is the bending rigidity of the bridge after being damaged by disasters,is the bending stiffness of the jth cell, which is disaster damaged,/->Is the i-th order mode shape.
Step 16: the vibration toughness of the bridge is calculated through the vibration rigidity ratio of the bridge before and after disaster damage, and the calculation formula is as follows:
step 2: the spaceborne synthetic aperture radar (InSAR) is an all-weather, high-precision and large-area earth observation technology, and can obtain information such as elevation and deformation of a bridge. Detecting settlement of the bridge after the disaster by using the InSAR technology, and calculating settlement toughness of the bridge, wherein the concrete steps are as follows:
step 21: revisiting the target area through the PS-InSAR satellite, imaging at multiple angles, and repeatedly shooting the bridge body and the road surface to obtain elevation information of the bridge body and the road surface. The system extracts sedimentation information by using amplitude dispersion, phase, coherence coefficient and the like of the image after the acquired data are processed by correction registration, radiation calibration and the like.
Step 22: the method comprises the steps of analyzing amplitude stability coefficients of each pixel, extracting pixels which still have good coherence after long time interval to serve as PS points, researching phase change of the PS points, removing offset values of an atmospheric phase, a residual phase and a sight direction target object from an interference phase, analyzing to obtain deformation measurement values, and finally generating an average offset rate image of ground surface deformation, so that tiny foundation settlement is monitored. The calculation formula is as follows:
wherein:a differential interference phase for the i-th pixel; />Is the surface deformation quantity; />Is the atmospheric phase; />Is a random error phase; />Is the residual phase (caused by PS position elevation error and external DEM error); />Is the noise residual phase.
Step 23: by expert analysis, the sedimentation amount is maximizedAnalysis is carried out to determine the maximum acceptable sedimentation variant and +.>Calculating the settlement toughness R of the bridge after disasters set 。
Step 3: the high-resolution image of the bridge is obtained through unmanned aerial vehicle close-range photography, the image is subjected to feature analysis, and the bridge structural disease obtaining method is achieved, so that the working efficiency of bridge detection is effectively improved. And analyzing structural defects of the bridge after disasters, and calculating structural toughness of the bridge. The method comprises the following specific steps:
step 31: the unmanned aerial vehicle can reach the difficult position of traditional manpower detection, such as bridge beam supports, pier etc. position, and carry out succinct quick repeated sampling to detail part. Bridge piers, supports and Liang Tiying of the bridge after disasters are collected through unmanned aerial vehicles.
Step 32: the area of the bridge pier concrete with corrosion damage, scouring crack, cracking and other diseases is identified through an artificial intelligence algorithm and recorded as S 1 The method comprises the steps of carrying out a first treatment on the surface of the The area of structural diseases such as aging, fracture, dislocation, void and the like of the bridge support is identified and recorded as S 2 The method comprises the steps of carrying out a first treatment on the surface of the Identifying cracking of beam body concreteThe area of the fallen disease was recorded as S 3 。
Step 33: and determining the severity of different structural diseases by an expert analysis method in combination with the type, length and other characteristics of the bridge and the severity and type of disasters, and determining the weights w of different diseases.
Step 34: and calculating the structural toughness of the bridge by the areas and weights of different bridge structural diseases, wherein the calculation formula is as follows:
R str ＝S 1 ·w 1 +S 2 ·w 2 +S 3 ·w 3 (7)
wherein w is 1 Is the weight of diseases of the bridge pier structure, w 2 Is the weight of diseases of the support structure, w 3 Is the weight of the beam body structural diseases.
And 4, calculating the traffic toughness of the bridge in the road traffic network, wherein the concrete steps are as follows:
step 41, collecting traffic demand q passing through the bridge rs Shortest trip time in the absence of disastersShortest journey time after disaster +.>And calculating the topological structure of the traffic network where the bridge is positioned.
Step 42, calculating the passing toughness of the bridge, wherein the calculation formula is as follows:
and 5, recording repair time t from the beginning of disaster to the repair of the bridge to the original state, and calculating the safety toughness of the bridge, wherein the calculation formula is as follows:
a method for evaluating safety toughness of a highway bridge after disaster is developed on a main channel bridge of a Shanghai city long Jiang Sui bridge, and a toughness evaluation framework scheme shown in figure 2 is adopted. The strain sensor is used for detecting the vertical deformation of the cross section of the bridge, as shown in fig. 3, then the mode shape of the bridge before and after the disaster is calculated, then the initial stiffness contribution ratio and the post-disaster stiffness contribution ratio of the bridge are calculated, and the vibration toughness of the bridge is calculated through the stiffness contribution ratio before and after the disaster. And acquiring bridge data by using the InSAR satellite, and processing and analyzing the data to obtain the settlement of the bridge after the disaster. And 5 experts analyze and judge the bridge characteristics and the corresponding settlement amount to give the settlement toughness of the bridge. The unmanned aerial vehicle is used for collecting high-precision images of structures such as piers, supports and beam bodies, and different diseases are identified and separated through a convolutional neural network, as shown in fig. 4. And determining weights of different bridge structural diseases through expert analysis, and calculating the structural toughness of the bridge. Calculating a road network topological structure in a traffic road network range of the Yangtze river tunnel bridge, recording an initial state, average vehicle shortest travel time and traffic demand after disasters, and then calculating the traffic toughness of the bridge. And finally, recording the repair time of the bridge after the disaster, and calculating the safety toughness of the bridge according to the determined index.
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit performs the respective methods and processes described above, for example, the methods S1 to S5. For example, in some embodiments, methods S1-S5 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S5 in any other suitable manner (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (10)

1. The method for evaluating the safety toughness of the highway bridge after disaster is characterized by comprising the following steps of:
s1, measuring the length and the vertical displacement of the bridge, and calculating the vibration toughness R of the bridge according to the modal shape and the rigidity contribution ratio of the bridge vir ；
S2, acquiring settlement data of the bridge after the bridge is subjected to disasters, and calculating settlement toughness R of the bridge set ；
S3, obtaining a high-resolution image of the bridge through unmanned aerial vehicle close-range photography, performing feature analysis on the image to obtain bridge structural disease features after disasters, and calculating structural toughness R of the bridge str ；
S4, calculating the passing toughness V of the bridge through the topological structure of the traffic network where the bridge with traffic demand and travel time is located tra ；
S5, recording repair time t from when the bridge is subjected to disaster to when the bridge is repaired to an original state, and combining vibration toughness R vir Subsidence property R set Structural toughness R str Toughness V of passing through tra And calculating the safety toughness R of the bridge.
2. The method for evaluating the safety toughness after a highway bridge disaster according to claim 1, wherein said step S1 comprises the following substeps:
s11, measuring the length L of the bridge, and detecting the vertical displacement y of the bridge by using a bridge strain sensor;
s12, calculating the stiffness contribution ratio R of the jth unit of the ith-order mode shape of the bridge before damage according to the mode shape of the bridge before disaster ij The expression is:
where k (x) is the initial bending stiffness of the bridge, k j Is the initial bending stiffness of the jth cell, Φ i (x) Is the i-th order mode shape;
s13, calculating the stiffness contribution ratio of the ith-order mode shape and the jth unit of the damaged bridge according to the mode shape of the damaged bridgeThe expression is:
wherein k is * (x) Is the bending rigidity of the bridge after being damaged by disasters,is the bending stiffness of the jth cell, which is disaster damaged,/->Is the i-th order mode shape;
step S14, disaster passingThe vibration rigidity ratio of the bridge before and after the damage is calculated to obtain the vibration toughness R of the bridge vir ：
Where n is the number of units.
3. The method for evaluating the safety toughness after a highway bridge disaster according to claim 1, wherein said step S2 comprises the following substeps:
s21, revisiting a target area through a PS-InSAR satellite, imaging at multiple angles, repeatedly shooting a bridge body and a road surface to obtain elevation information, and extracting sedimentation data by using amplitude dispersion, phase and coherence coefficient of an image after correction registration and radiometric calibration treatment;
s22, extracting pixels with set coherence after a set time interval as PS points by analyzing amplitude stability coefficients of each pixel, removing offset values of an atmospheric phase, a residual phase and a sight direction target object from an interference phase according to phase change of the PS points, analyzing to obtain deformation measurement values, and finally generating an average offset rate graph of surface deformation so as to monitor tiny foundation settlement;
s23, analyzing the settlement amount by an expert analysis methodAnd bridge characteristics, giving the settlement toughness R of the bridge after disasters set 。
4. The method for evaluating the safety toughness after a highway bridge disaster according to claim 1, wherein said step S3 comprises the following substeps:
s31, collecting images of bridge piers, bridge supports and beam bodies of bridges after disasters through unmanned aerial vehicles;
s32, identifying the bridge pier disease area, the bridge bearing disease area and the beam body disease area through an artificial intelligent algorithm;
step S33, determining the severity of different structural diseases by combining the type, the length characteristics and the severity and the type of disasters of the bridge through an expert analysis method, and determining the weights w of the different diseases;
s34, calculating structural toughness R of the bridge through the disease areas and weights of different bridge structures str 。
5. The method for evaluating the safety toughness of a highway bridge post-disaster according to claim 4, wherein the pier diseases in the step S32 include corrosion damage, erosion crack and cracking disease phenomena of pier concrete; bridge bearing diseases include aging, fracture, dislocation and void phenomena; beam body diseases include corrosion damage, scour cracking and cracking.
6. The method for evaluating the post-disaster safety toughness of a highway bridge according to claim 4, wherein the structural toughness R of the bridge is str The computational expression is:
R str ＝S 1 ·w 1 +S 2 ·w 2 +S 3 ·w 3
wherein S is 1 、S 2 、S 3 Respectively the disease area of the bridge pier structure, the disease area of the support structure and the concrete disease area of the beam body, w 1 、w 2 、w 3 Is the corresponding weight.
7. The method for evaluating the post-disaster safety toughness of a highway bridge according to claim 1, wherein the passing toughness V of the bridge in step S4 tra The computational expression is:
wherein q is rs In order to pass the traffic demand through the bridge,is the shortest travel time before and after the occurrence of the disaster.
8. The method for evaluating the safety toughness of a highway bridge after disaster according to claim 1, wherein the safety toughness R of the bridge in the step S5 has the following expression:
9. an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-8.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-8.
CN202310539432.9A 2023-05-12 2023-05-12 Safety toughness assessment method for highway bridge after disaster Pending CN116611705A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310539432.9A CN116611705A (en) 2023-05-12 2023-05-12 Safety toughness assessment method for highway bridge after disaster

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310539432.9A CN116611705A (en) 2023-05-12 2023-05-12 Safety toughness assessment method for highway bridge after disaster

Publications (1)

Publication Number Publication Date
CN116611705A true CN116611705A (en) 2023-08-18

Family

ID=87682887

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310539432.9A Pending CN116611705A (en) 2023-05-12 2023-05-12 Safety toughness assessment method for highway bridge after disaster

Country Status (1)

Country Link
CN (1) CN116611705A (en)

Similar Documents

Publication Publication Date Title
An et al. Recent progress and future trends on damage identification methods for bridge structures
Alamdari et al. Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge
Kim et al. Automated bridge component recognition from point clouds using deep learning
Yu et al. Structural identification of an 18-story RC building in Nepal using post-earthquake ambient vibration and lidar data
WO2020207070A1 (en) Method and system for evaluating shenzhen sea water quality
US11585721B2 (en) Displacement component detection apparatus, displacement component detection method, and computer-readable recording medium
Quqa et al. Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks
Zhang et al. Vortex‐induced vibration measurement of a long‐span suspension bridge through noncontact sensing strategies
WO2019126972A1 (en) Deformation information extraction method using insar, terminal, and storage medium
JP2023139099A (en) Learning data collecting apparatus, learning data collecting method, and program
CN114705689A (en) Unmanned aerial vehicle-based method and system for detecting cracks of outer vertical face of building
JP7351849B2 (en) Structure damage cause estimation system, damage cause estimation method, and damage cause estimation server
US20220138970A1 (en) Structural vibration monitoring method based on computer vision and motion compensation
Soleymani et al. Damage detection and monitoring in heritage masonry structures: Systematic review
CN112411371A (en) Highway bridge comprehensive detection method and system based on mobile sensing and vision
CN108108568B (en) Method for eliminating low-frequency sampling indexes affecting online safety monitoring of bridge structure due to temperature
Zhang et al. The application of deep learning in bridge health monitoring: a literature review
Zhou et al. Mechanical state inversion method for structural performance evaluation of existing suspension bridges using 3D laser scanning
Moeinifard et al. Lost vibration test data recovery using convolutional neural network: a case study
Chegeni et al. New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique
Wang et al. Target-free recognition of cable vibration in complex backgrounds based on computer vision
Hassani et al. Smart bridge monitoring
CN116611705A (en) Safety toughness assessment method for highway bridge after disaster
Zhang et al. Multi-resource fusion of nondestructive evaluation data for bridge deck assessment using discrete wavelet transform and Dempster-Shafer theory
Peng et al. Computer vision-based displacement identification and its application to bridge condition assessment under operational conditions

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination