CN117807853B - Non-contact damage prediction method for steel arch bridge suspender and related products - Google Patents
Non-contact damage prediction method for steel arch bridge suspender and related products Download PDFInfo
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Abstract
The invention relates to the technical field of steel arch bridge suspender life detection, in particular to a steel arch bridge suspender non-contact damage prediction method and related products, wherein the method comprises the steps of obtaining a total magnetic field intensity measurement value of any rusted area; acquiring a boom corrosion loss coefficient at a corresponding position; obtaining a resistance probability density function of the structural member; obtaining a combined tension value of the suspender under the combined load effect; obtaining a load probability density function of the boom under a generalized effect; obtaining the corrosion damage probability of the suspender through the resistance probability density function and the load probability density function; the invention can acquire the total magnetic field intensity measured value of any rusted area in a non-contact way, accurately predict the resistance change of the suspender under the actual working condition, calculate the corrosion damage probability of the suspender according to the load probability density function and the resistance probability density function, judge the risk through the set damage risk value and predict the health state of the suspender.
Description
Technical Field
The invention relates to the technical field of steel arch bridge suspender life detection, in particular to a non-contact damage prediction method for a steel arch bridge suspender and a related product.
Background
The steel arch bridge is widely applied to bridge engineering due to the unique structural characteristics and attractive appearance. In steel arch bridges, the boom is a critical stressed member, the health of which is directly related to the overall safety of the bridge. However, due to the influence of factors such as environmental corrosion, overload operation, material aging and the like, the suspender can be damaged by corrosion, fatigue crack and the like, and the safe operation of the bridge is seriously threatened.
Currently, health monitoring of bridge booms mainly relies on traditional contact detection techniques, such as electromagnetic flaw detection, ultrasonic flaw detection, and the like. Although the methods can evaluate the damage condition of the interior and the surface of the suspender to a certain extent, the methods have a plurality of defects such as complicated detection process, high detection cost, strict requirements on environmental conditions and the like. In addition, the contact detection method generally needs to conduct traffic control on the bridge, and influences normal use of the bridge.
Disclosure of Invention
The invention aims to solve the technical problem that the contact type detection difficulty is high, and aims to provide a non-contact type damage prediction method for a steel arch bridge suspender and related products, so that the rust damage of the suspender is predicted and analyzed under the non-contact condition.
The invention is realized by the following technical scheme:
a non-contact damage prediction method for a steel arch bridge suspender comprises the following steps:
constructing a rust damage magnetic field function of the boom, and acquiring a total magnetic field intensity measurement value of any rust area through the rust damage magnetic field function;
acquiring a boom corrosion loss coefficient at a corresponding position through a magnetic field intensity measurement value;
obtaining a resistance probability density function of the structural member;
establishing a finite element model of the steel arch bridge to obtain a combined tension value of the suspender under the combined load action;
obtaining a load probability density function of the boom under the generalized effect based on the combined tension value;
Obtaining the corrosion damage probability of the suspender through the resistance probability density function and the load probability density function;
Setting a damage risk value, and setting the suspender to be in a damage risk state if the corrosion damage probability of the suspender is larger than the damage risk value; otherwise, the boom is set in a non-damage risk state.
Specifically, the method of the rust damage magnetic field function of the boom comprises the following steps:
Determining the position of a corrosion area of the suspender, and obtaining the axial length of the corrosion area And circumferential width/>；
Determination of axial tension of boom at rusted areaAnd radial stress/>；
Calculating to obtain magnetic field detection pointDistance from axially negative magnetic charge of rusted region/>Calculating to obtain magnetic field detection point/>Distance from positive magnetic charge in axial direction of rusted region/>Calculating to obtain magnetic field detection point/>Distance from axially downward negative magnetic charge of rusted region/>Calculating to obtain magnetic field detection point/>Distance from positive magnetic charge under the axial direction of rusted region/>；
Respectively calculating the rust areas、/>、/>Magnetic field strength measurements in three directions/>、/>、/>The calculation formula of the magnetic field strength measurement value comprises:
；
；
；
calculation of total magnetic field strength measurement for rusted region ；
Wherein,Is the axial magnetization of the position of the rusted area in the non-rusted state,/>Is the radial magnetization of the position of the rusted area in the non-rusted state,/>Is the magnetic flux of rusted area,/>Is the diameter of the hanging rod,/>For the connection line between the center of the rusted area and the central axis of the suspender and the magnetic field detection point/>Included angle of connecting line between central axis of suspender,/>Is the/>, of the center of the rusted areaAxis coordinates,/>Is the/>, of the center of the rusted areaAxis coordinates,/>Is the/>, of the center of the rusted areaAnd (5) axis coordinates.
Optionally, the distance、/>、/>、/>The calculation method of (1) comprises the following steps:
establishing by taking a magnetic field detection point as an origin Coordinate system, wherein/>The shaft is parallel to the central axis of the suspender;
Determining a magnetic field detection point Vector of extreme points of upper, lower, left and right of the rusted area:
；
；
；
。
specifically, the corrosion loss coefficient of the boom is calculated Wherein/>Is the average value of the total magnetic field intensity of the non-rusted suspender,/>The total magnetic field intensity of the rusted area is measured;
the method for obtaining the resistance probability density function of the structural member comprises the following steps:
determining bridge structural resistance influence factors of the steel arch bridge: uncertainty of material performance, uncertainty of geometric parameters of structural members and uncertainty of calculation modes;
Building a material performance uncertainty probability density model, a structural member geometric parameter uncertainty probability density model and a calculation mode uncertainty probability density model, and obtaining a bias mean value of a probability density function And coefficient of variation/>Wherein the deviation average value and the variation coefficient are both taken according to the axle center tension member;
obtaining a resistance probability density function of a structural member using an indirect method Wherein, the method comprises the steps of, wherein,Calculated for theoretical resistance of the non-rusted boom,/>Load level carried for steel arch bridge,/>Is a natural constant.
Specifically, the combined load includes dead load, live load, temperature load, and environmental load, the environmental load being geological load, wind load, ice and snow load, natural disaster load, and/or water load;
the load probability density function is Wherein/>For the theoretical tension value of the boom under combined load,/>Load level carried for steel arch bridge,/>Is a natural constant.
Specifically, a boom rust damage probability density function is constructed,Wherein/>For a load level of/>Resistance obtained by calculation of the probability density function of resistance,/>For a load level of/>Load obtained by calculation of the probability density function of resistance,/>For a load level of/>Load probability density function at that time.
Optionally, the method of determining the rust area comprises:
acquiring a high-resolution image of the boom through the unmanned aerial vehicle, and preprocessing the image;
Identifying a corrosion area of the suspender through the trained convolutional neural network model, and acquiring an initial contour of the corrosion area through a Canny edge detection algorithm;
Based on the initial contour, acquiring the actual contour of the rusted area by utilizing a contour detection algorithm;
performing rectangular fitting on the actual contour by using a rectangular fitting algorithm, and enabling two sides of the fitted rectangle to be parallel to the central axis of the suspender;
The minimum circumscribed rectangle of the actual profile is obtained by fitting and is used as the rust area of the boom.
The non-contact damage prediction terminal for the steel arch bridge suspender comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the non-contact damage prediction method for the steel arch bridge suspender when executing the computer program.
A computer readable storage medium storing a computer program which when executed by a processor implements a steel arch bridge boom non-contact damage prediction method as described above.
A computer program product comprising computer programs/instructions which when executed by a processor implement a steel arch bridge boom non-contact damage prediction method as described above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
According to the invention, the total magnetic field intensity measured value of any rusted area can be obtained in a non-contact manner by constructing the rusted damage magnetic field function of the suspender, the resistance change of the suspender under the actual working condition can be predicted more accurately by obtaining the resistance probability density function of the structural member, the healthy state of the suspender is predicted by constructing the finite element model of the steel arch bridge, considering the suspender pulling force under the combined load effect, constructing the load probability density function to obtain the load probability density distribution, calculating the rusted damage probability of the suspender according to the load probability density function and the resistance probability density function, judging the risk by the set damage risk value, and predicting the healthy state of the suspender.
According to the invention, through the rust damage magnetic field function, non-contact detection of the rust loss of the suspender is realized, secondary damage possibly brought by a traditional contact detection method is avoided, the influence of detection on normal use of a bridge is greatly reduced, and the detection efficiency is improved.
By combining the resistance probability density function and the load probability density function, various factors such as material performance, structural characteristics, load change and the like can be comprehensively considered, the corrosion damage probability of the suspender is predicted more accurately, and the reliability and the accuracy of damage prediction are improved.
Drawings
Fig. 1 is a schematic flow chart of a method for predicting non-contact damage of a steel arch bridge boom according to the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and embodiments, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent. It is to be understood that the specific embodiments described herein are merely illustrative of the substances, and not restrictive of the invention.
It should be further noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
Embodiments of the present invention and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1, a non-contact damage prediction method for a steel arch bridge boom includes:
firstly, constructing a rust damage magnetic field function of a boom, and acquiring a total magnetic field intensity measurement value of any rust area through the rust damage magnetic field function; and obtaining the corrosion loss coefficient of the boom at the corresponding position through the magnetic field intensity measurement value.
By establishing a magnetic field model, the rust degree of the boom can be evaluated in a non-contact manner. The rust of the boom can affect the magnetism, and the rust condition of the boom can be deduced by measuring the change of the magnetic field, so that a rust damage magnetic field function is constructed, and the damage condition of the boom is predicted based on the relationship between the magnetic field and the substance state (such as the rust degree). In actual use, the unmanned aerial vehicle is equipped with a magnetic field generating source and a magnetic field detector, the boom is placed in the magnetic field by the magnetic field generating source, and the magnetic field strength of the rusted area can be obtained by the magnetic field detector.
The measured magnetic field strength is converted into a more visual parameter, namely a corrosion loss coefficient, so as to evaluate the loss degree of the hanger rod, and the corrosion loss coefficient can be quantitatively calculated by comparing the magnetic field strength of the corroded area and the non-corroded area.
And secondly, obtaining a resistance probability density function of the structural member.
By establishing a resistance probability density function, the resistance change of the structural member under the influence of uncertainty is quantitatively analyzed, and the uncertainty of factors such as material performance, structural geometric parameters, loading effect and the like is considered by the model. The resistance distribution of the structural member under different conditions can be predicted by a probability statistical method.
Thirdly, a finite element model of the steel arch bridge is established, a combined tension value of the suspender under the action of combined load is obtained, finite element analysis is a conventional computer simulation technology, the response of the structure under the action of various loads can be simulated, and the stress state of the whole structure can be obtained by dividing the structure into a limited number of small units and analyzing each unit. The finite element model of the steel arch bridge is constructed, wherein the finite element model comprises material properties, geometric dimensions, boundary conditions and the like, then combined loads such as dead load, live load, temperature load and the like are applied, and the combined tension value of the suspender is calculated.
And obtaining a load probability density function of the boom under the generalized effect based on the combined tension value.
In actual conditions, the load carried by the boom may vary due to various factors, such as traffic flow, environmental impact, and the like. The load probability density function can describe the probability distribution of the boom's force at different load levels.
Fourthly, obtaining the corrosion damage probability of the suspender through a resistance probability density function and a load probability density function; the corrosion damage probability refers to the possibility of corrosion damage of the boom in consideration of the boom material resistance and the actual bearing load. By combining the resistance probability density function and the load probability density function, the safety of the boom in different rusting states can be quantitatively evaluated.
Fifthly, setting a damage risk value, and setting the suspender to be in a damage risk state if the corrosion damage probability of the suspender is larger than the damage risk value; otherwise, the boom is set in a non-damage risk state. The corrosion damage probability of the suspender can be directly sent, and the suspender with the damage risk value larger than the damage risk value can be sent, so that the early warning of the service life of the suspender is realized.
Example two
The rust damage magnetic field function method of the boom comprises the following steps:
S1, determining the position of a corrosion area of a suspender, and obtaining the axial length of the corrosion area And circumferential width/>; The location and size of the rust area is the basis for evaluating its impact on the overall performance of the boom. Typically by visual inspection, image processing techniques or other non-contact detection methods. In this embodiment, the unmanned aerial vehicle carries the high-definition camera to shoot the real-time image of the boom, and the corresponding position is determined through the image processing technology.
S2, determining the axial tension of the suspender at the rusted areaAnd radial stress/>; The stress state can influence the magnetization characteristic of the rusted area, so that the stress of any position of the suspender can be analyzed by establishing a finite element model of the steel arch bridge, and after the rusted area is determined, the axial tension/>, of the corresponding position can be obtainedAnd radial stress/>。
S3, calculating to obtain a magnetic field detection pointDistance from axially negative magnetic charge of rusted region/>Calculating to obtain magnetic field detection point/>Distance from positive magnetic charge in axial direction of rusted region/>Calculating to obtain magnetic field detection point/>Distance from axially downward negative magnetic charge of rusted region/>Calculating to obtain magnetic field detection point/>Distance from positive magnetic charge under the axial direction of rusted region/>；
In the present embodiment, the magnetic field detection points are provided with a magnetic field generation source and a magnetic field detector, and in the magnetic field model, the magnetic change of the rusted region can be simplified to be the distribution of magnetic charges. Magnetic charge is used to denote the ability of an object to produce a magnetic effect in a magnetic field. Here, the positive and negative magnetic charges simulate magnetization changes in the rusted region, respectively.
In this embodiment, the unmanned aerial vehicle flies along the boom with a magnetic field generator that produces a steady, known strength and known distribution magnetic field alongside the boom. The rusted area of the boom will change the distribution of the magnetic field. When a magnetic field is generated by a magnetic field generator on the unmanned aerial vehicle, the magnetic characteristics (such as magnetic permeability change) of the rusted area can cause the magnetic field to change. The magnetic field detector detects the point of the magnetic fieldThe magnetic field after the influence of the rusted region is measured.
The magnetic field generator in this embodiment needs to be able to generate a magnetic field of sufficient strength and stability at a distance from the boom so that the magnetic change in the rusted area can be effectively detected.
The magnetic field generated by the magnetic field generator may interfere with the proper operation of the magnetic field detector, measures (e.g., using shielding materials, a reasonably designed spatial layout, etc.) need to be taken to minimize such interference, and since the magnetic field measurements may be disturbed by the electromagnetic field of the drone itself, appropriate algorithms need to be employed during the data processing and analysis stages to eliminate such potential disturbances.
Distance of、/>、/>、/>The calculation method of (1) comprises the following steps:
s31, establishing a reference coordinate system by taking the magnetic field detection point as an origin Coordinate system, wherein/>The shaft is parallel to the central axis of the boom.
S32, determining a magnetic field detection pointVector of extreme points of upper, lower, left and right of the rusted area:
； Respectively representing the position vectors of the negative magnetic charge and the positive magnetic charge in the axial direction from the magnetic field detection point P to the rust area; /(I) ；; Respectively representing the position vectors of the negative magnetic charge and the positive magnetic charge in the axial direction from the magnetic field detection point P to the rust area;
Wherein, Is the diameter of the hanging rod,/>Is a connecting line between the center of the rust area and the central axis of the boom and a magnetic field detection pointIncluded angle of connecting line between central axis of suspender,/>Is the/>, of the center of the rusted areaAxis coordinates,/>Is the/>, of the center of the rusted areaAxis coordinates,/>Is the/>, of the center of the rusted areaAnd (5) axis coordinates.
S33, calculating the length of each vector to obtain each distance. By calculating the Euclidean length of the vector (typically the square root of the sum of the squares of the components of the vector), the actual distance of the field detection point to each magnetic charge point in the rusted region can be obtained.
S4, respectively calculating the corrosion areas、/>、/>Magnetic field strength measurements in three directions/>、/>、/>The calculation formula of the magnetic field strength measurement value comprises:
；
；
；
Wherein, Is the axial magnetization of the position of the rusted area in the non-rusted state,/>Is the radial magnetization of the position of the rusted area in the non-rusted state. The magnetization in the non-rusted state is obtained by experimental or theoretical calculation according to the magnetic field strength generated by the magnetic field generator. /(I)The magnetic flux, which is the rusted area, is detected by a magnetic field detector.
S5, calculating the total magnetic field intensity measurement value of the rusted area。
Example III
Calculating corrosion loss coefficient of suspenderWherein/>Is the average value of the total magnetic field intensity of the non-rusted suspender,/>The total magnetic field intensity of the rusted area is measured;
the method for obtaining the resistance probability density function of the structural member comprises the following steps:
Determining bridge structural resistance influence factors of the steel arch bridge: material performance uncertainty-considering the influence of variation of material characteristics on structural performance, structural member geometric parameter uncertainty-considering geometric dimension variation due to manufacturing errors, assembly errors and the like, calculation mode uncertainty-involving assumptions and approximations in modeling and calculation processes;
Building a material performance uncertainty probability density model, a structural member geometric parameter uncertainty probability density model and a calculation mode uncertainty probability density model, and obtaining a bias mean value of a probability density function And coefficient of variation/>Wherein the deviation average value and the variation coefficient are both taken according to the axle center tension member; the skewness mean reflects the asymmetry of the probability density distribution and the coefficient of variation reflects the degree of dispersion relative to the mean.
Obtaining a resistance probability density function of a structural member using an indirect methodWherein, the method comprises the steps of, wherein,Calculated for theoretical resistance of the non-rusted boom,/>Load level carried for steel arch bridge,/>Is a natural constant.
A large amount of data is collected regarding the steel arch bridge material properties, geometric parameters, and calculation patterns, from which statistical methods (e.g., fitting distributions, regression analysis, etc.) can be used to determine the specific form of probability density function for each uncertainty. The usual distributions include normal distribution, lognormal distribution, beta distribution, etc., the form of the probability density function is determined, then parameters such as mean, standard deviation, skewness, kurtosis, etc. are required to be estimated, and these can be achieved by statistical methods (such as maximum likelihood estimation, bayesian estimation, etc.), and finally the probability density function is established, which can be implemented by those skilled in the art.
Example IV
The combined load includes dead load, live load, temperature load and environmental load, the environmental load being geological load, wind load, ice and snow load, natural disaster load and/or water load.
Dead load is a constant load created by the weight of the structure itself. The live load is a load that varies during use, such as a vehicle, a pedestrian, or the like. The temperature load is a load caused by a temperature change.
Geological loading involves the influence of geological conditions on the bridge, such as earthquakes, foundation settlement, soil pressure, etc. In particular in seismic active areas, geological loading may be an important consideration in design to ensure stability and seismic performance of the bridge.
Wind load is an important external load of a bridge structure, strong wind can cause the bridge to generate wind vibration, and vibration load is applied to the structure, so that the direction, speed and transverse load of wind are required to be considered, and the bridge can safely run under various meteorological conditions.
In cold climates, snow and ice may load the bridge, including the weight of snow, the impact of ice coating and melting processes on the structure.
Natural disaster loading is the destructive effect that natural disasters such as floods, geologic landslides, etc. may have on bridges. Factors such as water currents, geological changes and the like possibly caused by the natural disasters need to be considered so as to ensure that the bridge has enough stability and tolerance when the disasters occur.
In the case of bridges crossing rivers or bodies of water, the water currents may create hydrodynamic loads on the bridge. This includes consideration of the flow rate of water flow, water level variation, floods, etc. The design needs to ensure that the bridge is stable under water load and prevent damage to the bridge structure from water flow.
The load probability density function isWherein/>For the theoretical tension value of the boom under combined load,/>Load level carried for steel arch bridge,/>Is a natural constant.
Constructing a density function of the rust damage probability of the suspender,Wherein/>For a load level of/>Resistance obtained by calculation of the probability density function of resistance,/>For a load level of/>Load obtained by calculation of the probability density function of resistance,/>For a load level of/>The probability density function of load when describing the probability distribution of the boom to withstand different load levels.
The calculation involves a probability density function for the load,From/>Integration to infinity reflects the probability of rusting damage occurring with actual loads exceeding structural member resistance.
Example five
The method for determining the rust area comprises the following steps:
acquiring a high-resolution image of the boom through the unmanned aerial vehicle, and preprocessing the image; the unmanned aerial vehicle is used for carrying the high-resolution camera to acquire the image of the boom, so that the flexibility and the safety of acquiring the image can be improved, and particularly, the image can be acquired at a height or an angle which is difficult to access. Image preprocessing includes sharpening, denoising, contrast enhancement, etc. of the image to better identify the rusted region.
Identifying a corrosion area of the suspender through the trained convolutional neural network model, and acquiring an initial contour of the corrosion area through a Canny edge detection algorithm; CNN is a deep learning algorithm, is good at processing image recognition tasks, and can accurately recognize and locate rusted areas in images; canny edge detection is an edge detection algorithm that can clearly identify the contours of objects in an image.
Based on the initial contour, acquiring the actual contour of the rusted area by utilizing a contour detection algorithm; a contour detection algorithm is used to obtain the precise contour of the rusted region, and more accurately delineate the shape and size of the rusted region.
Performing rectangular fitting on the actual contour by using a rectangular fitting algorithm, and enabling two sides of the fitted rectangle to be parallel to the central axis of the suspender; fitting the accurate contour into a rectangle, enabling two sides of the rectangle after fitting to be parallel to the central axis of the suspender, simplifying geometric representation of the rust area, and facilitating subsequent analysis and calculation.
The minimum circumscribed rectangle of the actual profile is obtained by fitting and is used as the rust area of the boom.
Example six
A non-contact damage prediction terminal for a steel arch bridge suspender comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the non-contact damage prediction method for the steel arch bridge suspender when executing the computer program.
The memory may be used to store software programs and modules, and the processor executes various functional applications of the terminal and data processing by running the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an execution program required for at least one function, and the like.
The storage data area may store data created according to the use of the terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
A computer readable storage medium storing a computer program which when executed by a processor implements a steel arch bridge boom non-contact damage prediction method as above.
Computer readable media may include computer storage media and communication media without loss of generality. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instruction data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the ones described above. The above-described system memory and mass storage devices may be collectively referred to as memory.
A computer program product comprising computer programs/instructions which when executed by a processor implement a steel arch bridge boom non-contact damage prediction method as described above.
The computer program product comprises a computer program or set of instructions for performing specific tasks or implementing specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored on various forms of computer storage media, such as memory, hard disk, solid state drive, optical disk, or other forms of digital storage devices. Either in the form of compiled binary code or in the form of scripts or bytecodes that can be executed by an interpreter. The program product enables the processor to process data in a specific order and manner through well-designed algorithms and logic instructions to perform various functions such as data analysis, user interaction, device control, etc.
It will be appreciated by persons skilled in the art that the above embodiments are provided for clarity of illustration only and are not intended to limit the scope of the invention. Other variations or modifications of the above-described invention will be apparent to those of skill in the art, and are still within the scope of the invention.
Claims (8)
1. A method for predicting non-contact damage to a steel arch bridge boom, comprising:
constructing a rust damage magnetic field function of the boom, and acquiring a total magnetic field intensity measurement value of any rust area through the rust damage magnetic field function;
obtaining the corrosion loss coefficient of the boom at the corresponding position through the magnetic field intensity measurement Wherein H 0 is the average value of the total magnetic field intensity of the non-rusted suspender, and H is the total magnetic field intensity measurement value of the rusted area;
obtaining a resistance probability density function R (t) of the structural member;
establishing a finite element model of the steel arch bridge to obtain a combined tension value of the suspender under the combined load action;
obtaining load probability density function of suspender under generalized effect based on combined tension value Wherein F is a theoretical tension value of the suspender under the combined load action, t is a load level borne by the steel arch bridge, and e is a natural constant;
Obtaining the corrosion damage probability of the suspender through the resistance probability density function and the load probability density function;
Setting a damage risk value, and setting the suspender to be in a damage risk state if the corrosion damage probability of the suspender is larger than the damage risk value; otherwise, setting the suspender in a non-damage risk state;
The method for acquiring the resistance probability density function of the structural member comprises the following steps:
determining bridge structural resistance influence factors of the steel arch bridge: material performance uncertainty, structural member geometry uncertainty, and computation mode uncertainty;
Establishing a material performance uncertainty probability density model, a structural member geometric parameter uncertainty probability density model and a calculation mode uncertainty probability density model, and obtaining a skewness mean lambda and a variation coefficient c of a probability density function, wherein the skewness mean and the variation coefficient take values according to an axle center tension member;
obtaining a resistance probability density function of a structural member using an indirect method Wherein r is a theoretical resistance calculation value of a non-rusted suspender, t is a load level borne by the steel arch bridge, and e is a natural constant;
wherein, the probability of damage to the corrosion of the suspender is obtained by constructing the probability density function of damage to the corrosion of the suspender, Wherein R is the resistance obtained by calculation of the resistance probability density function when the load level is t, S (t) is the load probability density function when the load level is t, and S is the load obtained by calculation of the resistance probability density function when the load level is t.
2. A method for predicting non-contact damage to a steel arch bridge boom as recited in claim 1, wherein the method for obtaining a total magnetic field strength measurement for any rusted area comprises:
determining the position of a corrosion area of the boom, and acquiring the axial length a and the circumferential width gamma of the corrosion area;
Determining axial tension sigma a and radial stress sigma h of the boom at the rust area;
Calculating to obtain a distance r 1 between the magnetic field detection point P and the axially-upward negative magnetic charge of the rusted area, calculating to obtain a distance r 2 between the magnetic field detection point P and the axially-upward positive magnetic charge of the rusted area, calculating to obtain a distance r 3 between the magnetic field detection point P and the axially-downward negative magnetic charge of the rusted area, and calculating to obtain a distance r 4 between the magnetic field detection point P and the axially-downward positive magnetic charge of the rusted area;
The calculation formula for calculating the magnetic field intensity measured value H x、Hy、Hz of the rusted area in the three directions of X, Y, Z respectively comprises the following steps:
calculation of total magnetic field strength measurement for rusted region
Wherein M a is the axial magnetization of the position of the rusted region in the non-rusted state, M h is the radial magnetization of the position of the rusted region in the non-rusted state, B is the magnetic flux of the rusted region, D is the diameter of the hanging rod,The included angle is the included angle between the connecting line between the center of the rusted area and the central axis of the suspender and the connecting line between the magnetic field detection point P and the central axis of the suspender, X is the X-axis coordinate of the center of the rusted area, Y is the Y-axis coordinate of the center of the rusted area, and Z is the Z-axis coordinate of the center of the rusted area.
3. A method of predicting non-contact damage to a steel arch bridge boom as recited in claim 2, wherein the method of calculating the distance r 1、r2、r3、r4 comprises:
establishing an XYZ coordinate system by taking a magnetic field detection point as an origin, wherein a Z axis is parallel to the central axis of the boom;
Determining vectors of the magnetic field detection point P and upper, lower, left and right extreme points of the rusted area:
And calculating the length of each vector to obtain each distance.
4. A method of predicting non-contact damage to a steel arch bridge boom as recited in claim 1, wherein the combined load comprises dead load, live load, temperature load, and environmental load, the environmental load being geological load, wind load, ice and snow load, natural disaster load, and/or water load.
5. A method of predicting non-contact failure of a steel arch bridge boom as recited in claim 2, wherein the method of determining the rust area comprises:
acquiring a high-resolution image of the boom through the unmanned aerial vehicle, and preprocessing the image;
Identifying a corrosion area of the suspender through the trained convolutional neural network model, and acquiring an initial contour of the corrosion area through a Canny edge detection algorithm;
Based on the initial contour, acquiring the actual contour of the rusted area by utilizing a contour detection algorithm;
performing rectangular fitting on the actual contour by using a rectangular fitting algorithm, and enabling two sides of the fitted rectangle to be parallel to the central axis of the suspender;
The minimum circumscribed rectangle of the actual profile is obtained by fitting and is used as the rust area of the boom.
6. A steel arch bridge boom non-contact damage prediction terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a steel arch bridge boom non-contact damage prediction method as claimed in any one of claims 1-5 when executing the computer program.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a method of steel arch bridge boom non-contact damage prediction as recited in any one of claims 1-5.
8. A computer program product comprising computer program/instructions which, when executed by a processor, implements a method of non-contact damage prediction of a steel arch bridge boom according to any one of claims 1 to 5.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101261246A (en) * | 2007-03-09 | 2008-09-10 | 清华大学 | Pipeline crack far-field vortex checking method |
| KR101598823B1 (en) * | 2015-04-14 | 2016-03-02 | 주식회사 이제이텍 | Safety Test Method For Suspension Bridge Cable |
| CN106198716A (en) * | 2016-09-18 | 2016-12-07 | 中国石油大学(华东) | A kind of circumference electromagnetic field inner-walls of duct flaw detection system and quantitative evaluating method |
| CN106968173A (en) * | 2017-04-28 | 2017-07-21 | 西南大学 | Main rope of suspension bridge corrosion monitoring system and method based on magnetic field |
| CN109034492A (en) * | 2018-08-16 | 2018-12-18 | 交通运输部公路科学研究所 | A kind of prediction technique of bridge pull sling puccinia graminis erosion risk of breakage |
| CN113152273A (en) * | 2021-04-28 | 2021-07-23 | 重庆交通大学 | Detection device and detection method for boom diseases |
| CN114511088A (en) * | 2022-01-25 | 2022-05-17 | 中铁第四勘察设计院集团有限公司 | Bayesian model updating method and system for structure damage recognition |
| CN116359328A (en) * | 2023-03-31 | 2023-06-30 | 广东省第四建筑工程有限公司 | Steel wire rope magnetic flux leakage detection method and detection system for realizing same |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ITBO20070756A1 (en) * | 2007-11-16 | 2009-05-17 | Filippo Bastianini | DEVICE FOR MONITORING THE HEALTH STATUS OF STRUCTURES |
-
2024
- 2024-02-29 CN CN202410225741.3A patent/CN117807853B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101261246A (en) * | 2007-03-09 | 2008-09-10 | 清华大学 | Pipeline crack far-field vortex checking method |
| KR101598823B1 (en) * | 2015-04-14 | 2016-03-02 | 주식회사 이제이텍 | Safety Test Method For Suspension Bridge Cable |
| CN106198716A (en) * | 2016-09-18 | 2016-12-07 | 中国石油大学(华东) | A kind of circumference electromagnetic field inner-walls of duct flaw detection system and quantitative evaluating method |
| CN106968173A (en) * | 2017-04-28 | 2017-07-21 | 西南大学 | Main rope of suspension bridge corrosion monitoring system and method based on magnetic field |
| CN109034492A (en) * | 2018-08-16 | 2018-12-18 | 交通运输部公路科学研究所 | A kind of prediction technique of bridge pull sling puccinia graminis erosion risk of breakage |
| CN113152273A (en) * | 2021-04-28 | 2021-07-23 | 重庆交通大学 | Detection device and detection method for boom diseases |
| CN114511088A (en) * | 2022-01-25 | 2022-05-17 | 中铁第四勘察设计院集团有限公司 | Bayesian model updating method and system for structure damage recognition |
| CN116359328A (en) * | 2023-03-31 | 2023-06-30 | 广东省第四建筑工程有限公司 | Steel wire rope magnetic flux leakage detection method and detection system for realizing same |
Non-Patent Citations (8)
| Title |
|---|
| Broken wire detection in stay cables based on guided waves;Jiang Xu等;《2010 International Conference on Mechanic Automation and Control Engineering》;20100803;第1207-1210页 * |
| 基于压磁效应的锈蚀钢筋应力状态检测试验研究;金伟良等;《海洋工程》;20171130;第35卷(第06期);第62-70页 * |
| 基于磁场强度检测的悬索桥缆索锈蚀风险概率计算方法;姚国文等;《科学技术与工程》;20200818;第20卷(第23期);第9609-9614页 * |
| 基于超声导波的钢桥面板纵肋对接焊缝疲劳裂纹检测方法;张清华等;《中国公路学报》;20220630;第35卷(第06期);第101-112页 * |
| 桥梁拉吊索无损检测与对策;李世卿等;《交通世界(建养.机械)》;20111031(第10期);第198-199页 * |
| 桥梁施工缆索吊悬索索力叠加算法;杜桃明等;《2022年工业建筑学术交流会论文集（下册）》;20221031;全文 * |
| 桥梁杆索无损检测技术研究;郭骞;《辽宁省交通高等专科学校学报》;20130815;第15卷(第04期);第86-90页 * |
| 郭骞.桥梁杆索无损检测技术研究.《辽宁省交通高等专科学校学报》.2013,第15卷(第04期),第86-90页. * |
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