CN106049752A - Building slope roof beam with good property - Google Patents

Abstract

The invention relates to a building slope roof beam with good property. The building slope roof beam comprises a building slope roof beam body and a non-destructive detection device, wherein the non-destructive detection device is connected with the building slope roof beam body. The building slope roof beam is characterized in that the building slope roof beam is built by the following method of (1) according to the to-be-built building structure, utilizing computer aided design to build a slope roof beam structure model on a building structure model; (2) according to the slope roof beam structure model, calculating the stress conditions of an oblique beam, a support base and/or a flat beam of the slope roof beam, and arranging additional ribs in the slope roof beam structure according to the stress centralizing conditions of the beams and the support base; (3) arranging angle supports on the connecting part of the slope beams of the slope roof beam structure, and the connecting part of the slope beam and the flat beam. The building slope roof beam has the advantages that the building cost of the slope roof beam structure is reduced, and the safety of the slope roof beam structure is improved.

Description

A kind of building sloping roof beam of good performance

Technical field

The present invention relates to building field, be specifically related to a kind of building sloping roof beam of good performance.

Background technology

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 detect.Electromagnetic nondestructive is changed to basis for estimation with material electromagnetic performance, comes material Material and component implement defects detection and performance test.In correlation technique, although Pulsed eddy current testing technology has obtained deep grinding Study carefully and quickly develop, but still suffer from that testing result is not accurate enough, information excavating is the most deep enough, testing result is had The problems such as effect classification.

Summary of the invention

For the problems referred to above, the present invention provides a kind of building sloping roof beam of good performance.

The purpose of the present invention realizes by the following technical solutions:

A kind of building sloping roof beam of good performance, is connected including building sloping roof beam with building sloping roof beam The cannot-harm-detection device, it is characterized in that, described building sloping roof beam is adopted and is built with the following method: (1) is according to building of planning to build Building thing structure utilizes computer-aided design to build sloping roof beam structural model on fabric structure model；(2) according to room, slope Face girder construction model calculates the cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge, concentrates according to the stress of beam, bearing Situation lays additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam and cant beam junction or cant beam and flat-topped ridge Junction arranges angle torr.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

The invention have the benefit that and decrease the cost building sloping roof beam structure, improve sloping roof beam knot simultaneously The degree of safety of structure.Economic and practical, calculated the bigger position of stress of sloping roof beam structure by computer-aided design, at stress relatively Additional muscle is arranged in big junction, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The method of the present invention is by reality Engineering design is summed up to be proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, and reinforcement manner more closes Reason, safer.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is the schematic diagram of building sloping roof beam of the present invention.

Fig. 2 is the schematic diagram of the cannot-harm-detection device of the present invention.

Reference:

Based on temporal signatures detection module 1, carry based on frequency domain character detection module 2, comprehensive detection module 3, temporal signatures Take submodule 11, defects detection submodule 12 based on time domain, pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24, defects detection submodule 25 based on frequency domain.

Detailed description of the invention

The invention will be further described with the following Examples.

Application scenarios 1

Seeing Fig. 1, Fig. 2, the one building of good performance sloping roof beam of the present embodiment, including building sloping roof beam And the cannot-harm-detection device being connected with building sloping roof beam, it is characterized in that, described building sloping roof beam uses such as lower section Method builds: (1) utilizes computer-aided design to build pitched roof on fabric structure model according to the fabric structure planned to build Girder construction model；(2) cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge are calculated according to sloping roof beam structural model, Stress according to beam, bearing concentrates situation to lay additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam with oblique Beam junction or cant beam arrange angle torr with flat-topped ridge junction.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

This preferred embodiment is economic and practical, is calculated the bigger position of stress of sloping roof beam structure by computer-aided design Putting, additional muscle is arranged in the junction bigger at stress, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The side of the present invention Method is summed up by actual engineering design and is proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, Er Qiepei Muscle mode is more reasonable, safer.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

This preferred embodiment decreases the cost building sloping roof beam structure, improves the safety of sloping roof beam structure simultaneously Degree.

Preferably, the cannot-harm-detection device includes based on temporal signatures detection module 1, based on frequency domain character detection module 2 and Comprehensive detection module 3, particularly as follows:

(1) based on temporal signatures detection module 1, it includes that temporal signatures extracts submodule 11, defect based on time domain inspection Survey submodule 12；Described temporal signatures extracts submodule 11 for using the temporal signatures extracting method of improvement to extract temporal signatures Value；Described defects detection submodule based on time domain 12 is for using the automatic classifying identification method of improvement to building pitched roof Beam defect carries out detection and identifies, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule Block 21, for defect area time domain response and reference zone time domain response are carried out fast Fourier transform, 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 Carry out difference processing after process again, calculate difference frequency domain response；Described normalized submodule 22 is for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for according to collection Skin effect selects the frequency being suitable to detect building sloping roof beam defect, and based on the frequency chosen to difference normalizing Change frequency domain response and be optimized process；Described frequency domain character extracts the submodule 24 difference normalization frequency domain after extracting optimization The differential peak spectrum of response, characteristic frequency differential amplitude spectrum and difference cross zero frequency as can be used for characterizing building sloping roof beam The frequency domain character value of Material Physics attribute；Described defects detection submodule based on frequency domain 25 is for using the automatic classification of improvement Recognition methods carries out detection and identifies, to obtain testing result S based on frequency domain building sloping roof beam defect₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, adopt The defect type of tested building sloping roof beam it is determined with predetermined defect classifying identification rule.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off Interference, it is achieved that the accurate detection of building sloping roof beam defect.

Preferably, described temporal signatures extracting method based on improvement extracts temporal signatures value, including:

(1) use pulse eddy current sensor that building sloping roof beam defect is detected, adjust pulse eddy current sensor And the lift-off distance between tested building sloping roof beam surface, it is thus achieved that defect area time domain response q (t), chooses tested building The time domain response at thing sloping roof beam zero defect position is as reference zone time domain response c (t)；

(2) defect area time domain response q (t) and reference zone time domain response c (t) are carried out difference and normalized, Obtaining difference normalization time domain response S (t), definition process formula is:

$S\left(t\right)=\frac{q\left(t\right)}{{\mathrm{\ξ}}_{1}max\left(q\right(t\left)\right)}-\frac{c\left(t\right)}{{\mathrm{\ξ}}_{2}max\left(c\right(t\left)\right)}$

In formula, ξ₁、ξ₂For the coefficient adjustment factor set, 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 as can be used for table Levy the temporal signatures value of building sloping roof beam Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies building sloping roof beam defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression of described gaussian radial basis function kernel function Formula is K (x, y)=exp{-γ ‖ x-y ‖², use particle swarm optimization algorithm that parameter γ of RBF function is optimized；

(2) perform training algorithm, use training data to obtain support vector cassification model；

(3) training data is tested, unknown building sloping roof beam defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on Testing result S of frequency domain₂Process, obtain final detection result, by final detection result with in data base correspond to different The calibration result of degree of impairment compares, and selects the calibration result corresponding with final detection result, according to the damage pre-build Mapping relations between traumatic condition condition and calibration result, obtain the degree of impairment corresponding with described calibration result, and then determine tested The defect type of building sloping roof beam.

Described degree of impairment includes equivalent size, depth of defect.

Described based on the frequency chosen, difference normalization frequency domain response is optimized process, including:

(1) according to the impulse eddy current response signal data structure data matrix D of multiple building sloping roof beam defects:

In formula, d_ijExpression i-th defect impulse eddy current response signal value at the frequency that jth is chosen, i=1, 2 ..., p, j=1,2 ..., q；

(2) it is standardized each impulse eddy current response signal value in data matrix D processing, the arteries and veins after definition standardization Rush eddy current response signal value d_ij' computing formula be:

${d_{ij}}^{\′}=\frac{2d_{ij}-\stackrel{\‾}{d_j}-\stackrel{\‾}{d_i}}{\sqrt{\frac{1}{p-1}{\Σ}_{i=1}^p{(d_{ij}-\stackrel{\‾}{d_j})}^2}+\sqrt{\frac{1}{q-1}{\Σ}_{j=1}^q{(d_{ij}-\stackrel{\‾}{d_i})}^2}},(i=1,2,\mathrm{...},p,j=1,2,\mathrm{...},q)$

In formula,

Then the impulse eddy current response at the frequency that jth is chosen of p defect constitutes vector and is:

d_j=(d_1j′,d_2j′,…,d_pj′)^T

(3) calculate the response of each impulse eddy current and constitute vector d₁,d₂,…,d_qCorrelation coefficient square at q the frequency chosen Battle array C:

In formula, c_mn(m=1,2 ..., q, n=1,2 ..., q) it is d_mAnd d_nCorrelation coefficient；

(4) k optimization frequency N is determined_rReflect the resultant effect of q the frequency chosen, r=1,2 ..., k, k ＜ q, excellent Change frequency matrix to be represented by:

$\left\{\begin{array}{c}{N}_1=h_{11}{d}_1+h_{12}{d}_2+...h_{11}{d}_q\\ N_2=h_{21}{d}_1+h_{22}{d}_2+...h_{2q}{d}_q\\ \mathrm{.........}\\ N_k=h_{k1}{d}_1+h_{k2}{d}_2+...h_{kq}{d}_q\end{array}\right.$

In formula, h_rjRepresenting q the frequency chosen weight coefficient on optimization frequency, weighting coefficient matrix H is expressed as:

Weight coefficient h_rjCalculation be:

1) to characteristic equation ,=0 solves | λ E-C |, asks for each eigenvalue λ_j(j=1,2 ..., q), by each eigenvalue λ_j (j=1,2 ..., q) arrange according to descending order, λ₁＞ λ₂＞ ... ＞ λ_q, and ask for eigenvalue λ_j(j=1,2 ..., Q) character pair vector e_j, it is desirable to | | e_j| |=1, i.e.

2) the r optimization frequency N is defined_rContribution rate G to resultant effect_r:

$G_r=\frac{{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j},(r=1,2,...,k)$

3) k optimization frequency N is calculated_rContribution rate of accumulative total L:

$L=\frac{{\Σ}_{r=1}^k{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j}$

K is the minima meeting L-90% ＞ 0；

4) weight coefficient is calculated:

Detection data are standardized processing by this preferred embodiment, facilitate different characteristic value to carry out linear combination, improve Calculate speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection by mistake Difference, and improve the Classification and Identification rate to building sloping roof beam defect, it is simple to follow-up study and the problem of solution, improve product matter Amount.

The lift-off distance that this application scene adjusts between pulse eddy current sensor and tested building sloping roof beam surface is 0.4mm, sets coefficient adjustment factor ξ₁=0.9, ξ₂=0.9, the Classification and Identification rate of building sloping roof beam defect is improve 5%.

Application scenarios 2

Seeing Fig. 1, Fig. 2, the one building of good performance sloping roof beam of the present embodiment, including building sloping roof beam And the cannot-harm-detection device being connected with building sloping roof beam, it is characterized in that, described building sloping roof beam uses such as lower section Method builds: (1) utilizes computer-aided design to build pitched roof on fabric structure model according to the fabric structure planned to build Girder construction model；(2) cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge are calculated according to sloping roof beam structural model, Stress according to beam, bearing concentrates situation to lay additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam with oblique Beam junction or cant beam arrange angle torr with flat-topped ridge junction.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

This preferred embodiment is economic and practical, is calculated the bigger position of stress of sloping roof beam structure by computer-aided design Putting, additional muscle is arranged in the junction bigger at stress, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The side of the present invention Method is summed up by actual engineering design and is proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, Er Qiepei Muscle mode is more reasonable, safer.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

This preferred embodiment decreases the cost building sloping roof beam structure, improves the safety of sloping roof beam structure simultaneously Degree.

Preferably, the cannot-harm-detection device includes based on temporal signatures detection module 1, based on frequency domain character detection module 2 and Comprehensive detection module 3, particularly as follows:

(1) based on temporal signatures detection module 1, it includes that temporal signatures extracts submodule 11, defect based on time domain inspection Survey submodule 12；Described temporal signatures extracts submodule 11 for using the temporal signatures extracting method of improvement to extract temporal signatures Value；Described defects detection submodule based on time domain 12 is for using the automatic classifying identification method of improvement to building pitched roof Beam defect carries out detection and identifies, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule Block 21, for defect area time domain response and reference zone time domain response are carried out fast Fourier transform, 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 Carry out difference processing after process again, calculate difference frequency domain response；Described normalized submodule 22 is for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for according to collection Skin effect selects the frequency being suitable to detect building sloping roof beam defect, and based on the frequency chosen to difference normalizing Change frequency domain response and be optimized process；Described frequency domain character extracts the submodule 24 difference normalization frequency domain after extracting optimization The differential peak spectrum of response, characteristic frequency differential amplitude spectrum and difference cross zero frequency as can be used for characterizing building sloping roof beam The frequency domain character value of Material Physics attribute；Described defects detection submodule based on frequency domain 25 is for using the automatic classification of improvement Recognition methods carries out detection and identifies, to obtain testing result S based on frequency domain building sloping roof beam defect₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, adopt The defect type of tested building sloping roof beam it is determined with predetermined defect classifying identification rule.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off Interference, it is achieved that the accurate detection of building sloping roof beam defect.

Preferably, described temporal signatures extracting method based on improvement extracts temporal signatures value, including:

(1) use pulse eddy current sensor that building sloping roof beam defect is detected, adjust pulse eddy current sensor And the lift-off distance between tested building sloping roof beam surface, it is thus achieved that defect area time domain response q (t), chooses tested building The time domain response at thing sloping roof beam zero defect position is as reference zone time domain response c (t)；

(2) defect area time domain response q (t) and reference zone time domain response c (t) are carried out difference and normalized, Obtaining difference normalization time domain response S (t), definition process formula is:

$S\left(t\right)=\frac{q\left(t\right)}{{\mathrm{\ξ}}_{1}max\left(q\right(t\left)\right)}-\frac{c\left(t\right)}{{\mathrm{\ξ}}_{2}max\left(c\right(t\left)\right)}$

In formula, ξ₁、ξ₂For the coefficient adjustment factor set, 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 as can be used for table Levy the temporal signatures value of building sloping roof beam Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies building sloping roof beam defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression of described gaussian radial basis function kernel function Formula is K (x, y)=exp{-γ ‖ x-y ‖², use particle swarm optimization algorithm that parameter γ of RBF function is optimized；

(2) perform training algorithm, use training data to obtain support vector cassification model；

(3) training data is tested, unknown building sloping roof beam defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on Testing result S of frequency domain₂Process, obtain final detection result, by final detection result with in data base correspond to different The calibration result of degree of impairment compares, and selects the calibration result corresponding with final detection result, according to the damage pre-build Mapping relations between traumatic condition condition and calibration result, obtain the degree of impairment corresponding with described calibration result, and then determine tested The defect type of building sloping roof beam.

Described degree of impairment includes equivalent size, depth of defect.

Described based on the frequency chosen, difference normalization frequency domain response is optimized process, including:

(1) according to the impulse eddy current response signal data structure data matrix D of multiple building sloping roof beam defects:

In formula, d_ijExpression i-th defect impulse eddy current response signal value at the frequency that jth is chosen, i=1, 2 ..., p, j=1,2 ..., q；

(2) it is standardized each impulse eddy current response signal value in data matrix D processing, the arteries and veins after definition standardization Rush eddy current response signal value d_ij' computing formula be:

${d_{ij}}^{\′}=\frac{2d_{ij}-\stackrel{\‾}{d_j}-\stackrel{\‾}{d_i}}{\sqrt{\frac{1}{p-1}{\Σ}_{i=1}^p{(d_{ij}-\stackrel{\‾}{d_j})}^2}+\sqrt{\frac{1}{q-1}{\Σ}_{j=1}^q{(d_{ij}-\stackrel{\‾}{d_i})}^2}},(i=1,2,\mathrm{...},p,j=1,2,\mathrm{...},q)$

In formula,

Then the impulse eddy current response at the frequency that jth is chosen of p defect constitutes vector and is:

d_j=(d_1j′,d_2j′,…,d_pj′)^T

(3) calculate the response of each impulse eddy current and constitute vector d₁,d₂,…,d_qCorrelation coefficient square at q the frequency chosen Battle array C:

In formula, c_mn(m=1,2 ..., q, n=1,2 ..., q) it is d_mAnd d_nCorrelation coefficient；

(4) k optimization frequency N is determined_rReflect the resultant effect of q the frequency chosen, r=1,2 ..., k, k ＜ q, excellent Change frequency matrix to be represented by:

$\left\{\begin{array}{c}{N}_1=h_{11}{d}_1+h_{12}{d}_2+...h_{11}{d}_q\\ N_2=h_{21}{d}_1+h_{22}{d}_2+...h_{2q}{d}_q\\ \mathrm{.........}\\ N_k=h_{k1}{d}_1+h_{k2}{d}_2+...h_{kq}{d}_q\end{array}\right.$

In formula, h_rjRepresenting q the frequency chosen weight coefficient on optimization frequency, weighting coefficient matrix H is expressed as:

Weight coefficient h_rjCalculation be:

1) to characteristic equation ,=0 solves | λ E-C |, asks for each eigenvalue λ_j(j=1,2 ..., q), by each eigenvalue λ_j (j=1,2 ..., q) arrange according to descending order, λ₁＞ λ₂＞ ... ＞ λ_q, and ask for eigenvalue λ_j(j=1,2 ..., Q) character pair vector e_j, it is desirable to | | e_j| |=1, i.e.

2) the r optimization frequency N is defined_rContribution rate G to resultant effect_r:

$G_r=\frac{{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j},(r=1,2,...,k)$

3) k optimization frequency N is calculated_rContribution rate of accumulative total L:

$L=\frac{{\Σ}_{r=1}^k{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j}$

K is the minima meeting L-90% ＞ 0；

4) weight coefficient is calculated:

Detection data are standardized processing by this preferred embodiment, facilitate different characteristic value to carry out linear combination, improve Calculate speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection by mistake Difference, and improve the Classification and Identification rate to building sloping roof beam defect, it is simple to follow-up study and the problem of solution, improve product matter Amount.

The lift-off distance that this application scene adjusts between pulse eddy current sensor and tested building sloping roof beam surface is 0.6mm, sets coefficient adjustment factor ξ₁=1.1, ξ₂=1.1, the Classification and Identification rate of building sloping roof beam defect is improve 4%.

Application scenarios 3

Seeing Fig. 1, Fig. 2, the one building of good performance sloping roof beam of the present embodiment, including building sloping roof beam And the cannot-harm-detection device being connected with building sloping roof beam, it is characterized in that, described building sloping roof beam uses such as lower section Method builds: (1) utilizes computer-aided design to build pitched roof on fabric structure model according to the fabric structure planned to build Girder construction model；(2) cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge are calculated according to sloping roof beam structural model, Stress according to beam, bearing concentrates situation to lay additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam with oblique Beam junction or cant beam arrange angle torr with flat-topped ridge junction.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

This preferred embodiment is economic and practical, is calculated the bigger position of stress of sloping roof beam structure by computer-aided design Putting, additional muscle is arranged in the junction bigger at stress, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The side of the present invention Method is summed up by actual engineering design and is proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, Er Qiepei Muscle mode is more reasonable, safer.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

This preferred embodiment decreases the cost building sloping roof beam structure, improves the safety of sloping roof beam structure simultaneously Degree.

Preferably, the cannot-harm-detection device includes based on temporal signatures detection module 1, based on frequency domain character detection module 2 and Comprehensive detection module 3, particularly as follows:

(1) based on temporal signatures detection module 1, it includes that temporal signatures extracts submodule 11, defect based on time domain inspection Survey submodule 12；Described temporal signatures extracts submodule 11 for using the temporal signatures extracting method of improvement to extract temporal signatures Value；Described defects detection submodule based on time domain 12 is for using the automatic classifying identification method of improvement to building pitched roof Beam defect carries out detection and identifies, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule Block 21, for defect area time domain response and reference zone time domain response are carried out fast Fourier transform, 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 Carry out difference processing after process again, calculate difference frequency domain response；Described normalized submodule 22 is for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for according to collection Skin effect selects the frequency being suitable to detect building sloping roof beam defect, and based on the frequency chosen to difference normalizing Change frequency domain response and be optimized process；Described frequency domain character extracts the submodule 24 difference normalization frequency domain after extracting optimization The differential peak spectrum of response, characteristic frequency differential amplitude spectrum and difference cross zero frequency as can be used for characterizing building sloping roof beam The frequency domain character value of Material Physics attribute；Described defects detection submodule based on frequency domain 25 is for using the automatic classification of improvement Recognition methods carries out detection and identifies, to obtain testing result S based on frequency domain building sloping roof beam defect₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, adopt The defect type of tested building sloping roof beam it is determined with predetermined defect classifying identification rule.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off Interference, it is achieved that the accurate detection of building sloping roof beam defect.

Preferably, described temporal signatures extracting method based on improvement extracts temporal signatures value, including:

(1) use pulse eddy current sensor that building sloping roof beam defect is detected, adjust pulse eddy current sensor And the lift-off distance between tested building sloping roof beam surface, it is thus achieved that defect area time domain response q (t), chooses tested building The time domain response at thing sloping roof beam zero defect position is as reference zone time domain response c (t)；

(2) defect area time domain response q (t) and reference zone time domain response c (t) are carried out difference and normalized, Obtaining difference normalization time domain response S (t), definition process formula is:

$S\left(t\right)=\frac{q\left(t\right)}{{\mathrm{\ξ}}_{1}max\left(q\right(t\left)\right)}-\frac{c\left(t\right)}{{\mathrm{\ξ}}_{2}max\left(c\right(t\left)\right)}$

In formula, ξ₁、ξ₂For the coefficient adjustment factor set, 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 as can be used for table Levy the temporal signatures value of building sloping roof beam Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies building sloping roof beam defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression of described gaussian radial basis function kernel function Formula is K (x, y)=exp{-γ ‖ x-y ‖², use particle swarm optimization algorithm that parameter γ of RBF function is optimized；

(2) perform training algorithm, use training data to obtain support vector cassification model；

(3) training data is tested, unknown building sloping roof beam defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on Testing result S of frequency domain₂Process, obtain final detection result, by final detection result with in data base correspond to different The calibration result of degree of impairment compares, and selects the calibration result corresponding with final detection result, according to the damage pre-build Mapping relations between traumatic condition condition and calibration result, obtain the degree of impairment corresponding with described calibration result, and then determine tested The defect type of building sloping roof beam.

Described degree of impairment includes equivalent size, depth of defect.

Described based on the frequency chosen, difference normalization frequency domain response is optimized process, including:

(1) according to the impulse eddy current response signal data structure data matrix D of multiple building sloping roof beam defects:

In formula, d_ijExpression i-th defect impulse eddy current response signal value at the frequency that jth is chosen, i=1, 2 ..., p, j=1,2 ..., q；

(2) it is standardized each impulse eddy current response signal value in data matrix D processing, the arteries and veins after definition standardization Rush eddy current response signal value d_ij' computing formula be:

${d_{ij}}^{\′}=\frac{2d_{ij}-\stackrel{\‾}{d_j}-\stackrel{\‾}{d_i}}{\sqrt{\frac{1}{p-1}{\Σ}_{i=1}^p{(d_{ij}-\stackrel{\‾}{d_j})}^2}+\sqrt{\frac{1}{q-1}{\Σ}_{j=1}^q{(d_{ij}-\stackrel{\‾}{d_i})}^2}},(i=1,2,\mathrm{...},p,j=1,2,\mathrm{...},q)$

In formula,

Then the impulse eddy current response at the frequency that jth is chosen of p defect constitutes vector and is:

d_j=(d_1j′,d_2j′,…,d_pj′)^T

(3) calculate the response of each impulse eddy current and constitute vector d₁,d₂,…,d_qCorrelation coefficient square at q the frequency chosen Battle array C:

In formula, c_mn(m=1,2 ..., q, n=1,2 ..., q) it is d_mAnd d_nCorrelation coefficient；

(4) k optimization frequency N is determined_rReflect the resultant effect of q the frequency chosen, r=1,2 ..., k, k ＜ q, excellent Change frequency matrix to be represented by:

$\left\{\begin{array}{c}{N}_1=h_{11}{d}_1+h_{12}{d}_2+...h_{11}{d}_q\\ N_2=h_{21}{d}_1+h_{22}{d}_2+...h_{2q}{d}_q\\ \mathrm{.........}\\ N_k=h_{k1}{d}_1+h_{k2}{d}_2+...h_{kq}{d}_q\end{array}\right.$

In formula, h_rjRepresenting q the frequency chosen weight coefficient on optimization frequency, weighting coefficient matrix H is expressed as:

Weight coefficient h_rjCalculation be:

1) to characteristic equation ,=0 solves | λ E-C |, asks for each eigenvalue λ_j(j=1,2 ..., q), by each eigenvalue λ_j (j=1,2 ..., q) arrange according to descending order, λ₁＞ λ₂＞ ... ＞ λ_q, and ask for eigenvalue λ_j(j=1,2 ..., Q) character pair vector e_j, it is desirable to | | e_j| |=1, i.e.

2) the r optimization frequency N is defined_rContribution rate G to resultant effect_r:

$G_r=\frac{{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j},(r=1,2,...,k)$

3) k optimization frequency N is calculated_rContribution rate of accumulative total L:

$L=\frac{{\Σ}_{r=1}^k{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j}$

K is the minima meeting L-90% ＞ 0；

4) weight coefficient is calculated:

Detection data are standardized processing by this preferred embodiment, facilitate different characteristic value to carry out linear combination, improve Calculate speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection by mistake Difference, and improve the Classification and Identification rate to building sloping roof beam defect, it is simple to follow-up study and the problem of solution, improve product matter Amount.

The lift-off distance that this application scene adjusts between pulse eddy current sensor and tested building sloping roof beam surface is 0.8mm, sets coefficient adjustment factor ξ₁=0.9, ξ₂=1.1, the Classification and Identification rate of building sloping roof beam defect is improve 4.5%.

Application scenarios 4

Seeing Fig. 1, Fig. 2, the one building of good performance sloping roof beam of the present embodiment, including building sloping roof beam And the cannot-harm-detection device being connected with building sloping roof beam, it is characterized in that, described building sloping roof beam uses such as lower section Method builds: (1) utilizes computer-aided design to build pitched roof on fabric structure model according to the fabric structure planned to build Girder construction model；(2) cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge are calculated according to sloping roof beam structural model, Stress according to beam, bearing concentrates situation to lay additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam with oblique Beam junction or cant beam arrange angle torr with flat-topped ridge junction.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

This preferred embodiment is economic and practical, is calculated the bigger position of stress of sloping roof beam structure by computer-aided design Putting, additional muscle is arranged in the junction bigger at stress, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The side of the present invention Method is summed up by actual engineering design and is proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, Er Qiepei Muscle mode is more reasonable, safer.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

This preferred embodiment decreases the cost building sloping roof beam structure, improves the safety of sloping roof beam structure simultaneously Degree.

Preferably, the cannot-harm-detection device includes based on temporal signatures detection module 1, based on frequency domain character detection module 2 and Comprehensive detection module 3, particularly as follows:

(1) based on temporal signatures detection module 1, it includes that temporal signatures extracts submodule 11, defect based on time domain inspection Survey submodule 12；Described temporal signatures extracts submodule 11 for using the temporal signatures extracting method of improvement to extract temporal signatures Value；Described defects detection submodule based on time domain 12 is for using the automatic classifying identification method of improvement to building pitched roof Beam defect carries out detection and identifies, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule Block 21, for defect area time domain response and reference zone time domain response are carried out fast Fourier transform, 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 Carry out difference processing after process again, calculate difference frequency domain response；Described normalized submodule 22 is for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for according to collection Skin effect selects the frequency being suitable to detect building sloping roof beam defect, and based on the frequency chosen to difference normalizing Change frequency domain response and be optimized process；Described frequency domain character extracts the submodule 24 difference normalization frequency domain after extracting optimization The differential peak spectrum of response, characteristic frequency differential amplitude spectrum and difference cross zero frequency as can be used for characterizing building sloping roof beam The frequency domain character value of Material Physics attribute；Described defects detection submodule based on frequency domain 25 is for using the automatic classification of improvement Recognition methods carries out detection and identifies, to obtain testing result S based on frequency domain building sloping roof beam defect₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, adopt The defect type of tested building sloping roof beam it is determined with predetermined defect classifying identification rule.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off Interference, it is achieved that the accurate detection of building sloping roof beam defect.

Preferably, described temporal signatures extracting method based on improvement extracts temporal signatures value, including:

(1) use pulse eddy current sensor that building sloping roof beam defect is detected, adjust pulse eddy current sensor And the lift-off distance between tested building sloping roof beam surface, it is thus achieved that defect area time domain response q (t), chooses tested building The time domain response at thing sloping roof beam zero defect position is as reference zone time domain response c (t)；

(2) defect area time domain response q (t) and reference zone time domain response c (t) are carried out difference and normalized, Obtaining difference normalization time domain response S (t), definition process formula is:

$S\left(t\right)=\frac{q\left(t\right)}{{\mathrm{\ξ}}_{1}max\left(q\right(t\left)\right)}-\frac{c\left(t\right)}{{\mathrm{\ξ}}_{2}max\left(c\right(t\left)\right)}$

In formula, ξ₁、ξ₂For the coefficient adjustment factor set, 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 as can be used for table Levy the temporal signatures value of building sloping roof beam Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies building sloping roof beam defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression of described gaussian radial basis function kernel function Formula is K (x, y)=exp{-γ ‖ x-y ‖², use particle swarm optimization algorithm that parameter γ of RBF function is optimized；

(2) perform training algorithm, use training data to obtain support vector cassification model；

(3) training data is tested, unknown building sloping roof beam defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on Testing result S of frequency domain₂Process, obtain final detection result, by final detection result with in data base correspond to different The calibration result of degree of impairment compares, and selects the calibration result corresponding with final detection result, according to the damage pre-build Mapping relations between traumatic condition condition and calibration result, obtain the degree of impairment corresponding with described calibration result, and then determine tested The defect type of building sloping roof beam.

Described degree of impairment includes equivalent size, depth of defect.

Described based on the frequency chosen, difference normalization frequency domain response is optimized process, including:

(1) according to the impulse eddy current response signal data structure data matrix D of multiple building sloping roof beam defects:

In formula, d_ijExpression i-th defect impulse eddy current response signal value at the frequency that jth is chosen, i=1, 2 ..., p, j=1,2 ..., q；

(2) it is standardized each impulse eddy current response signal value in data matrix D processing, the arteries and veins after definition standardization Rush eddy current response signal value d_ij' computing formula be:

${d_{ij}}^{\′}=\frac{2d_{ij}-\stackrel{\‾}{d_j}-\stackrel{\‾}{d_i}}{\sqrt{\frac{1}{p-1}{\Σ}_{i=1}^p{(d_{ij}-\stackrel{\‾}{d_j})}^2}+\sqrt{\frac{1}{q-1}{\Σ}_{j=1}^q{(d_{ij}-\stackrel{\‾}{d_i})}^2}},(i=1,2,\mathrm{...},p,j=1,2,\mathrm{...},q)$

In formula,

Then the impulse eddy current response at the frequency that jth is chosen of p defect constitutes vector and is:

d_j=(d_1j′,d_2j′,…,d_pj′)^T

(3) calculate the response of each impulse eddy current and constitute vector d₁,d₂,…,d_qCorrelation coefficient square at q the frequency chosen Battle array C:

In formula, c_mn(m=1,2 ..., q, n=1,2 ..., q) it is d_mAnd d_nCorrelation coefficient；

(4) k optimization frequency N is determined_rReflect the resultant effect of q the frequency chosen, r=1,2 ..., k, k ＜ q, excellent Change frequency matrix to be represented by:

$\left\{\begin{array}{c}{N}_1=h_{11}{d}_1+h_{12}{d}_2+...h_{11}{d}_q\\ N_2=h_{21}{d}_1+h_{22}{d}_2+...h_{2q}{d}_q\\ \mathrm{.........}\\ N_k=h_{k1}{d}_1+h_{k2}{d}_2+...h_{kq}{d}_q\end{array}\right.$

In formula, h_rjRepresenting q the frequency chosen weight coefficient on optimization frequency, weighting coefficient matrix H is expressed as:

Weight coefficient h_rjCalculation be:

1) to characteristic equation ,=0 solves | λ E-C |, asks for each eigenvalue λ_j(j=1,2 ..., q), by each eigenvalue λ_j (j=1,2 ..., q) arrange according to descending order, λ₁＞ λ₂＞ ... ＞ λ_q, and ask for eigenvalue λ_j(j=1,2 ..., Q) character pair vector e_j, it is desirable to | | e_j| |=1, i.e.

2) the r optimization frequency N is defined_rContribution rate G to resultant effect_r:

$G_r=\frac{{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j},(r=1,2,...,k)$

3) k optimization frequency N is calculated_rContribution rate of accumulative total L:

$L=\frac{{\Σ}_{r=1}^k{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j}$

K is the minima meeting L-90% ＞ 0；

4) weight coefficient is calculated:

Detection data are standardized processing by this preferred embodiment, facilitate different characteristic value to carry out linear combination, improve Calculate speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection by mistake Difference, and improve the Classification and Identification rate to building sloping roof beam defect, it is simple to follow-up study and the problem of solution, improve product matter Amount.

The lift-off distance that this application scene adjusts between pulse eddy current sensor and tested building sloping roof beam surface is 1.0mm, sets coefficient adjustment factor ξ₁=1.1, ξ₂=0.9, the Classification and Identification rate of building sloping roof beam defect is improve 5.6%.

Application scenarios 5

Seeing Fig. 1, Fig. 2, the one building of good performance sloping roof beam of the present embodiment, including building sloping roof beam And the cannot-harm-detection device being connected with building sloping roof beam, it is characterized in that, described building sloping roof beam uses such as lower section Method builds: (1) utilizes computer-aided design to build pitched roof on fabric structure model according to the fabric structure planned to build Girder construction model；(2) cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge are calculated according to sloping roof beam structural model, Stress according to beam, bearing concentrates situation to lay additional muscle in sloping roof beam structure；(3) at sloping roof beam structure cant beam with oblique Beam junction or cant beam arrange angle torr with flat-topped ridge junction.

Preferably, it is characterised in that described cant beam or flat-topped ridge are provided with girder and secondary beam, described girder and secondary beam concentration power Place arranges additional hooping.

This preferred embodiment is economic and practical, is calculated the bigger position of stress of sloping roof beam structure by computer-aided design Putting, additional muscle is arranged in the junction bigger at stress, improves Stability Analysis of Structures and the quality of sloping roof beam structure.The side of the present invention Method is summed up by actual engineering design and is proved, the sloping roof beam structure steel content built through the present invention reduces about 10%, Er Qiepei Muscle mode is more reasonable, safer.

Preferably, it is characterised in that described bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge respectively with limit Muscle anchoring indulged by bearing, intermediate support.

This preferred embodiment decreases the cost building sloping roof beam structure, improves the safety of sloping roof beam structure simultaneously Degree.

Preferably, the cannot-harm-detection device includes based on temporal signatures detection module 1, based on frequency domain character detection module 2 and Comprehensive detection module 3, particularly as follows:

(1) based on temporal signatures detection module 1, it includes that temporal signatures extracts submodule 11, defect based on time domain inspection Survey submodule 12；Described temporal signatures extracts submodule 11 for using the temporal signatures extracting method of improvement to extract temporal signatures Value；Described defects detection submodule based on time domain 12 is for using the automatic classifying identification method of improvement to building pitched roof Beam defect carries out detection and identifies, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes pretreatment submodule 21, normalized submodule 22, frequency domain Optimize submodule 23, frequency domain character extracts submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule Block 21, for defect area time domain response and reference zone time domain response are carried out fast Fourier transform, 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 Carry out difference processing after process again, calculate difference frequency domain response；Described normalized submodule 22 is for difference frequency domain Response is normalized, and then obtains difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for according to collection Skin effect selects the frequency being suitable to detect building sloping roof beam defect, and based on the frequency chosen to difference normalizing Change frequency domain response and be optimized process；Described frequency domain character extracts the submodule 24 difference normalization frequency domain after extracting optimization The differential peak spectrum of response, characteristic frequency differential amplitude spectrum and difference cross zero frequency as can be used for characterizing building sloping roof beam The frequency domain character value of Material Physics attribute；Described defects detection submodule based on frequency domain 25 is for using the automatic classification of improvement Recognition methods carries out detection and identifies, to obtain testing result S based on frequency domain building sloping roof beam defect₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, adopt The defect type of tested building sloping roof beam it is determined with predetermined defect classifying identification rule.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off Interference, it is achieved that the accurate detection of building sloping roof beam defect.

Preferably, described temporal signatures extracting method based on improvement extracts temporal signatures value, including:

(1) use pulse eddy current sensor that building sloping roof beam defect is detected, adjust pulse eddy current sensor And the lift-off distance between tested building sloping roof beam surface, it is thus achieved that defect area time domain response q (t), chooses tested building The time domain response at thing sloping roof beam zero defect position is as reference zone time domain response c (t)；

(2) defect area time domain response q (t) and reference zone time domain response c (t) are carried out difference and normalized, Obtaining difference normalization time domain response S (t), definition process formula is:

$S\left(t\right)=\frac{q\left(t\right)}{{\mathrm{\ξ}}_{1}max\left(q\right(t\left)\right)}-\frac{c\left(t\right)}{{\mathrm{\ξ}}_{2}max\left(c\right(t\left)\right)}$

In formula, ξ₁、ξ₂For the coefficient adjustment factor set, 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 as can be used for table Levy the temporal signatures value of building sloping roof beam Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies building sloping roof beam defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression of described gaussian radial basis function kernel function Formula is K (x, y)=exp{-γ ‖ x-y ‖², use particle swarm optimization algorithm that parameter γ of RBF function is optimized；

(2) perform training algorithm, use training data to obtain support vector cassification model；

(3) training data is tested, unknown building sloping roof beam defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on Testing result S of frequency domain₂Process, obtain final detection result, by final detection result with in data base correspond to different The calibration result of degree of impairment compares, and selects the calibration result corresponding with final detection result, according to the damage pre-build Mapping relations between traumatic condition condition and calibration result, obtain the degree of impairment corresponding with described calibration result, and then determine tested The defect type of building sloping roof beam.

Described degree of impairment includes equivalent size, depth of defect.

Described based on the frequency chosen, difference normalization frequency domain response is optimized process, including:

(1) according to the impulse eddy current response signal data structure data matrix D of multiple building sloping roof beam defects:

In formula, d_ijExpression i-th defect impulse eddy current response signal value at the frequency that jth is chosen, i=1, 2 ..., p, j=1,2 ..., q；

(2) it is standardized each impulse eddy current response signal value in data matrix D processing, the arteries and veins after definition standardization Rush eddy current response signal value d_ij' computing formula be:

${d_{ij}}^{\′}=\frac{2d_{ij}-\stackrel{\‾}{d_j}-\stackrel{\‾}{d_i}}{\sqrt{\frac{1}{p-1}{\Σ}_{i=1}^p{(d_{ij}-\stackrel{\‾}{d_j})}^2}+\sqrt{\frac{1}{q-1}{\Σ}_{j=1}^q{(d_{ij}-\stackrel{\‾}{d_i})}^2}},(i=1,2,\mathrm{...},p,j=1,2,\mathrm{...},q)$

In formula,

Then the impulse eddy current response at the frequency that jth is chosen of p defect constitutes vector and is:

d_j=(d_1j′,d_2j′,…,d_pj′)^T

(3) calculate the response of each impulse eddy current and constitute vector d₁,d₂,…,d_qCorrelation coefficient square at q the frequency chosen Battle array C:

In formula, c_mn(m=1,2 ..., q, n=1,2 ..., q) it is d_mAnd d_nCorrelation coefficient；

(4) k optimization frequency N is determined_rReflect the resultant effect of q the frequency chosen, r=1,2 ..., k, k ＜ q, excellent Change frequency matrix to be represented by:

$\left\{\begin{array}{c}{N}_1=h_{11}{d}_1+h_{12}{d}_2+...h_{11}{d}_q\\ N_2=h_{21}{d}_1+h_{22}{d}_2+...h_{2q}{d}_q\\ \mathrm{.........}\\ N_k=h_{k1}{d}_1+h_{k2}{d}_2+...h_{kq}{d}_q\end{array}\right.$

In formula, h_rjRepresenting q the frequency chosen weight coefficient on optimization frequency, weighting coefficient matrix H is expressed as:

Weight coefficient h_rjCalculation be:

1) to characteristic equation ,=0 solves | λ E-C |, asks for each eigenvalue λ_j(j=1,2 ..., q), by each eigenvalue λ_j (j=1,2 ..., q) arrange according to descending order, λ₁＞ λ₂＞ ... ＞ λ_q, and ask for eigenvalue λ_j(j=1,2 ..., Q) character pair vector e_j, it is desirable to | | e_j| |=1, i.e.

2) the r optimization frequency N is defined_rContribution rate G to resultant effect_r:

$G_r=\frac{{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j},(r=1,2,...,k)$

3) k optimization frequency N is calculated_rContribution rate of accumulative total L:

$L=\frac{{\Σ}_{r=1}^k{\mathrm{\λ}}_r}{{\Σ}_{j=1}^q{\mathrm{\λ}}_j}$

K is the minima meeting L-90% ＞ 0；

4) weight coefficient is calculated:

Detection data are standardized processing by this preferred embodiment, facilitate different characteristic value to carry out linear combination, improve Calculate speed；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection by mistake Difference, and improve the Classification and Identification rate to building sloping roof beam defect, it is simple to follow-up study and the problem of solution, improve product matter Amount.

The lift-off distance that this application scene adjusts between pulse eddy current sensor and tested building sloping roof beam surface is 1.2mm, sets coefficient adjustment factor ξ₁=1, ξ₂=1, the Classification and Identification rate of building sloping roof beam defect is improve 4%.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. a building sloping roof beam of good performance, including building sloping roof beam and is connected with building sloping roof beam The cannot-harm-detection device, is characterized in that, described building sloping roof beam is adopted and built with the following method: (1) is according to the building planned to build Thing structure utilizes computer-aided design to build sloping roof beam structural model on fabric structure model；(2) according to pitched roof Girder construction model calculates the cant beam of sloping roof beam, bearing and/or the stressing conditions of flat-topped ridge, concentrates feelings according to the stress of beam, bearing Condition lays additional muscle in sloping roof beam structure；(3) connect with flat-topped ridge with cant beam junction or cant beam at sloping roof beam structure cant beam The place of connecing arranges angle torr.

One the most according to claim 1 building of good performance sloping roof beam, is characterized in that, it is characterised in that described Cant beam or flat-topped ridge are provided with at girder and secondary beam, described girder and secondary beam concentration power and arrange additional hooping.

One the most according to claim 2 building of good performance sloping roof beam, is characterized in that, it is characterised in that described Bearing includes intermediate support and limit bearing；Described cant beam or flat-topped ridge indulge muscle anchoring respectively with limit bearing, intermediate support.