CN111929307A - On-site nondestructive testing and evaluating method for corrosion degree of existing steel structural member - Google Patents
On-site nondestructive testing and evaluating method for corrosion degree of existing steel structural member Download PDFInfo
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- 230000007797 corrosion Effects 0.000 title claims abstract description 130
- 238000005260 corrosion Methods 0.000 title claims abstract description 130
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 25
- 239000010959 steel Substances 0.000 title claims abstract description 25
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- 238000009659 non-destructive testing Methods 0.000 title claims abstract description 15
- 238000001514 detection method Methods 0.000 claims abstract description 54
- 238000011156 evaluation Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000004140 cleaning Methods 0.000 claims abstract description 3
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 claims description 35
- 238000013461 design Methods 0.000 claims description 11
- 238000005516 engineering process Methods 0.000 claims description 6
- 239000003973 paint Substances 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000003384 imaging method Methods 0.000 claims description 5
- 239000003086 colorant Substances 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000032798 delamination Effects 0.000 claims description 2
- 230000003628 erosive effect Effects 0.000 claims description 2
- 238000011065 in-situ storage Methods 0.000 claims 1
- 238000012360 testing method Methods 0.000 abstract description 4
- 238000011158 quantitative evaluation Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000013441 quality evaluation Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000005530 etching Methods 0.000 description 1
- PHTXVQQRWJXYPP-UHFFFAOYSA-N ethyltrifluoromethylaminoindane Chemical compound C1=C(C(F)(F)F)C=C2CC(NCC)CC2=C1 PHTXVQQRWJXYPP-UHFFFAOYSA-N 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
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Abstract
The invention provides a field nondestructive testing evaluation method for the corrosion degree of an existing steel structural member, which comprises the following steps: s1: grading the corrosion degree of the surface property of the member; s2: judging whether the corrosion grade of the current component needs quantitative detection and evaluation, and continuing the subsequent steps if necessary; s3: cleaning the surface of the component; s4: measuring the area without the etch pit by using an ultrasonic thickness gauge to obtain the wall thickness; s5: setting a detection area and sticking a registration target; s6: setting scanning parameters of three-dimensional laser scanning equipment; s7: scanning the detection area by using three-dimensional laser scanning equipment to obtain point cloud data; s8: simplifying the point cloud data of each plate; s9: and analyzing and processing the point cloud data to obtain quantitative corrosion degree data and obtain a qualitative evaluation result. The invention discloses a field nondestructive testing evaluation method for the corrosion degree of an existing steel structural member, and belongs to a nondestructive mode, and the method is high in testing speed and good in testing precision.
Description
Technical Field
The invention relates to the field of nondestructive testing evaluation, in particular to a field nondestructive testing evaluation method for the corrosion degree of an existing steel structural member.
Background
The corrosion is an important factor influencing the durability of a steel structure, and for the existing steel member, the corrosion not only weakens the section of the member, but also can cause the brittle failure of the member, so that the structure collapses. Therefore, when the existing steel structure house or the member is detected, effective measures need to be taken to accurately evaluate the corrosion degree of the member. At present, the evaluation method of the corrosion degree of the steel member mainly comprises a quality evaluation method and a depth evaluation method, wherein the quality evaluation method needs to sample the corrosion member and weigh the corrosion member in a laboratory to calculate the corrosion rate, but most of the existing members do not have the condition of damaging the sample during field detection, so the quality evaluation method is not suitable for field detection; the depth evaluation method judges the corrosion degree by measuring the residual thickness of the component on site or measuring the typical corrosion pit depth, however, the residual thickness measurement cannot reflect the pitting characteristics, the error of the corrosion pit depth measured on site is large, the light condition is often poor during detection, hundreds of corrosion pits may exist on the surface of the component, and the maximum corrosion pit depth cannot be accurately measured, so the depth evaluation method has great limitation in the field detection.
The three-dimensional laser scanning obtains a three-dimensional space data source of a target object by measuring the horizontal direction, the slant distance and the reflection intensity of the surface of an object contacted by a laser spot, is a non-contact active detection technology, has the advantages of high sampling rate, high scanning speed, high measurement precision, long measurement range and the like, and is widely applied to the building surveying and mapping industry. In the aspect of defect detection, for point cloud data acquired by scanning an outer vertical surface by laser, the coordinates of a defect area are obviously different from those of a surrounding normal area, and the position and the size of the defect can be quantitatively identified by performing three-dimensional imaging through a signal processing algorithm. Because the existing steel member is corroded mostly in a punctiform state, the maximum corrosion pit depth is an important factor for judging the corrosion degree of the member, and the corrosion pit condition on the surface of the member can be rapidly and accurately scanned by a three-dimensional laser scanning technology, so that the maximum corrosion pit depth can be rapidly and nondestructively identified, and the corrosion degree of the member can be further evaluated. The three-dimensional laser scanning technology is combined with the existing steel member corrosion degree evaluation method to form a set of nondestructive rapid detection evaluation system, the corrosion degree rapid detection of large-area members can be realized, the detection precision can meet the engineering requirements, and the method has important application potential in steel structure detection.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a field nondestructive testing and evaluating method for the corrosion degree of the existing steel structural member, which belongs to a nondestructive mode, and has the advantages of high testing speed and good testing precision. The method comprises two ways of qualitative evaluation and quantitative evaluation. And (4) qualitative evaluation, namely, rapidly and qualitatively grading the component by using the corrosion property, and directly evaluating the corrosion degree of the component. And quantitative evaluation, namely quantitatively evaluating the corrosion degree of the member when the mechanical property of the member cannot be directly evaluated according to the qualitative grading result. The quantitative evaluation utilizes three-dimensional laser scanning equipment to scan the surface characteristics of the component, utilizes a portable computer to perform post-processing such as registration, denoising, partitioning, analysis, data extraction, digital imaging and the like on point cloud data, and calculates and processes results such as the component volume corrosion rate, the area corrosion rate, the maximum corrosion depth, the corrosion pit distribution image and the like, so that the accurate reflection of the component corrosion degree is realized, and a guidance basis is provided for the subsequent safety evaluation.
In order to achieve the purpose, the invention provides a field nondestructive testing and evaluating method for the corrosion degree of an existing steel structural member, which comprises the following steps:
s1: visually inspecting a component to be detected, and preliminarily grading the corrosion degree of the surface property of the component according to a plurality of preset corrosion grades; part of the corrosion grades can directly obtain qualitative evaluation results, and the other part of the corrosion grades need quantitative detection and evaluation;
s2: judging whether the corrosion grade of the current component needs quantitative detection and evaluation, if so, continuing the subsequent steps, otherwise, ending the steps;
s3: cleaning the surface of the component;
s4: measuring the area of the component to be measured, where no corrosion pit appears, by using an ultrasonic thickness gauge to obtain the wall thickness;
s5: arranging a detection area in an area where the member to be detected has an erosion pit, and pasting registration targets on the surface of the member in the detection area at equal intervals;
s6: setting scanning parameters of a three-dimensional laser scanning device;
s7: scanning the detection area by using the three-dimensional laser scanning equipment to obtain point cloud data; registering point clouds generated at three-dimensional laser scanning positions at different positions by using the registration target based on a closest point iterative registration method and a coordinate transformation technology to form complete point cloud information of the surface of the component in the detection area;
s8: dividing the component into three flat panel components, namely a web plate, an upper flange and a lower flange, segmenting and extracting point cloud, denoising by using a k-nearest neighbor point cloud denoising algorithm, re-sampling the point cloud data according to a distance threshold set by detection precision, and simplifying the point cloud data of each panel component;
s9: the point cloud data J for a single plateiAnd analyzing and processing to obtain quantitative corrosion degree data of the surface, the plate or the component, and obtaining a qualitative evaluation result according to the quantitative corrosion degree data.
Preferably, in the step S1, the corrosion degree of the surface shape of the component includes six corrosion grades, and the corrosion grades include:
in the first stage, the surface anticorrosive paint of the component is complete;
in the second stage, the anticorrosive paint on the surface of the component falls off, but the component is not rusted;
thirdly, the surface of the component has the rust, and the surface is still flat and has no obvious pitting corrosion;
fourthly, pitting exists on the surface of the component, and certain pits exist, but the pits are not connected into a large number of pieces;
a fifth stage, connecting a large number of component corrosion pits into a piece, wherein the component surface has a corrosion delamination phenomenon; and
a sixth stage, the member is rusted, rusted or broken in the thickness direction;
the fourth stage requires the quantitative detection evaluation; the rest rust grades can directly obtain the qualitative evaluation result; the first stage, second stage and third stage directly obtain the component substantially free of rust, the component performance can meet the qualitative assessment results required by design or specification; the fifth and sixth stages directly achieved severe rusting and could not be continued as the result of the qualitative assessment of the structural load bearing member.
Preferably, in the step S6, the wavelength, the sampling frequency and the scanning speed of the three-dimensional laser scanning device are set according to the required detection distance, the detection accuracy and the registration target pitch; the three-dimensional laser scanning equipment adopts handheld three-dimensional laser scanning equipment.
Preferably, the step of analyzing and processing the point cloud data of the single plate in S9 further includes the steps of:
s91: selecting the plate internal point from the point cloud as a coordinate origin, establishing a local coordinate system, and dividing the point cloud into a front side and a back side;
s92: calculating the point cloud data of the surface by mean value to obtain an average z coordinate or r coordinate, representing the average height after rusting, and obtaining the average height z of an area without rusting by the original design drawing, the member specification table and the actually measured wall thicknessi,norm；
S93: for the plate with both sides capable of scanning, the average thickness of the plate after corrosion
And
are respectively aThe average coordinates of said front and said back;
for the plate with only one side capable of scanning, the plate with only one side capable of scanning comprises a round pipe and double-spliced angle steel, and the average thickness of the plate is equal to that of the plate after rusting
d represents the thickness of the rustless area of the plate;
bulk rust rate of the platei＝di/d；
S94: for the plate with only one side capable of being scanned, the maximum rust depth h of the point cloud with one sidei＝|zi,norm|-|zi,minL, wherein zi,minRepresenting the minimum value of the absolute value of the z coordinate in the point cloud;
for the plate with both sides capable of being scanned, the corrosion depth of the two sides of the plate is taken as a larger value, and the maximum corrosion depth H of the plate is obtainedi；
S95: converting the single-side point cloud data if the z coordinate value z of the single pointi＜zi,normIf the point belongs to the pit sunken area, the coordinate of the point is kept unchanged; otherwise, the point belongs to a constant region, the z coordinate of the point is taken as 0, and the transformed point cloud data is stored as a new data set Jia(ii) a J pair by using k-nearest neighbor point cloud denoising algorithmiaAfter denoising, point cloud imaging is carried out by using detection software, the positions of pit-etched sunken areas and normal areas are respectively represented by using different colors, then the images are converted into binary images by using a threshold segmentation principle, and the area A of the pit-etched areas is automatically extracted by calculating pixel points of the binary imagesia(ii) a According to the area A of the pit areaiaCalculating the area corrosion rate eta of the platei；
S96: repeating the steps S91-S95 on the rest plates to obtain the rusted volume rust rate of each plateiMaximum rust depth HiAnd area corrosion rate ηi(ii) a Carrying out weighted average on the corrosion rates of different plates of the same member according to the sectional area of the plates to obtain the whole of the memberTaking the maximum rust depth of all the plates as the maximum rust depth H of the member; the quantitative rust extent data includes an overall volume rust rate, an area rust rate η, and a maximum rust depth H of the component.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
(1) the mode that qualitative detection and quantitative determination combine is adopted, and the direct qualitative judgement of carrying out is carried out to it to most non-corrosion or serious component of corrosion, only needs to carry out quantitative determination to the component of little part moderate corrosion, and detection efficiency is high, and the range of application is wide, is applicable to the detection of on-the-spot large tracts of land component.
(2) And the quantitative evaluation is nondestructive testing, and the mechanical property of the component is not influenced.
(3) The quantitative evaluation can accurately calculate the volume corrosion rate, the area corrosion rate and the maximum corrosion depth, can accurately position the corrosion pit position, and has high detection and evaluation accuracy.
Drawings
FIG. 1 is a flow chart of an existing steel structural member corrosion degree on-site nondestructive testing evaluation method according to an embodiment of the invention;
FIG. 2 is a schematic representation of a typical H-steel component corrosion cross-section of an embodiment of the present invention;
fig. 3 is a partially enlarged view of the area a of fig. 2.
Detailed Description
The following description of the preferred embodiments of the present invention will be provided in conjunction with the accompanying drawings 1-3, and will make the functions and features of the invention better understood.
Referring to FIG. 1, an existing hot-rolled H-section steel member 1 having a cross-sectional dimension of H300X 10X 15 is assumed. The method for the field nondestructive testing and evaluation of the corrosion degree of the existing steel structural member 1 comprises the following steps:
s1: visually inspecting the component 1 to be detected, and preliminarily grading the corrosion degree of the surface property of the component 1 according to a plurality of preset corrosion grades; and a part of corrosion grades can directly obtain a qualitative evaluation result, and the other part of corrosion grades need quantitative detection and evaluation.
Preferably, in the step S1, the corrosion degree of the surface shape of the component 1 includes six corrosion grades, and the corrosion grades include:
in the first stage, the surface of the component 1 is complete in anticorrosive paint;
in the second stage, the anticorrosive paint on the surface of the component 1 falls off, but the component 1 is not rusted;
thirdly, the surface of the component 1 is provided with the rust, the surface is still flat, and no obvious pitting corrosion exists;
fourthly, pitting corrosion exists on the surface of the component 1, and certain corrosion pits exist, but the corrosion pits are not connected into a large number of pieces;
in the fifth stage, a large number of corrosion pits of the component 1 are connected into a sheet, and the surface of the component 1 is corroded and layered; and
sixth, the member 1 is rusted, rusted or broken in the thickness direction;
the fourth stage needs quantitative detection and evaluation; the qualitative evaluation result can be directly obtained from the rest corrosion grades; the first stage, the second stage and the third stage are basically free of obvious corrosion, the performance and the section of the component 1 are basically consistent with those of the component 1 which is not corroded, the component 1 which is basically free of corrosion is directly obtained, and the performance of the component 1 can meet the qualitative evaluation result of the design or specification requirement; and the fifth-level and the sixth-level severe rust corrosion show that the mechanical property and the residual section of the member 1 are greatly reduced, and the member 1 has almost no ductility, so that the severe rust corrosion is directly obtained and cannot be continuously used as the qualitative evaluation result of the structural bearing member 1. Only the fourth stage requires the subsequent detection of pitting corrosion on the surface of the component 1.
S2: and judging whether the corrosion grade of the current component 1 needs quantitative detection and evaluation, if so, continuing the subsequent steps, and otherwise, ending the steps.
S3: the surface of the component 1 is cleaned.
S4: and measuring the areas, without the corrosion pits, of the flange and the web of the component 1 to be measured by using an ultrasonic thickness gauge to obtain the wall thickness.
S5: and arranging a detection area in the area where the member 1 to be detected has the corrosion pits, and pasting registration targets on the surface of the member 1 in the detection area at equal intervals.
In the embodiment, a detection area is arranged in a 1000mm area of the span of the selected component 1, and a registration target is pasted on the web and the middle surfaces of the upper and lower flanges in the detection area at intervals of 200 mm.
S6: and setting scanning parameters of a three-dimensional laser scanning device.
In the step S6, setting the wavelength, sampling frequency and scanning speed of the three-dimensional laser scanning equipment according to the required detection distance, detection precision and registration target distance; the three-dimensional laser scanning equipment adopts handheld three-dimensional laser scanning equipment.
In this embodiment, the portable three-dimensional laser scanning device is set to use green light according to the laser propagation theory, the wavelength of the green light is set to be 532nm, the scanning speed is 200000 dots per second, the dot spacing is 0.05mm, and the detection distance is 1 m.
S7: scanning the detection area by using three-dimensional laser scanning equipment to obtain point cloud data; and registering the point clouds generated at the three-dimensional laser scanning positions at different positions by using a registration target based on a closest point iterative registration method and a coordinate transformation technology to form complete point cloud information of the surface of the component 1 in the detection area.
S8: dividing the component 1 into three flat panel components including a web plate, an upper flange and a lower flange according to the type of the preset component 1, segmenting and extracting point cloud, denoising by using a k-nearest neighbor point cloud denoising algorithm, resampling point cloud data according to a distance threshold set by detection precision, and simplifying the point cloud data of each panel component;
s9: taking a web as an example, the design section of a web plate in the detection area is 1000 multiplied by 244 multiplied by 10, and the point cloud data J of the webiAnd (4) carrying out analysis treatment to obtain quantitative corrosion degree data of the plate or the member 1, and obtaining a qualitative evaluation result according to the quantitative corrosion degree data.
The step of analyzing and processing the point cloud data of the single plate in the step S9 further includes the steps of:
s91: for the planar plate, selecting the inner point of the plate as the origin of coordinates in the point cloud, establishing a local coordinate system,wherein the z axis is the thickness direction to realize that all z coordinates of the point clouds on the two surfaces of the plate in the thickness direction are respectively more than 0 and less than 0, and the point clouds are divided into JipFront and back Jin(ii) a For the circular tube, an axis equation and a section curve equation of the outer surface of the circular tube are fitted by using a least square method under a global coordinate system, any point on the axis is selected as a coordinate origin, and a local three-dimensional axis coordinate system is established, wherein the r axis is the thickness direction of the circular tube. Taking a web as an example, selecting a point on the axis of the cross section of the web as a coordinate origin to establish a local coordinate system, wherein the design coordinate z of the front surface of the web is 5mm, and the design coordinate z of the back surface of the web is-5 mm.
S92: calculating the point cloud data of a plane (curved surface) by mean value to obtain an average z coordinate or r coordinate, representing the average height after rusting, and obtaining the average height z of an area without rusting by the original design drawing, a member specification table and the actually measured wall thicknessi,norm；
Firstly, the z coordinate (r coordinate) is averaged to obtain
Extracting the maximum value z of the absolute value of the z coordinate (r coordinate)i,maxAnd define zi,norm＝zi,maxAs z-coordinate (r-coordinate) of the normal surface of the component 1, where there is a predetermined tolerance, in order to prevent individual protrusions of the surface of the component 1.
S93: for both-sided scannable plates, the average thickness of the plate after tarnishing
And
the average coordinates of the front and back sides respectively;
for the plate with only one side capable of scanning, the plate with only one side capable of scanning comprises a circular tube and double-spliced angle steel, and the average thickness of the plate after being corroded
d represents the thickness of the rustless area of the plate; obtained through original design drawings, component specification tables or actual measurement;
bulk rust ratio of platei＝di/d；
S94: for a plate with only one side capable of being scanned, the maximum rust depth h of a point cloud on the single sidei＝|zi,norm|-|zi,minL, wherein zi,minRepresenting the minimum value of the absolute value of the z coordinate in the point cloud;
for the plate with two surfaces capable of being scanned, the corrosion depth of the two surfaces of the plate is taken as a larger value, and the maximum corrosion depth H of the plate is obtainedi；
S95: converting the single-side point cloud data if the z coordinate value z of the single pointi＜zi,normIf the point belongs to the pit sunken area, the coordinate of the point is kept unchanged; otherwise, the point belongs to a normal region, the z coordinate of the point is taken as 0, and the transformed point cloud data is stored as a new data set JiaA recessed portion indicating an etching pit in the detection region; j pair by using k-nearest neighbor point cloud denoising algorithmiaAfter denoising, point cloud imaging is carried out by using detection software, the positions of pit-etched sunken areas and normal areas are respectively represented by using different colors, then the images are converted into binary images by using a threshold segmentation principle, and the area A of the pit-etched areas is automatically extracted by calculating pixel points of the binary imagesia(ii) a According to the area A of the pit areaiaCalculating the area corrosion rate eta of the platei(ii) a Taking the sum of the areas of the front surface and the back surface of each plate as the total corrosion area, and defining etai＝(Aiap+Aian) The area corrosion rate of the plate is/2A, wherein A is the total area of the single-sided area, AiapIs the area of the front side pit area, AianThe area of the reverse etch pit area.
S96: repeating the steps S91-S95 on the upper flange and the lower flange in the embodiment to obtain the volume corrosion rate of each plate after being corrodediMaximum rust depth HiAnd area corrosion rate ηi(ii) a If necessary, the corrosion of the component 1 is carried outPerforming overall evaluation, namely performing weighted average on the corrosion rates of flanges and webs in the detection area according to the sectional areas of the design specifications of the plate pieces to obtain the overall volume corrosion rate and the area corrosion rate eta of the member 1, and taking the maximum corrosion depth of all the plate pieces as the maximum corrosion depth H of the member 1; quantitative rust severity data includes the overall volumetric rust rate, the area rust rate η and the maximum rust depth H of the member 1.
While the present invention has been described in detail and with reference to the embodiments thereof as illustrated in the accompanying drawings, it will be apparent to one skilled in the art that various changes and modifications can be made therein. Therefore, certain details of the embodiments are not to be interpreted as limiting, and the scope of the invention is to be determined by the appended claims.
Claims (4)
1. An on-site nondestructive testing and evaluating method for the corrosion degree of an existing steel structural member comprises the following steps:
s1: visually inspecting a component to be detected, and preliminarily grading the corrosion degree of the surface property of the component according to a plurality of preset corrosion grades; part of the corrosion grades can directly obtain qualitative evaluation results, and the other part of the corrosion grades need quantitative detection and evaluation;
s2: judging whether the corrosion grade of the current component needs quantitative detection and evaluation, if so, continuing the subsequent steps, otherwise, ending the steps;
s3: cleaning the surface of the component;
s4: measuring the area of the component to be measured, where no corrosion pit appears, by using an ultrasonic thickness gauge to obtain the wall thickness;
s5: arranging a detection area in an area where the member to be detected has an erosion pit, and pasting registration targets on the surface of the member in the detection area at equal intervals;
s6: setting scanning parameters of a three-dimensional laser scanning device;
s7: scanning the detection area by using the three-dimensional laser scanning equipment to obtain point cloud data; registering point clouds generated at three-dimensional laser scanning positions at different positions by using the registration target based on a closest point iterative registration method and a coordinate transformation technology to form complete point cloud information of the surface of the component in the detection area;
s8: dividing the component into three flat panel components, namely a web plate, an upper flange and a lower flange, segmenting and extracting point cloud, denoising by using a k-nearest neighbor point cloud denoising algorithm, re-sampling the point cloud data according to a distance threshold set by detection precision, and simplifying the point cloud data of each panel component;
s9: the point cloud data J for a single plateiAnd analyzing and processing to obtain quantitative corrosion degree data of the surface, the plate or the component, and obtaining a qualitative evaluation result according to the quantitative corrosion degree data.
2. The in-situ nondestructive testing evaluation method for corrosion degree of existing steel structural member of claim 1, wherein in said step S1, the corrosion degree of said member surface property includes six corrosion grades, said corrosion grades include:
in the first stage, the surface anticorrosive paint of the component is complete;
in the second stage, the anticorrosive paint on the surface of the component falls off, but the component is not rusted;
thirdly, the surface of the component has the rust, and the surface is still flat and has no obvious pitting corrosion;
fourthly, pitting exists on the surface of the component, and certain pits exist, but the pits are not connected into a large number of pieces;
a fifth stage, connecting a large number of component corrosion pits into a piece, wherein the component surface has a corrosion delamination phenomenon; and
a sixth stage, the member is rusted, rusted or broken in the thickness direction;
the fourth stage requires the quantitative detection evaluation; the rest rust grades can directly obtain the qualitative evaluation result; the first stage, second stage and third stage directly obtain the component substantially free of rust, the component performance can meet the qualitative assessment results required by design or specification; the fifth and sixth stages directly achieved severe rusting and could not be continued as the result of the qualitative assessment of the structural load bearing member.
3. The on-site nondestructive testing evaluation method for the rust degree of the existing steel structural member according to claim 2, wherein in the step S6, the wavelength, sampling frequency and scanning speed of the three-dimensional laser scanning device are set according to the required detection distance, detection accuracy and the registration target spacing; the three-dimensional laser scanning equipment adopts handheld three-dimensional laser scanning equipment.
4. The method for on-site nondestructive testing evaluation of corrosion degree of existing steel structural member according to claim 3, wherein said step of analyzing and processing said point cloud data of said individual plate member in S9 further comprises the steps of:
s91: selecting the plate internal point from the point cloud as a coordinate origin, establishing a local coordinate system, and dividing the point cloud into a front side and a back side;
s92: calculating the point cloud data of the surface by mean value to obtain an average z coordinate or r coordinate, representing the average height after rusting, and obtaining the average height z of an area without rusting by the original design drawing, the member specification table and the actually measured wall thicknessi,norm；
S93: for the plate with both sides capable of scanning, the average thickness of the plate after corrosion
And z is the average coordinate of the front and back sides, respectively;
for the plate with only one side capable of scanning, the plate with only one side capable of scanning comprises a round pipe and double-spliced angle steel, and the average thickness of the plate is equal to that of the plate after rusting
d represents the thickness of the rustless area of the plate;
bulk rust rate of the platei＝di/d；
S94: for the plate with only one side capable of being scanned, the maximum rust depth h of the point cloud with one sidei＝|zi，norm|-|zi，minL, wherein zi，minRepresenting the minimum value of the absolute value of the z coordinate in the point cloud;
for the plate with both sides capable of being scanned, the corrosion depth of the two sides of the plate is taken as a larger value, and the maximum corrosion depth H of the plate is obtainedi；
S95: converting the single-side point cloud data if the z coordinate value z of the single pointi＜zi，normIf the point belongs to the pit sunken area, the coordinate of the point is kept unchanged; otherwise, the point belongs to a constant region, the z coordinate of the point is taken as 0, and the transformed point cloud data is stored as a new data set Jia(ii) a J pair by using k-nearest neighbor point cloud denoising algorithmiaAfter denoising, point cloud imaging is carried out by using detection software, the positions of pit-etched sunken areas and normal areas are respectively represented by using different colors, then the images are converted into binary images by using a threshold segmentation principle, and the area A of the pit-etched areas is automatically extracted by calculating pixel points of the binary imagesia(ii) a According to the area A of the pit areaiaCalculating the area corrosion rate eta of the platei；
S96: repeating the steps S91-S95 on the rest plates to obtain the rusted volume rust rate of each plateiMaximum rust depth HiAnd area corrosion rate ηi(ii) a Carrying out weighted average on the corrosion rates of different plates of the same member according to the sectional areas of the plates to obtain the integral volume corrosion rate and the area corrosion rate eta of the member, and taking the maximum corrosion depth of all the plates as the maximum corrosion depth H of the member; the quantitative rust extent data includes an overall volume rust rate, an area rust rate η, and a maximum rust depth H of the component.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112859681A (en) * | 2021-01-07 | 2021-05-28 | 南京渐起网络科技有限公司 | Intelligent monitoring method for safety and stability of building steel structure based on big data analysis and cloud monitoring platform |
| CN114354337A (en) * | 2021-12-10 | 2022-04-15 | 广东电网有限责任公司 | Method and device for detecting tensile strength of hardware in corrosion state and terminal equipment |
| CN114636499A (en) * | 2022-03-16 | 2022-06-17 | 天津大学 | On-site detection and evaluation method for residual bearing capacity of rusty welded hollow ball joint |
| CN116012297A (en) * | 2022-12-02 | 2023-04-25 | 广东机电职业技术学院 | Terahertz-based rail surface damage detection method, terahertz-based rail surface damage detection system and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1761871A (en) * | 2003-02-21 | 2006-04-19 | 吉多·D·K·德莫莱奇 | Method and apparatus for scanning corrosion and surface defects |
| JP2006194792A (en) * | 2005-01-14 | 2006-07-27 | Hiroshima Univ | Method for predicting strength degradation of corrosion structure |
| CN101071132A (en) * | 2006-05-11 | 2007-11-14 | 上海市建筑科学研究院有限公司 | Concrete chloride ion permeation property quick rust testing method |
| CN104088472A (en) * | 2014-07-10 | 2014-10-08 | 汕头市建设工程质量监督检测站 | Method for detecting and restoring reinforced concrete structures of coastal building |
| CN109540778A (en) * | 2019-01-15 | 2019-03-29 | 华北水利水电大学 | A kind of the quantitative assessment device and test method of 7N01 aluminium alloy exfoliation Corrosion |
| CN109658398A (en) * | 2018-12-12 | 2019-04-19 | 华中科技大学 | A kind of surface defects of parts identification and appraisal procedure based on three-dimensional measurement point cloud |
| CN109884178A (en) * | 2019-03-08 | 2019-06-14 | 重庆交通大学 | The steel bar corrosion information collecting device and steel bar corrosion detection method of concrete structure |
| JP2019203706A (en) * | 2018-05-21 | 2019-11-28 | 株式会社横河ブリッジ | Rust evaluation photographing device, rust evaluation system, and rust evaluation method |
-
2020
- 2020-08-07 CN CN202010789220.2A patent/CN111929307B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1761871A (en) * | 2003-02-21 | 2006-04-19 | 吉多·D·K·德莫莱奇 | Method and apparatus for scanning corrosion and surface defects |
| JP2006194792A (en) * | 2005-01-14 | 2006-07-27 | Hiroshima Univ | Method for predicting strength degradation of corrosion structure |
| CN101071132A (en) * | 2006-05-11 | 2007-11-14 | 上海市建筑科学研究院有限公司 | Concrete chloride ion permeation property quick rust testing method |
| CN104088472A (en) * | 2014-07-10 | 2014-10-08 | 汕头市建设工程质量监督检测站 | Method for detecting and restoring reinforced concrete structures of coastal building |
| JP2019203706A (en) * | 2018-05-21 | 2019-11-28 | 株式会社横河ブリッジ | Rust evaluation photographing device, rust evaluation system, and rust evaluation method |
| CN109658398A (en) * | 2018-12-12 | 2019-04-19 | 华中科技大学 | A kind of surface defects of parts identification and appraisal procedure based on three-dimensional measurement point cloud |
| CN109540778A (en) * | 2019-01-15 | 2019-03-29 | 华北水利水电大学 | A kind of the quantitative assessment device and test method of 7N01 aluminium alloy exfoliation Corrosion |
| CN109884178A (en) * | 2019-03-08 | 2019-06-14 | 重庆交通大学 | The steel bar corrosion information collecting device and steel bar corrosion detection method of concrete structure |
Non-Patent Citations (1)
| Title |
|---|
| 王煜成 等: "锈蚀对既有钢结构性能影响研究进展" * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112859681A (en) * | 2021-01-07 | 2021-05-28 | 南京渐起网络科技有限公司 | Intelligent monitoring method for safety and stability of building steel structure based on big data analysis and cloud monitoring platform |
| CN114354337A (en) * | 2021-12-10 | 2022-04-15 | 广东电网有限责任公司 | Method and device for detecting tensile strength of hardware in corrosion state and terminal equipment |
| CN114354337B (en) * | 2021-12-10 | 2023-09-05 | 广东电网有限责任公司 | Tensile strength detection method and device for hardware fitting in rust state and terminal equipment |
| CN114636499A (en) * | 2022-03-16 | 2022-06-17 | 天津大学 | On-site detection and evaluation method for residual bearing capacity of rusty welded hollow ball joint |
| CN114636499B (en) * | 2022-03-16 | 2023-09-15 | 天津大学 | On-site detection and evaluation method for residual bearing capacity of rusted welding hollow ball node |
| CN116012297A (en) * | 2022-12-02 | 2023-04-25 | 广东机电职业技术学院 | Terahertz-based rail surface damage detection method, terahertz-based rail surface damage detection system and storage medium |
| CN116012297B (en) * | 2022-12-02 | 2023-08-22 | 广东机电职业技术学院 | Terahertz-based rail surface damage detection method, terahertz-based rail surface damage detection system and storage medium |
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