CN109341989A - A kind of Bridge Influence Line recognition methods that can reject vehicle power effect - Google Patents
A kind of Bridge Influence Line recognition methods that can reject vehicle power effect Download PDFInfo
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- CN109341989A CN109341989A CN201811016637.4A CN201811016637A CN109341989A CN 109341989 A CN109341989 A CN 109341989A CN 201811016637 A CN201811016637 A CN 201811016637A CN 109341989 A CN109341989 A CN 109341989A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
Abstract
The invention belongs to safety of structure detection technique fields, disclose a kind of Bridge Influence Line recognition methods that can reject vehicle power effect, and method includes: that (1) pre-processes the bridge response data that sensor measures using empirical mode decomposition；(2) it is identified to by the response of pretreated bridge using Quasi-static Method.The power superposition item that bridge can be responded by being pre-processed using empirical mode decomposition to bridge response is rejected, to obtain the quasi_static response of bridge, is further useful for influencing line identification.This method can handle the structural response data measured on various bridge types by sensor, have good future in engineering applications without understanding any bridge structure information in advance simultaneously.
Description
Technical field
The invention belongs to safety of structure detection technique fields, and in particular to one kind can reject the bridge of vehicle power effect
Beam influences line recognition methods.
Technical background
In recent years, the promotion with the continuous development and people of high-speed rail technology to trip efficiency requirements, in the world
Built high-speed railway total mileage is unprecedented to be improved.Since most operating mileages of high-speed railway are all in height
On bridge formation, the safe condition of high-speed rail bridge is also had been to be concerned by more and more people.High-speed rail bridge is arranged due to constantly bearing high speed
The power of vehicle loads, and the load effect being subject to is more complicated than common highway bridge, how to monitor high metal bridge in real time
The military service performance of beam becomes a significant problem concerning passenger survival property safety.
It is also worth noting that occurring in recent years a kind of based on the damnification recognition method for influencing line variation.Bridge
The response curve that line is bridge a certain specific position when unit load passes through bridge is influenced, as a kind of statical feature of bridge,
It influences line and contains a large amount of structural information.As the index of a structural damage, the rigid of bridge can directly be reacted by influencing line
Degree and flexibility information.In addition to this, the location information that line further comprises bridge is influenced, it can be by influencing the variation of line to damage
It is positioned.Since load running track is single, being particularly suitable for influences line to it identifies railroad bridge.But bullet train mistake
It is biggish that generation is compared in quasi-static bridge response when dynamic effect caused by when bridge can make bridge response pass a bridge with low speed train
Deviation, how from dynamic effect identification obtain influence line become high-speed railway bridge influence line identification a difficult point ask
Topic.
For the semi-static load situation that low speed load train is passed a bridge, influencing line knowledge method for distinguishing has had many researchs
Achievement.OBerin proposes a kind of matrix method at first, which carrys out the identification from measured data, influences line.This method establish it is a kind of and
The relevant matrix of train information, by identifying Bridge Influence Line to matrix inversion.Sio-Song Leng is estimated using maximum likelihood
Meter method is deduced a kind of influence line identification formula, and this method can consider the effect of multiple load vehicles simultaneously, thus
Give a kind of more accurate influence line estimation.Chen uses the influence line that regularization method obtains identification first and is not inconsistent
The fluctuation for closing physical significance is eliminated.Recognize that bridge response can be expressed as influencing line and train information letter
Several convolution influences line by the method identification of fast Fourier transform deconvolution.This method can greatly improve influence line
The efficiency of identification, and also have certain guarantee on accuracy of identification.In the case where considering dynamic effect, Wang Ningbo et al. is logical
It crosses multinomial and SIN function to be respectively fitted static(al) effect and dynamic effect, identification has obtained certain specific bridge types
Influence line.And for other more common situations, up for further studying.
Summary of the invention
The purpose of the present invention is one kind can reject the Bridge Influence Line recognition methods of vehicle power effect.
Technical solution of the present invention:
A kind of Bridge Influence Line recognition methods that can reject vehicle power effect, steps are as follows:
Step 1 pre-processes the bridge response message that sensor measures using empirical mode decomposition
(1) local maximum and local minimum of bridge response signal R (t) are found out；
(2) minimum interpolation and maximum interpolating function are calculated separately using cubic spline interpolation method, and is corresponded to
Signal envelope emin(t) and emax(t)；
(3) local mean value M (t)=(e of signal maximum and minimum envelope is calculatedmin(t)+emax(t))/2。
(4) original input signal is subtracted into local mean value and obtains oscillator signal H (t)=R (t)-M (t)；
(5) when H (t) meets the condition of intrinsic mode functions, H (t) becomes an imf (t)；Otherwise, with H (t) replacement step
(1) R (t) in is simultaneously repeated the above process since step (1)；One intrinsic mode functions must satisfy 2 conditions: 1) function exists
In entire time range, the number of Local Extremum and zero crossing is equal, or at most poor one；2) at any point in time, office
The envelope of portion's maximum value and the envelope average value of local minimum are necessary for 0；
(6) imf is enabled1(t)=H (t), then imf1It (t) is first imf (t), corresponding surplus Res1(t)=R (t)-
imf1(t), by Res1(t) above-mentioned all steps are repeated as new signal, obtains second imf (t) component, and so on, it obtains
To Res1(t)-imf2(t)=Res2(t) ... Resn-1(t)-imfn(t)=Resn(t)；When intrinsic mode functions can not be further continued for mentioning
When taking, stop screening process；
(7) pass through decomposable process above, R (t) is finally broken down into n imf (t) component and a surplus Resn(t)；
Original signal R (t) is expressed asSurplus Res after treatmentnIt (t) is exactly that bridge standard is quiet
State response；
Step 2 carries out influence line identification using Quasi-static Method to by the response of pretreated bridge
After the pretreatment of empirical mode decomposition, bridge dynamic response is converted to the response of the bridge under quasi-static situation,
It is identified using classical quasi-static influence line identification model.
Beneficial effects of the present invention:
(1) Bridge Influence Line recognition methods of the invention is compared with original influence line recognition methods, can be arranged high speed
Bridge structure response when vehicle loads is identified, is influenced line identification for high-speed rail bridge and is provided theoretical basis；
(2) Bridge Influence Line recognition methods of the invention has stringent theoretical basis, is rung based on bridge load and bridge
The synchronous acquisition information answered, and combine advanced Statistical error algorithm, it is ensured that have by the influence line of the system identification higher
Precision；
(3) Bridge Influence Line recognition methods of the invention uses simple, can be to acquisition without predicting bridge structure information
Obtained bridge dynamic response data is directly handled, to obtain influencing line.
Detailed description of the invention
Fig. 1 for the used algorithm of the present invention implementation flow chart；
Fig. 2 is the model of vibration that the load vehicle simulated in embodiment of the present invention method is passed a bridge；
Fig. 3 is that bridge dynamic response caused by vehicle is loaded in embodiment of the present invention method；
Fig. 4 is the quasi_static response obtained after being handled in embodiment of the present invention method by empirical mode decomposition；
Fig. 5 is the influence line identified in embodiment of the present invention method to quasi_static response after processing；
Specific embodiment
The present invention is described in further detail with a numerical example with reference to the accompanying drawing.
Influence line recognition methods of the invention point " the bridge response message that sensor is measured using empirical mode decomposition into
Row pretreatment " and " being identified to by the response of pretreated bridge using Quasi-static Method " two steps, specific embodiment
It has been given above, the application method and feature of invention is next illustrated in conjunction with an example.
Implement to calculate: 72km/h Biaxial stress vehicle, which is passed a bridge, influences line identification
In this numerical example, we simulate the for-wheel vehicle that a speed is 72km/h and pass through simply supported beam, pass through foundation
Vehicle bridge coupling vibration model responds to analyze the amount of deflection of span centre.The wheelbase of for-wheel vehicle is 4m, and freely-supported beam length is 16m.By vehicle
Body is simplified to three mass blocks, and quality is respectively: m1=524kg；m2=297kg；m3=6451kg.The concrete condition of model is shown in
Fig. 2.
In this example, the differential equation of motion of four-degree-of-freedom vehicle and distributed mass system simply supported beam is established, is passed through
The time-histories that ordinary differential system obtains the response of simply supported beam span centre is solved, as shown in Figure 3.Deflection of bridge span when bullet train is passed a bridge
Response is segmented into quasi_static response and power superposition item two parts, can be divided two parts by empirical mode decomposition
Solution, decomposition result are shown in Fig. 4.
After being rejected to dynamic effect, it can be identified with quasi-static influence line recognition methods to line is influenced, in this calculation
In example, we are using regularization LSQR iterative method.Identify that obtained influence line and the comparison of true impact line are shown in Fig. 5.It can be with
Find out, the influence line identified and true value coincide substantially.This method is one kind of Bridge Influence Line when identifying dynamically load
Effective ways.
Claims (1)
1. the Bridge Influence Line recognition methods that one kind can reject vehicle power effect, which is characterized in that steps are as follows:
Step 1 pre-processes the bridge response message that sensor measures using empirical mode decomposition
(1) local maximum and local minimum of bridge response signal R (t) are found out；
(2) minimum interpolation and maximum interpolating function are calculated separately using cubic spline interpolation method, and obtained to induction signal
Envelope emin(t) and emax(t)；
(3) local mean value M (t)=(e of signal maximum and minimum envelope is calculatedmin(t)+emax(t))/2；
(4) original input signal is subtracted into local mean value and obtains oscillator signal H (t)=R (t)-M (t)；
(5) when H (t) meets the condition of intrinsic mode functions, H (t) becomes an imf (t)；Otherwise, in H (t) replacement step (1)
R (t) and repeated the above process since step (1)；One intrinsic mode functions must satisfy 2 conditions: 1) function is entire
In time range, the number of Local Extremum and zero crossing is equal, or at most poor one；2) at any point in time, part is most
The envelope being worth greatly and the envelope average value of local minimum are necessary for 0；
(6) imf is enabled1(t)=H (t), then imf1It (t) is first imf (t), corresponding surplus Res1(t)=R (t)-imf1(t),
By Res1(t) above-mentioned all steps are repeated as new signal, obtains second imf (t) component, and so on, obtain Res1
(t)-imf2(t)=Res2(t) ... Resn-1(t)-imfn(t)=Resn(t)；When intrinsic mode functions can not be further continued for extracting,
Stop screening process；
(7) pass through decomposable process above, R (t) is finally broken down into n imf (t) component and a surplus Resn(t)；It is original
Signal R (t) is expressed asSurplus Res after treatmentnIt (t) is exactly the quasi-static sound of bridge
It answers；
Step 2 carries out influence line identification using Quasi-static Method to by the response of pretreated bridge
After the pretreatment of empirical mode decomposition, bridge dynamic response is converted to the response of the bridge under quasi-static situation, uses
Classical quasi-static influence line identification model is identified.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811016637.4A CN109341989B (en) | 2018-09-03 | 2018-09-03 | Bridge influence line identification method capable of eliminating vehicle power effect |
| PCT/CN2019/073826 WO2020048090A1 (en) | 2018-09-03 | 2019-01-30 | Bridge influence line identification method capable of eliminating vehicle power effect |
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| CN201811016637.4A CN109341989B (en) | 2018-09-03 | 2018-09-03 | Bridge influence line identification method capable of eliminating vehicle power effect |
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| CN109341989B CN109341989B (en) | 2020-06-12 |
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| WO (1) | WO2020048090A1 (en) |
Cited By (3)
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| CN110261305A (en) * | 2019-06-18 | 2019-09-20 | 南京东南建筑机电抗震研究院有限公司 | Based on across footpaths continuous bridge damnification recognition methods such as the multispan for influencing line |
| CN112485030A (en) * | 2020-11-09 | 2021-03-12 | 深圳市桥博设计研究院有限公司 | Bridge structure dynamic monitoring method, system and equipment based on frequency coupling |
| CN114414122A (en) * | 2022-01-07 | 2022-04-29 | 大连理工大学 | Sensing device for measuring multi-component force of wheel |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112485030A (en) * | 2020-11-09 | 2021-03-12 | 深圳市桥博设计研究院有限公司 | Bridge structure dynamic monitoring method, system and equipment based on frequency coupling |
| CN112485030B (en) * | 2020-11-09 | 2023-03-14 | 深圳市桥博设计研究院有限公司 | Bridge structure dynamic monitoring method, system and equipment based on frequency coupling |
| CN114414122A (en) * | 2022-01-07 | 2022-04-29 | 大连理工大学 | Sensing device for measuring multi-component force of wheel |
| CN114414122B (en) * | 2022-01-07 | 2022-09-27 | 大连理工大学 | Sensing device for measuring multi-component force of wheel |
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| Publication number | Publication date |
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| CN109341989B (en) | 2020-06-12 |
| WO2020048090A1 (en) | 2020-03-12 |
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