CN105241660A - High-speed rail large-scale bridge performance evaluation method based on health monitoring data - Google Patents

High-speed rail large-scale bridge performance evaluation method based on health monitoring data Download PDF

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CN105241660A
CN105241660A CN201510759672.5A CN201510759672A CN105241660A CN 105241660 A CN105241660 A CN 105241660A CN 201510759672 A CN201510759672 A CN 201510759672A CN 105241660 A CN105241660 A CN 105241660A
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bridge
data
value
evaluation
health monitoring
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CN105241660B (en
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施洲
蒲黔辉
张贵忠
闫志刚
赵钰
岳青
吴来义
张同刚
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Southwest Jiaotong University
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Abstract

The invention discloses a high-speed rail large-scale bridge performance evaluation method based on health monitoring data, which relates to the field of bridge engineering detection. The high-speed rail large-scale bridge performance evaluation method comprises the steps of arranging an automatic sensor hardware equipment system on a high-speed rail bridge in an optimized manner, achieving automatic data acquisition, carrying out denoise processing, time domain statistical processing, frequency domain processing and the like on measured data in sequence, and evaluating bridge running safety performance based on the health monitoring data. The high-speed rail large-scale bridge performance evaluation method meets the actual requirements of high-speed rail bridge detection and evaluation, can reflect the running safety of the high-speed rail bridge accurately and timely, and provides important guarantee for the safe running of high-speed trains; the evaluation results obtained through adopting the method are unified with the high-speed rail conventional detection and evaluation results, thereby facilitating the actual application of the method for the bridge managers; and decision-making suggestions for targeted bridge maintenance and repair are made on the basis of the evaluation results obtained through adopting the method, thereby being conductive to implementing timely maintenance and dynamic maintenance, and greatly improving the bridge overhaul efficiency.

Description

Based on the high ferro large bridge Reliable Evaluating Methods of Their Performance of health monitoring data
Technical field
The present invention relates to science of bridge building detection field, be specially the high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data.
Background technology
Along with the development of ultra-large type bridge, the job requirement of detecting appraisal in operation of corresponding bridge is also more and more higher, and tradition is evaluated as main method with manual detection and is difficult to adapt to ultra-large type bridge machinery evaluation needs.In this context, bridge health monitoring system is also constantly developed, and one of study hotspot becoming construction applications.Bridge health monitoring system by arranging various kinds of sensors, signal transmission apparatus etc. on bridge, ambient parameters and all kinds of dynamic response of bridge structure are carried out to the dynamic monitoring of real-time robotization, and on the basis of Monitoring Data, real-time assessment and safe early warning are made to problems such as the load-bearing capacity of bridge structure, component damages.
Along with the development of high-speed railway large bridge, for ensureing that bullet train safety and steady passes through bridge, the operative performance such as, road-ability stressed to the vibration of bridge structure, dynamic deformation, local fatigue propose higher requirement.And at present, high-speed railway bridge all adopts daytime high density to drive a vehicle continuously, night 24 points ~ 4:00 AM " Window time " to use conventional methods be manually main inspection maintenance, also exist and check blind area, particularly new material, new construction, the new equipment of high-speed rail bridge employing, checks on the quality and is difficult to ensure.Therefore health monitoring systems is one of important channel solving current high-speed railway large bridge check and evaluation, meanwhile, in the health monitoring systems of high-speed railway large bridge, except structural damage, load-bearing capacity are monitored, more pay close attention to the monitoring and evaluation of the bullet train operational safety performance of bridge.
The technical scheme of prior art one
In the health monitoring systems of existing highway large bridge and general fast railway, the load-bearing capacity of more concern bridges, carries out evaluation and the early warning such as bridge damnification position, degree of injury with non-destructive tests, damage criterion etc.
(1) development course of bridge structural damage identification and correction
As far back as the sixties in 20th century, just start to adopt dynamic date to carry out the integrality of evaluation structure in Aero-Space, mechanical engineering circle, cause the concern of field of civil engineering subsequently.In recent decades, the development of the dynamometry analysis Instrument equipment of test modal analysis theory and technology and some advanced persons, provides strong guarantee for existing engineering structure obtains modal parameter and dynamic response parameter accurately.Just in this context, the damage check assessment method based on dynamic test is developed rapidly.Compared with traditional detection means, the advantage of Dynamic testing is to can't harm, it is convenient to test, can uninterrupted traffic can hold the state of structure on the whole.Nearly two during the last ten years, and dynamic method damage check has become Chinese scholars with assessment and paid close attention to and the problem studying very active.
The research of this respects such as, modal strain energy (MSE) as poor in frequency change duplicate ratio, modal assurance criterion (MAC), Curvature Mode Method, flexibility matrix based on the damage criterion of dynamic survey parameter and index method relatively early, and is constantly developed.This type of damage criterion and index method all can adopt numerical simulation to know method for distinguishing and obtain good validation, but this class methods weak point needs a large amount of and necessary accurate measured data.Afterwards, neural network, genetic algorithm, wavelet transformation, Hilbert-Huang conversion, data fusion scheduling algorithm and instrument are introduced in non-destructive tests successively, enrich and have developed damage identification theory and method.The research contents of associated injury identification is more and more extensive, and wherein testing signal process, test data process, the measure of dynamic test error-reduction, power optimizing the locations of the measuring points and the application etc. in health monitoring are the important branch's content in non-destructive tests field.In all kinds of research method, be important component part wherein based on the Optimization inversion class non-destructive tests algorithm of modal parameter and Modifying model always.
(2) based on Modifying model theory and the algorithm of modal parameter
Bridge structure carries out FEM updating according to actual measurement kinetic parameter also can realize non-destructive tests.Berman just by the norm of minimizing Weighted quality, rigidity error as far back as nineteen eighty-three, is zero as constraint condition using the symmetry of parameter matrix, orthogonality and residue modal vector, have modified mass matrix and stiffness matrix.Subsequently, the sensitivity computing method of eigenwert and proper vector is employed for the research of structure partial damage characteristic.Bicanic & Chen adopts Gauss-Newton least square method or Direct Iterative Method to carry out Modifying model to identify structural damage, and result shows only to need several rank natural frequency test data can obtain good recognition result.Zhang Qiwei etc. take Jiang Yin Bridge as engineering background, have studied the FEM updating utilizing on-the-spot Ambient Vibration Measurements to carry out Suspension bridge structure.Ancestor Zhou Hong, Gao Minglin, summer camphor tree China system further investigation continuous rigid frame bridge FEM updating problem, correction model is effectively applied to damage in bridge health monitoring and safety assessment.Li Zheng, Li Zhongxian revise concrete material model, have effectively carried out the stressed behavioral study under xoncrete structure cyclic loading, and have studied its application in the identification of high pier seismic Damage.Yu Ling and Xu Peng proposes a heavy Multiple Damaged Locations in Structures recognition methods based on Filled function ant group algorithm (CACO), for non-destructive tests constrained optimization method extends new research means.The people such as the war prosperous and Xia He of man [propose a kind of method utilizing bridge structure online dynamic response evaluation structure damage under Vehicle Load based on damage sensitivity, widen the damnification recognition method based on dynamic test.Lin Xiankun, an order more waits people for Prestressed Continuous Box Beam bridge, utilizes accelerating genetic algorithm, based on 7 rank modal parameters before environmental excitation modal test, revises the initial finite element model of this bridge, obtain good result.The people such as Zhang Shilei, Chen Shaofeng carry out model parameter correction according to when a small amount of measurement data to a steel truss model based on just drilling analytic approach, and sum up and be a set ofly applicable to the method for large and complex structure Modifying model and provide calculation procedure.The people such as Wang Lei, Yu Sheng revise across rigid-lid hypothesis bridge finite element model greatly certain prestressed concrete using model analysis frequency as input vector based on radial base neural net, and obtaining can the structural finite element model of the physical state of reflect structure more realistically.The people such as Han Fang, Zhong Dongwang propose a kind of model modification method based on information fusion and bayesian theory, and the method makes full use of prior imformation, and iterative computation amount is less, may extend to large complicated nonlinear organization.Qi Quanquan, Xin Kegui is for the nonuniqueness of finite element structure Modifying model result under incomplete complex mode condition, utilize add mass and do not add the dynamic test data of mass 2 group model and mass matrix poor, in conjunction with differential concept, a kind of new Modifying model algorithm of having derived.
Yuan Aimin, the people such as Dai Hang take into account the constraint of bridge structure boundary condition and parametric sensitivity, carry out FEM updating to survey modal parameter to a cable-stayed bridge, research thinks that boundary condition rigidity should as corrected parameter, and its correction result reflects the actual conditions of bridge.Shi Zhou etc. are middle in the damage based on modal parameter is studied proposes the boundary condition correction that application constraint optimization identification method carries out bridge structural model, and boundary condition and component elasticity modulus combine correction, also tentatively discuss the Damage Identification of Bridge Structure considering boundary condition variation.
(3) development of large road bridges health monitoring systems
At present, the isostructural health monitoring systems of large road bridges is constantly developed, and one of study hotspot becoming construction applications.Bridge health monitoring obtains good progress in Contents for Monitoring, monitoring sensor, optimizing the locations of the measuring points, monitor signal process, evaluation algorithm etc.But in current health monitoring systems, be limited to the many factors such as monitoring measuring point quantity, monitor signal noise, the bridge structural damage identification based on health monitoring data is theoretical still exists many difficulties with the aspect such as algorithm, bridge technology status index.
The shortcoming of prior art one
Be mainly used in large road bridges at current health monitoring systems, be limited to the many factors such as monitoring measuring point quantity, monitor signal noise, the bridge structural damage identification based on health monitoring data is theoretical still exists many difficulties with the aspect such as algorithm, bridge technology status index.The practical implementation of large road bridges health monitoring systems existing at present shows, except effectively monitoring except the data of some, the work practicality such as follow-up bridge analysis evaluation are relatively poor.To there is particular problem as follows in the bridge performance evaluation of existing large road bridges health monitoring systems:
1) the more concern bridge structural damage identifications of existing highway bridge health monitoring systems, bearing capacity safety, be thus also not suitable for the more concern bullet trains of high-speed railway large bridge pass through bridge time operation safety;
2) quantity and error etc. of measured data is limited to, monitoring system still can not effectively realize the damage of bridge structure and component whether, the identification of degree of injury, damage reason location, not yet effectively can make the load-bearing capacity of bridge structure and the applicability of bridge and passing judgment on;
3) health monitoring assessment method, conclusion etc. also can not effectively be connected with traditional highway, railroad bridge detecting appraisal and Bearing Capacity Evaluation etc.;
4) health monitoring systems fails to realize proposing effective maintenance decision recommendation to the maintenance of bridge on the basis of bridge performance evaluation.
The technical scheme of prior art two
High-speed railway large bridge still carries out test and assessment in a conventional manner, according to " High-speed Railway Bridges tunnel buildings repair rule (try) " (TG/GW114-2011), " calibrating of high-speed railway bridge operative performance specifies (trying) " (TG/GW209-2014) etc. carry out traditional with manual detection, be assessed as master.With the inspection system that " during phase property Jian Cha ﹑ Lin Jian Cha ﹑ Shui literary composition Guan Ce ﹑ Zhuan item Jian Cha ﹑ certificate test etc. " are content, and high-speed railway bridge structure technology state is evaluated in contrast corresponding " High-speed Railway Bridges tunnel buildings state evaluation standard ".
The shortcoming of prior art two
Current high-speed railway bridge Main Basis " rule (trying) repaired by High-speed Railway Bridges tunnel buildings " (TG/GW114-2011), " high-speed railway bridge operative performance calibrating regulation (trying) " (TG/GW209-2014) etc. carry out conventional sense maintenance, the basic pipe mode of supporting with reference to common railway is carried out, based on traditional manual detection, under the specified conditions of high-speed railway circuit operation on daytime " night, 24 points ~ 4:00 AM was safeguarded ", the manpower of testing at substantial and material resources, and it is low to there is detecting appraisal efficiency, the problems such as evaluation accuracy is weak.
Summary of the invention
The object of the present invention is to provide a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data, for there is " skylight at night " manual detection in current high-speed railway large bridge detecting appraisal, the problem such as detecting appraisal efficiency is low, accuracy is weak, and because of measuring point limited amount, test noise impact and the problem such as non-destructive tests, bridge performance evaluation difficulty that causes in current existing span bridge health monitoring system.
In order to overcome the above problems, the technical solution used in the present invention is as follows, a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data, comprise the following steps: the sensor hardware device systems of preferred arrangement robotization on high-speed rail bridge, realize the data acquisition of robotization, after preliminary noise reduction process, Time-domain Statistics process and frequency domain process are carried out successively to measured data, carry out the judge of bridge operation security performance based on health monitoring data.
As preferably, the content measurement of preferred arrangement focuses on the monitoring of the special component of vibration monitoring of engineering structure, bearing power and rail temperature expansion and cotraction regulator, and measuring point quantity optimization is arranged in the position of controlling sections and least favorable value.
As preferably, the preliminary noise reduction process of Monitoring Data is as follows:
No signal, signal sampling frequency, signal quantity peak value and signal intensity situation is had to carry out the reliability assessment of health monitoring data with data; And the dynamic datas such as acceleration are carried out to the low-pass filtering of 20 ~ 50Hz, carry out 0 average value processing, signal reconstruction process if desired.
As preferably, the Time-domain Statistics process of measured data is as follows:
(1) peak value process:
For sample frequency higher than in 20Hz and above dynamic monitoring data process, in specified time interval △ T, extract maximum, the minimum peak D of each value max(ti), D min(ti), i.e. the result such as minimax acceleration, displacement amplitude, wheel rail force, as shown in formula (1):
D Max(ti)=Max{D(tx)}(1a)
D Min(ti)=Min{D(tx)}(1b)
In formula, D (tx) is bridge dynamic monitoring data sequence in the △ T cycle;
The time interval △ T of dynamic monitoring data process is according to following formula value:
ΔT=(L B+L T)/v+5(2)
In formula, L bfor overall length of bridge, unit is m; L tfor train length, unit is m; V is the conventional passage rate of train, and unit is m/s; The 5s increased is the time span redundance improving data;
For the plan static monitoring techniques data of sample frequency lower than 20Hz (usual frequency is at below 1Hz), record its maximum, minimum peak S respectively with cycle regular hour △ T equally max(ti), S min(ti), as formula (3):
S Max(ti)=Max{S(tx)}(3a)
S Min(ti)=Min{S(tx)}(3b)
In formula, S (tx) is that static monitoring techniques data sequence intended by △ T cycle bridge, remembers that each quasi-peak value result is PV (i), stores and for subsequent use with certain data layout;
(2) the timely Forewarning Measures of peak value
In all kinds of actual measurement dynamic data △ T time, to be recorded as DS according to time interval end for subsequent use for extreme value, in evaluation processing procedure, consider special data processing, in maximal value record in △ T time interval, immediately with method spacer maximum value writing time that maximal value substitutes, can instant alarming during to ensure to occur that maximum actual measurement value exceeds standard in △ T time interval;
(3) peak value statistical treatment
After the process of dynamic data segmentation peak value, by peak-data series in the △ T of gained together with plan static monitoring techniques DS result PV (i), carry out next step statistical study process, statistics analysis of the inspected data process, for dissimilar data, carries out corresponding average Av with the different cycles to dynamic data segmentation peak value and plan static monitoring techniques data sequence pV(i), variances sigma vPVthe statistical study process of (i);
(4) variable quantity process
According to peak value and the statistical treatment of health monitoring data, by the mean value Av of current peak results PV (i) with corresponding data in the past pV(i-1) subtract each other, obtain " variable quantity " V of all kinds of Monitoring Data pV(i), as formula (4):
V PV(i)=PV(i)-A vPV(i-1)(4)。
As preferably, the frequency domain process of measured data is as follows:
Adopt Fourier transform simultaneously, adopt wavelet analysis and HHT transform analysis to carry out the reconstruction processing of Modal Parameter Identification and time domain data, remember that the frequency values data after 3 kinds of frequency transformations are respectively F fFT(ti), F wT(ti), F hHT(ti):
The difference of the frequency values after conversion process and last period average is respectively Δ F f, Δ F w, Δ F h.
ΔF F=F fft(ti)-F Av(i-1)(5a)
ΔF W=F wt(ti)-F Av(i-1)(5b)
ΔF H=F HHt(ti)-F Av(i-1)(5c)
Note F fFT(ti) fusion weight coefficient is ω f, its value is as shown in formula 6a ~ 6c:
ω F = 0.05 , i f ΔF F F A v ( i - 1 ) > 58 % - - - ( 6 a )
ω F = 1.0 , i f ΔF F F A v ( i - 1 ) ≤ 20 % - - - ( 6 b )
&omega; F = 1.5 - 2.5 &Delta;F F F A v ( i - 1 ) , i f 20 % < &Delta;F F F A v ( i - 1 ) &le; 58 % - - - ( 6 c )
Equally, F wT(ti), F hHT(ti) fusion weight coefficient is ω w, ω h, its value is with formula 5;
Frequency resultant F after fusion w(ti) be shown below:
F W(ti)=[ω F·F FFt(ti)+ω w·F wt(ti)+ω H·F HHt(ti)]/(ω FwH)(6)。
As preferably, the judge carrying out bridge operation security performance based on health monitoring data is as follows:
(1) Peak Intensity Method evaluation, evaluate with the compare of analysis that maximum, minimum peak set PV (i) of all kinds of Monitoring Data of health monitoring systems in △ T time interval carry out 3 levels with the bridge operation safety judgment threshold value system preset, the judge conclusion of Peak Intensity Method evaluation obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ";
In the evaluation process of Peak Intensity Method, according to the evaluation that specific bridge sets " calculated value ", " calibrating general value ", " safety limit " 3 levels carry out structure different performance situation respectively according to respective evaluation index threshold value, characterize difference and the degradation of actual loading response and theory state respectively, bridge actual loading responds and the contrast situation of normal safe driving conditions, the operation safety performance of bridge structure lay in situation;
(2) evaluation is become during high-speed rail bridge peak value based on health monitoring data
The comparative analysis of situation and statistical parameter of changing in being increased in time by all kinds of monitoring parameter peak value is evaluated, characterize bridge structure develop in time and produce change, deterioration situation; Become the evaluation same assessment method adopting 4 threshold value 5 intervals during peak value, comprise practical frequency, acceleration, dynamic stress, dynamic strain for the monitoring parameter becoming rating method during peak value; The judge conclusion becoming rating method during peak value obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ";
(3) high-speed rail bridge based on health monitoring data is totally evaluated
Based on " Peak Intensity Method evaluation ", " becoming evaluation during peak value ", propose overall deliberated index It, overall vibration deliberated index Id, damage field index Ip (i), respectively the operation safety of high-speed railway bridge is totally passed judgment on, overall vibration situation is passed judgment on, differentiate the damage field position that bridge is general.
Beneficial effect of the present invention is as follows: the high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data that the present invention proposes, by the sensor hardware device system of preferred arrangement robotization on high-speed rail bridge, realize the data acquisition of robotization, after rough handling, time-frequency domain process are carried out to measured data, carry out the judge of bridge operation security performance based on health monitoring data, and provide the suggestion of bridge maintenance Maintenance Assistant Decision-making.By the bridge monitoring of robotization with rationally evaluate, effectively can reduce the workload that bridge conventional sense is evaluated, overcome the difficulty that high ferro large bridge pipe is foster.Method of the present invention meets the actual demand of high-speed railway bridge detecting appraisal, promptly and accurately can react the security of high-speed railway bridge operation, for the safe operation of bullet train provides important leverage; The evaluation result of this method, also with the unification mutually of high-speed rail bridge conventional sense evaluation conclusion, is convenient to the practical application of system bridge management personnel; The basis of this method evaluation conclusion also proposes the decision recommendation of pointed bridge maintenance maintenance, is convenient to realize timely maintenance, Dynamic Maintenance, greatly improves the efficiency of bridge maintenance.
Accompanying drawing explanation
Fig. 1 is overall procedure schematic diagram of the present invention;
Fig. 2 is detailed process schematic diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, to above-mentioned being described in more detail with other technical characteristic and advantage of the present invention.
Based on a high ferro large bridge Reliable Evaluating Methods of Their Performance for health monitoring data, namely analyzing and processing is carried out to the signal data of health monitoring systems collection, and carry out the method for bridge security operative performance evaluation on this basis; Concrete step is as follows:
(1) based on the health monitoring optimizing the locations of the measuring points of assessment of performance
Quantity and the quality of bridge measured data is depended on based on the accuracy of the high ferro large bridge assessment of performance result of health monitoring data and reliability, for carrying out smoothly of ensureing that follow-up bridge performance evaluates, in bridge health monitoring system, first should carry out the preferred arrangement of health monitoring measuring point according to the feature of high-speed railway bridge and assessment of performance content and method thereof.
Health monitoring optimizing the locations of the measuring points based on assessment of performance is divided into two levels, and one is the optimization of content measurement; Next is the optimization of measuring point quantity; The principle optimized is guaranteed performance evaluation needs, and measuring point is fewer but better.
In the optimization of content measurement, should in conjunction with the design feature of bridge, specify bridge evaluating target, except being related to the evaluation project of bullet train operation security as except acceleration, the natural frequency of vibration, displacement amplitude etc. (the calibrating project related in " high-speed railway bridge operative performance calibrating regulation (trying) " (TG/GW209-2014)), also should consider the evaluation of special construction bridge structure component, as bearing power, rail temperature expansion and cotraction regulator, bar Suo Li, local fatigue stress etc.
In the optimization of measuring point quantity, adhere to the principle of " fewer but better ", measuring point is laid in the positions such as representativeness, key, least favorable value.
(2) health monitoring data rough handling
In the rough handling of high-speed railway bridge health monitoring data, first carry out the reliability assessment of signal data, and carry out the rough handlings such as the noise reduction of signal data.
1) reliability assessment of signal data: have no signal, signal sampling frequency, signal quantity peak value, signal intensity situation to carry out test and assessment to measured data, in time signal fault warning is carried out to the measured signal data do not met the demands, and reminds the corresponding project of inspection.
2) rough handling such as noise reduction of signal data: object is to eliminate because of environment RANDOM WIND, oscillatory load, the interference such as neighbourhood noise and the Monitoring Data signal noise, measuring point exceptional value etc. that cause.The rough handling of signal data is mainly denoising, deburring, abnormality value removing, elimination trend term etc.The method of signal transacting is mainly filtering, signal reconstruction etc.
3) rough handling method and flow process: the content such as acceleration, displacement amplitude, first with the low-pass filtering of 20 ~ 50Hz, then carry out 0 average value processing, carry out signal reconstruction if desired; The content such as vertical displacement, dynamic stress, first with the low-pass filtering of 20 ~ 50Hz, then carries out abnormality value removing process, carries out signal reconstruction if desired.
(3) Time Domain Processing of health monitoring data
1) peak value process:
For sample frequency higher than in 20Hz and above dynamic monitoring data process, in specified time interval △ T, extract maximum, the minimum peak D of each value max(ti), D min(ti), i.e. the result such as minimax acceleration, displacement amplitude, wheel rail force, as shown in formula (1).Follow-up data process will be applied to and bridge is evaluated.
D Max(ti)=Max{D(tx)}(1a)
D Min(ti)=Min{D(tx)}(1b)
In formula, D (tx) is bridge dynamic monitoring data sequence in the △ T cycle.
The time interval △ T of dynamic monitoring data process is according to following formula value:
ΔT=(L B+L T)/v+5(2)
In formula, L bfor overall length of bridge, unit is m; L tfor train length, unit is m; V is the conventional passage rate of train, and unit is m/s; The 5s increased is the time span redundance improving data.
For sample frequency lower than the plan static monitoring techniques data of the usual frequency of 20Hz at below 1Hz, its maximum, minimum peak S is recorded respectively equally with cycle regular hour △ T (this cycle according to test data type, can be chosen in the scope to a couple of days at several minutes flexibly) max(ti), S min(ti), as formula (3):
S Max(ti)=Max{S(tx)}(3a)
S Min(ti)=Min{S(tx)}(3b)
In formula, S (tx) is bridge plan static monitoring techniques data sequence in the △ T cycle.Remember that each quasi-peak value result is PV (i), with certain data layout store and for subsequent use.
2) the timely Forewarning Measures of peak value
In all kinds of actual measurement dynamic data △ T time, to be recorded as DS according to time interval end for subsequent use for extreme value.In evaluation processing procedure, consider special data processing, in maximal value record in △ T time interval, immediately with method spacer maximum value writing time that maximal value substitutes, so that ensure can instant alarming when occurring that maximum actual measurement value exceeds standard in △ T time interval.
3) peak value statistical treatment
After the process of dynamic data segmentation peak value, peak-data series in the △ T of gained, together with plan static monitoring techniques data sequence result PV (i), is carried out next step statistical study process.Statistics analysis of the inspected data process, for dissimilar data, carries out corresponding average Av with the different cycles to dynamic data segmentation peak value and plan static monitoring techniques data sequence pV(i), variances sigma vPVthe statistical study process of (i).Data after all kinds of statistical treatment can be applicable to bridge operation safety assessment, Monitoring Data fail-safe analysis etc.
4) variable quantity process
According to peak value and the statistical treatment of health monitoring data, by the mean value Av of current peak results PV (i) with corresponding data in the past pV(i-1) subtract each other, obtain " variable quantity " V of all kinds of Monitoring Data pVi (), as formula (4).Variable quantity will be applied to bridge operation safety assessment, Monitoring Data fail-safe analysis etc.
V PV(i)=PV(i)-A vPV(i-1)(4)
(4) data frequency domain process
In the frequency domain process of health monitoring dynamic data, adopt Fourier transform (FastFourierTransforms) simultaneously, ((Hilbert-HuangTransform) analyzes the reconstruction processing etc. of carrying out Modal Parameter Identification and time domain data to adopt wavelet analysis (WaveletTransforms) and HHT conversion.Remember that the frequency values data after 3 kinds of frequency transformations are respectively F fFT(ti), F wT(ti), F hHT(ti).
The difference of the frequency values after conversion process and last period average is respectively Δ F f, Δ F w, Δ F h.
ΔF F=F fft(ti)-F Av(i-1)(5a)
ΔF W=F wt(ti)-F Av(i-1)(5b)
ΔF H=F HHt(ti)-F Av(i-1)(5c)
Note F fFT(ti) fusion weight coefficient is ω f, its value is as shown in formula 6a ~ 6c.
&omega; F = 0.05 , i f &Delta;F F F A v ( i - 1 ) > 58 % - - - ( 6 a )
&omega; F = 1.0 , i f &Delta;F F F A v ( i - 1 ) &le; 20 % - - - ( 6 b )
&omega; F = 1.5 - 2.5 &Delta;F F F A v ( i - 1 ) , i f 20 % < &Delta;F F F A v ( i - 1 ) &le; 58 % - - - ( 6 c )
Equally, F wT(ti), F hHT(ti) fusion weight coefficient is ω w, ω h, its value is with formula 6.
Frequency resultant F after fusion w(ti) be shown below:
F W(ti)=[ω F·F FFt(ti)+ω w·F wt(ti)+ω H·F HHt(ti)]/(ω FwH)(7)
Adopt frequency domain data integration technology, the easy and advantage in time invarinat data of integrated application FFT, wavelet transformation have the advantage of higher rate respectively at the low-band signal at bridge structure, HHT converts advantage in non-stationary, Nonlinear harmonic oscillator.By comparison, the measure such as preferred, provide frequency domain data recognition success rate and precision.
(5) based on the large-scale high-speed rail bridge Peak Intensity Method evaluation of health monitoring data
Peak Intensity Method evaluation based on health monitoring data to be compared judge with the Indentification model in 4 threshold value 5 intervals of presetting with maximum, the minimum peak PV (i) of all kinds of Monitoring Data of health monitoring systems in △ T time interval, TH1 ~ TH4 is respectively default 4 threshold values from small to large, it is partitioned into 5 threshold intervals, and the concrete value of TH1 ~ TH4 is determined according to computational analysiss such as concrete bridge structure type and parameters.The judge conclusion of Peak Intensity Method evaluation obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ".Concrete Peak Intensity Method evaluation is as shown in table 1.
Vertical displacement amplitude surveyed by table 1 and calculated value contrasts evaluation form
Between area of interest Evaluation conclusion Deliberated index value V A1
PV(i)<TH1 Excellent 2
TH1≤PV(i)<TH2 Well 1
TH2≤PV(i)<TH3 Normally 0
TH3≤PV(i)≤TH4 Deterioration -1
PV(i)>TH4 Deterioration is serious -2
Based in the large-scale high-speed rail bridge Peak Intensity Method evaluation of health monitoring data, bridge operation security compares from 3 different sides evaluation.According to the evaluation that specific bridge sets " calculated value ", " calibrating general value ", " safety limit " 3 aspects carry out structure different performance situation respectively according to respective evaluation index threshold value.
Calculated value, i.e. the dynamic response end value of vehicle bridge coupling vibration calculating.Characterize difference and the degradation of the response of bridge actual loading and theory state.
Calibrating general value, refers in bridge normal operation process, for ensureing that all kinds of parameters that the good operative performance of bridge structure and Ride Quality of High-speed Train and passenger comfort set are as the limit value of vibration frequency, displacement amplitude, acceleration etc.Characterize the contrast situation of the response of bridge actual loading and normal safe driving conditions.
Safety limit, refers in bridge normal operation process, for ensureing bridge structure load-bearing safety, bullet train running safety and safety limit corresponding to all kinds of parameters that set.The operation security performance deposit situation of direct sign bridge structure.
At this, illustrate that the assessment method of all kinds of actual measurement parameter is as follows with the evaluation of the Peak Intensity Method of the vertical displacement amplitude of certain bridge health monitoring:
Remember the actual measurement vertical displacement amplitude peak A that the rough handling of row data and peak value process obtain into ti, reject without after the vertical displacement amplitude peak value of surveying during train passage by itself and corrected Calculation value A cicontrast is passed judgment on, and specifically passes judgment on interval with method in table 2, " deliberated index value V in table a1" be the parameter being applied to follow-up Comprehensive Assessment.
Vertical displacement amplitude surveyed by table 2 and calculated value contrasts evaluation form
Measured value/calculated value Evaluation conclusion Deliberated index value V A1
~<0.40 Vibrate minimum 2
0.40≤~<0.70 Vibrate less 1
0.70≤~<1.00 Normal vibration 0
1.00≤~≤1.20 Vibrate larger -1
~>1.20 Vibration significantly -2
Vertical displacement amplitude A tiwith normal limit A nicontrast evaluation method is with corrected Calculation value, and its deliberated index value is designated as V a1c.Vertical displacement amplitude measured value A tiwith safety limit A siwhether can carry out directly contrast evaluation to transfinite, corresponding judging quota value is respectively 0 ,-2.
(6) evaluation is become during high-speed rail bridge peak value based on health monitoring data
Becoming rating method during peak value based on health monitoring data is the comparative analysis assessment method of situation and statistical parameter of changing in being increased in time by all kinds of monitoring parameter peak value, the situation such as change, deterioration that sign bridge structure develops in time and produces.The evaluation same assessment method adopting 4 threshold value 5 intervals is become during peak value.Monitoring parameter for becoming rating method during peak value comprises practical frequency, acceleration, dynamic displacement, dynamic strain etc.
The judge conclusion becoming rating method during peak value obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ".
Same to survey vertical displacement amplitude, by vertical displacement amplitude actual measurement peak A tiin the monitoring periods of certain hour length, on average obtain vertical displacement amplitude average peak be designated as all actual measurement vertical displacement amplitude peak A in this monitoring periods tistatistical variance be designated as σ a.In the evaluation of vertical displacement amplitude variations amount, by the actual measurement vertical displacement amplitude A in current monitoring periods tiwith previous period from practical measurement mean value difference be designated as vertical displacement amplitude variations value Δ A i, vertical displacement amplitude variations value Δ A ibe σ with vertical displacement amplitude peak variance in last monitoring periods acontrast as variable quantity deliberated index, corresponding deliberated index value is designated as V a2.
According to the vertical displacement amplitude characteristic of high-speed railway large bridge, on the basis of vehicle bridge coupling vibration available data, and in conjunction with the variation range of actual bridge bridge vertical displacement amplitude, carry out health monitoring actual measurement vertical displacement amplitude assessment method in table 3.
The evaluation of vertical displacement amplitude variations surveyed by table 3
Vertical displacement amplitude variations value Evaluation conclusion Deliberated index value V A2
<0.8σ A Change minimum 2
0.8~1.2σ A Conventional change 1
1.2~2.0σ A Change bigger than normal 0
2.0~3.0σ A Larger change -1
>3.0σ A Marked change -2
(7) high-speed rail bridge based on health monitoring data is totally evaluated
1) overall deliberated index I t
For carrying out the evaluation of high ferro large bridge performance further on the whole, becoming on the basis of evaluation when Peak Intensity Method evaluation, peak value, building the overall judging quota of bridge performance further, the operation safety of high-speed railway bridge is totally passed judgment on.
The peak value deliberated index value that note high ferro span bridge health monitors all kinds of parameter is V si, becoming deliberated index value during peak value is V ai.The overall deliberated index I of bridge tas follows:
I t = 1 N t ( &Sigma;V s i + &Sigma;V a i ) - - - ( 8 )
N in above formula tfor the total number of correspondence whole " deliberated index value ".
Overall deliberated index I tthe threshold interval passed judgment on and judge conclusion are in table 4.The bridge state evaluation grade unification mutually that bridge rating specifies with " rule (trying) repaired by High-speed Railway Bridges tunnel buildings " (TG/GW114-2011).
The overall deliberated index I of table 4 tevaluation form
2) overall vibration deliberated index I d
More pay close attention to the problem of bridge vibration characteristic for high-speed railway large bridge, build overall vibration deliberated index I d, its overall vibration situation is passed judgment on.
It is V that note high ferro span bridge health monitors the peak value deliberated index value of all kinds of vibration monitoring parameters as acceleration, the natural frequency of vibration, displacement amplitude etc. dsi, becoming deliberated index value during peak value is V dai.It is as follows for bridge overall deliberated index:
I d = 1 N d ( &Sigma;V d s i + &Sigma;V d a i ) - - - ( 9 )
N in above formula dfor the total number of corresponding coupled vibration test parameter " deliberated index value ".
Overall deliberated index I dthe threshold interval passed judgment on and judge conclusion are in table 5.The bridge state evaluation grade unification mutually that bridge rating specifies with " rule (trying) repaired by High-speed Railway Bridges tunnel buildings " (TG/GW114-2011).
Table 5 overall vibration deliberated index Id evaluation form
Note: in table, evaluation interval value in Id interval can adjust according to specific bridge.
3) damage field index I p(i)
Become on the basis of evaluation when the evaluation of health monitoring data peaks and the peak value of high ferro large bridge, build the discriminant criterion I of bridge damnification position p, in order to differentiate the damage field position that bridge is general.
Note high ferro span bridge health monitoring sensor is mainly arranged in vertical bridge to diverse location xi place, and m cross section, is designated as S altogether i(xi), wherein (i, 1 ~ m), remember that the peak value deliberated index value of monitoring parameter on each cross section is V ssj, becoming deliberated index value during peak value is V saj.Bridge damnification zone index I pi () is as follows:
I p ( i ) = 1 N i ( &Sigma;V s s j + &Sigma;V s a j ) - - - ( 10 )
N in above formula ifor the total number of coupled vibration test parameter " deliberated index value " on corresponding i cross section.
Damage field index I pi threshold interval that () passes judgment on and judge conclusion are in table 6.Bridge state evaluation grade " AA, A1, B, C " unification mutually that bridge rating specifies with " rule (trying) repaired by High-speed Railway Bridges tunnel buildings " (TG/GW114-2011).
Table 6 damage field index I p(i) evaluation form
Note: in table, evaluation interval value in Ip interval can adjust according to specific bridge.
(8) decision-making supported by the dynamic pipe based on evaluation result
On the basis of high ferro span bridge health monitoring evaluation, the suggestion of dynamic Maintenance Decision making is proposed, manual detection should be started in time and detect judge targetedly according to the possible damage field of early warning when early warning " starts artificial special monitoring " in monitoring evaluation, timely problem and the disease solving discovery, prevents further expanding of disease.Evaluate of good performance in evaluation, can the proper extension conventional sense cycle.

Claims (6)

1. the high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data, it is characterized in that, comprise the following steps: the sensor hardware device systems of preferred arrangement robotization on high-speed rail bridge, realize the data acquisition of robotization, after preliminary noise reduction process, Time-domain Statistics process and frequency domain process are carried out successively to measured data, carry out the judge of bridge operation security performance based on health monitoring data.
2. a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data according to claim 1, it is characterized in that, the content measurement of preferred arrangement focuses on the monitoring of the special component of vibration monitoring of engineering structure, bearing power and rail temperature expansion and cotraction regulator, and measuring point quantity optimization is arranged in the position of controlling sections and least favorable value.
3. a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data according to claim 1 and 2, it is characterized in that, the preliminary noise reduction process of Monitoring Data is as follows:
No signal, signal sampling frequency, signal quantity peak value and signal intensity situation is had to carry out the reliability assessment of health monitoring data with data; And the dynamic datas such as acceleration are carried out to the low-pass filtering of 20 ~ 50Hz, carry out 0 average value processing, signal reconstruction process if desired.
4. a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data according to claim 3, it is characterized in that, the Time-domain Statistics process of measured data is as follows:
(1) peak value process:
For sample frequency higher than in 20Hz and above dynamic monitoring data process, in specified time interval △ T, extract maximum, the minimum peak D of each value max(ti), D min(ti), i.e. the result such as minimax acceleration, displacement amplitude, wheel rail force, as shown in formula (1):
D Max(ti)=Max{D(tx)}(1a)
D Min(ti)=Min{D(tx)}(1b)
In formula, D (tx) is bridge dynamic monitoring data sequence in the △ T cycle;
The time interval △ T of dynamic monitoring data process is according to following formula value:
ΔT=(L B+L T)/v+5(2)
In formula, L bfor overall length of bridge, unit is m; L tfor train length, unit is m; V is the conventional passage rate of train, and unit is m/s; The 5s increased is the time span redundance improving data;
For sample frequency lower than the plan static monitoring techniques data of the usual frequency of 20Hz at below 1Hz, record its maximum, minimum peak S respectively with cycle regular hour △ T equally max(ti), S min(ti), as formula (3):
S Max(ti)=Max{S(tx)}(3a)
S Min(ti)=Min{S(tx)}(3b)
In formula, S (tx) is that static monitoring techniques data sequence intended by △ T cycle bridge, remembers that each quasi-peak value result is PV (i), stores and for subsequent use with certain data layout;
(2) the timely Forewarning Measures of peak value
In all kinds of actual measurement dynamic data △ T time, to be recorded as DS according to time interval end for subsequent use for extreme value, in evaluation processing procedure, consider special data processing, in maximal value record in △ T time interval, immediately with method spacer maximum value writing time that maximal value substitutes, can instant alarming during to ensure to occur that maximum actual measurement value exceeds standard in △ T time interval;
(3) peak value statistical treatment
After the process of dynamic data segmentation peak value, by peak-data series in the △ T of gained together with plan static monitoring techniques DS result PV (i), carry out next step statistical study process, statistics analysis of the inspected data process, for dissimilar data, carries out corresponding average Av with the different cycles to dynamic data segmentation peak value and plan static monitoring techniques data sequence pV(i), variances sigma vPVthe statistical study process of (i);
(4) variable quantity process
According to peak value and the statistical treatment of health monitoring data, by the mean value Av of current peak results PV (i) with corresponding data in the past pV(i-1) subtract each other, obtain " variable quantity " V of all kinds of Monitoring Data pV(i), as formula (4):
V PV(i)=PV(i)-A vPV(i-1)(4)。
5. a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data according to claim 4, it is characterized in that, the frequency domain process of measured data is as follows:
Adopt Fourier transform simultaneously, adopt wavelet analysis and HHT transform analysis to carry out the reconstruction processing of Modal Parameter Identification and time domain data, remember that the frequency values data after 3 kinds of frequency transformations are respectively F fFT(ti), F wT(ti), F hHT(ti):
The difference of the frequency values after conversion process and last period average is respectively Δ F f, Δ F w, Δ F h.
AF F=F fft(ti)-F Av(i-1)(5a)
AF W=F wt(ti)-F Av(i-1)(5b)
AF H=F HHt(ti)-F Av(i-1)(5c)
Note F fFT(ti) fusion weight coefficient is ω f, its value is as shown in formula 6a ~ 6c:
&omega; F = 0.05 , i f &Delta;F F F A v ( i - 1 ) > 58 % - - - ( 6 a )
&omega; F = 1.0 , i f &Delta;F F F A v ( i - 1 ) &le; 20 % - - - ( 6 b )
&omega; F = 1.5 - 2.5 &Delta;F F F A v ( i - 1 ) , i f 20 % < &Delta;F F F A v ( i - 1 ) &le; 58 % - - - ( 6 c )
Equally, F wT(ti), F hHT(ti) fusion weight coefficient is ω w, ω h, its value is with formula 5;
Frequency resultant F after fusion w(ti) be shown below:
F W(ti)=[F p·F FFt(ti)+ω W·F wt(ti)+ω H·F HHt(ti)]/(ω FWH)(6)。
6. a kind of high ferro large bridge Reliable Evaluating Methods of Their Performance based on health monitoring data according to claim 1, it is characterized in that, the judge carrying out bridge operation security performance based on health monitoring data is as follows:
(1) Peak Intensity Method evaluation, evaluate with the compare of analysis that maximum, minimum peak set PV (i) of all kinds of Monitoring Data of health monitoring systems in △ T time interval carry out 3 levels with the bridge operation safety judgment threshold value system preset, the judge conclusion of Peak Intensity Method evaluation obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ";
In the evaluation process of Peak Intensity Method, according to the evaluation that specific bridge sets " calculated value ", " calibrating general value ", " safety limit " 3 levels carry out structure different performance situation respectively according to respective evaluation index threshold value, characterize difference and the degradation of actual loading response and theory state respectively, bridge actual loading responds and the contrast situation of normal safe driving conditions, the operation safety performance of bridge structure lay in situation;
(2) evaluation is become during high-speed rail bridge peak value based on health monitoring data
The comparative analysis of situation and statistical parameter of changing in being increased in time by all kinds of monitoring parameter peak value is evaluated, characterize bridge structure develop in time and produce change, deterioration situation; Become the evaluation same assessment method adopting 4 threshold value 5 intervals during peak value, comprise practical frequency, acceleration, dynamic stress, dynamic strain for the monitoring parameter becoming rating method during peak value; The judge conclusion becoming rating method during peak value obtains corresponding 5 kinds " evaluation conclusions " according to 5 different judge intervals, and indicates different " deliberated index value ";
(3) high-speed rail bridge based on health monitoring data is totally evaluated
Based on " Peak Intensity Method evaluation ", " becoming evaluation during peak value ", propose overall deliberated index It, overall vibration deliberated index Id, damage field index Ip (i), respectively the operation safety of high-speed railway bridge is totally passed judgment on, overall vibration situation is passed judgment on, differentiate the damage field position that bridge is general.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105718658A (en) * 2016-01-21 2016-06-29 上海巨一科技发展有限公司 Large-size bridge online evaluating system
CN106568557A (en) * 2016-10-31 2017-04-19 东南大学 High speed railway bridge vehicle-bridge vibration performance safety early warning method
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040237662A1 (en) * 2003-06-02 2004-12-02 Sayed Nassar Conveyor diagnostic system
CN101281117A (en) * 2008-05-29 2008-10-08 上海交通大学 Wide span rail traffic bridge damnification recognition method
CN102147597A (en) * 2010-02-10 2011-08-10 广州大学 Health monitoring system for structures of great building and bridge
CN202066527U (en) * 2011-03-06 2011-12-07 东北石油大学 Intelligent bridge health monitor based on Zigbee module
CN202433035U (en) * 2011-12-23 2012-09-12 西安迅腾科技有限责任公司 Bridge health monitoring data acquisition instrument with remote parameter configuration function
CN204086219U (en) * 2014-09-22 2015-01-07 东北大学 A kind of bridge health monitoring system
CN104660622A (en) * 2013-11-18 2015-05-27 田荣侠 A bridge structure health monitoring system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040237662A1 (en) * 2003-06-02 2004-12-02 Sayed Nassar Conveyor diagnostic system
CN101281117A (en) * 2008-05-29 2008-10-08 上海交通大学 Wide span rail traffic bridge damnification recognition method
CN102147597A (en) * 2010-02-10 2011-08-10 广州大学 Health monitoring system for structures of great building and bridge
CN202066527U (en) * 2011-03-06 2011-12-07 东北石油大学 Intelligent bridge health monitor based on Zigbee module
CN202433035U (en) * 2011-12-23 2012-09-12 西安迅腾科技有限责任公司 Bridge health monitoring data acquisition instrument with remote parameter configuration function
CN104660622A (en) * 2013-11-18 2015-05-27 田荣侠 A bridge structure health monitoring system
CN204086219U (en) * 2014-09-22 2015-01-07 东北大学 A kind of bridge health monitoring system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
周毅 等: "基于监测数据的跨海斜拉桥强/台风作用效应研究", 《土木工程学报》 *

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