CN106013614A - Concrete-filled steel tubular column with defect recognition function - Google Patents

Abstract

The invention relates to a concrete-filled steel tubular column with a defect recognition function. The concrete-filled steel tubular column comprises a reinforced concrete column and a nondestructive testing device connected with the reinforced concrete column, wherein the reinforced concrete column comprises reinforced concrete prefabricated components and reinforcing steel bars. The concrete-filled steel tubular column is characterized in that the reinforced concrete column is composed of multiple reinforced concrete cylindrical prefabricated components, longitudinal reinforcing steel bar locating meshes and longitudinal reinforcing steel bars, multiple reinforced concrete cylindrical prefabricated components are overlapped into a column mold, the longitudinal reinforcing steel bar locating meshes matched with the inner cavities of cylindrical prefabricated components are arranged between joints of every two adjacent reinforced concrete cylindrical prefabricated components, and the longitudinal reinforcing steel bars are interpenetrated into grids of the longitudinal reinforcing steel bar locating meshes and are connected with multiple reinforced concrete cylindrical prefabricated components in series. The concrete-filled steel tubular column provided by the invention is short in construction period, small in engineering difficulty, high in construction efficiency and worthy of popularization and application.

Description

A kind of steel core concrete column with defect recognition function

Technical field

The present invention relates to armored concrete field, be specifically related to a kind of steel core concrete column with defect recognition function.

Background technology

Non-Destructive Testing is premised on not destroying measurand internal structure and Practical Performance, to measurand inside or the thing on surface Rationality energy, state characteristic detect.Electromagnetic nondestructive is changed to basis for estimation with material electromagnetic performance, comes material and structure Part implements defects detection and performance test.In correlation technique, although Pulsed eddy current testing technology has obtained in-depth study with quick Development, but still suffer from that testing result is not accurate enough, information excavating is the most deep enough, testing result is not carried out effective classification etc. Problem.

Summary of the invention

For the problems referred to above, the present invention provides a kind of steel core concrete column with defect recognition function.

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

A kind of steel core concrete column with defect recognition function, including reinforced column and the nothing that is connected with reinforced column Damaging detection device, described reinforced column includes precast member for reinforcing bar concrete, reinforcing bar, it is characterized in that: armored concrete Post is made up of polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal reinforcement, polylith reinforced concrete tube Shape prefabricated components are overlapped into post mould, arrange and tubular prefabricated components inner chamber between adjacent reinforced concrete tubular prefabricated components seam The longitudinal reinforcement location mesh sheet adapted, longitudinal reinforcement is interspersed in the mesh sheet grid of longitudinal reinforcement location, concatenates polylith reinforced concrete Soil tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

The invention have the benefit that whole construction is not required to assembling reinforcement cage, be also not required to on-the-spot formwork and short construction period, Engineering difficulty is little, and efficiency of construction is high.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limitation of the invention, for Those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtains the attached of other according to the following drawings Figure.

Fig. 1 is reinforced column circular columns longitudinal section of the present invention sectional structure schematic diagram.

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

Reference:

Based on temporal signatures detection module 1, extract son based on frequency domain character detection module 2, comprehensive detection module 3, temporal signatures Module 11, defects detection submodule 12 based on time domain, pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract 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, a kind of steel core concrete column with defect recognition function of the present embodiment, including reinforced column And the cannot-harm-detection device being connected with reinforced column, described reinforced column includes precast member for reinforcing bar concrete, steel Muscle, is characterized in that: reinforced column is by polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal steel Muscle form, polylith armored concrete tubular prefabricated components are overlapped into post mould, adjacent reinforced concrete tubular prefabricated components seam it Between the longitudinal reinforcement location mesh sheet adapted with tubular prefabricated components inner chamber is set, longitudinal reinforcement is interspersed in longitudinal reinforcement and positions mesh sheet In grid, concatenate polylith armored concrete tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of rectangular column.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of circular columns.

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, defects detection based on time domain Module 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 reinforced column defect Carry out detection to identify, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes that pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule 21 for carrying out fast Fourier transform to defect area time domain response and reference zone time domain response, obtains defect area frequency domain and rings Should be with reference zone frequency domain response, and after defect area frequency domain response and reference zone frequency domain response are normalized respectively Carry out difference processing again, calculate difference frequency domain response；Described normalized submodule 22 is for carrying out difference frequency domain response Normalized, and then obtain difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for choosing according to kelvin effect Go out the frequency being suitable to that reinforced column defect is detected, and based on the frequency chosen, difference normalization frequency domain response is carried out Optimization processes；Described frequency domain character extract the submodule 24 difference normalization frequency domain response after extracting optimization differential peak spectrum, Characteristic frequency differential amplitude spectrum and difference cross zero frequency as the frequency domain character that can be used for characterizing reinforced column Material Physics attribute Value；Reinforced column is lacked by described defects detection submodule based on frequency domain 25 for using the automatic classifying identification method of improvement It is trapped into row detection to identify, to obtain testing result S based on frequency domain₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, use pre- Determine defect classifying identification rule and be determined the defect type of tested reinforced column.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off to disturb, Achieve the accurate detection of reinforced column defect.

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

(1) use pulse eddy current sensor that reinforced column defect is detected, adjust pulse eddy current sensor and tested steel Lift-off distance between reinforced concrete post surface, it is thus achieved that defect area time domain response q (t), chooses tested reinforced column intact Fall into the time domain response at position 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, obtain 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 characterize reinforcing bar The temporal signatures value of concrete column Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies reinforced column defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression formula of described gaussian radial basis function kernel function For 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 reinforced column defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on frequency domain Testing result S₂Process, obtain final detection result, by final detection result with in data base correspond to different degree of impairments Calibration result compare, select the calibration result corresponding with final detection result, according to the degree of impairment pre-build and mark Determine the mapping relations between result, obtain the degree of impairment corresponding with described calibration result, and then determine tested reinforced column Defect type.

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 reinforced column 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 pulse whirlpool after definition standardization Stream 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 matrix C at q the frequency chosen:

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 and calculate speed Degree；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection error, And improve the Classification and Identification rate to reinforced column defect, it is simple to follow-up study and the problem of solution, improve product quality.

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

Application scenarios 2

Seeing Fig. 1, Fig. 2, a kind of steel core concrete column with defect recognition function of the present embodiment, including reinforced column And the cannot-harm-detection device being connected with reinforced column, described reinforced column includes precast member for reinforcing bar concrete, steel Muscle, is characterized in that: reinforced column is by polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal steel Muscle form, polylith armored concrete tubular prefabricated components are overlapped into post mould, adjacent reinforced concrete tubular prefabricated components seam it Between the longitudinal reinforcement location mesh sheet adapted with tubular prefabricated components inner chamber is set, longitudinal reinforcement is interspersed in longitudinal reinforcement and positions mesh sheet In grid, concatenate polylith armored concrete tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of rectangular column.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of circular columns.

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, defects detection based on time domain Module 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 reinforced column defect Carry out detection to identify, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes that pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule 21 for carrying out fast Fourier transform to defect area time domain response and reference zone time domain response, obtains defect area frequency domain and rings Should be with reference zone frequency domain response, and after defect area frequency domain response and reference zone frequency domain response are normalized respectively Carry out difference processing again, calculate difference frequency domain response；Described normalized submodule 22 is for carrying out difference frequency domain response Normalized, and then obtain difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for choosing according to kelvin effect Go out the frequency being suitable to that reinforced column defect is detected, and based on the frequency chosen, difference normalization frequency domain response is carried out Optimization processes；Described frequency domain character extract the submodule 24 difference normalization frequency domain response after extracting optimization differential peak spectrum, Characteristic frequency differential amplitude spectrum and difference cross zero frequency as the frequency domain character that can be used for characterizing reinforced column Material Physics attribute Value；Reinforced column is lacked by described defects detection submodule based on frequency domain 25 for using the automatic classifying identification method of improvement It is trapped into row detection to identify, to obtain testing result S based on frequency domain₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, use pre- Determine defect classifying identification rule and be determined the defect type of tested reinforced column.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off to disturb, Achieve the accurate detection of reinforced column defect.

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

(1) use pulse eddy current sensor that reinforced column defect is detected, adjust pulse eddy current sensor and tested steel Lift-off distance between reinforced concrete post surface, it is thus achieved that defect area time domain response q (t), chooses tested reinforced column intact Fall into the time domain response at position 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, obtain 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 characterize reinforcing bar The temporal signatures value of concrete column Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies reinforced column defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression formula of described gaussian radial basis function kernel function For 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 reinforced column defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on frequency domain Testing result S₂Process, obtain final detection result, by final detection result with in data base correspond to different degree of impairments Calibration result compare, select the calibration result corresponding with final detection result, according to the degree of impairment pre-build and mark Determine the mapping relations between result, obtain the degree of impairment corresponding with described calibration result, and then determine tested reinforced column Defect type.

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 reinforced column 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 pulse whirlpool after definition standardization Stream 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 matrix C at q the frequency chosen:

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 and calculate speed Degree；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection error, And improve the Classification and Identification rate to reinforced column defect, it is simple to follow-up study and the problem of solution, improve product quality.

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

Application scenarios 3

Seeing Fig. 1, Fig. 2, a kind of steel core concrete column with defect recognition function of the present embodiment, including reinforced column And the cannot-harm-detection device being connected with reinforced column, described reinforced column includes precast member for reinforcing bar concrete, steel Muscle, is characterized in that: reinforced column is by polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal steel Muscle form, polylith armored concrete tubular prefabricated components are overlapped into post mould, adjacent reinforced concrete tubular prefabricated components seam it Between the longitudinal reinforcement location mesh sheet adapted with tubular prefabricated components inner chamber is set, longitudinal reinforcement is interspersed in longitudinal reinforcement and positions mesh sheet In grid, concatenate polylith armored concrete tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of rectangular column.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of circular columns.

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, defects detection based on time domain Module 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 reinforced column defect Carry out detection to identify, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes that pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule 21 for carrying out fast Fourier transform to defect area time domain response and reference zone time domain response, obtains defect area frequency domain and rings Should be with reference zone frequency domain response, and after defect area frequency domain response and reference zone frequency domain response are normalized respectively Carry out difference processing again, calculate difference frequency domain response；Described normalized submodule 22 is for carrying out difference frequency domain response Normalized, and then obtain difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for choosing according to kelvin effect Go out the frequency being suitable to that reinforced column defect is detected, and based on the frequency chosen, difference normalization frequency domain response is carried out Optimization processes；Described frequency domain character extract the submodule 24 difference normalization frequency domain response after extracting optimization differential peak spectrum, Characteristic frequency differential amplitude spectrum and difference cross zero frequency as the frequency domain character that can be used for characterizing reinforced column Material Physics attribute Value；Reinforced column is lacked by described defects detection submodule based on frequency domain 25 for using the automatic classifying identification method of improvement It is trapped into row detection to identify, to obtain testing result S based on frequency domain₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, use pre- Determine defect classifying identification rule and be determined the defect type of tested reinforced column.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off to disturb, Achieve the accurate detection of reinforced column defect.

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

(1) use pulse eddy current sensor that reinforced column defect is detected, adjust pulse eddy current sensor and tested steel Lift-off distance between reinforced concrete post surface, it is thus achieved that defect area time domain response q (t), chooses tested reinforced column intact Fall into the time domain response at position 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, obtain 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 characterize reinforcing bar The temporal signatures value of concrete column Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies reinforced column defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression formula of described gaussian radial basis function kernel function For 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 reinforced column defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on frequency domain Testing result S₂Process, obtain final detection result, by final detection result with in data base correspond to different degree of impairments Calibration result compare, select the calibration result corresponding with final detection result, according to the degree of impairment pre-build and mark Determine the mapping relations between result, obtain the degree of impairment corresponding with described calibration result, and then determine tested reinforced column Defect type.

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 reinforced column 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 pulse whirlpool after definition standardization Stream 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 matrix C at q the frequency chosen:

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 and calculate speed Degree；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection error, And improve the Classification and Identification rate to reinforced column defect, it is simple to follow-up study and the problem of solution, improve product quality.

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

Application scenarios 4

Seeing Fig. 1, Fig. 2, a kind of steel core concrete column with defect recognition function of the present embodiment, including reinforced column And the cannot-harm-detection device being connected with reinforced column, described reinforced column includes precast member for reinforcing bar concrete, steel Muscle, is characterized in that: reinforced column is by polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal steel Muscle form, polylith armored concrete tubular prefabricated components are overlapped into post mould, adjacent reinforced concrete tubular prefabricated components seam it Between the longitudinal reinforcement location mesh sheet adapted with tubular prefabricated components inner chamber is set, longitudinal reinforcement is interspersed in longitudinal reinforcement and positions mesh sheet In grid, concatenate polylith armored concrete tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of rectangular column.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of circular columns.

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, defects detection based on time domain Module 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 reinforced column defect Carry out detection to identify, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes that pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule 21 for carrying out fast Fourier transform to defect area time domain response and reference zone time domain response, obtains defect area frequency domain and rings Should be with reference zone frequency domain response, and after defect area frequency domain response and reference zone frequency domain response are normalized respectively Carry out difference processing again, calculate difference frequency domain response；Described normalized submodule 22 is for carrying out difference frequency domain response Normalized, and then obtain difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for choosing according to kelvin effect Go out the frequency being suitable to that reinforced column defect is detected, and based on the frequency chosen, difference normalization frequency domain response is carried out Optimization processes；Described frequency domain character extract the submodule 24 difference normalization frequency domain response after extracting optimization differential peak spectrum, Characteristic frequency differential amplitude spectrum and difference cross zero frequency as the frequency domain character that can be used for characterizing reinforced column Material Physics attribute Value；Reinforced column is lacked by described defects detection submodule based on frequency domain 25 for using the automatic classifying identification method of improvement It is trapped into row detection to identify, to obtain testing result S based on frequency domain₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, use pre- Determine defect classifying identification rule and be determined the defect type of tested reinforced column.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off to disturb, Achieve the accurate detection of reinforced column defect.

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

(1) use pulse eddy current sensor that reinforced column defect is detected, adjust pulse eddy current sensor and tested steel Lift-off distance between reinforced concrete post surface, it is thus achieved that defect area time domain response q (t), chooses tested reinforced column intact Fall into the time domain response at position 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, obtain 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 characterize reinforcing bar The temporal signatures value of concrete column Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies reinforced column defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression formula of described gaussian radial basis function kernel function For 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 reinforced column defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on frequency domain Testing result S₂Process, obtain final detection result, by final detection result with in data base correspond to different degree of impairments Calibration result compare, select the calibration result corresponding with final detection result, according to the degree of impairment pre-build and mark Determine the mapping relations between result, obtain the degree of impairment corresponding with described calibration result, and then determine tested reinforced column Defect type.

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 reinforced column 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 pulse whirlpool after definition standardization Stream 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 matrix C at q the frequency chosen:

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 and calculate speed Degree；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection error, And improve the Classification and Identification rate to reinforced column defect, it is simple to follow-up study and the problem of solution, improve product quality.

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

Application scenarios 5

Seeing Fig. 1, Fig. 2, a kind of steel core concrete column with defect recognition function of the present embodiment, including reinforced column And the cannot-harm-detection device being connected with reinforced column, described reinforced column includes precast member for reinforcing bar concrete, steel Muscle, is characterized in that: reinforced column is by polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal steel Muscle form, polylith armored concrete tubular prefabricated components are overlapped into post mould, adjacent reinforced concrete tubular prefabricated components seam it Between the longitudinal reinforcement location mesh sheet adapted with tubular prefabricated components inner chamber is set, longitudinal reinforcement is interspersed in longitudinal reinforcement and positions mesh sheet In grid, concatenate polylith armored concrete tubular prefabricated components.

Preferably, described armored concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangle There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of rectangular column.

Preferably, described armored concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular There is interior step platform cylinder another port, and described longitudinal reinforcement location mesh sheet is ring, above has grid.

This preferred embodiment short construction period, engineering difficulty are little, and efficiency of construction is high, it is adaptable to need the place of circular columns.

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, defects detection based on time domain Module 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 reinforced column defect Carry out detection to identify, to obtain testing result S based on time domain₁；

(2) based on frequency domain character detection module 2, it includes that pretreatment submodule 21, normalized submodule 22, frequency domain are excellent Beggar's module 23, frequency domain character extract submodule 24 and defects detection submodule 25 based on frequency domain；Described pretreatment submodule 21 for carrying out fast Fourier transform to defect area time domain response and reference zone time domain response, obtains defect area frequency domain and rings Should be with reference zone frequency domain response, and after defect area frequency domain response and reference zone frequency domain response are normalized respectively Carry out difference processing again, calculate difference frequency domain response；Described normalized submodule 22 is for carrying out difference frequency domain response Normalized, and then obtain difference normalization frequency domain response；Described frequency domain optimizes submodule 23 for choosing according to kelvin effect Go out the frequency being suitable to that reinforced column defect is detected, and based on the frequency chosen, difference normalization frequency domain response is carried out Optimization processes；Described frequency domain character extract the submodule 24 difference normalization frequency domain response after extracting optimization differential peak spectrum, Characteristic frequency differential amplitude spectrum and difference cross zero frequency as the frequency domain character that can be used for characterizing reinforced column Material Physics attribute Value；Reinforced column is lacked by described defects detection submodule based on frequency domain 25 for using the automatic classifying identification method of improvement It is trapped into row detection to identify, to obtain testing result S based on frequency domain₂；

(3) comprehensive detection module 3, for according to testing result S based on time domain₁With testing result S based on frequency domain₂, use pre- Determine defect classifying identification rule and be determined the defect type of tested reinforced column.

This preferred embodiment, by the way of temporal signatures detection and frequency domain character detection combine, effectively inhibits lift-off to disturb, Achieve the accurate detection of reinforced column defect.

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

(1) use pulse eddy current sensor that reinforced column defect is detected, adjust pulse eddy current sensor and tested steel Lift-off distance between reinforced concrete post surface, it is thus achieved that defect area time domain response q (t), chooses tested reinforced column intact Fall into the time domain response at position 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, obtain 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 characterize reinforcing bar The temporal signatures value of concrete column Material Physics attribute.

The automatic classifying identification method of described improvement carries out detection and identifies reinforced column defect, including:

(1) select gaussian radial basis function kernel function (RBF) as Kernel function, the expression formula of described gaussian radial basis function kernel function For 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 reinforced column defect is predicted.

Described predetermined defect classifying identification rule is: use weighted mean method to testing result S based on time domain₁With based on frequency domain Testing result S₂Process, obtain final detection result, by final detection result with in data base correspond to different degree of impairments Calibration result compare, select the calibration result corresponding with final detection result, according to the degree of impairment pre-build and mark Determine the mapping relations between result, obtain the degree of impairment corresponding with described calibration result, and then determine tested reinforced column Defect type.

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 reinforced column 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 pulse whirlpool after definition standardization Stream 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,…, _qCorrelation matrix C at q the frequency chosen:

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 and calculate speed Degree；Optimized by frequency domain, improve detection efficiency；Comprehensive detection module 3 is set, it is possible to farthest reduce detection error, And improve the Classification and Identification rate to reinforced column defect, it is simple to follow-up study and the problem of solution, improve product quality.

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

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than to scope Restriction, although having made to explain to the present invention with reference to preferred embodiment, it will be understood by those within the art that, Technical scheme can be modified or equivalent, without deviating from the spirit and scope of technical solution of the present invention.

Claims (3)

1. there is a steel core concrete column for defect recognition function, including reinforced column and be connected with reinforced column The cannot-harm-detection device, described reinforced column includes precast member for reinforcing bar concrete, reinforcing bar, it is characterized in that: reinforced concrete Earth pillar is made up of polylith armored concrete tubular prefabricated components, longitudinal reinforcement location mesh sheet, longitudinal reinforcement, polylith armored concrete Tubular prefabricated components are overlapped into post mould, arrange and in tubular prefabricated components between adjacent reinforced concrete tubular prefabricated components seam The longitudinal reinforcement location mesh sheet that chamber adapts, longitudinal reinforcement is interspersed in the mesh sheet grid of longitudinal reinforcement location, and concatenation polylith reinforcing bar mixes Solidifying soil tubular prefabricated components.

A kind of steel core concrete column with defect recognition function the most according to claim 1, is characterized in that, described steel Reinforced concrete tubular prefabricated components are rectangular cylinder, and rectangular cylinder Single port has outer step platform, rectangular cylinder another port to have interior step to put down Platform, described longitudinal reinforcement location mesh sheet is rectangular loop, above has grid.

A kind of steel core concrete column with defect recognition function the most according to claim 2, is characterized in that, described steel Reinforced concrete tubular prefabricated components are circular cylinder, and circular cylinder Single port has outer step platform, circular cylinder another port to have interior step to put down Platform, described longitudinal reinforcement location mesh sheet is ring, above has grid.