CN114609358A - Method for evaluating residual performance of existing rusted steel structure - Google Patents
Method for evaluating residual performance of existing rusted steel structure Download PDFInfo
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Abstract
A residual performance evaluation method for an existing rusted steel structure is characterized in that a topography measuring instrument is used for obtaining surface topography data of the rusted steel structure, size parameters and topography parameters of a surface corrosion pit of the rusted steel structure are analyzed and counted, and a statistical distribution model of each parameter of the rusted pit is established; carrying out a mechanical experiment of the corrosion steel structure, obtaining key corrosion pit parameters for inducing overall instability or damage of the structure, and counting the bearing indexes of the corrosion steel structure under different key corrosion pit parameters; verifying the finite element model by adopting an experimental result through a reverse reconstruction method; establishing different key rust pit sample libraries, fitting a relationship between the bearing capacity of the corroded steel structure and the key corrosion pit parameter by adopting a least square method, and establishing a method for predicting the residual bearing capacity of the corroded steel structure. The method has the characteristics of simple and convenient operation, high evaluation precision and the like.
Description
Technical Field
The invention relates to a method for evaluating the residual bearing performance of a steel structure, in particular to a comprehensive technical method for determining the residual bearing performance evaluation of a corroded steel structure by combining microscopic morphology measurement, mechanical tests and finite element analysis and establishing the relationship between the mechanical characteristics and the morphology characteristics of the corroded steel structure.
Background
The problem of corrosion of a steel structure in a long-term service process is a great problem which troubles the technical field of civil engineering. The rusted pits on the surface of the steel structure can weaken the original design size of the structure and reduce the bearing capacity of the structure; stress concentration can be caused, the plasticity and the fracture toughness of the material are degraded, and the structure is easy to fracture and damage before the design bearing requirement is not met. The corrosion has great influence on the safety and stability of the steel structure, and the performance evaluation of the existing steel structure mainly depends on the evaluation of the corrosion degree, namely a corrosion surface quantification method and a characterization result. For a long time, the existing steel structure corrosion result is quantified mostly by adopting indexes such as corrosion depth, corrosion quality loss rate, corrosion section thickness loss rate and the like, and detailed damage outline and appearance information of a corrosion surface are not determined, so that a series of technical short boards appear on performance evaluation of the existing steel structure, for example, the technical innovation and development urgently needed under the situation of existing steel structure reinforcement and modification and performance improvement steep increase in China are severely limited, and the effective popularization of a double-carbon policy in the civil engineering field is not facilitated.
Utilize traditional steel construction corrosion quantization means, inevitably lead to measurement efficiency poor, the working cycle is long, apparent defect calculates complicacy and statistics not thorough scheduling problem, and because the artifical required equipment variety of measuring is many, the high scheduling problem is required to the operating condition, the modernization and the intelligent development of existing steel construction performance evaluation technique have seriously been restricted, consequently need urgently a new technology of corrosion steel construction surface integrated measurement and residual properties evaluation that testing method is convenient, high-efficient and intelligent effective integration, in order to break through a great deal of problems that traditional steel construction detection and evaluation faced.
Disclosure of Invention
The invention aims to provide the method for evaluating the residual bearing performance of the existing rusted steel structure, which is simple and convenient to operate and high in evaluation precision.
The purpose of the invention is realized as follows: a method for evaluating residual bearing performance of an existing rusted steel structure comprises the following steps:
step (1), measuring the appearance of the surface of a corroded steel structure;
the measuring equipment adopts a non-contact morphology measuring instrument with high sensitivity and convenient operation, and the measuring precision is 5 microns; before measurement, firstly, physically removing surface floating rust on two sides of a rusted steel structure, respectively erecting and fixing measuring instruments, taking a measuring point on each of an upper scanning surface and a lower scanning surface as a reference point, wherein a connecting line of the reference points on the upper scanning surface and the lower scanning surface is parallel to the thickness direction of a structural plate, surface coordinate values (x, y and z) of the reference points can be directly read by the two topographers, and the actual thickness of the structure at the reference point is | zOn the upper part-zLower part∣，zOn the upper partMeasuring the z-direction coordinate, z, of a control point for surface scanning on a structureLower partThe z-direction coordinates of the control points are measured for the scanning of the surface under the structure. And setting the plane where the minimum height of the measuring point of the scanning surface is as a zero potential surface, and converting the rest measuring points by taking the zero potential surface as reference to define the actual corrosion depth. And associating the corresponding measuring points of the upper scanning surface and the lower scanning surface along the thickness direction of the parallel structure, and calculating to obtain the actual thickness of the structure at any measuring point of the scanning surface by taking the associated thickness of the measuring control points of the upper scanning surface and the lower scanning surface as a standard.
Determining surface topography characteristic parameters of a rust pit steel structure;
determining effective ranges of all rust pits according to the fact that slopes of continuous measuring points on the surfaces on two sides of the boundary of the rust pit have different signs, further calculating the depth, the width and the adjacent space distance of the rust pits, converting the independent rust pits into parameter models such as semi-circles or semi-ellipses, namely a three-dimensional model of the rust pit, calculating a morphology characterization parameter D of the actual rust surface of the rust steel structure by adopting a box counting method according to the measured depth of the rust pit and the reconstructed three-dimensional surface of the rust steel structure based on a fractal means;
wherein L is1Length of measuring range, L2Measuring the width of the range,. epsiloniThe length of the bottom side of the cubic box is measured,
measuring the height of the cubic box;
step (3), establishing a rust pit parameter distribution statistical model, namely a rust pit parameter evolution model;
adopting a rust pit parameter probability density distribution function to control and characterize the evolution rule of the depth, the width and the adjacent distance of the rust pit in the whole corrosion process, adjusting the characterization significance by using the n value, and establishing a rust pit distribution statistical model:
wherein P (t) is the probability density distribution function of rust pit parameter, t is the corrosion time, χ2The method is characterized in that chi-square distribution symbols are represented, w and c are the width and depth of a rust pit on the surface of a rusted steel structure respectively, D is the fractal dimension of the rusted surface, e is a normal natural number, r is an abnormal integral function, and n is any integer greater than 0;
step (4), performing a corrosion steel structure mechanical experiment;
loading a rusted steel structure by adopting an MTS loading device, measuring the surface strain results of two sides of the rusted steel structure in real time by adopting two full-field strain test systems in the experimental process, determining a region with severe plastic strain rapid accumulation through accumulation analysis, and defining the region as a key rusted pit causing instability or damage of the rusted steel structure, namely defining the position with maximum continuous strain accumulation rate and final state as a key rusted pit region, and determining the width and depth of the rusted pit;
step (5), evaluating the residual bearing capacity of the rusted steel structure;
establishing a finite element model of the rusted steel structure by using a finite element means and a reverse reconstruction method; verifying the correctness of the finite element model by utilizing the relationship between the rust pit parameters obtained in the step (4) and the bearing performance index; on the premise of the correctness of the finite element model, establishing a key rust pit size parameter and morphology parameter sample library by using the rust pit evolution model established in the step (2), respectively establishing the finite element model, analyzing the change rule of the bearing performance index of the rusted steel structure under different rust pit parameters, and obtaining a residual bearing capacity prediction model of the rusted steel structure by least square fitting:
β(F,Δ)＝α0+α1*w+α2*c+α3*D (3)
in the formula, beta is an estimated value of the bearing capacity of the rusted steel structure; w is the depth of the rust pit; c is the width of the rust pit; d is the fractal dimension of the surface of the rust pit, namely the morphology characterization parameter; f is a load application type of the finite element model; the limiting deformation value of the delta corrosion steel structure; alpha is alpha0，α1，α2，α3Respectively fitting parameters of a load-displacement curve of a finite element result of a steel structure load test by using a least square method.
The invention can comprehensively and accurately quantify the complex surface appearance of the rusted steel structure, firstly, a non-contact appearance collector is adopted to measure the appearance data of the rusted surface of the structure, the industrialized sampling result of the appearance data of the rusted steel structure is transposed and converted, the size parameters and the distribution characteristics of all rust pits on the surface of the rusted steel structure are researched, so that the appearance characteristics of the rusted surface of the steel structure are comprehensively mastered, then, the key rust pits causing the damage of the steel structures with different corrosion degrees are accurately obtained by utilizing the method of combining the mechanical test and the full-field strain test, the lower limit bearing levels and the fracture indexes of the size and the appearance characteristics of the different key rust pits are counted and used as the input parameters of finite element simulation of the rusted steel structure. And finally, establishing a refined finite element model of the rusted steel component containing different key rusts by adopting a reverse reconstruction method based on the surface characteristic evolution law of the rusted steel, and further filling a relation example of mechanical indexes of the rusted steel structure and the corresponding key rusts by using an analysis result, so as to provide a performance evaluation method of the rusted steel structure considering the corrosion morphology evolution characteristics, thereby widening the industrialized detection range of the rusted steel structure and achieving the purpose of improving the performance evaluation result precision.
Compared with the prior art, the invention has the beneficial effects that:
1. the method adopts portable shape measurement as a structural performance evaluation means, not only reduces the types of in-service steel structure detection tools, but also greatly improves the detection precision and efficiency, and is suitable for industrial inspection of large-size steel structures.
2. The method comprehensively considers the size, shape, spatial distribution and morphological characteristics of the rusted surface, perfects the quantification means of the random complex rusted surface, improves the defect characterization precision of the complex random rusted surface, and effectively avoids the problem of repeated characterization or lack of characterization information of the rusted complex surface.
3. The method provides a statistical method for the random surface space thickness of the rusty member by utilizing the space coordinate system principle, establishes a bearing performance degradation model based on the real member size, solves the problem of transition conservation assessment brought by the traditional equivalent method, and solves the problems of poor measurement efficiency, long working period, complex apparent defect calculation, incomplete statistics and the like in the traditional steel structure detection technology.
4. The invention adopts the structural performance test based on the appearance characteristics, can well avoid the problem of prediction precision distortion caused by the original corrosion section reduction method, introduces the local defects and the complex appearance of the corrosion surface into an evaluation mechanism, establishes the corresponding relation between the random appearance characteristics and the residual bearing capacity, and greatly improves the prediction quality of the residual bearing capacity of the corrosion steel structure.
5. The method is based on the nondestructive detection technology, realizes accurate prediction of the residual bearing capacity on the basis of the nondestructive existing structure, and has the advantages of simple operation, universal equipment and great industrial popularization significance.
Drawings
FIG. 1 is a flow chart of the evaluation technique of the present invention.
FIG. 2-1 is a diagram of a measurement of the topography of a rusted surface.
FIG. 2-2 is a sectional view of the rusted steel structure.
FIG. 3 is a plot of rust surface measurement data identification versus rust pit parameter conversion.
Fig. 4-1 is a schematic diagram of a three-dimensional model of rust pits on the rusted surface shown in fig. 3.
Fig. 4-2 is a schematic diagram of a calculation method of a fractal dimension of a corrosion surface.
FIG. 5 is a schematic diagram of a mechanical test of a rusted steel structure and a key rust pit determination method.
FIG. 6-1 is a topological reconstruction of the thickness direction of the rusted steel structure.
And 6-2, the corrosion steel structure is integrally reversely reconstructed.
FIG. 7 is a schematic diagram of a method for predicting the residual bearing capacity of a rusted steel structure.
In FIGS. 2-1 and 2-2: 1 is a topography test system, 2 is a measuring area sideline, 3 is a measuring object, 4 is a measuring structure initial surface, 5 is an actual corrosion surface highest point, 6 is an actual corrosion surface lowest point, 7 is an actual corrosion surface, 8 is a maximum residual thickness, and 9 is a corrosion depth.
In fig. 3: 9 is the depth of the rust, 10 is the slope of the connecting line of adjacent measuring points on the corrosion surface, 11 is the width of the rust pit, and 12 is the depth of the rust pit.
In FIGS. 4-1 and 4-2: 13 is a rust pit three-dimensional model, 14 is a rust pit model long axis radius, 15 is a rust pit model short axis radius, 16 is a reconstructed rust surface of a rust steel structure, 17 is a box counting method unit height, 18 is a box counting method unit square bottom side length, 19 is a length of a corrosion surface topography analysis area, and 20 is a width of the corrosion surface topography analysis area. In fig. 5: 22 is a loading device, 23 is a full-field strain testing system, and 24 is a plastic strain rapid accumulation area on the surface of a rusted steel structure. And 25 is a key rust pit.
In FIG. 6-1, 26 is the control point of the upper surface of the rusted steel structure, and 27 is the control point of the lower surface of the rusted steel structure. In fig. 6-2, 28 is a control section of a rusted steel structure, and 29 is a reconstructed model of the rusted steel structure.
Detailed Description
Concrete construction steps
In fig. 2, a grinding machine is used for removing rust on the surface of a rusted steel structure 3, a topography test system 1 is used for scanning the surface topography of the steel structure after rust removal, and the scanning area is the coverage area of a sideline 2 of the measurement area. After the steel structure is corroded, the initial surface 4 of the original structure is corroded and falls off, and is reduced to an actual corrosion surface 7 through deposition, wherein the actual surface comprises two characteristic points, namely a highest point 5 and a lowest point 6, of the actual corrosion surface. The maximum residual thickness 8 of the structure and the depth of erosion 9 at each measured point can be obtained by conversion of the actual erosion surface 7 measured data.
In fig. 3, based on the actual measurement data of the corrosion depth 9 of each point on the corrosion surface in fig. 2, the corrosion depth 9 of the corrosion surface 7 in the same pit is analyzed by determining whether the slope 10 of the line connecting adjacent measurement points on the corrosion surface is the same number or not, and the maximum depth and the maximum width are taken as the pit width 11 and the pit depth 12.
In fig. 4-1, the independent pits (all pits on the surface of the independent pits include key pits, and since the pits need to be quantized, their shapes should be transformed equivalently, and their sizes are quantized by the major and minor half axes) are transformed into parametric models such as regular semi-circles or semi-ellipses, i.e., the three-dimensional model 13 of the pits. The major semi-axis 14 of the three-dimensional model 13 of the rust pit corresponds to the depth 12 of the rust pit, and the minor semi-axis 15 corresponds to the width 11 of the rust pit. And measuring the corrosion depth 9 of the structure surface according to the graph 2, reconstructing the three-dimensional surface 16 of the corroded steel structure, and calculating the appearance characterization parameters of the actual corrosion surface 7 of the corroded structure by adopting a box counting method based on a fractal means, such as the parameter D in the graph 4-2. In fig. 4-2, the cube box with height 17 and bottom side length 18 is used to cover and reconstruct the surface 16 of the corroded steel structure, and the topographic characteristic parameters D of the corroded surface with length 19 and width 20 can be calculated by the fractal dimension calculation formula 21 (h in the formula is the measured data of the corrosion depth 9 of each point on the corroded surface), and the calculation result is used as the topographic characteristic parameters of the corroded steel structure 3.
In FIG. 4-2, the feature characterization parameter D of the corroded steel structure is as follows:
wherein L is1Length of measuring range, L2Measuring the width of the range,. epsiloniThe length of the bottom side of the cubic box is measured,
measuring the height of the cubic box;
in fig. 3, the distribution statistical model of the rusted steel structure 3 is obtained by counting all the width 11 of the rusted pit, the depth 12 of the rusted pit and the fractal dimension of the shape:
wherein P (t) is a probability density distribution function of a rust pit parameter, χ2The method is characterized in that the method is a chi-square distribution symbol, t is corrosion time, w and c are respectively the width 11 and the depth 12 of a rusted pit on the surface of a rusted steel structure, D is a calculated value of a fractal dimension of the rusted surface (formula 1), e is a normal natural number, f is an abnormal integral function, and n is any integer greater than 0.
In fig. 5, the MTS loading device 22 is used to load the corroded steel structure 3 (full field strain testing corrosion steel structure mechanical test process, aiming at determining and positioning the key corrosion pit), the full field strain testing system 23 is used in the test process to measure the surface strain result of the corroded steel structure 3 in real time, and through accumulation analysis, a severe plastic strain accumulation region 24 is determined to serve as a key corrosion pit 25 for causing instability or damage of the corroded steel structure 3, and the width 11 and the depth 12 of the corrosion pit are determined.
In fig. 6-1, the reference point member actual thickness is determined using the upper and lower surface control points 26 and 27 of the rusted steel structure 3 as reference points. In FIG. 6-2, the height relationship between adjacent corrosion surface points is used to construct a steel member section 28 in the whole area where the control points are located, and an entire member reconstruction model 29 is reconstructed by extension topology (here, the measurement result of the surface topography of the corrosion steel structure is a large amount of point cloud data (constructed by fixed spaced points, each point coordinate is (x, y, Z)), the difference between the x and y adjacent data points is the set measurement step distance of the topography test system 1, and Z is the corrosion surface measurement height at the position of each data point, therefore, when constructing the corrosion steel structure finite element model, each measurement point is connected in the x and y directions in sequence to form a corrosion surface topology network, then the structure is divided into a plurality of equal parts in the Z direction, in order to ensure the convergence of the finite element calculation result, the single layer units equally divided in the Z direction of the structure should be provided with the minimum corrosion parameter of the pit close, finally, the corrosion surface topology network is constructed in sequence on each equal layer in the Z direction, forming a finite element model of the rusted steel structure, also called a finite element topological model).
In fig. 7, the result of the loading test of the rusted steel structure 3 is used as a determination index to verify the accuracy of the finite element simulation. On the premise of ensuring the accuracy of the finite element modeling method of the rusted steel structure, a sample library of key rust pit size parameters and appearance parameters is constructed by using the rust pit evolution model established in the figure 3, then finite element models are respectively established for mechanical analysis (the result is shown in a table 31), and then a calculation model of the residual bearing capacity of the rusted steel structure based on the key rust pit size and appearance characteristics is established by using least square fitting, and is shown in a formula (3).
Examples
Table 31 calculation result of corrosion surface parameters and finite element axis compression of rusted 32# C channel steel test piece
Taking an actual 32# C channel steel corrosion test piece as an example, the surface appearance of the test piece is measured, and the key rust pit parameters and the local fractal dimension of the test piece are determined. Establishing a key pit parameter and morphology parameter model library according to dimensionless relation among all parameters in the pit evolution process, then sequentially establishing a finite element model containing a series of key pits with horizontal sizes by utilizing reverse reconstruction, and carrying out axle center stressed finite element simulation to obtain a bearing force value under the corresponding key pit. And then, a residual bearing capacity calculation model is established by fitting the key rust pit parameters, the surface fractal dimension and the bearing capacity value of the rusted channel steel test piece, and the residual bearing capacity calculation model is used as the residual bearing capacity evaluation standard of the rusted channel steel test piece in the embodiment. For other channel steel members, after key rust pit parameters are obtained through measurement, the residual bearing capacity of the other channel steel members can be directly evaluated by utilizing the established evaluation standard.
1. Measuring size parameters (w, c) and morphology parameters (D) of a rust pit on the surface of the structure by adopting the existing rust steel structure morphology test; pit size parameter extraction using fig. 3 and fig. 4-1; pit morphology parameter extraction utilizes fig. 4-2. And (3) carrying out statistical analysis on the rust pit size parameter evolution rule, and establishing a statistical model of each parameter (formula 2 in the specification).
2. And then carrying out a mechanical experiment of the rusted steel structure, wherein the load type is represented by F. In the test process, a strain field on the surface of a test piece is measured by using DIC (digital computer), and rust pit parameters and morphology parameters of a plastic strain accumulation severe region (namely a crack initiation region) are determined based on the rule that a fracture crack is easy to initiate in the plastic strain accumulation severe region, and the rust pit is called as a key rust pit.
And then, counting key rust pit parameters and bearing performance indexes of each rusted member.
3. And (3) establishing a finite element model of the rusted steel structure by using a finite element means and adopting a reverse reconstruction method, comparing the relationship between the rust pit parameters and the bearing performance indexes obtained in the step (2), and verifying the correctness of the finite element model. On the premise of the correctness of the finite element model, establishing a key rust pit size parameter and morphology parameter sample library by using the rust pit evolution model established in step 1, respectively establishing the finite element model, analyzing the change rule of the bearing performance index of the rusted steel structure under different rust pit parameters, and obtaining a rusted steel structure bearing capacity prediction model (indicated by a formula (3) in the specification) through fitting (least square fitting).
4. When the residual bearing capacity of an actual structure is predicted, the development grade of a key rust pit is predicted only by testing the size parameter and the morphology parameter of the rust pit on the rusted surface, and then the calculation is carried out according to a residual bearing capacity prediction model of a rusted steel structure.
5. A finite element approach is utilized.
1) By adopting the structural performance test based on the morphological characteristics, the problem of prediction precision distortion caused by the original corrosion section reduction method can be well avoided, the local defects and the complex morphology of the corrosion surface are introduced into an evaluation mechanism, the corresponding relation between the random morphological characteristics and the residual bearing capacity is established, and the prediction quality of the residual bearing capacity of the corrosion steel structure is greatly improved;
2) the method is based on a nondestructive testing technology, accurate prediction of the residual bearing capacity of the existing structure is realized on the basis of no damage, the operation is simple, the equipment is universal, and the industrial popularization significance is great.
Claims (1)
1. A residual performance evaluation method for existing rusted steel structures is characterized by comprising the following steps: the method comprises the following steps:
step (1), measuring the surface appearance of the rusted steel structure
The measuring equipment adopts a non-contact morphology measuring instrument with high sensitivity and convenient operation, and the measuring precision is 5 microns; before measurement, firstly, physically removing the surface rust on two sides of the rusted steel structure, respectively erecting and fixing the measuring instruments, taking a measuring point on each of the upper and lower scanning surfaces as a reference point, wherein a connecting line of the reference points on the upper and lower surfaces is parallel to the thickness direction of the structural plate, surface coordinate values (x, y, z) of the reference points can be directly read by the two topographers, and the actual thickness of the structure at the reference point is |. zOn the upper part-zLower part∣，zOn the upper partMeasuring the z-direction coordinate, z, of a control point for surface scanning on a structureLower partScanning a coordinate in a z direction of a measurement control point for the lower surface of the structure, setting a plane where a minimum height of a measurement point of a scanning surface is located as a zero potential surface, converting and defining an actual corrosion depth by taking the zero potential surface as a reference for the rest measurement points, associating corresponding measurement points of the upper scanning surface and the lower scanning surface along the thickness direction of the parallel structure, and calculating to obtain the actual thickness of the structure at any measurement point of the scanning surface by taking the associated thicknesses of the measurement control points of the upper scanning surface and the lower scanning surface as a standard;
step (2) determining surface topography characteristic parameters of rust pit steel structure
Determining effective ranges of all rust pits according to the necessarily different signs of the slopes of the continuous measuring points on the surfaces on the two sides of the boundary of the rust pit, and further calculating the depth, the width, the adjacent space distance and the local surface fractal parameters of the rust pit; converting the independent rust pit into a parameter model such as a semicircle or a semiellipse, namely a three-dimensional rust pit model (13), and calculating a morphology characterization parameter D of the actual rusted surface of the rusted steel structure by adopting a box counting method based on a fractal means according to the measured rust pit depth (9) and the reconstructed rusted steel structure three-dimensional surface (16);
wherein L is1Length of measuring range, L2Measuring the width of the range,. epsiloniThe length of the bottom side of the cubic box is measured,
measuring the height of the cubic box;
step (3), a rust pit parameter distribution statistical model is established, namely the rust pit parameter evolution model adopts a rust pit parameter probability density distribution function to control and characterize the evolution rule of the depth, the width and the adjacent spacing of the rust pit in the whole corrosion process, and utilizes the n value to adjust the characterization significance, and the rust pit distribution statistical model is established:
wherein P (t) is the probability density distribution function of rust pit parameter, t is the corrosion time, χ2The method is characterized in that a chi-square distribution symbol is adopted, w and c are respectively the width (11) and the depth (12) of a rust pit on the surface of a rusted steel structure, D is a calculated value of a fractal dimension of the rusted surface (formula 1), e is a constant natural number, f is an abnormal integral function, and n is any integer greater than 0;
step (4), corrosion steel structure mechanics experiment
Loading a rusted steel structure by adopting an MTS loading device (22), measuring the strain results of the surfaces of the two sides of the rusted steel structure in real time by adopting two full-field strain testing systems (23) in the experimental process, determining a severe plastic strain rapid accumulation region (24) through accumulation analysis, and using the severe plastic strain rapid accumulation region as a key rust pit (25) for causing instability or damage of the rusted steel structure, namely defining the maximum continuous positions of the strain accumulation rate and the final state as a key rust pit region, and determining the width (11) and the depth (12) of the rust pit;
step (5), evaluating the residual bearing capacity of the rusted steel structure
Establishing a finite element model of the rusted steel structure by using a finite element means and a reverse reconstruction method; verifying the correctness of the finite element model by utilizing the relationship between the rust pit parameters obtained in the step (4) and the bearing performance index; on the premise of the correctness of the finite element model, establishing a key rust pit size parameter and morphology parameter sample library by using the rust pit evolution model established in the step (2), respectively establishing the finite element model, analyzing the change rule of the bearing performance index of the rusted steel structure under different rust pit parameters, and obtaining a residual bearing capacity prediction model of the rusted steel structure by least square fitting:
β(F,Δ)＝α0+α1*w+α2*c+α3*D (3)
in the formula, beta is an estimated value of the bearing capacity of the rusted steel structure; w is the depth of the rust pit; c is the width of the rust pit; d is the fractal dimension of the surface of the rust pit, namely the morphology characterization parameter; f is a load application type of the finite element model; the limiting deformation value of the delta corrosion steel structure; alpha is alpha0，α1，α2，α3Respectively fitting parameters of a load-displacement curve of a finite element result of a steel structure load test by using a least square method.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5505030A (en) * | 1994-03-14 | 1996-04-09 | Hardcore Composites, Ltd. | Composite reinforced structures |
| EP1122533A2 (en) * | 2000-02-01 | 2001-08-08 | Kabushiki Kaisha Toshiba | Deterioration diagnostic method and equipment thereof |
| CA2344092A1 (en) * | 2001-04-12 | 2002-10-12 | Dalhousie University | A method and apparatus for the measurement of corrosion and damage in installed bolts, rods and bars |
| WO2007038378A2 (en) * | 2005-09-23 | 2007-04-05 | Uit, L.L.C. | Method of metal performance improvement and protection against degradation and suppression thereof by ultrasonic impact |
| CN103572705A (en) * | 2013-11-21 | 2014-02-12 | 西南科技大学 | Construction method of prefabricated reinforced concrete block prefabricated bridge pier |
| CN104964920A (en) * | 2015-06-05 | 2015-10-07 | 西安建筑科技大学 | Apparatus and method for simulating natural corrosion of reinforced concrete |
| CN106290133A (en) * | 2016-08-12 | 2017-01-04 | 西安石油大学 | A kind of image measuring method evaluating oil pipe Pitting corrosion behavior |
| CN108711145A (en) * | 2018-03-29 | 2018-10-26 | 西安石油大学 | A kind of erosion profile based on three-dimensional image analysis describes means of interpretation |
| CN109437672A (en) * | 2018-11-26 | 2019-03-08 | 西南科技大学 | A kind of Rock soil similar material and preparation method thereof that intensive parameter is controllable |
| CN109471998A (en) * | 2018-11-01 | 2019-03-15 | 上海海事大学 | A kind of corrosion fatigue method for predicting residual useful life based on three-dimensional fractal dimension |
| CN109827855A (en) * | 2018-08-30 | 2019-05-31 | 长沙理工大学 | Seasonality corrosion couples down Reinforced Concrete Bridge life-span prediction method with fatigue |
| CN111931647A (en) * | 2020-08-10 | 2020-11-13 | 西安建筑科技大学 | Steel structure surface rust pit identification, extraction and evaluation equipment, method and storage medium |
| CN112697691A (en) * | 2021-01-15 | 2021-04-23 | 中国人民解放军海军航空大学青岛校区 | Research method for corrosion damage distribution rule of aviation alloy steel in service environment |
| CN113484225A (en) * | 2021-06-04 | 2021-10-08 | 苏州热工研究院有限公司 | Method for evaluating corrosion state of steel bar in concrete |
| CN113836696A (en) * | 2021-08-25 | 2021-12-24 | 河海大学 | Method for checking ultimate bearing capacity of gate in continuous corrosion state |
| CN114036798A (en) * | 2021-11-15 | 2022-02-11 | 国网福建省电力有限公司经济技术研究院 | Corrosion steel analysis method based on corrosion pit fusion and application thereof |
-
2022
- 2022-03-24 CN CN202210292484.6A patent/CN114609358B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5505030A (en) * | 1994-03-14 | 1996-04-09 | Hardcore Composites, Ltd. | Composite reinforced structures |
| EP1122533A2 (en) * | 2000-02-01 | 2001-08-08 | Kabushiki Kaisha Toshiba | Deterioration diagnostic method and equipment thereof |
| CA2344092A1 (en) * | 2001-04-12 | 2002-10-12 | Dalhousie University | A method and apparatus for the measurement of corrosion and damage in installed bolts, rods and bars |
| WO2007038378A2 (en) * | 2005-09-23 | 2007-04-05 | Uit, L.L.C. | Method of metal performance improvement and protection against degradation and suppression thereof by ultrasonic impact |
| CN103572705A (en) * | 2013-11-21 | 2014-02-12 | 西南科技大学 | Construction method of prefabricated reinforced concrete block prefabricated bridge pier |
| CN104964920A (en) * | 2015-06-05 | 2015-10-07 | 西安建筑科技大学 | Apparatus and method for simulating natural corrosion of reinforced concrete |
| CN106290133A (en) * | 2016-08-12 | 2017-01-04 | 西安石油大学 | A kind of image measuring method evaluating oil pipe Pitting corrosion behavior |
| CN108711145A (en) * | 2018-03-29 | 2018-10-26 | 西安石油大学 | A kind of erosion profile based on three-dimensional image analysis describes means of interpretation |
| WO2020042753A1 (en) * | 2018-08-30 | 2020-03-05 | 长沙理工大学 | Method for predicting service life of reinforced concrete bridge under conditions of seasonal corrosion and fatigue coupling |
| CN109827855A (en) * | 2018-08-30 | 2019-05-31 | 长沙理工大学 | Seasonality corrosion couples down Reinforced Concrete Bridge life-span prediction method with fatigue |
| CN109471998A (en) * | 2018-11-01 | 2019-03-15 | 上海海事大学 | A kind of corrosion fatigue method for predicting residual useful life based on three-dimensional fractal dimension |
| CN109437672A (en) * | 2018-11-26 | 2019-03-08 | 西南科技大学 | A kind of Rock soil similar material and preparation method thereof that intensive parameter is controllable |
| CN111931647A (en) * | 2020-08-10 | 2020-11-13 | 西安建筑科技大学 | Steel structure surface rust pit identification, extraction and evaluation equipment, method and storage medium |
| CN112697691A (en) * | 2021-01-15 | 2021-04-23 | 中国人民解放军海军航空大学青岛校区 | Research method for corrosion damage distribution rule of aviation alloy steel in service environment |
| CN113484225A (en) * | 2021-06-04 | 2021-10-08 | 苏州热工研究院有限公司 | Method for evaluating corrosion state of steel bar in concrete |
| CN113836696A (en) * | 2021-08-25 | 2021-12-24 | 河海大学 | Method for checking ultimate bearing capacity of gate in continuous corrosion state |
| CN114036798A (en) * | 2021-11-15 | 2022-02-11 | 国网福建省电力有限公司经济技术研究院 | Corrosion steel analysis method based on corrosion pit fusion and application thereof |
Non-Patent Citations (3)
| Title |
|---|
| REN SONGBO 等: "Fractal characteristic of corroded steel surface and application to the fracture analyses", 《CONSTRUCTION AND BUILDING MATERIALS》 * |
| REN SONGBO 等: "Measurement pitting morphology characteristic of corroded steel surface and fractal reconstruction model", 《MEASUREMENT》 * |
| 宋嘉文 等: "锈蚀钢筋力学性能研究", 《低温建筑技术》 * |
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