CN106012789B - A kind of steel structure bridge with Non-Destructive Testing function - Google Patents

A kind of steel structure bridge with Non-Destructive Testing function Download PDF

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CN106012789B
CN106012789B CN201610609311.7A CN201610609311A CN106012789B CN 106012789 B CN106012789 B CN 106012789B CN 201610609311 A CN201610609311 A CN 201610609311A CN 106012789 B CN106012789 B CN 106012789B
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易开全
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Guangzhou loyalty Engineering Inspection Co., Ltd.
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    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D1/00—Bridges in general
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
    • G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
    • G01N27/90—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents
    • G01N27/9046—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents by analysing electrical signals
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D2101/00—Material constitution of bridges
    • E01D2101/30—Metal

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Abstract

A kind of steel structure bridge with Non-Destructive Testing function of the present invention, the cannot-harm-detection device being connected including steel structure bridge and with steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on the supporting plate of the support column composition at supporting surface both ends, the bottom of the support column is provided with an inclined plane, the support frame of two triangular shapes is provided between two support columns of the supporting plate, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse is outside, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, cooperate and install with hypotenuse, a through hole is offered on the inner side right-angle side of support frame as described above, a "T"-shaped connector is provided between two support frames.Bridge floor of the present invention is steady, and bearing capacity is big, and the cooperation between bridge floor and support frame more fastens.

Description

A kind of steel structure bridge with Non-Destructive Testing function
Technical field
The present invention relates to bridge field, and in particular to a kind of steel structure bridge with Non-Destructive Testing function.
Background technology
Steel structure bridge is the bridge built using steel construction, and its main body is alloy steel products, and group is carried out after producing parts Dress and welding, it is a kind of novel bridge mode of construction of current more fashion.
Non-Destructive Testing is premised on not destroying measurand internal structure and practical performance, to measurand inside or table The physical property in face, state characteristic are detected.Electromagnetic nondestructive is become with material electromagnetic performance turns to basis for estimation, to material Material and component implement defects detection and performance test.In correlation technique, although Pulsed eddy current testing technology has obtained deep grind Study carefully and quickly develop, but still suffer from that testing result is not accurate enough, information excavating is not deep enough, does not have to testing result The problems such as effect classification.
The content of the invention
In view of the above-mentioned problems, the present invention provides a kind of steel structure bridge with Non-Destructive Testing function.
The purpose of the present invention is realized using following technical scheme：
A kind of steel structure bridge with Non-Destructive Testing function, including steel structure bridge and the Non-Destructive Testing that is connected with steel structure bridge Device, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on the support of the support column composition at supporting surface both ends Plate, the bottom of the support column are provided with an inclined plane, and two triangular shapes are provided between two support columns of the supporting plate Support frame, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse is outside, the support column bottom Inclined-plane is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, is offered on the inner side right-angle side of support frame as described above One through hole, a "T"-shaped connector is provided between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
Beneficial effects of the present invention are：Being acted on by the support frame of both ends perforate, flooding action is strong, and anti-impact force is strong, and Utilize a "T"-shaped connector so that bridge floor is steady, and bearing capacity is big, and the cooperation between bridge floor and support frame more fastens.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not form any limit to the present invention System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings Other accompanying drawings.
Fig. 1 is the schematic diagram of steel structure bridge of the present invention.
Fig. 2 is the schematic diagram of the cannot-harm-detection device of the present invention.
Reference：
Carried based on temporal signatures detection module 1, based on frequency domain character detection module 2, comprehensive detection module 3, temporal signatures Take submodule 11, based on time domain the defects of detection sub-module 12, pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24, based on frequency domain the defects of detection sub-module 25.
Embodiment
The invention will be further described with the following Examples.
Application scenarios 1
Referring to Fig. 1, Fig. 2, a kind of steel structure bridge with Non-Destructive Testing function of the present embodiment, including steel structure bridge and with The connected the cannot-harm-detection device of steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on supporting surface The supporting plate of the support column composition at both ends, the bottom of the support column are provided with an inclined plane, two support columns of the supporting plate it Between be provided with the support frames of two triangular shapes, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse Outwards, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, support frame as described above Inner side right-angle side on offer a through hole, be provided with a "T"-shaped connector between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
This preferred embodiment is acted on by the support frame of both ends perforate, and flooding action is strong, and anti-impact force is strong.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
This preferred embodiment is compared with traditional steel structure bridge, and bridge floor is more steady, and bearing capacity is bigger, bridge floor and support frame Between cooperation more fasten.
Preferably, the cannot-harm-detection device is included based on temporal signatures detection module 1, based on the and of frequency domain character detection module 2 Comprehensive detection module 3, it is specially：
(1) temporal signatures detection module 1 is based on, it is included temporal signatures extracting sub-module 11, examined based on the defects of time domain Survey submodule 12；The temporal signatures extracting sub-module 11 is used for using improved temporal signatures extracting method extraction temporal signatures Value；The defects of being based on time domain detection sub-module 12 is used for using improved automatic classifying identification method to steel structure bridge defect Detection identification is carried out, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module 2 is based on, it includes pre-processing submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24 and detection sub-module 25 the defects of based on frequency domain；The pretreatment submodule Block 21 is used to carry out Fast Fourier Transform (FFT) to defect area time domain response and reference zone time domain response, obtains defect area frequency Domain response and reference zone frequency domain response, and defect area frequency domain response and reference zone frequency domain response are normalized respectively Difference processing is carried out after processing again, calculates difference frequency domain response；The normalized submodule 22 is used for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；The frequency domain optimization submodule 23 is used for according to collection Skin effect selects the frequency suitable for being detected to steel structure bridge defect, and normalizes frequency domain to difference based on the frequency of selection Response optimizes processing；The difference normalization frequency domain response that the frequency domain character extracting sub-module 24 is used to extract after optimizing Differential peak spectrum, specific frequency differential amplitude spectrum and difference, which are crossed zero frequency and be used as, can be used for characterizing steel structure bridge Material Physics attribute Frequency domain character value；The defects of being based on frequency domain detection sub-module 25 is used for using improved automatic classifying identification method to steel Structure bridge defect carries out detection identification, to obtain the testing result S based on frequency domain2；
(3) comprehensive detection module 3, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, adopt The defects of being determined tested steel structure bridge with predetermined defect classifying identification rule type.
This preferred embodiment effectively inhibits lift-off by way of temporal signatures detection and frequency domain character detection are combined Interference, realizes the accurate detection of steel structure bridge defect.
Preferably, it is described based on improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor is with being tested Lift-off distance between steel structure bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect portion The time domain response of position is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), Difference normalization time domain response S (t) is obtained, definition process formula is：
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for table Levy the temporal signatures value of steel structure bridge Material Physics attribute.
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions, the expression of the gaussian radial basis function are used as from gaussian radial basis function (RBF) Formula is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted.
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on The testing result S of frequency domain2Handled, obtain final detection result, final detection result is different from corresponding in database The calibration result of degree of impairment is compared, and calibration result corresponding with final detection result is selected, according to the damage pre-established Mapping relations between condition of the injury condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determined tested The defects of steel structure bridge type.
The degree of impairment includes equivalent size, depth of defect.
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent i-th of defect j-th selection frequency at impulse eddy current response signal value, i=1, 2 ..., p, j=1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the arteries and veins after definition standardization Punching vortex response signal value dij' calculation formula be：
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCoefficient correlation square at q frequency of selection Battle array C：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k ＜ q are excellent Change frequency matrix to be represented by：
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj (j=1,2 ..., q) is arranged, λ according to descending order1＞ λ2＞ ... ＞ λq, and ask for eigenvalue λj(j=1,2 ..., Q) character pair vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
K is the minimum value for meeting L-90% ＞ 0；
4) weight coefficient is calculated：
This preferred embodiment is standardized to detection data, facilitates different characteristic value to carry out linear combination, is improved Calculating speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, detection can be farthest reduced and miss Difference, and the Classification and Identification rate to steel structure bridge defect is improved, it is easy to follow-up study and solves the problems, such as, improves product quality.
The lift-off distance that this application scene is adjusted between pulse eddy current sensor and tested steel structure bridge surface is 0.4mm, Set coefficient adjustment factor ξ1=0.9, ξ2=0.9,5% is improved to the Classification and Identification rate of steel structure bridge defect.
Application scenarios 2
Referring to Fig. 1, Fig. 2, a kind of steel structure bridge with Non-Destructive Testing function of the present embodiment, including steel structure bridge and with The connected the cannot-harm-detection device of steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on supporting surface The supporting plate of the support column composition at both ends, the bottom of the support column are provided with an inclined plane, two support columns of the supporting plate it Between be provided with the support frames of two triangular shapes, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse Outwards, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, support frame as described above Inner side right-angle side on offer a through hole, be provided with a "T"-shaped connector between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
This preferred embodiment is acted on by the support frame of both ends perforate, and flooding action is strong, and anti-impact force is strong.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
This preferred embodiment is compared with traditional steel structure bridge, and bridge floor is more steady, and bearing capacity is bigger, bridge floor and support frame Between cooperation more fasten.
Preferably, the cannot-harm-detection device is included based on temporal signatures detection module 1, based on the and of frequency domain character detection module 2 Comprehensive detection module 3, it is specially：
(1) temporal signatures detection module 1 is based on, it is included temporal signatures extracting sub-module 11, examined based on the defects of time domain Survey submodule 12；The temporal signatures extracting sub-module 11 is used for using improved temporal signatures extracting method extraction temporal signatures Value；The defects of being based on time domain detection sub-module 12 is used for using improved automatic classifying identification method to steel structure bridge defect Detection identification is carried out, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module 2 is based on, it includes pre-processing submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24 and detection sub-module 25 the defects of based on frequency domain；The pretreatment submodule Block 21 is used to carry out Fast Fourier Transform (FFT) to defect area time domain response and reference zone time domain response, obtains defect area frequency Domain response and reference zone frequency domain response, and defect area frequency domain response and reference zone frequency domain response are normalized respectively Difference processing is carried out after processing again, calculates difference frequency domain response；The normalized submodule 22 is used for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；The frequency domain optimization submodule 23 is used for according to collection Skin effect selects the frequency suitable for being detected to steel structure bridge defect, and normalizes frequency domain to difference based on the frequency of selection Response optimizes processing；The difference normalization frequency domain response that the frequency domain character extracting sub-module 24 is used to extract after optimizing Differential peak spectrum, specific frequency differential amplitude spectrum and difference, which are crossed zero frequency and be used as, can be used for characterizing steel structure bridge Material Physics attribute Frequency domain character value；The defects of being based on frequency domain detection sub-module 25 is used for using improved automatic classifying identification method to steel Structure bridge defect carries out detection identification, to obtain the testing result S based on frequency domain2；
(3) comprehensive detection module 3, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, adopt The defects of being determined tested steel structure bridge with predetermined defect classifying identification rule type.
This preferred embodiment effectively inhibits lift-off by way of temporal signatures detection and frequency domain character detection are combined Interference, realizes the accurate detection of steel structure bridge defect.
Preferably, it is described based on improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor is with being tested Lift-off distance between steel structure bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect portion The time domain response of position is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), Difference normalization time domain response S (t) is obtained, definition process formula is：
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for table Levy the temporal signatures value of steel structure bridge Material Physics attribute.
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions, the expression of the gaussian radial basis function are used as from gaussian radial basis function (RBF) Formula is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted.
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on The testing result S of frequency domain2Handled, obtain final detection result, final detection result is different from corresponding in database The calibration result of degree of impairment is compared, and calibration result corresponding with final detection result is selected, according to the damage pre-established Mapping relations between condition of the injury condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determined tested The defects of steel structure bridge type.
The degree of impairment includes equivalent size, depth of defect.
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent i-th of defect j-th selection frequency at impulse eddy current response signal value, i=1, 2 ..., p, j=1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the arteries and veins after definition standardization Punching vortex response signal value dij' calculation formula be：
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCoefficient correlation square at q frequency of selection Battle array C：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k ＜ q are excellent Change frequency matrix to be represented by：
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj (j=1,2 ..., q) is arranged, λ according to descending order1＞ λ2＞ ... ＞ λq, and ask for eigenvalue λj(j=1,2 ..., Q) character pair vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
K is the minimum value for meeting L-90% ＞ 0；
4) weight coefficient is calculated：
This preferred embodiment is standardized to detection data, facilitates different characteristic value to carry out linear combination, is improved Calculating speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, detection can be farthest reduced and miss Difference, and the Classification and Identification rate to steel structure bridge defect is improved, it is easy to follow-up study and solves the problems, such as, improves product quality.
The lift-off distance that this application scene is adjusted between pulse eddy current sensor and tested steel structure bridge surface is 0.6mm, Set coefficient adjustment factor ξ1=1.1, ξ2=1.1,4% is improved to the Classification and Identification rate of steel structure bridge defect.
Application scenarios 3
Referring to Fig. 1, Fig. 2, a kind of steel structure bridge with Non-Destructive Testing function of the present embodiment, including steel structure bridge and with The connected the cannot-harm-detection device of steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on supporting surface The supporting plate of the support column composition at both ends, the bottom of the support column are provided with an inclined plane, two support columns of the supporting plate it Between be provided with the support frames of two triangular shapes, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse Outwards, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, support frame as described above Inner side right-angle side on offer a through hole, be provided with a "T"-shaped connector between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
This preferred embodiment is acted on by the support frame of both ends perforate, and flooding action is strong, and anti-impact force is strong.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
This preferred embodiment is compared with traditional steel structure bridge, and bridge floor is more steady, and bearing capacity is bigger, bridge floor and support frame Between cooperation more fasten.
Preferably, the cannot-harm-detection device is included based on temporal signatures detection module 1, based on the and of frequency domain character detection module 2 Comprehensive detection module 3, it is specially：
(1) temporal signatures detection module 1 is based on, it is included temporal signatures extracting sub-module 11, examined based on the defects of time domain Survey submodule 12；The temporal signatures extracting sub-module 11 is used for using improved temporal signatures extracting method extraction temporal signatures Value；The defects of being based on time domain detection sub-module 12 is used for using improved automatic classifying identification method to steel structure bridge defect Detection identification is carried out, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module 2 is based on, it includes pre-processing submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24 and detection sub-module 25 the defects of based on frequency domain；The pretreatment submodule Block 21 is used to carry out Fast Fourier Transform (FFT) to defect area time domain response and reference zone time domain response, obtains defect area frequency Domain response and reference zone frequency domain response, and defect area frequency domain response and reference zone frequency domain response are normalized respectively Difference processing is carried out after processing again, calculates difference frequency domain response；The normalized submodule 22 is used for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；The frequency domain optimization submodule 23 is used for according to collection Skin effect selects the frequency suitable for being detected to steel structure bridge defect, and normalizes frequency domain to difference based on the frequency of selection Response optimizes processing；The difference normalization frequency domain response that the frequency domain character extracting sub-module 24 is used to extract after optimizing Differential peak spectrum, specific frequency differential amplitude spectrum and difference, which are crossed zero frequency and be used as, can be used for characterizing steel structure bridge Material Physics attribute Frequency domain character value；The defects of being based on frequency domain detection sub-module 25 is used for using improved automatic classifying identification method to steel Structure bridge defect carries out detection identification, to obtain the testing result S based on frequency domain2；
(3) comprehensive detection module 3, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, adopt The defects of being determined tested steel structure bridge with predetermined defect classifying identification rule type.
This preferred embodiment effectively inhibits lift-off by way of temporal signatures detection and frequency domain character detection are combined Interference, realizes the accurate detection of steel structure bridge defect.
Preferably, it is described based on improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor is with being tested Lift-off distance between steel structure bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect portion The time domain response of position is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), Difference normalization time domain response S (t) is obtained, definition process formula is：
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for table Levy the temporal signatures value of steel structure bridge Material Physics attribute.
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions, the expression of the gaussian radial basis function are used as from gaussian radial basis function (RBF) Formula is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted.
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on The testing result S of frequency domain2Handled, obtain final detection result, final detection result is different from corresponding in database The calibration result of degree of impairment is compared, and calibration result corresponding with final detection result is selected, according to the damage pre-established Mapping relations between condition of the injury condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determined tested The defects of steel structure bridge type.
The degree of impairment includes equivalent size, depth of defect.
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent i-th of defect j-th selection frequency at impulse eddy current response signal value, i=1, 2 ..., p, j=1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the arteries and veins after definition standardization Punching vortex response signal value dij' calculation formula be：
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCoefficient correlation square at q frequency of selection Battle array C：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k ＜ q are excellent Change frequency matrix to be represented by：
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj (j=1,2 ..., q) is arranged, λ according to descending order1＞ λ2＞ ... ＞ λq, and ask for eigenvalue λj(j=1,2 ..., Q) character pair vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
K is the minimum value for meeting L-90% ＞ 0；
4) weight coefficient is calculated：
This preferred embodiment is standardized to detection data, facilitates different characteristic value to carry out linear combination, is improved Calculating speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, detection can be farthest reduced and miss Difference, and the Classification and Identification rate to steel structure bridge defect is improved, it is easy to follow-up study and solves the problems, such as, improves product quality.
The lift-off distance that this application scene is adjusted between pulse eddy current sensor and tested steel structure bridge surface is 0.8mm, Set coefficient adjustment factor ξ1=0.9, ξ2=1.1,4.5% is improved to the Classification and Identification rate of steel structure bridge defect.
Application scenarios 4
Referring to Fig. 1, Fig. 2, a kind of steel structure bridge with Non-Destructive Testing function of the present embodiment, including steel structure bridge and with The connected the cannot-harm-detection device of steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on supporting surface The supporting plate of the support column composition at both ends, the bottom of the support column are provided with an inclined plane, two support columns of the supporting plate it Between be provided with the support frames of two triangular shapes, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse Outwards, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, support frame as described above Inner side right-angle side on offer a through hole, be provided with a "T"-shaped connector between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
This preferred embodiment is acted on by the support frame of both ends perforate, and flooding action is strong, and anti-impact force is strong.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
This preferred embodiment is compared with traditional steel structure bridge, and bridge floor is more steady, and bearing capacity is bigger, bridge floor and support frame Between cooperation more fasten.
Preferably, the cannot-harm-detection device is included based on temporal signatures detection module 1, based on the and of frequency domain character detection module 2 Comprehensive detection module 3, it is specially：
(1) temporal signatures detection module 1 is based on, it is included temporal signatures extracting sub-module 11, examined based on the defects of time domain Survey submodule 12；The temporal signatures extracting sub-module 11 is used for using improved temporal signatures extracting method extraction temporal signatures Value；The defects of being based on time domain detection sub-module 12 is used for using improved automatic classifying identification method to steel structure bridge defect Detection identification is carried out, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module 2 is based on, it includes pre-processing submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24 and detection sub-module 25 the defects of based on frequency domain；The pretreatment submodule Block 21 is used to carry out Fast Fourier Transform (FFT) to defect area time domain response and reference zone time domain response, obtains defect area frequency Domain response and reference zone frequency domain response, and defect area frequency domain response and reference zone frequency domain response are normalized respectively Difference processing is carried out after processing again, calculates difference frequency domain response；The normalized submodule 22 is used for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；The frequency domain optimization submodule 23 is used for according to collection Skin effect selects the frequency suitable for being detected to steel structure bridge defect, and normalizes frequency domain to difference based on the frequency of selection Response optimizes processing；The difference normalization frequency domain response that the frequency domain character extracting sub-module 24 is used to extract after optimizing Differential peak spectrum, specific frequency differential amplitude spectrum and difference, which are crossed zero frequency and be used as, can be used for characterizing steel structure bridge Material Physics attribute Frequency domain character value；The defects of being based on frequency domain detection sub-module 25 is used for using improved automatic classifying identification method to steel Structure bridge defect carries out detection identification, to obtain the testing result S based on frequency domain2；
(3) comprehensive detection module 3, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, adopt The defects of being determined tested steel structure bridge with predetermined defect classifying identification rule type.
This preferred embodiment effectively inhibits lift-off by way of temporal signatures detection and frequency domain character detection are combined Interference, realizes the accurate detection of steel structure bridge defect.
Preferably, it is described based on improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor is with being tested Lift-off distance between steel structure bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect portion The time domain response of position is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), Difference normalization time domain response S (t) is obtained, definition process formula is：
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for table Levy the temporal signatures value of steel structure bridge Material Physics attribute.
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions, the expression of the gaussian radial basis function are used as from gaussian radial basis function (RBF) Formula is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted.
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on The testing result S of frequency domain2Handled, obtain final detection result, final detection result is different from corresponding in database The calibration result of degree of impairment is compared, and calibration result corresponding with final detection result is selected, according to the damage pre-established Mapping relations between condition of the injury condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determined tested The defects of steel structure bridge type.
The degree of impairment includes equivalent size, depth of defect.
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent i-th of defect j-th selection frequency at impulse eddy current response signal value, i=1, 2 ..., p, j=1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the arteries and veins after definition standardization Punching vortex response signal value dij' calculation formula be：
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCoefficient correlation square at q frequency of selection Battle array C：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k ＜ q are excellent Change frequency matrix to be represented by：
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj (j=1,2 ..., q) is arranged, λ according to descending order1＞ λ2＞ ... ＞ λq, and ask for eigenvalue λj(j=1,2 ..., Q) character pair vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
K is the minimum value for meeting L-90% ＞ 0；
4) weight coefficient is calculated：
This preferred embodiment is standardized to detection data, facilitates different characteristic value to carry out linear combination, is improved Calculating speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, detection can be farthest reduced and miss Difference, and the Classification and Identification rate to steel structure bridge defect is improved, it is easy to follow-up study and solves the problems, such as, improves product quality.
The lift-off distance that this application scene is adjusted between pulse eddy current sensor and tested steel structure bridge surface is 1.0mm, Set coefficient adjustment factor ξ1=1.1, ξ2=0.9,5.6% is improved to the Classification and Identification rate of steel structure bridge defect.
Application scenarios 5
Referring to Fig. 1, Fig. 2, a kind of steel structure bridge with Non-Destructive Testing function of the present embodiment, including steel structure bridge and with The connected the cannot-harm-detection device of steel structure bridge, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on supporting surface The supporting plate of the support column composition at both ends, the bottom of the support column are provided with an inclined plane, two support columns of the supporting plate it Between be provided with the support frames of two triangular shapes, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse Outwards, the inclined-plane of the support column bottom is arranged on the inclined edge surfaces of support frame, is cooperated and is installed with hypotenuse, support frame as described above Inner side right-angle side on offer a through hole, be provided with a "T"-shaped connector between two support frames.
Preferably, the "T"-shaped connector includes a cross bar and a montant, and the both ends of the cross bar are sleeved on two supports In the through hole of frame, and fixed, fixed between the upper end of the montant and supporting surface by the second fixed block, institute by the first fixed block State between the first fixed block and cross bar by being welded to connect, by being welded to connect between second fixed block and supporting surface.
This preferred embodiment is acted on by the support frame of both ends perforate, and flooding action is strong, and anti-impact force is strong.
Preferably, it is in " C " font between the supporting surface and support column, it is orthogonal between two support columns and supporting surface.
This preferred embodiment is compared with traditional steel structure bridge, and bridge floor is more steady, and bearing capacity is bigger, bridge floor and support frame Between cooperation more fasten.
Preferably, the cannot-harm-detection device is included based on temporal signatures detection module 1, based on the and of frequency domain character detection module 2 Comprehensive detection module 3, it is specially：
(1) temporal signatures detection module 1 is based on, it is included temporal signatures extracting sub-module 11, examined based on the defects of time domain Survey submodule 12；The temporal signatures extracting sub-module 11 is used for using improved temporal signatures extracting method extraction temporal signatures Value；The defects of being based on time domain detection sub-module 12 is used for using improved automatic classifying identification method to steel structure bridge defect Detection identification is carried out, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module 2 is based on, it includes pre-processing submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracting sub-module 24 and detection sub-module 25 the defects of based on frequency domain；The pretreatment submodule Block 21 is used to carry out Fast Fourier Transform (FFT) to defect area time domain response and reference zone time domain response, obtains defect area frequency Domain response and reference zone frequency domain response, and defect area frequency domain response and reference zone frequency domain response are normalized respectively Difference processing is carried out after processing again, calculates difference frequency domain response；The normalized submodule 22 is used for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；The frequency domain optimization submodule 23 is used for according to collection Skin effect selects the frequency suitable for being detected to steel structure bridge defect, and normalizes frequency domain to difference based on the frequency of selection Response optimizes processing；The difference normalization frequency domain response that the frequency domain character extracting sub-module 24 is used to extract after optimizing Differential peak spectrum, specific frequency differential amplitude spectrum and difference, which are crossed zero frequency and be used as, can be used for characterizing steel structure bridge Material Physics attribute Frequency domain character value；The defects of being based on frequency domain detection sub-module 25 is used for using improved automatic classifying identification method to steel Structure bridge defect carries out detection identification, to obtain the testing result S based on frequency domain2；
(3) comprehensive detection module 3, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, adopt The defects of being determined tested steel structure bridge with predetermined defect classifying identification rule type.
This preferred embodiment effectively inhibits lift-off by way of temporal signatures detection and frequency domain character detection are combined Interference, realizes the accurate detection of steel structure bridge defect.
Preferably, it is described based on improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor is with being tested Lift-off distance between steel structure bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect portion The time domain response of position is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), Difference normalization time domain response S (t) is obtained, definition process formula is：
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for table Levy the temporal signatures value of steel structure bridge Material Physics attribute.
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions, the expression of the gaussian radial basis function are used as from gaussian radial basis function (RBF) Formula is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted.
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on The testing result S of frequency domain2Handled, obtain final detection result, final detection result is different from corresponding in database The calibration result of degree of impairment is compared, and calibration result corresponding with final detection result is selected, according to the damage pre-established Mapping relations between condition of the injury condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determined tested The defects of steel structure bridge type.
The degree of impairment includes equivalent size, depth of defect.
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent i-th of defect j-th selection frequency at impulse eddy current response signal value, i=1, 2 ..., p, j=1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the arteries and veins after definition standardization Punching vortex response signal value dij' calculation formula be：
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCoefficient correlation square at q frequency of selection Battle array C：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k ＜ q are excellent Change frequency matrix to be represented by：
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj (j=1,2 ..., q) is arranged, λ according to descending order1＞ λ2＞ ... ＞ λq, and ask for eigenvalue λj(j=1,2 ..., Q) character pair vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
K is the minimum value for meeting L-90% ＞ 0；
4) weight coefficient is calculated：
This preferred embodiment is standardized to detection data, facilitates different characteristic value to carry out linear combination, is improved Calculating speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, detection can be farthest reduced and miss Difference, and the Classification and Identification rate to steel structure bridge defect is improved, it is easy to follow-up study and solves the problems, such as, improves product quality.
The lift-off distance that this application scene is adjusted between pulse eddy current sensor and tested steel structure bridge surface is 1.2mm, Set coefficient adjustment factor ξ1=1, ξ ＞2=1,4% is improved to the Classification and Identification rate of steel structure bridge defect.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent substitution, without departing from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. a kind of steel structure bridge with Non-Destructive Testing function, including steel structure bridge and the Non-Destructive Testing dress that is connected with steel structure bridge Put, it is characterized in that, described steel structure bridge includes a supporting surface and is arranged on the supporting plate of the support column composition at supporting surface both ends, The bottom of the support column is provided with an inclined plane, and the support of two triangular shapes is provided between two support columns of the supporting plate Frame, support frame as described above central aperture, the rectangular triangle of support frame as described above, hypotenuse is outside, the inclined-plane of the support column bottom It is arranged on the inclined edge surfaces of support frame, cooperates and install with hypotenuse, it is logical that one is offered on the inner side right-angle side of support frame as described above Hole, a "T"-shaped connector is provided between two support frames；
The cannot-harm-detection device is included based on temporal signatures detection module, based on frequency domain character detection module and comprehensive detection module, Specially：
(1) temporal signatures detection module is based on, it includes temporal signatures extracting sub-module, detects submodule based on the defects of time domain Block；The temporal signatures extracting sub-module is used for using improved temporal signatures extracting method extraction temporal signatures value；The base Detection sub-module is used to carry out detection knowledge to steel structure bridge defect using improved automatic classifying identification method in the time domain the defects of Not, to obtain the testing result S based on time domain1；
(2) frequency domain character detection module is based on, it includes pre-processing submodule, normalized submodule, frequency domain optimization submodule Block, frequency domain character extracting sub-module and detection sub-module the defects of based on frequency domain；The pretreatment submodule is used for defect area Domain time domain response and reference zone time domain response carry out Fast Fourier Transform (FFT), obtain defect area frequency domain response and reference zone Frequency domain response, and difference is carried out again after defect area frequency domain response and reference zone frequency domain response are normalized respectively Processing, calculates difference frequency domain response；The normalized submodule is used to difference frequency domain response be normalized, And then obtain difference normalization frequency domain response；The frequency domain optimization submodule is used to select suitable for steel structure according to kelvin effect The frequency that bridge defect is detected, and processing is optimized to difference normalization frequency domain response based on the frequency of selection；It is described Differential peak spectrum, the specific frequency difference for the difference normalization frequency domain response that frequency domain character extracting sub-module is used to extract after optimizing Amplitude spectrum and difference cross zero frequency as available for the frequency domain character value for characterizing steel structure bridge Material Physics attribute；It is described to be based on frequency The defects of domain, detection sub-module was used to carry out detection identification to steel structure bridge defect using improved automatic classifying identification method, with Obtain the testing result S based on frequency domain2；
(3) comprehensive detection module, for according to the testing result S based on time domain1With the testing result S based on frequency domain2, using pre- Determine the defects of defect classifying identification rule is determined tested steel structure bridge type；
The improved temporal signatures extracting method extraction temporal signatures value, including：
(1) steel structure bridge defect is detected using pulse eddy current sensor, adjustment pulse eddy current sensor and tested steel structure Lift-off distance between bridge surface, defect area time domain response q (t) is obtained, choose tested steel structure bridge zero defect position Time domain response is as reference zone time domain response c (t)；
(2) difference and normalized are carried out to defect area time domain response q (t) and reference zone time domain response c (t), obtained Difference normalization time domain response S (t), definition process formula are：
<mrow> <mi>S</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>q</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>&amp;xi;</mi> <mn>1</mn> </msub> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mfrac> <mrow> <mi>c</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>&amp;xi;</mi> <mn>2</mn> </msub> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow>
In formula, ξ1、ξ2For the coefficient adjustment factor of setting, ξ1、ξ2Span be [0.9,1.1]；
(3) extract difference normalization time domain response S (t) the differential peak time and difference zero-crossing timing is used as and can be used for characterizing steel The temporal signatures value of structure bridge Material Physics attribute；
The improved automatic classifying identification method carries out detection identification to steel structure bridge defect, including：
(1) Kernel functions are used as from gaussian radial basis function (RBF), the expression formula of the gaussian radial basis function is K (x, y)=exp {-γ ‖ x-y ‖2, the parameter γ of RBF functions is optimized using particle swarm optimization algorithm；
(2) training algorithm is performed, support vector cassification model is obtained using training data；
(3) training data is tested, unknown steel structure bridge defect is predicted；
The predetermined defect classifying identification rule is：Using weighted mean method to the testing result S based on time domain1With based on frequency domain Testing result S2Handled, final detection result is obtained, by final detection result from corresponding to different damages in database The calibration result of situation is compared, and calibration result corresponding with final detection result is selected, according to the damage feelings pre-established Mapping relations between condition and calibration result, degree of impairment corresponding with the calibration result is obtained, and then determine tested steel structure The defects of bridge type；
The degree of impairment includes equivalent size, depth of defect；
It is described that processing is optimized to difference normalization frequency domain response based on the frequency of selection, including：
(1) data matrix D is constructed according to the impulse eddy current response signal data of multiple steel structure bridge defects：
In formula, dijRepresent the impulse eddy current response signal value at the frequency of i-th of defect selection at j-th, i=1,2 ..., p, j =1,2 ..., q；
(2) each impulse eddy current response signal value in data matrix D is standardized, the pulse whirlpool after definition standardization Flow response signal value dij' calculation formula be：
<mrow> <msup> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&amp;prime;</mo> </msup> <mo>=</mo> <mfrac> <mrow> <mn>2</mn> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <mover> <msub> <mi>d</mi> <mi>j</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>-</mo> <mover> <msub> <mi>d</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <msqrt> <mrow> <mfrac> <mn>1</mn> <mrow> <mi>p</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>p</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <mover> <msub> <mi>d</mi> <mi>j</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>+</mo> <msqrt> <mrow> <mfrac> <mn>1</mn> <mrow> <mi>q</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>q</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <mover> <msub> <mi>d</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>p</mi> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow>
In formula,
Then the impulse eddy current response composition vector at the frequency of p defect selection at j-th is：
dj=(d1j′,d2j′,…,dpj′)T
(3) calculate each impulse eddy current response and form vectorial d1,d2,…,dqCorrelation matrix C at q frequency of selection：
In formula, cmn(m=1,2 ..., q, n=1,2 ..., q) is dmAnd dnCoefficient correlation；
(4) k optimization frequency N is determinedrTo reflect the resultant effect of q frequency of selection, r=1,2 ..., k, k<Q, optimization frequency Matrix is represented by：
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>h</mi> <mn>11</mn> </msub> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>h</mi> <mn>12</mn> </msub> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>+</mo> <mo>...</mo> <msub> <mi>h</mi> <mn>11</mn> </msub> <msub> <mi>d</mi> <mi>q</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>N</mi> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi>h</mi> <mn>21</mn> </msub> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>h</mi> <mn>22</mn> </msub> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>+</mo> <mo>...</mo> <msub> <mi>h</mi> <mrow> <mn>2</mn> <mi>q</mi> </mrow> </msub> <msub> <mi>d</mi> <mi>q</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>.........</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>N</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>h</mi> <mrow> <mi>k</mi> <mn>1</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>h</mi> <mrow> <mi>k</mi> <mn>2</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>+</mo> <mo>...</mo> <msub> <mi>h</mi> <mrow> <mi>k</mi> <mi>q</mi> </mrow> </msub> <msub> <mi>d</mi> <mi>q</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
In formula, hrjWeight coefficient of the q frequency for representing to choose on optimization frequency, weighting coefficient matrix H are expressed as：
Weight coefficient hrjCalculation be：
1) to characteristic equation | λ E-C |=0 solves, and asks for each eigenvalue λj(j=1,2 ..., q), by each eigenvalue λj(j= 1,2 ..., q) arranged according to descending order, λ1>λ2>…>λq, and ask for eigenvalue λj(j=1,2 ..., q) it is corresponding Characteristic vector ej, it is desirable to | | ej| |=1, i.e.,
2) r-th of optimization frequency N is definedrTo the contribution rate G of resultant effectr：
<mrow> <msub> <mi>G</mi> <mi>r</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> <mrow> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>q</mi> </msubsup> <msub> <mi>&amp;lambda;</mi> <mi>j</mi> </msub> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow>
3) k optimization frequency N is calculatedrContribution rate of accumulative total L：
<mrow> <mi>L</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>k</mi> </msubsup> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> </mrow> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>q</mi> </msubsup> <msub> <mi>&amp;lambda;</mi> <mi>j</mi> </msub> </mrow> </mfrac> </mrow>
K is to meet L-90%>0 minimum value；
4) weight coefficient is calculated：
2. a kind of steel structure bridge with Non-Destructive Testing function according to claim 1, it is characterized in that, the "T"-shaped company Fitting includes a cross bar and a montant, and the both ends of the cross bar are sleeved in the through hole of two support frames, and is fixed by first Block is fixed, and is fixed between the upper end of the montant and supporting surface by the second fixed block, is passed through between first fixed block and cross bar It is welded to connect, by being welded to connect between second fixed block and supporting surface.
3. a kind of steel structure bridge with Non-Destructive Testing function according to claim 2, it is characterized in that, the supporting surface with It is in " C " font between support column, it is orthogonal between two support columns and supporting surface.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10123099A (en) * 1996-10-18 1998-05-15 Non Destructive Inspection Co Ltd Apparatus and method for evaluating metallic material
CN1277288A (en) * 1999-06-01 2000-12-20 株式会社大林组 Viaduct bridge substructure and its design method
CN103257182A (en) * 2013-06-07 2013-08-21 电子科技大学 Pulse vortexing defect quantitative detection method and detection system
CN203160420U (en) * 2013-04-08 2013-08-28 李涛 H-shaped steel-structure support frame and H-shaped steel-structure bridge thereof
CN103940626A (en) * 2014-04-01 2014-07-23 上海交通大学 Method for evaluating remaining service life of orthotropic steel deck slab on active service after fatigue cracking
CN203807927U (en) * 2014-03-12 2014-09-03 福建巨岸建设工程有限公司 Steel structure bridge
CN203923875U (en) * 2014-04-25 2014-11-05 天津市核通建筑钢结构有限公司 A kind of steel structure bridge
CN205157501U (en) * 2015-12-11 2016-04-13 傅水娟 A send -receiver device for ultrasonic non -destructive testing equipment

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10123099A (en) * 1996-10-18 1998-05-15 Non Destructive Inspection Co Ltd Apparatus and method for evaluating metallic material
CN1277288A (en) * 1999-06-01 2000-12-20 株式会社大林组 Viaduct bridge substructure and its design method
CN203160420U (en) * 2013-04-08 2013-08-28 李涛 H-shaped steel-structure support frame and H-shaped steel-structure bridge thereof
CN103257182A (en) * 2013-06-07 2013-08-21 电子科技大学 Pulse vortexing defect quantitative detection method and detection system
CN203807927U (en) * 2014-03-12 2014-09-03 福建巨岸建设工程有限公司 Steel structure bridge
CN103940626A (en) * 2014-04-01 2014-07-23 上海交通大学 Method for evaluating remaining service life of orthotropic steel deck slab on active service after fatigue cracking
CN203923875U (en) * 2014-04-25 2014-11-05 天津市核通建筑钢结构有限公司 A kind of steel structure bridge
CN205157501U (en) * 2015-12-11 2016-04-13 傅水娟 A send -receiver device for ultrasonic non -destructive testing equipment

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