CN114609358B - Residual performance evaluation method for existing rust steel structure - Google Patents
Residual performance evaluation method for existing rust steel structure Download PDFInfo
- Publication number
- CN114609358B CN114609358B CN202210292484.6A CN202210292484A CN114609358B CN 114609358 B CN114609358 B CN 114609358B CN 202210292484 A CN202210292484 A CN 202210292484A CN 114609358 B CN114609358 B CN 114609358B
- Authority
- CN
- China
- Prior art keywords
- rust
- steel structure
- pit
- parameter
- measuring
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/20—Metals
- G01N33/204—Structure thereof, e.g. crystal structure
- G01N33/2045—Defects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/20—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring contours or curvatures, e.g. determining profile
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Crystallography & Structural Chemistry (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)
Abstract
Aiming at the residual performance evaluation method of the existing rust steel structure, the surface morphology data of the rust steel structure is obtained by using a morphology measuring instrument, the surface pit size parameter and the morphology parameter of the rust steel structure are analyzed and counted, and a statistic distribution model of each parameter of the rust pit is established; carrying out a rust steel structure mechanics experiment, acquiring key pit parameters for inducing the integral instability or damage of the structure, and counting the bearing indexes of the rust steel structure under different key pit parameters; verifying the finite element model by adopting an experimental result through a reverse reconstruction method; and (3) establishing different key rust pit sample libraries, and adopting a least square method to fit the relation between the bearing capacity of the rust steel structure and key rust pit parameters, so as to establish a prediction method for the residual bearing capacity of the rust steel structure. The method has the characteristics of simplicity and convenience in operation, high evaluation accuracy 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 evaluating the residual bearing performance of the rust-resistant steel structure by establishing the relationship between the mechanical characteristics and the morphological characteristics of the rust-resistant steel structure through the combination of microscopic morphology measurement, mechanical test and finite element analysis.
Background
The rust problem of the steel structure in the long-term service process is always a serious problem puzzling the technical field of civil engineering. Rust pits on the surface of the rusted steel structure not only weaken the original design size of the structure, but also reduce the bearing capacity of the structure; stress concentration can be also caused, the plasticity and fracture toughness of the material are deteriorated, and the structure is extremely easy to fracture and destroy before the design bearing requirement is not met. The corrosion has a great influence on the safety and stability of the steel structure, and the performance evaluation of the existing steel structure is mainly based on the evaluation of the corrosion degree, namely the corrosion surface quantization method and the characterization result. For a long time, the corrosion result of the existing steel structure is quantified by adopting indexes such as corrosion depth, corrosion quality loss rate, corrosion section thickness loss rate and the like, and detailed damage profile and morphology information of the corrosion surface are not clearly shown, so that a series of technical short plates appear on performance evaluation of the existing steel structure, such as large manual inspection error, too general corrosion quantification, neglecting of rust damage weak areas of failure sections, too conservative reinforcement measures and the like, which severely restrict the technical innovation and development of urgent demands of China under the conditions of reinforcement transformation and steep performance improvement of the existing steel structure, and are not beneficial to effective popularization of double-carbon policies in the civil engineering field.
The problems of poor measurement efficiency, long working period, complex apparent defect calculation, incomplete statistics and the like are necessarily caused by utilizing the traditional steel structure corrosion quantification means, and the modernization and intelligent development of the existing steel structure performance evaluation technology are seriously restricted due to the problems of multiple equipment types, high operation condition requirements and the like required by manual measurement, so that a new technology for comprehensive measurement and residual performance evaluation of the corrosion steel structure surface, which is convenient, efficient and intelligent and effectively integrated, is needed to break through the problems faced by the traditional steel structure detection evaluation.
Disclosure of Invention
The invention aims to provide the method for evaluating the residual bearing performance of the existing rust steel structure, which is simple and convenient to operate and high in evaluation accuracy.
The purpose of the invention is realized in the following way: a method for evaluating residual bearing performance of an existing rust steel structure comprises the following steps:
step (1), measuring the surface morphology 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 rust on two sides of a rust steel structure, respectively erecting and fixing a measuring instrument, taking each measuring point of an upper scanning surface and a lower scanning surface as reference points, connecting lines of the upper reference points and the lower reference points are parallel to the thickness direction of the structure plate, and the surface coordinate values (x, y, z) of the upper reference points and the lower reference points can be directly read by two appearance instruments, wherein the actual thickness of the structure at the reference points is z Upper part -z Lower part(s) ∣，z Upper part Measuring the z-direction coordinate, z, of a control point for scanning the upper surface of a structure Lower part(s) The z-direction coordinates of the control points are measured for the lower surface scan of the structure. And setting a plane where the minimum height of the measuring points of the scanning surface is located as a zero potential surface, and converting and defining the actual corrosion depth by taking the zero potential surface as a reference for the rest measuring points. And (3) correlating 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 correlation thickness of the measuring control points of the upper scanning surface and the lower scanning surface as a standard.
Step (2), determining surface morphology characteristic parameters of the rust pit steel structure;
determining the effective range of all the rust pits according to the difference of the continuous measuring point connecting line slopes on the two sides of the boundary of the rust pits, further calculating the depth, the width and the adjacent space distance of the rust pits, converting the independent rust pits into semi-circular or semi-elliptical and other parametric models, namely a rust pit three-dimensional model, calculating the morphological 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 pits and the reconstructed rust steel structure three-dimensional surface and based on fractal means;
wherein L is 1 Measuring the length of the range, L 2 Measuring the width of the range epsilon i Measuring cubeThe bottom side length of the box is equal to the length of the bottom side of the box,
measuring the height of the cube box;
step (3), establishing a rust pit parameter distribution statistical model, namely a rust pit parameter evolution model;
the rust pit parameter probability density distribution function is adopted to control and represent the evolution rule of the rust pit depth, width and adjacent intervals in the whole corrosion process, the n value is used for adjusting the representation meaning, and a rust pit distribution statistical model is established:
wherein P (t) is the probability density distribution function of the pit parameter, t is the corrosion time, χ 2 The method is characterized in that the symbols are chi-square distribution, w and c are the rust pit width and the rust pit depth of the surface of a rust steel structure respectively, D is the fractal dimension of the rust surface, e is a normal natural number, f is an abnormal integral function, and n is any integer greater than 0;
step (4), a rust steel structure mechanics experiment;
the method comprises the steps of loading a rusted steel structure by adopting an MTS loading device, measuring the strain results of the two sides of the rusted steel structure in real time by adopting two full-field strain testing systems in the experimental process, determining a rapid accumulation area of severe plastic strain through accumulation analysis, serving as a key rust pit for inducing instability or damage of the rusted steel structure, namely defining the position with the largest strain accumulation rate and the largest final state as a key rust pit area, and determining the width and the depth of the rust pit;
step (5), evaluating the residual bearing capacity of the rusted steel structure;
establishing a rust steel structure finite element model by using a finite element means and a reverse reconstruction method; verifying the correctness of the finite element model by utilizing the relation between the rust pit parameters and the bearing performance indexes obtained in the step (4); on the premise of correctness of the finite element model, the rust pit evolution model established in the step (2) is utilized to establish a key rust pit size parameter and morphology parameter sample library, the finite element models are respectively established again, the rust steel structure bearing performance index change rule under different rust pit parameters is analyzed, and the rust steel structure residual bearing capacity prediction model is obtained through least square fitting:
β(F,Δ)＝α 0 +α1*w+α 2 *c+α 3 *D (3)
wherein, beta is an estimated bearing capacity value of the rust steel structure; w is the pit depth; c is the pit width; d is the fractal dimension of the surface of the rust pit, namely the appearance characterization parameter; f is the load type applied by the finite element model; the limit deformation value of the delta rust steel structure; alpha 0 ，α 1 ，α 2 ，α 3 And fitting load-displacement curve fitting parameters of the finite element result of the steel structure load test by using a least square method.
According to the method, the complex surface morphology of the rusted steel structure is comprehensively and accurately quantified, firstly, structural rusted surface morphology data are measured by a non-contact morphology acquisition instrument, the size parameters and the distribution characteristics of all rusted pits on the surface of the rusted steel structure are researched by transposing and converting industrial sampling results of the structural rusted surface morphology data, so that the morphological characteristics of the rusted surface of the steel structure are comprehensively mastered, then, key rusted pits which cause damage of the steel structure with different rusting degrees are accurately obtained by a method of combining a mechanical test and a full-field strain test, and the critical rusted pit sizes, ultimate bearing levels and fracture indexes under the morphological characteristics are counted and are used as input parameters of finite element simulation of the rusted steel structure. And finally, based on the corrosion steel surface feature evolution law, establishing a corrosion steel member refined finite element model containing different key pits by adopting a reverse reconstruction method, and further filling a relation calculation example of corrosion steel structure mechanical indexes and corresponding key pits by utilizing an analysis result, so as to further provide a corrosion steel structure performance evaluation method considering corrosion morphology evolution features, thereby widening the corrosion steel structure industrialized detection range and achieving the purpose of improving the performance evaluation result accuracy.
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, thereby not only reducing the types of in-service steel structure detection tools, but also greatly improving the detection precision and efficiency, and being suitable for industrialized inspection of a large amount of 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 repeated characterization or missing characterization information problem of the complex rusted surface.
3. The method utilizes the principle of a space coordinate system, provides a random surface space thickness statistical method of the rusted component, establishes a bearing performance degradation model based on the size of the real component, solves the problem of evaluation transition conservation caused 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 faced by the traditional steel structure detection technology.
4. The method adopts the structural performance test based on the morphological characteristics, can well avoid the problem of prediction accuracy distortion caused by the original rust section folding and subtracting method, introduces the local defects of the rust surface and the complex morphology thereof into an evaluation mechanism, establishes the corresponding relation between the random morphological characteristics and the residual bearing capacity, and greatly improves the prediction quality of the residual bearing capacity of the rust steel structure.
5. The invention realizes accurate prediction of the residual bearing capacity on the basis of the existing structure without damage based on the nondestructive detection technology, has simple operation, universal equipment and great significance in industrial popularization.
Drawings
FIG. 1 is a flow chart of an evaluation technique of the present invention.
FIG. 2-1 is a graph of rust surface topography measurements.
Fig. 2-2 is a section view of the rust steel structure morphology measurement.
FIG. 3 is a graph of rust surface measurement data identification versus rust pit parameter conversion.
FIG. 4-1 is a schematic view of a three-dimensional model of the rust pit of the rust surface shown in FIG. 3.
Fig. 4-2 is a schematic diagram of a method for calculating the fractal dimension of a rusted surface.
FIG. 5 is a schematic diagram of a rust steel structural mechanics test and a key rust pit determination method.
Fig. 6-1 is a topological reconstruction of the thickness direction of a rusted steel structure.
Fig. 6-2 is an overall reverse reconstruction of a rust-steel structure.
FIG. 7 is a schematic diagram of a method for predicting residual bearing capacity of a rusted steel structure.
Fig. 2-1 and 2-2: 1 is a topography test system, 2 is a measuring area boundary line, 3 is a measuring object, 4 is a measuring structure initial surface, 5 is an actual rust surface highest point, 6 is an actual rust surface lowest point, 7 is an actual rust surface, 8 is a maximum residual thickness, and 9 is a rust depth.
In fig. 3: and 9 is the rust depth, 10 is the slope of the connecting line of adjacent measuring points of the corrosion surface, 11 is the rust pit width, and 12 is the rust pit depth.
In fig. 4-1 and 4-2: 13 is a three-dimensional model of a rust pit, 14 is a long-axis radius of the rust pit model, 15 is a short-axis radius of the rust pit model, 16 is a rust steel structure reconstruction rust surface, 17 is a box counting unit height, 18 is a square bottom side length of the box counting unit, 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: and 22 is a loading device, 23 is a full-field strain testing system, and 24 is a rapid plastic strain accumulation area of the surface of the rusted steel structure. 25 is a critical rust pit.
In fig. 6-1, 26 is the control point of the upper surface of the rust-resistant steel structure, and 27 is the control point of the lower surface of the rust-resistant steel structure. In fig. 6-2, 28 is a control section of a rust-resistant steel structure, and 29 is a rust-resistant steel structure reconstruction model.
Detailed Description
Specific construction steps
In fig. 2, a polisher is used for carrying out surface rust removal on a rust-removed steel structure 3, a morphology test system 1 is used for scanning the surface morphology of the rust-removed steel structure, and a scanning area is the coverage area of a measuring area boundary line 2. After the steel structure is corroded, the original structure initial surface 4 is degraded and falls off, and the actual corrosion surface 7 is reduced by deposition, wherein the actual surface comprises two characteristic points of an actual corrosion surface highest point 5 and an actual corrosion surface lowest point 6. The maximum residual thickness 8 of the structure and the depth of corrosion 9 at each measurement point can be obtained by scaling the actual corrosion surface 7 measured data.
In fig. 3, based on the measured data of the corrosion depth 9 of each point of the corrosion surface in fig. 2, the corrosion depth 9 of the corrosion surface 7 in the same pit is analyzed by using whether the slope 10 of the connecting line of adjacent measurement points of the corrosion surface is equal to the reference number to determine the pit (i.e., pit) range, and the maximum depth and the maximum width of the corrosion depth 9 are taken as the pit width 11 and the pit depth 12.
In fig. 4-1, the independent pits (where the independent pits refer to all pits on the rusted surface, and also include the key pits), and since parameters of the pits need to be quantized, the equivalent transformation of the shape of the pits should be performed first, and the dimensions of the pits are quantized by long and short half shafts) are transformed into a parametric model, such as a regular semicircle or a semi-ellipse, that is, a three-dimensional model 13 of the pits. The long half shaft 14 of the pit three-dimensional model 13 corresponds to the pit depth 12, and the short half shaft 15 corresponds to the pit width 11. According to FIG. 2, the corrosion depth 9 of the structure surface is measured, the three-dimensional surface 16 of the rusted steel structure is reconstructed, and based on a fractal measure, the appearance characterization parameter of the actual corrosion surface 7 of the rusted structure is calculated by adopting a box counting method, as the parameter D in FIG. 4-2. In fig. 4-2, when the surface 16 of the rusted steel structure is covered by the regular cube box with the height 17 and the bottom edge length 18, the corrosion surface topography characteristic parameters D of the length 19 and the width 20 can be calculated by a fractal dimension calculation formula 21 (h is measured data of corrosion depth 9 of each point of the corrosion surface in the formula), and the calculated result is taken as the surface topography characteristic parameter of the rusted steel structure 3.
In fig. 4-2, the morphological characterization parameter D of the rust steel structure:
wherein L is 1 Measuring the length of the range, L 2 Measuring the width of the range epsilon i The bottom side length of the cube box is measured,
measuring the height of the cube box;
in fig. 3, all pit widths 11, pit depths 12 and morphology fractal dimensions on the surface of the rust steel structure 3 are counted to obtain a distribution statistical model:
wherein P (t) is a probability density distribution function of pit parameters, χ 2 The method is characterized in that a chi-square distribution symbol is adopted, t is corrosion time, w and c are corrosion pit width 11 and pit depth 12 of the surface of a rusted steel structure respectively, D is a fractal dimension calculated value (formula 1) of the rusted surface, e is a normal natural number, gamma is an abnormal integral function, and n is any integer greater than 0.
In fig. 5, the rust steel structure 3 is loaded by an MTS loading device 22 (a full-field strain test rust steel structure mechanical test process is performed for determining and positioning a key rust pit), a full-field strain test system 23 is used in the test process to measure the surface strain result of the rust steel structure 3 in real time, a severe plastic strain accumulation area 24 is determined through accumulation analysis, the severe plastic strain accumulation area is used as a key rust pit 25 for inducing destabilization or damage of the rust steel structure 3, and the rust pit width 11 and the depth 12 of the rust 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 rust-steel structure 3 as reference points. In fig. 6-2, the steel member section 28 of the whole area where the control point is located is constructed by using the height relation of adjacent points of the corrosion surface, and the whole structural reconstruction model 29 is constructed by extending the topology reconstruction (herein, the measurement result of the surface appearance of the corrosion steel structure is a large amount of point cloud data (constructed by fixed interval points, the coordinates of each point are (x, y and Z), the difference value between the adjacent points x and y is the measurement step distance of the appearance test system 1, and Z is the measurement height of the corrosion surface where each data point is located.
In fig. 7, the accuracy of the finite element simulation was verified by using the result of the loading test of the rust steel structure 3 as a determination index. On the premise of ensuring the accuracy of the rust steel structure finite element modeling method, a rust pit evolution model established in fig. 3 is utilized to establish a key rust pit size parameter and morphology parameter sample library, a finite element model is respectively established to carry out mechanical analysis (the result is shown in table 31), and then a least square fitting method is utilized to establish a rust steel structure residual bearing capacity calculation model based on the key rust pit size and morphology characteristics, wherein the calculation model is shown in formula (3).
Examples
Table 31 corrosion 32#c channel steel test piece corrosion surface parameter and finite element axis compression calculation result
Taking an actual 32#C channel steel rust test piece as an example, measuring the surface morphology of the test piece, and determining the key rust pit parameters and the local fractal dimension of the test piece. According to dimensionless relation among parameters in the rust pit evolution process, establishing a key rust pit parameter and morphology parameter model library, then sequentially establishing a finite element model containing a series of size horizontal key rust pits by reverse reconstruction, and performing axle center compression finite element simulation to obtain bearing capacity values under the corresponding key rust pits. And then, a residual bearing capacity calculation model is established by fitting the key 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 an evaluation standard of the residual bearing capacity of the rusted channel steel test piece in the embodiment. For other channel steel components, after key pit parameters are obtained through measurement, the residual bearing capacity of the channel steel components can be directly estimated by using the established estimation standard.
1. Measuring the size parameters (w, c) and the shape parameters (D) of the rust pit on the surface of the structure by adopting the shape test of the existing rust steel structure; pit size parameter extraction using fig. 3 and 4-1; pit morphology parameter extraction utilizes fig. 4-2. And (3) statistically analyzing 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 rust steel structure mechanics experiment, wherein the load type is represented by F. The testing process utilizes DIC to measure the strain field of the surface of a test piece, and based on the rule that fracture cracks are easy to initiate in a severe plastic strain accumulation area, the rust pit parameters and the morphology parameters of the severe plastic strain accumulation area (namely the crack initiation area) are determined, and the rust pit is called a key rust pit.
And then, counting key rust pit parameters and bearing performance indexes of each rust member.
3. And (3) establishing a rust steel structure finite element model by utilizing a finite element means and adopting a reverse reconstruction method, comparing the relation between the rust pit parameters and the bearing performance indexes obtained in step (2), and verifying the correctness of the finite element model. On the premise of correctness of the finite element model, a rust pit evolution model established by 1 is utilized, a key rust pit size parameter and morphology parameter sample library is established, the finite element model is established again respectively, the rust steel structure bearing performance index change rule under different rust pit parameters is analyzed, and a rust steel structure bearing capacity prediction model (indicated by a formula (3) in the specification) is obtained through fitting (least square fitting).
4. When the residual bearing capacity of the actual structure is predicted, the method only needs to test the size parameter and the shape parameter of the rust pit on the rust surface, predicts the development grade of the key rust pit, and then calculates according to the residual bearing capacity prediction model of the rust steel structure.
5. Finite element means are utilized.
1) The structural performance test based on the morphological characteristics can well avoid the problem of prediction accuracy distortion caused by the original rust section folding and subtracting method, introduce the local defects of the rust surface and the complex morphology thereof into an evaluation mechanism, establish the corresponding relation between the random morphological characteristics and the residual bearing capacity, and greatly improve the prediction quality of the residual bearing capacity of the rust steel structure;
2) The method is based on a nondestructive testing technology, realizes accurate prediction of the residual bearing capacity on the basis of the existing nondestructive structure, is simple to operate, has common equipment and has great industrial popularization significance.
Claims (1)
1. A method for evaluating the residual performance of an existing rust steel structure is characterized by comprising the following steps: the method comprises the following steps:
step (1), measuring the surface morphology 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 rust on two sides of a rust steel structure, respectively erecting and fixing a measuring instrument, taking each measuring point of an upper scanning surface and a lower scanning surface as reference points, connecting lines of the upper reference points and the lower reference points are parallel to the thickness direction of the structure plate, and the surface coordinate values (x, y, z) of the upper reference points and the lower reference points can be directly read by two appearance instruments, wherein the actual thickness of the structure at the reference points is z Upper part -z Lower part(s) ∣，z Upper part Measuring the z-direction coordinate, z, of a control point for scanning the upper surface of a structure Lower part(s) For the z-direction coordinate of a scanning measurement control point on the lower surface of the structure, setting the plane where the minimum height of the measurement point of the scanning surface is located as a zero potential surface, converting and defining the actual corrosion depth by taking the zero potential surface as a reference of other measurement points, correlating the 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 correlation thickness of the measurement control points of the upper scanning surface and the lower scanning surface as a standard;
step (2), determining the surface morphology characteristic parameters of the rust pit steel structure
Determining the effective range of all the rust pits according to the continuous measuring point connecting line slope difference numbers of the surfaces at the two sides of the rust pit boundary, and further calculating the depth, width, adjacent space distance and local surface fractal parameters of the rust pits; the independent rust pit is converted into a semi-circular or semi-elliptical parameter model, namely a rust pit three-dimensional model (13), and the shape characterization parameter D of the actual rust surface of the rust steel structure is calculated by adopting a box counting method based on a fractal means according to the measured corrosion depth (9) and the reconstructed rust steel structure three-dimensional surface (16);
wherein L is 1 Measuring the length of the range, L 2 Measuring the width of the range epsilon i The bottom side length of the cube box is measured,
measuring the height of the cube 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 rust pit depth, width and adjacent intervals in the whole corrosion process, and n values are used for adjusting characterization significance, so that the rust pit distribution statistical model is established:
p(t)：
wherein P (t) is the probability density distribution function of the pit parameter, t is the corrosion time, χ 2 For chi-square distribution symbols, w and c are respectively the rust pit width (11) and the rust pit depth (12) of the rust steel structure surface, D is a morphological characterization parameter, namely a calculated value of the fractal dimension of the rust surface, see formula 1, e is a normal natural number, f is an abnormal integral function, and n is any integer greater than 0;
step (4), rust steel structure mechanics experiment
The method comprises the steps of loading a rusted steel structure by adopting an MTS loading device (22), measuring the strain results 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 rapid accumulation area (24) of severe plastic strain through accumulation analysis, serving as a key rust pit (25) for causing instability or damage of the rusted steel structure, namely defining the position with the largest strain accumulation rate and the largest final state as a key rust pit area, and determining the rust pit width (11) and the rust pit depth (12);
step (5), evaluating residual bearing capacity of rusted steel structure
Establishing a rust steel structure finite element model by using a finite element means and a reverse reconstruction method; verifying the correctness of the finite element model by utilizing the relation between the rust pit parameters and the bearing performance indexes obtained in the step (4); on the premise of correctness of the finite element model, the rust pit evolution model established in the step (2) is utilized to establish a key rust pit size parameter and morphology parameter sample library, the finite element models are respectively established again, the rust steel structure bearing performance index change rule under different rust pit parameters is analyzed, and the rust steel structure residual bearing capacity prediction model is obtained through least square fitting:
β(F, Δ)＝α 0 +α1*w+α 2 *c+α 3 *D (3)
wherein, beta is an estimated bearing capacity value of the rust steel structure; w is the pit depth; c is the pit width; d is the fractal dimension of the surface of the rust pit, namely the appearance characterization parameter; f is the load type applied by the finite element model; the limit deformation value of the delta rust steel structure; alpha 0 ，α 1 ，α 2 ，α 3 And fitting load-displacement curve fitting parameters of the finite element result of the steel structure load test by using a least square method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210292484.6A CN114609358B (en) | 2022-03-24 | 2022-03-24 | Residual performance evaluation method for existing rust steel structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210292484.6A CN114609358B (en) | 2022-03-24 | 2022-03-24 | Residual performance evaluation method for existing rust steel structure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114609358A CN114609358A (en) | 2022-06-10 |
| CN114609358B true CN114609358B (en) | 2023-06-06 |
Family
ID=81865919
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210292484.6A Active CN114609358B (en) | 2022-03-24 | 2022-03-24 | Residual performance evaluation method for existing rust steel structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114609358B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118584067B (en) * | 2024-08-07 | 2024-10-01 | 深圳市宏源建设科技有限公司 | Support hanger defect detection system |
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 |
|---|
| Fractal characteristic of corroded steel surface and application to the fracture analyses;Ren Songbo 等;《Construction and Building Materials》(第340期);第1-19页 * |
| Measurement pitting morphology characteristic of corroded steel surface and fractal reconstruction model;Ren Songbo 等;《Measurement》(第第190期期);第1-15页 * |
| 锈蚀钢筋力学性能研究;宋嘉文 等;《低温建筑技术》(第6期);第8-10页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114609358A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Shear-related roughness classification and strength model of natural rock joint based on fuzzy comprehensive evaluation | |
| Xu | Estimating the effects of corrosion pits on the fatigue life of steel plate based on the 3D profile | |
| CN112347668B (en) | Steel bridge deck fatigue reliability assessment method based on probabilistic fracture mechanics | |
| Santos et al. | Comparison of Methods for Texture Assessment of Concrete Surfaces. | |
| CN110502855B (en) | Method for identifying damage of uniform-section continuous beam with support reaction influence line curvature | |
| CN114609358B (en) | Residual performance evaluation method for existing rust steel structure | |
| CN104834806A (en) | Joint roughness coefficient size effect sampling representativeness evaluation method | |
| Karolczuk et al. | Modelling of stress gradient effect on fatigue life using Weibull based distribution function | |
| CN113935090A (en) | Random traffic flow fine simulation method for bridge vehicle-induced fatigue analysis | |
| CN115659737A (en) | Beam member damage identification method based on curvature mode and discrete wavelet transform | |
| Cheng et al. | Effect of surface topography on stress concentration factor | |
| CN111089544A (en) | Tunnel monitoring measurement data analysis method based on maximum entropy method reliability theory | |
| CN117781882B (en) | Industrial hardware detects MES data acquisition analysis system | |
| CN117421919A (en) | Safety assessment method and system for service life of vehicle body | |
| CN109490334B (en) | Nondestructive testing method for T-shaped forge piece by using residual stress prediction model | |
| Wang et al. | Probabilistic study on the macro-crack initiation of the rib-to-deck welded joint on orthotropic steel deck | |
| Kasai et al. | Application of risk curve for statistical analysis of backside corrosion in the bottom floors of oil storage tanks | |
| CN111579647A (en) | Concrete member corrosion degree detection method and system based on analytic hierarchy process | |
| CN113312812B (en) | Method for generating initial geometric defects of cold-formed steel member | |
| Wang et al. | A method for the damage detection of pile foundation in high-pile wharf based on a curvature mode deletion model | |
| CN113240637B (en) | Wall flatness informatization detection method and system based on machine learning | |
| CN115711587A (en) | Method for determining minimum sample number of section line strips for structural surface three-dimensional roughness evaluation | |
| CN115081813A (en) | Method for evaluating anti-slip performance of steel slag asphalt mixture pavement wearing layer | |
| CN114036798A (en) | Corrosion steel analysis method based on corrosion pit fusion and application thereof | |
| CN116413133B (en) | Compressive strength monitoring method and system for special steel |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |