CN109024198A - A kind of road face internal injury detection method based on passive source arbitrary excitation - Google Patents
A kind of road face internal injury detection method based on passive source arbitrary excitation Download PDFInfo
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- CN109024198A CN109024198A CN201810780974.4A CN201810780974A CN109024198A CN 109024198 A CN109024198 A CN 109024198A CN 201810780974 A CN201810780974 A CN 201810780974A CN 109024198 A CN109024198 A CN 109024198A
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- surface wave
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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- Engineering & Computer Science (AREA)
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- Civil Engineering (AREA)
- Structural Engineering (AREA)
- Road Repair (AREA)
Abstract
The invention discloses a kind of road face internal injury detection methods based on passive source arbitrary excitation, comprising the following steps: firstly, in road face surface layout acceleration transducer, the composition detection array；Then, using environment fine motion as driving source, sensor acquires the vibration signal in road face, passes it to computer；Secondly, computer pre-processes the vibration data for each sensor being collected into；Again, with spatial autocorrelation method, the phase velocity of surface wave is acquired, extracts surface wave dispersion curve；Finally, judging whether have damage inside face according to surface wave depth-phase velocity curve.The present invention is diagnosed with damage position and degree of the passive source surface wave etection theory to road face, is scientifically assessed the bearing capacity of the duty status in road face, damaged condition and road face, provides new method to solve non-destructive testing inside road face.
Description
Technical field
The present invention relates to road face detection field more particularly to a kind of road face internal exergy dissipation triages based on passive source arbitrary excitation
Survey method.
Background technique
With the fast development of air-transport industry and road haulage, aviation and highway safety thing are caused by road surface damage
Therefore occur often, surface damage detection in road becomes an important research topic.Current main road face detection method has flexure inspection
Survey, core boring sampling, Ground Penetrating Radar etc.:
Deflection testing is a kind of common methods of face structure estimating, and sensor is distributed in away from load center 2.5-5m
In the range of, under the control of the computer, the weight of certain mass is freely fallen from certain altitude, impact force applies arteries and veins to road face
Load is rushed, face short time set is caused, the surface deformation of structural level is then detected by the sensor of distribution different distance, then
Computer is transmitted a signal to, by data inverse and the bearing capacity for evaluating face.
Ground Penetrating Radar is by transmitting antenna to road surface launching pulse signal, this pulse signal is met during underground propagation
Reflection and refraction can be generated when to different conductive medium interfaces, receiving antenna is translated into digital letter after receiving reflection signal
Breath is transmitted to radar host computer and is handled, by analyzing by being incident on the propagation speed of the time interval and wave of reflection in the medium
Degree, just can calculate the thickness of different structure layer.Ground Penetrating Radar method and deflection testing method all have many advantages, such as it is efficient, intuitive, but
Disadvantage is also apparent from: operation and maintenance is costly, and road surface damage can only be found when being periodically detected, lacks timeliness.
Core boring sampling is to beat core boring sampling by the road automatic core boring sampling Che face, and core belt transect is gone back to laboratory and carries out pitch
The performance test of concrete, during experiment just it is observed that drilling the face Nei Dao structure sheaf the case where.Core boring sampling has
The features such as scientific, intuitive, but sampling process and time consumption of experimental process are long, and expense is relatively high, it is easy to fan out from point to area
Cause detection damage incomplete.
Can be seen that face detection by the above road face both domestic and external detection method is a time-consuming, costly process,
And lack timeliness.
Summary of the invention
Goal of the invention: it is directed to the above-mentioned prior art, proposes a kind of road face internal exergy dissipation triage based on passive source arbitrary excitation
Survey method provides a kind of new solution for the lossless real-time detection in road face.
A kind of technical solution: road face internal injury detection method based on passive source arbitrary excitation, comprising the following steps:
Step 1) is structured the formation form using nested equilateral triangle, and several MEMS3 axle accelerations sensings are arranged on road face surface
Device, the composition detection array；
Step 2), using environment fine motion as driving source, MEMS3 axle acceleration sensor acquires the vibration signal in road face, and
Pass to computer；
Step 3) carries out mean value after computer is collected into the vibration data of each MEMS3 axle acceleration sensor respectively
With go tilt component to handle, obtain the surface wave data of pretreated each sensor；
Step 4) chooses several Frequency points in preset frequency band equal intervals, for each Frequency point:
Step 4.1), the frequency centered on Frequency point carry out the surface wave data of pretreated each sensor
Bandpass filtering；
Step 4.2) acquires the surface of the Frequency point to the surface wave data application spatial autocorrelation method after bandpass filtering
The phase velocity of wave；
Step 5) after obtaining the phase velocity of the surface wave of all Frequency points, draws out surface wave frequency rate-phase velocity curve,
That is surface wave dispersion curve；
Step 6), it is deep according to the relationship and surface wave wavelength of surface wave frequency rate and surface wave wavelength and surface wave propagation
The relationship of degree draws out depth-phase velocity curve；
Step 7) checks surface wave depth-phase velocity curve, if surface wave depth-phase velocity curve is mutated,
There is damage in mutation depth in road face.
Further, in the step 1, the radius size of the detection array is determined according to depth of exploration, described half
Diameter is 1/4~1/3 size of depth of exploration, the arrangement detection array in radius region.
Further, the step 4.2) comprising the following specific steps
4.2.1), the data in each road are transformed into frequency domain with Fourier transformation；
4.2.2), the auto-power spectrum S in the center of circle and circumference record is calculated separately in frequency domain0(0, ω) and Sr(r, ω), and
The crosspower spectrum S (r, θ, ω) of center of circle record and circumference record；In formula, r is radius of a circle, and θ is station azimuth, and ω is angular frequency
Rate；
4.2.3), the spatial autocorrelation coefficient ρ of each station pair is calculated according to the following formulai(r, ω):
4.2.4), the azimuth averaging for calculating the spatial autocorrelation coefficient of each station pair obtains the space of the Frequency point certainly
Correlation coefficient ρ (r, ω):
ρ (r, ω)=averge (ρi(r,ω))
4.2.5), the surface phase velocity of wave c (ω) of the Frequency point, J in formula are calculated according to the following formula0(x) the of x is indicated
A kind of zero Bessel function:
The utility model has the advantages that the present invention carries out processing analysis to data with passive source surface wave etection theory, and then to the damage in road face
Hurt position and degree diagnosed, scientifically the bearing capacity of the duty status in road face, damaged condition and road face is assessed,
It is sounded an alarm in due course if road surface damage is serious, provides foundation and guidance for management works such as the maintenances and maintenance in road face, thus
Improve and ensure that face safe operation.
Detailed description of the invention
Fig. 1 is sensor arrangement figure；
Fig. 2 is ideal road face underground cavity surface wave depth-phase velocity curve graph.
Specific embodiment
Further explanation is done to the present invention with reference to the accompanying drawing.
A kind of road face internal injury detection method based on passive source arbitrary excitation, comprising the following steps:
Step 1) is structured the formation form using nested equilateral triangle, and several MEMS3 axle accelerations sensings are arranged on road face surface
Device, the composition detection array.The radius size of the detection array is determined according to depth of exploration, deep using 3 to 4 times of radius as exploration
Degree arranges the detection array in radius region.In the present embodiment, as shown in Figure 1,7 GY-25Z sensors are set on road face surface,
2 nested equilateral triangles are formed, the GY-25Z sensor on equilateral triangle is located in a great circle and a roundlet circumference.
Step 2), using environment fine motion as driving source, GY-25Z sensor acquires the vibration signal in road face, and passes to meter
Calculation machine.Specifically, selection carries out signal acquisition at night as far as possible in order to reduce human interference.
Step 3) after computer is collected into the vibration data of each GY-25Z sensor, carries out mean value respectively and goes to incline
Slope component processing, obtains the surface wave data of pretreated each sensor.
Step 4) chooses several Frequency points in preset frequency band equal intervals, for each Frequency point:
Step 4.1), the frequency centered on the Frequency point, to the surface wave data of pretreated each sensor into
Row bandpass filtering；
Step 4.2) acquires the surface of the Frequency point to the surface wave data application spatial autocorrelation method after bandpass filtering
The phase velocity of wave；Specifically comprise the following steps:
4.2.1), the data of each sensor acquisition become a track data again, and such as 10 sensors are corresponding with 10 numbers
According to the data in each road are transformed to frequency domain with Fourier transformation；
4.2.2), the auto-power spectrum S in the center of circle and circumference record is calculated separately in frequency domain0(0, ω) and Sr(r, ω), and
The center of circle records and the crosspower spectrum S (r, θ, ω) of circumference record, and in formula, r is radius of a circle, and θ is station azimuth, and ω is angular frequency
Rate；
4.2.3), the spatial autocorrelation coefficient ρ of each station pair is calculated according to the following formulai(r, ω):
4.2.4), the azimuth averaging for calculating the spatial autocorrelation coefficient of each station pair obtains the space of the Frequency point certainly
Correlation coefficient ρ (r, ω):
ρ (r, ω)=averge (ρi(r,ω))
The station, that is, acceleration transducer, the multiple stations form an array, in data processing, the station according to the station into
Row processing；
4.2.5), the surface phase velocity of wave c (ω) of the Frequency point, J in formula are calculated according to the following formula0(x) the of x is indicated
A kind of zero Bessel function:
Step 5) after obtaining the phase velocity of the surface wave of all Frequency points, draws out surface wave frequency rate-phase velocity curve,
That is surface wave dispersion curve.
Step 6), it is deep according to the relationship and surface wave wavelength of surface wave frequency rate and surface wave wavelength and surface wave propagation
The relationship of degree draws out depth-phase velocity curve.Specifically, the relationship of surface wave frequency rate and surface wave wavelength are as follows: surface wave
Wavelength is equal to surface phase velocity of wave divided by surface wave frequency rate.The relationship of surface wave wavelength and surface wave propagation depth are as follows: surface wave
Propagate the half that depth is about surface wave wavelength.
Step 7) checks surface wave depth-phase velocity curve, if surface wave depth-phase velocity curve is mutated,
There is damage in mutation depth in road face.Specifically, if there is damage, surface wave depth-phase velocity curve inside road face
It will appear "the" shape mutation as shown in Figure 2, abscissa states surface phase velocity of wave, and ordinate states surface wave depth.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of road face internal injury detection method based on passive source arbitrary excitation, which comprises the following steps:
Step 1) is structured the formation form using nested equilateral triangle, and several MEMS3 axle acceleration sensors, group is arranged on road face surface
At the detection array；
Step 2), using environment fine motion as driving source, MEMS3 axle acceleration sensor acquires the vibration signal in road face, and transmits
To computer；
Step 3) after computer is collected into the vibration data of each MEMS3 axle acceleration sensor, carries out mean value respectively and goes
Tilt component processing, obtains the surface wave data of pretreated each sensor；
Step 4) chooses several Frequency points in preset frequency band equal intervals, for each Frequency point:
Step 4.1), the frequency centered on Frequency point carry out band logical to the surface wave data of pretreated each sensor
Filtering；
Step 4.2) acquires the surface wave of the Frequency point to the surface wave data application spatial autocorrelation method after bandpass filtering
Phase velocity；
Step 5) after obtaining the phase velocity of the surface wave of all Frequency points, draws out surface wave frequency rate-phase velocity curve, i.e. table
Surface wave frequency dispersion curve；
Step 6), according to the relationship and surface wave wavelength of surface wave frequency rate and surface wave wavelength and surface wave propagation depth
Relationship draws out depth-phase velocity curve；
Step 7) checks surface wave depth-phase velocity curve, if surface wave depth-phase velocity curve is mutated, road face
There is damage in mutation depth.
2. the road face internal injury detection method according to claim 1 based on passive source arbitrary excitation, which is characterized in that
In the step 1, the radius size of the detection array determines that the radius is the 1/4 of depth of exploration according to depth of exploration
~1/3 size, the arrangement detection array in radius region.
3. the road face internal injury detection method according to claim 1 based on passive source arbitrary excitation, which is characterized in that
The step 4.2) comprising the following specific steps
4.2.1), the data in each road are transformed into frequency domain with Fourier transformation；
4.2.2), the auto-power spectrum S in the center of circle and circumference record is calculated separately in frequency domain0(0, ω) and Sr(r, ω) and the center of circle
The crosspower spectrum S (r, θ, ω) of record and circumference record；In formula, r is radius of a circle, and θ is station azimuth, and ω is angular frequency；
4.2.3), the spatial autocorrelation coefficient ρ of each station pair is calculated according to the following formulai(r, ω):
4.2.4), the azimuth averaging for calculating the spatial autocorrelation coefficient of each station pair, obtains the spatial autocorrelation of the Frequency point
Coefficient ρ (r, ω):
ρ (r, ω)=averge (ρi(r,ω))
4.2.5), the surface phase velocity of wave c (ω) of the Frequency point, J in formula are calculated according to the following formula0(x) first kind of x is indicated
Zero Bessel function:
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| CN112925029A (en) * | 2021-01-13 | 2021-06-08 | 天津大学 | Transient electromagnetic passive source exploration method |
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| CN108037187A (en) * | 2017-12-05 | 2018-05-15 | 南京航空航天大学 | Airfield runway based on Rayleigh waves comes to nothing monitoring method |
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| CN108037187A (en) * | 2017-12-05 | 2018-05-15 | 南京航空航天大学 | Airfield runway based on Rayleigh waves comes to nothing monitoring method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925029A (en) * | 2021-01-13 | 2021-06-08 | 天津大学 | Transient electromagnetic passive source exploration method |
| CN112925029B (en) * | 2021-01-13 | 2022-07-01 | 天津大学 | Transient electromagnetic passive source exploration method |
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