CN106053597A - Detection method of steel pipe concrete cavity defect extracted on basis of HHT characteristics - Google Patents
Detection method of steel pipe concrete cavity defect extracted on basis of HHT characteristics Download PDFInfo
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- CN106053597A CN106053597A CN201610340911.8A CN201610340911A CN106053597A CN 106053597 A CN106053597 A CN 106053597A CN 201610340911 A CN201610340911 A CN 201610340911A CN 106053597 A CN106053597 A CN 106053597A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/045—Analysing solids by imparting shocks to the workpiece and detecting the vibrations or the acoustic waves caused by the shocks
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/06—Visualisation of the interior, e.g. acoustic microscopy
- G01N29/0654—Imaging
- G01N29/069—Defect imaging, localisation and sizing using, e.g. time of flight diffraction [TOFD], synthetic aperture focusing technique [SAFT], Amplituden-Laufzeit-Ortskurven [ALOK] technique
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0232—Glass, ceramics, concrete or stone
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0234—Metals, e.g. steel
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
Abstract
The invention discloses a detection method of a steel pipe concrete cavity defect extracted on the basis of HHT characteristics. The method comprises the following steps: (1) applying mechanical impact on a detection point on the surface of steel pipe concrete to excite stress waves, using a sensor to receive the stress wave detection signals; (2) adopting an HHT algorithm to extract cavity characteristic signals from the stress wave detection signals to obtain a characteristic Hilbert marginal spectrum; (3) changing the detection point, repeating step (1) and (2); subjecting the characteristic Hilbert marginal spectrums corresponding to different detection points to an image wave train display; and judging the size of cavity of steel pipe concrete according to the characteristic Hilbert marginal spectrum. By accumulatively displaying the images of characteristic Hilbert marginal spectrums of multiple detection points, the situation of cavity defects in steel pipe concrete can be directly display and calculated, and the method has the advantages of strong reliability, and accurate detection result.
Description
Technical field
The present invention relates to the detection method of a kind of concrete filled steel tube cavity defect, carry based on HHT feature particularly to one
The detection method of the concrete filled steel tube cavity defect taken.
Background technology
Concrete filled steel tube is that a kind of wide popularization and application is in the complex building material of skyscraper, industrial premises and science of bridge building
Material.Concrete filled steel tubular member is bound by the steel pipe bundle of outer layer due to core concrete so that steel pipe and concrete are in holding capacity
Time can interact so that it is comprcssive strength, plasticity and toughness are significantly larger than traditional construction material.Steel tube concrete local soil type
Close structure it is crucial that concrete must be made to combine closely with steel pipe walls, so could realize designed by composite performance.Steel pipe
Concrete easily makes to exist inside concrete material cavity in construction, loose, construction joint, uneven microstructure cause concrete and steel
, there is, after curing in airtight condition condition and harden(ing)by itself, the contraction that a certain degree of volume is intrinsic in pipe weak bond so that at present in addition
The various concrete of design are difficult to reach the volumetric expansion of requirement and controllability, cause concrete glutinous formation de-with steel pipe walls de-
Empty so that the composite design performance of concrete filled steel tube is difficult to give full play to even lose efficacy, consequently, it is possible to cause building collapsing etc.
Accident.Therefore, carry out concrete filled steel tube cavity defect detection the military service of related building health is had great importance.
Currently, the method for detection concrete filled steel tube cavity defect mainly has artificial hammering method, direct core-drilling method, optical fiber
Sensing detection method, measure on stress pulse method etc..Directly core boring sampling method is used for checking detection, artificial hammering method for destructiveness detection
For empirical detection to the type of defect and defect size precision aspect all it cannot be guaranteed that, optical fiber sensing monitoring method needs pre-buried optical fiber
Sensor adds testing cost and is unfavorable for safeguarding.Measure on stress pulse method includes ultrasound examination and Impact echo.China
Existing " study for detecting concrete defect by ultrasonic technical regulation " is just focusing only on concrete to the defects detection of concrete filled steel tube
In the detection of defect, and this code explicitly points out, the defect inspection method proposed in this code be only applicable to " steel pipe walls and
Concrete strong bonding " defects detection of concrete filled steel tube, so for cavity defect ultrasound examination inapplicable.Impact
Echo method is a kind of lossless detection method based on stress wave, and its principle utilizes mechanical shock steel tube surface in short-term to produce stress
Ripple, this stress wave can propagate in the structure thus be reflected by internal flaw and outer surface, the stress wave meeting of roundtrip
Form a kind of characteristic modes, near stress wave shot point, accept echo-signal, obtained the dominant frequency of this mode by spectrum analysis
The internal cavity defect situation of size evaluation.Impact echo due to impact energy greatly, easily excite, to detection environmental requirement the highest
And the advantage such as applied range is considered as one the most promising concrete filled steel tube defect inspection method.
Affect Impact echo and accurately judge that the principal element calculated with size is to extract at concrete filled steel tube cavity defect
The characteristic modes of roundtrip and corresponding dominant frequency value between shock point and defect border.Because the existence of steel pipe and concrete are
The reasons such as heterogeneous material, stress wave route of transmission in structure becomes extremely complex, and special medical treatment mode can be by a lot of other
The interference of stress wave.Tradition directly fast Fourier transform is used to be difficult to extract the dominant frequency value of characteristic modes, and single-point master
The frequency corresponding concrete filled steel tube situation of coming to nothing of value can be once in bigger error and display can not preferably be visualized, so for this
Complicated unstable signal needs new analysis method to extract characteristic signal and imaging mode display cavity defect situation.
Summary of the invention
In order to solve the above-mentioned technical problem that the detection of present concrete filled steel tube cavity defect exists, the present invention provides a kind of one-tenth
This low, highly reliable, detection method of testing result concrete filled steel tube cavity defect based on HHT feature extraction accurately.
The present invention solves the technical scheme of above-mentioned technical problem and comprises the following steps:
Step 1: the test point on concrete filled steel tube surface applies mechanical shock elastic stress wave, utilizing sensor to receive should
Reeb detection signal；
Step 2: use the characteristic signal that comes to nothing in HHT algorithm extraction measure on stress pulse signal, obtain feature Hilbert limit
Spectrum；
Step 3: alteration detection point, repeats 1～2 steps；
Step 4: the feature Hilbert marginal spectrum corresponding to different test points is carried out the image wave train and shows；
Step 5: judge the size of coming to nothing of concrete filled steel tube according to feature Hilbert marginal spectrum image.
The method have technical effect that: 1, the present invention uses resonance voice chamber mode of coming to nothing to apply as Impact echo
In characteristic signal, be applied to detect below shock point for providing feasibility during cavity defect, by this mould for Impact echo
State can accurately calculate the size come to nothing；2, the present invention use HHT algorithm impact echo detection signal is processed, and
HHT algorithm is improved by the method using sawtooth interpolation method and extreme value continuation, end effect that effective suppression EMD decomposes and
Modal overlap, it is possible to effectively extract characteristic signal；3, by characteristic signal being carried out Hilbert marginal spectrum analysis, and carry out many
The feature Hilbert marginal spectrum wave train figure imaging of individual test point shows, makes Impact echo detect concrete filled steel tube cavity defect
Testing result is more accurately with directly perceived.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention.
Fig. 2 is steel tube concrete soil model and its situation of coming to nothing carrying out in the present invention testing；
Fig. 3 is in the present invention, outer wall of steel pipe to be carried out single-point one side axially to detect schematic diagram；
Fig. 4 is the flow chart carrying out using the characteristic signal that comes to nothing in HHT algorithm extraction measure on stress pulse signal in the present invention.
Fig. 5 shows the EMD decomposition result of the echo detection signal of measuring point 5 in the present invention；
Fig. 6 shows the EMD decomposition result of the echo detection signal of measuring point 20 in the present invention；
Fig. 7 shows the EMD decomposition result of the echo detection signal of measuring point 28 in the present invention；
Fig. 8 shows the image wave train result after using the inventive method to process.
Fig. 9 shows the image wave train result after using conventional fast Fourier transform to process.
Figure 10 shows that stress wave is at the propagation characteristic having the concrete filled steel tube come to nothing and without coming to nothing.
Detailed description of the invention
The invention will be further described with detailed description of the invention below in conjunction with the accompanying drawings.
A kind of concrete filled steel tube based on HHT feature extraction provided by the present invention comes to nothing measure on stress pulse signal processing side
Method, its flow process is as it is shown in figure 1, comprise the steps:
Step 1: detect concrete filled steel tubular member (see Fig. 2), at steel tube surface elastic stress wave and obtain stress
Ripple detection signal.
Wherein said default steel tube concrete earth model parameter includes: diameter of steel tube 50cm, thickness of steel pipe 2mm, steel pipe height
100cm, concrete grade C35；Having preset cavity defect in various degree, correspondence diameter of coming to nothing is followed successively by: 0cm, 5cm,
10cm、15cm、20cm；
Wherein stress wave excites and obtains the concrete grammar of measure on stress pulse signal and is: to described default steel tube concrete clay model
Type, carries out single-point one side at outer wall of steel pipe and axially detects (see Fig. 3), applies machine in short-term in steel tube surface correspondence top position of coming to nothing
Tool impacts, elastic stress wave, and the steel tube surface at distance shock point 1cm utilizes the time domain letter receiving sensor reception echo
Number, i.e. measure on stress pulse signal；The mechanical shock applied, for example, it is possible to for being time of contact 30us, amplitude is the half of 100N
Sinusoidal force.When obtaining measure on stress pulse signal, the sampling interval is set as that 10us, sampling time are set as 2ms.
Step 2: use the characteristic signal that comes to nothing in HHT algorithm extraction measure on stress pulse signal, obtain feature Hilbert limit
Spectrum, whole HHT method flow is shown in Fig. 4.Detailed process is as follows:
Step 2.1: the measure on stress pulse signal in step 1 is carried out EMD decomposition, obtains several intrinsic mode functions
IMF, it is specifically divided into following steps:
Step 2.1.1: corresponding Reeb detection signal x (n) carries out the lookup of extreme point, searches out all of maximum and pole
Little value, and at the several extreme point of two ends continuation of x (n)；And respectively maximum and minimum sequence are entered by sawtooth interpolation method
Row interpolation, extracts coenvelope and the lower envelope sequence of x (n).
Step 2.1.2: obtain curves of local mean value m (n) by upper and lower envelope.
Step 2.1.3: calculate h (n), h (n)=x (n)-m (n).
Step 2.1.4: judge whether h (n) meets condition IMF establishment condition.If meeting IMF establishment condition, walk
Rapid 2.1.5；If being unsatisfactory for establishment condition to make x (n)=h (n), return to step 2.1.1.
Wherein whether IMF establishment condition is for reaching to screen number of times, and screening number of times is S, typically takes 5.
Step 2.1.5: set i IMF as Ci(n), Ci(n)=x (n)-h (n), i=1,2,3 ...., N；
Step 2.1.6: if CiExtreme value count less than or equal to 1, terminate EMD process；Otherwise, x (n)=C is madeiN (), returns
Continue cycling through to step 2.1.1；
Step 2.2: extract and represent the IMF of come to nothing resonance voice chamber mode or concrete filled steel tube thickness mode as feature
IMF, can be to choose the IMF after 3 rank and 3 rank.
Step 2.3: feature IMF extracted in step 2.2 is carried out Hilbert conversion and obtains Hilbert time-frequency amplitude
Spectrum；Hilbert time-frequency amplitude spectrum is carried out time upper integral and obtains character pair Hilbert marginal spectrum.Wherein, to obtaining
Hilbert marginal spectrum sequence is normalized.Of course, 0 to 1 it is normalized to；
Step 3: the echo detection signal of different test points is repeated step 1～2；
Step 4: the feature Hilbert marginal spectrum after the normalization corresponding to different test points is carried out the image wave train and shows
Show.Concrete grammar is: the feature Hilbert marginal spectrum sequence corresponding to different measuring points after normalization is carried out Y-direction arrangement, Y
The numbering of axle scale correspondence detection measuring point, X-direction is corresponding instantaneous frequency, represents that instantaneous frequency values is corresponding by the depth of color
Amplitude.
The true instantaneous frequency of the feature Hilbert marginal spectrum representative feature signal of the most each measuring point, its peak value is corresponding
Concrete filled steel tube thickness frequency or the big small frequency that comes to nothing.Due to different cavity defect size characteristic of correspondence Hilbert limits
Varying in size of the frequency of spectrum peak, carries out the wave train by the feature Hilbert marginal spectrum of multiple measuring points and shows, by color pair
Than can intuitively contrast the cavity defect situation of concrete filled steel tube, the most concrete, can be according to cymometer corresponding to peak value
Calculate cavity defect diameter or closely knit place steel tube concrete soil thickness.
Fig. 8 shows the image wave train result after using the inventive method to process, and Fig. 9 shows in employing tradition quickly Fu
Image wave train result after leaf transformation process.
The result thoroughly doing away with Impact echo theoretical analysis and numerical method understands, when stress wave is propagated in concrete filled steel tube
Time, if concrete filled steel tube internal structure is closely knit, flawless in the case of, can reflect at performance outside, roundtrip
Stress wave can form a kind of special resonance mode i.e. transient state characteristic mode, such as Figure 10 .a.Answering that stress wave shot point receives
Echo detection signal also is carried out can be obtained by instantaneous resonant frequency by frequency-domain analysis by Reeb echo detection signal.By punching
Hitting echo method theoretical, i.e. formula is 1., utilizes this feature instantaneous frequency just can calculate the shock point distance from reflecting surface, i.e.
Steel tube concrete soil thickness H.
Wherein v1For stress wave spread speed in mixed earth, heretofore described steel tube concrete clay model
The velocity of wave of type is 4500m/s；f1It is characterized instantaneous frequency；
Occurring when steel pipe mixes the situation that earth separates below measuring point, stress wave is propagated in entering the air come to nothing, and passes
Can reflect when being multicast to concrete border, final this feature stress wave carries out the most anti-between shock point and concrete border
Penetrate, form a kind of transient state characteristic mode, can be described as resonance voice chamber mode of coming to nothing, such as Figure 10 .b.By impact echo law theory, i.e.
2. formula, obtains instantaneous resonant frequency corresponding to this transient state characteristic resonance mode from this echo detection signal and just can calculate and come to nothing
Size D, come to nothing the biggest, this transient state characteristic frequency is the lowest.
Wherein v2For stress wave spread speed in the regional air that comes to nothing, velocity of wave is 340m/s；f2For
Feature instantaneous frequency；
From Fig. 8 and Fig. 9 it can be seen that it can be found that echo detection signal from closely knit and the echo detection signal at place of coming to nothing
Frequency domain there are obvious difference, the frequency content at closely knit place are concentrated mainly on high band, and the frequency components at place of coming to nothing is obvious
Offset toward low frequency direction.
From figure 8, it is seen that use in the wave train figure after traditional fast Fourier transform process, it is impossible to tell difference
The frequency domain character of the echo detection signal under degree of coming to nothing, also cannot directly find the feature instantaneous frequency calculating size of coming to nothing.
From fig. 9, it can be seen that use in the feature Hilbert marginal spectrum wave train figure after the method process of the present invention, different
The Hilbert marginal spectrum of the echo detection signal under degree of coming to nothing occurs in that obvious frequency content difference.By formula 1 and public affairs
Formula 2, calculates size and thickness size situation of the coming to nothing one_to_one corresponding with detection model that comes to nothing.
A kind of concrete filled steel tube based on HHT feature extraction of the present invention comes to nothing measure on stress pulse signal processing method, passes through
Described mechanical shock, to concrete filled steel tube elastic stress wave, receives described measure on stress pulse signal and is carrying out HHT conversion, extracts spy
Levy modal components of coming to nothing and obtain feature Hilbert marginal spectrum.Image stack is carried out by several test point feature Hilbert marginal spectrums
Long-pending display can intuitively show and calculate the internal cavity defect situation of concrete filled steel tube.This method is highly reliable, and testing result is accurate
Really.
Claims (5)
1. the detection method of a concrete filled steel tube cavity defect based on HHT feature extraction, it is characterised in that include following step
Rapid:
Step 1: the test point on concrete filled steel tube surface applies mechanical shock elastic stress wave, utilizes sensor to receive stress wave
Detection signal；
Step 2: use the characteristic signal that comes to nothing in HHT algorithm extraction measure on stress pulse signal, obtain feature Hilbert marginal spectrum；
Step 3: alteration detection point, repeats 1～2 steps；
Step 4: the feature Hilbert marginal spectrum corresponding to different test points is carried out the image wave train and shows；
Step 5: judge the size of coming to nothing of concrete filled steel tube according to feature Hilbert marginal spectrum image.
The detection method of concrete filled steel tube cavity defect based on HHT feature extraction the most according to claim 1, described step
Rapid 2 specifically comprise the following steps that
Step 2.1: the measure on stress pulse signal in step 1 is carried out EMD decomposition, obtains several intrinsic mode functions IMF；
Step 2.2: the IMF after extracting 3 rank and 3 rank from above-mentioned several intrinsic mode functions IMF represents resonance of coming to nothing
The IMF of sound chamber mode or concrete filled steel tube thickness mode is as feature IMF；
Step 2.3: feature IMF extracted in step 2.2 is carried out Hilbert conversion and obtains Hilbert time-frequency amplitude spectrum；Right
Hilbert time-frequency amplitude spectrum carries out time upper integral and obtains character pair Hilbert marginal spectrum.
The detection method of concrete filled steel tube cavity defect based on HHT feature extraction the most according to claim 2, described step
Rapid 2.1 specifically comprise the following steps that
Step 2.1.1: corresponding Reeb detection signal x (n) carries out the lookup of extreme point, searches out all of maximum and minimum
Value, and at the several extreme point of two ends continuation of x (n)；And respectively maximum and minimum sequence are carried out by sawtooth interpolation method
Interpolation, extracts coenvelope and the lower envelope sequence of x (n)；
Step 2.1.2: obtain curves of local mean value m (n) by upper and lower envelope, n=1,2......, M, M are sampled point
Number；
Step 2.1.3: calculate h (n), h (n)=x (n)-m (n)；
Step 2.1.4: judge whether h (n) meets condition IMF establishment condition, whether IMF establishment condition is to reach to screen number of times,
Screening number of times is S, typically takes 5；If meeting IMF establishment condition, carry out step 2.1.5；If being unsatisfactory for establishment condition to make x
N ()=h (n), returns to step 2.1.1；
Step 2.1.5: set i IMF as Ci(n), Ci(n)=x (n)-h (n), i=1,2,3 ...., N, N are that EMD decomposition obtains
IMF exponent number；
Step 2.1.6: if CiExtreme value count less than or equal to 1, terminate EMD process；Otherwise, x (n)=C is madeiN (), returns to step
Rapid 2.1.1 continues cycling through.
The detection method of concrete filled steel tube cavity defect based on HHT feature extraction the most according to claim 1, to step
The Hilbert marginal spectrum sequence obtained in 2 is normalized, and is normalized to 0 to 1.
The detection method of concrete filled steel tube cavity defect based on HHT feature extraction the most according to claim 1, described step
Rapid 4 concretely comprise the following steps: the feature Hilbert marginal spectrum sequence corresponding to different measuring points after normalization is carried out Y-direction arrangement,
The numbering of Y-axis scale correspondence detection measuring point, X-direction is corresponding instantaneous frequency, represents that instantaneous frequency values is corresponding by the depth of color
Amplitude.
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| CN106638243A (en) * | 2016-11-09 | 2017-05-10 | 华南理工大学 | Cement concrete pavement slab bottom emptying identification robot and continuous identification method thereof |
| CN107132274A (en) * | 2017-06-16 | 2017-09-05 | 四川升拓检测技术股份有限公司 | A kind of method of testing of bridge prestress pore channel Grouted density |
| CN107607065A (en) * | 2017-09-22 | 2018-01-19 | 河海大学 | A kind of impact echo signal analysis method based on variation mode decomposition |
| CN107782787A (en) * | 2017-10-20 | 2018-03-09 | 杭州电子科技大学 | A kind of ultrasonic defect detection method |
| CN110082758A (en) * | 2019-05-23 | 2019-08-02 | 江苏高速公路工程养护技术有限公司 | A kind of road surface interlayer vacant analysis and method for maintaining |
| CN111475944B (en) * | 2020-04-03 | 2023-03-28 | 广西大学 | Quantitative analysis method for concrete filled steel tube top void area |
| CN111475944A (en) * | 2020-04-03 | 2020-07-31 | 广西大学 | Quantitative analysis method for concrete filled steel tube top void area |
| CN111783616A (en) * | 2020-06-28 | 2020-10-16 | 北京瓦特曼科技有限公司 | Data-driven self-learning-based nondestructive testing method |
| CN111783616B (en) * | 2020-06-28 | 2024-03-26 | 北京瓦特曼科技有限公司 | Nondestructive testing method based on data-driven self-learning |
| CN115047162A (en) * | 2022-06-24 | 2022-09-13 | 张家港沙龙精密管业有限公司 | Defect detection method and system for steel pipe heat treatment |
| CN115047162B (en) * | 2022-06-24 | 2024-02-06 | 张家港沙龙精密管业有限公司 | Defect detection method and system for heat treatment of steel pipe |
| CN115655887A (en) * | 2022-11-01 | 2023-01-31 | 广东建设职业技术学院 | Concrete strength prediction method |
| CN115655887B (en) * | 2022-11-01 | 2023-04-21 | 广东建设职业技术学院 | Concrete strength prediction method |
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