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2013 icossar sa_sm_fb_upload 2013 icossar sa_sm_fb_upload Presentation Transcript

  • Structural identification of the cable-stayed bridgeof the ANCRiSST SHM benchmark problemSapienza University of Rome – StroNGER s.r.l.S. Arangio, S. Mannucci, F. Bontempiemail: stefania.arangio@uniroma1.it, franco.bontempi@uniroma1.itNew York, June19th 2013
  • 2/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionPart IConclusionsPart IIThe ANCRiSST benchmark problemTraditional ad soft computing approaches forstructural identification ad damage detectionOutlineProcessing of monitoring data withEnhanced Frequency Domain Decomposition and Bayesian neural networks
  • 3/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionPart IConclusionsPart IIThe ANCRIiST benchmark problemTraditional ad soft computing approaches forstructural identification ad damage detectionOutlineProcessing of monitoring data withEnhanced Frequency Domain Decomposition and Bayesian neural networks View slide
  • 4/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionThe ANCRiSST benchmark problem• Consortium of 20 research institutions• Established in 2002 with the purpose of:• assessing current progresses on smart materials and structures technology• Developing synergies that facilitate joint research projects that cannot easily carriedout by individual centersIn October 2011 they opened forresearchers in the SHM community abenchmark problem based on a realbridge: the TianjinYonghe bridgehttp://smc.hit.edu.cn/ View slide
  • 5/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiDescription of the TianjinYonghe bridgeTianjin Hangu25.15 99.85 260 99.85 25.15• Cable-stayed bridge• Opened to traffic since December 1987• After 19 years of operation damages were detected and the bridge wasretrofitted• A sophisticated SHM system has been designed and implemented by theResearch Center of Structural Health Monitoring and Control of the HarbinInstitute of TechnologyIntroduction
  • 6/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiStructural Health Monitoring SystemTianjin Hangu2515 5600 5885 5900 5600 5600 5900 5885 5600 25151 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)Uniaxial/biaxial accelerometersHygrothermographAnemometer1, 3, 5, 7, 9 11, 13 2, 4, 6, 8, 10, 12, 14During 2008:• Continuous monitoring system• 14 uniaxial accelerometers on the bridge deck (downward and upward)• On the top of the tower: 1 biaxial accelerometer; 1 anemometer; 1 temperaturesensordownward and upwardIntroduction
  • 7/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. Bontempidamaged areaDamage situation 1Cracks at the closure segmentat both side spansAugust 2008:2 damages are detectedIntroduction
  • 8/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiDamage situation 2Damaged piersIntroduction
  • 9/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiAvailable data setHealth condition Damaged condition• Time histories of the accelerationsrecorded at the 14 deck sensorson January 1st and January 17th 2008(registration of 1 h for 24 h )• Environmental information(wind, temperature)• Biaxial accelerations at the top of thetower• Time histories of the accelerationsrecorded at the same 14 deck sensorson July 30th 2008(registration of 1 h for 24 h)• Accelerations collected by field testingAugust 7th to 10th 2008Introduction
  • 10/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionPart IConclusionsPart IIThe ANCRIiST benchmark problemTraditional ad soft computing approaches forstructural identification ad damage detectionOutlineProcessing of monitoring data withEnhanced Frequency Domain Decomposition and Bayesian neural networks
  • 11/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIMethods for structural identification ad damage detectionInput – outputtechniques• The structure has to be artificially excitedand in case of large structures it is not alwayspossible• The operation of the structure has to beinterruptedOnly outputTechniques• The excitation is given by the ambientvibration• Measurements in real operationalconditionsTraditionalmethodsSoft computingmethods• Time domainapproaches• Frequencydomainapproaches• Neuralnetworks• Geneticalgorithms• Fuzzy Logic
  • 12/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIEnhanced Frequency Domain Decomposition• Data collection and signal preprocessing• Construction of the the Power SpectralDensity matrix (PSD)• Whelch averaged modified periodgram method• 50 % overlapping and periodic Hamming windowing• Singular Value Decomposition (SVD) of the PSD• Identification of modal frequencies and mode shapes• Evaluation of the damping
  • 13/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIINonlinear feed-forward basis functions( ) ++= ∑∑ ==)2(01)1(0)1(1)2(, kDjjijiMjkjk bbxwgwfy wx∑=+=Dijijij bxwa1)1(0)1(( )kk afy =∑=+=Mjkjkjk bzwa1)2(0)2(( )jj agz =NEURAL NETWORKMODEL( ) ( )= ∑=Mjjjwfy1, xwx φoutput unitshidden unitsactivationsweightsbiasNeural network model
  • 14/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiTraditional learningtijtijtij www ∆+= −η1ijijWEW∂∂−=∆η Learning rateWeights updatingMinimization of asum of squares error functionModel fitting is obtained by modifications of the coefficients wt = correct valuey = network value( ) ++= ∑∑ ==)2(01)1(0)1(1)2(, kDjjijiMjkjk bbxwgwfy wxGradient descent algorithm [traingd]Conjugate gradient algorithm [traincg]Quasi – Newton algorithm [trainbfg]Levenberg – Marquardt algorithm [trainlm]( ){ } ∑∑∑== =+−=WiiNnoNttntn wxytE121 122;21 αw
  • 15/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiProbabilistic interpretation( ){ } −−∝ 2;2exp),,,( ww nn xytMxtpββ1) Probabilistic interpretationof the network output2) Probability modelfor the prediction error);( wxyt −=εGaussian µ = 0σD2 = 1/β3) Predictive PDFThe outputapproximates theconditional average ofthe target datahyperparameter4) Prior PDF( )−=22exp1),( wZMwpWαααGaussian µ = 0σw2 = 1/αhyperparameter
  • 16/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiNetwork learning as inference16=),,( MwDp β( )( ){ }−− ∑∑=NnNtnttnDoxytZ 12;2exp1wββLikelihood( ) ( )( )MDpMwpMwDpDwp,,,,,)M,,,(βααββα =Bayestheoremevidencepriorxlikelihoodposterior =( ) ( ){ } ∑∑∑ ==+−=WiiNnNtnttn wxytwEo12122;2αβw( ) ( ){ }∑∑=−=−NnNtnttnoxytMwDp12;2,,log wββ( ) ∑==−WiiwMwp122,logααmax (posterior) = min (negative log posterior)=− )M,,,(log βαDwp ( ) ( )=−− MwpMwDp ,log,,log αβ( )−=22exp1),( wZMwpWαααPrior( )Mwp( )MDwp ,
  • ( )( )( ){ }−−= ∑∑=NnNtnttnDoxytZMwDp12;2exp1,, wβββ( )−=22exp1),( wZMwpWααα( ){ }∑ ∑ ∑= =+−=−NnNtWiinttnowxytDwp1 122;2),,,(logαββαwMDATA PRE- PROCESSINGOUTPUTNETWORK ARCHITECTUREn°INPUTn°UNIT IN THE HIDDEN LAYERSPOSTERIOR: BAYES’ THEOREM( ) ( )( )MDpMwpMwDpDwp,,,,,),,,(βααββα =Mw = wMAP?yesINFERENCE OF NEW DATADATA POST PROCESSINGPROBABILISTIC MODEL• NOISE MODEL• PREDICTIVE PDF• LIKELIHOOD• PRIOR),,,( Mxtp βw( )MwDp ,, β),( Mwp αOPTIMIZATION(MINIMUM OF )),,,(log MβαDwp−noINPUTED EW( )Mwp( )MDwp ,1) Model fitting
  • 18/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiBayesian techniques for neural networks• Level 1 Model fitting: inferring the model parameters given themodel and the data• Level 2 Optimization of the hyperparameters α and β• Level 3 Model class selection: optimal model complexity• Level 4 Automatic relevance determination (ARD):evaluation of the relative importance of different inputsNetwork learning as inference (model fitting) is only one level inwhich Bayesian inference can be applied in the neural networkfieldHierarchical multi-level approachPartI
  • 19POSTERIOR FOR α, βTRAINING: OPTIMIZATIONw = wMAP??( ) ( )DMEVDMEV ii 1−>INFERENCE OF NEW DATACHOOSE MODEL Mi-1?POSTERIOR FOR Miα, β = αMP, βMPDATA PRE- PROCESSINGOUTPUTNETWORK MODEL MiN HIDDEN = iN INPUT = kPOSTERIOR FOR wyesDATA POST PROCESSINGPROBABILISTIC MODELnoINPUTCHOOSE INITIAL α, βINITIALIZE WEIGHTS wRE-ESTIMATION OF α, βyesnoWγ ≈yesnoi= i+1is α1,…,αk‘very large’?k= k-1yesno( ) ( )( )MDpMwpMwDpDwp,,,,,),,,(βααββα =M1st levelModel fitting
  • 20POSTERIOR FOR α, βTRAINING: OPTIMIZATIONw = wMAP??( ) ( )DMEVDMEV ii 1−>INFERENCE OF NEW DATACHOOSE MODEL Mi-1?POSTERIOR FOR Miα, β = αMP, βMPDATA PRE- PROCESSINGOUTPUTNETWORK MODEL MiN HIDDEN = iN INPUT = kPOSTERIOR FOR wyesDATA POST PROCESSINGPROBABILISTIC MODELnoINPUTCHOOSE INITIAL α, βINITIALIZE WEIGHTS wRE-ESTIMATION OF α, βyesnoWγ ≈yesnoi= i+1is α1,…,αk‘very large’?k= k-1yesno( ) ( )( )MDpMwpMwDpDwp,,,,,),,,(βααββα =M1st levelModel fitting2nd levelEvaluating the hyperparameters α, β( ) ( )( )MDpMpMDpDpβαβαβα,,,),,( =M
  • 21/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIssues in neural network design: selection of the optimal modelRULES OF THUMBS-…between the input layer size and the outputlayer size (Blum, 1992)- (Software Neuroshell, 2000)- (Berry and Lynoff, 1997)- n = dimension needed to capture 70-80% of thevariance(Boger and Guterman, 1997)OPTIMAL NUMBER OF UNITS)(32oI NNn +=INn ⋅<2examplesNn ⋅<301They aren’t rigorous methodsINPUTLAYEROUTPUTLAYERHIDDENLAYERSPartI
  • 22POSTERIOR FOR α, βTRAINING: OPTIMIZATIONw = wMAP??( ) ( )DMEVDMEV ii 1−>INFERENCE OF NEW DATACHOOSE MODEL Mi-1?POSTERIOR FOR Miα, β = αMP, βMPDATA PRE- PROCESSINGOUTPUTNETWORK MODEL MiN HIDDEN = iN INPUT = kPOSTERIOR FOR wyesDATA POST PROCESSINGPROBABILISTIC MODELnoINPUTCHOOSE INITIAL α, βINITIALIZE WEIGHTS wRE-ESTIMATION OF α, βyesnoWγ ≈yesnoi= i+1is α1,…,αk‘very large’?k= k-1yesno( ) ( )( )MDpMwpMwDpDwp,,,,,),,,(βααββα =M1st levelModel fitting2nd levelEvaluating the hyperparameters α, β3rd levelModel class selection( ) ( )MpMDpDMp ∝)(prior = constantevidence( ) ( )( )MDpMpMDpDpβαβαβα,,,),,( =M
  • POSTERIOR FOR α, βTRAINING: OPTIMIZATIONw = wMAP??( ) ( )DMEVDMEV ii 1−>INFERENCE OF NEW DATACHOOSE MODEL Mi-1?POSTERIOR FOR Miα, β = αMP, βMPDATA PRE- PROCESSINGOUTPUTNETWORK MODEL MiN HIDDEN = iN INPUT = kPOSTERIOR FOR wyesDATA POST PROCESSINGPROBABILISTIC MODELnoINPUTCHOOSE INITIAL α, βINITIALIZE WEIGHTS wRE-ESTIMATION OF α, βyesnoWγ ≈yesnoi= i+1is α1,…,αk‘very large’?k= k-1yesno( ) ( )( )MDpMwpMwDpDwp,,,,,),,,(βααββα =M1st levelModel fitting2nd levelEvaluating the hyperparameters α, β3rd levelModel class selection( ) ( )MpMDpDMp ∝)(prior = constantevidenceis α1,…,αk‘very large’?4th levelAutomatic Relevance Determination( ) ( )( )MDpMpMDpDpβαβαβα,,,),,( =M
  • 24/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionPart IConclusionsPart IIThe ANCRIiST benchmark problemTraditional ad soft computing approaches forstructural identification ad damage detectionOutlineProcessing of monitoring data withEnhanced Frequency Domain Decomposition and Bayesian neural networks
  • 25/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. Bontempi00,20,40,60 0,5 1 1,5 2 2,5 3H6H11H15H17H19H21SingularValues(health)f [Hz]00,10,20,30 0,5 1 1,5 2AverageSingularValues(health)f [Hz]EFDD: Singular Values Decomposition (undamaged)PartII
  • 26/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. Bontempi00,511,520 0,5 1 1,5 2AverageSingularValues(damaged)f [Hz]00,511,520 0,5 1 1,5 2 2,5 3H6H9H12H15H18H20H22H23H24EFDD: Singular Values Decomposition (damaged)PartII
  • 27/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiComparison of the mode shapesPartII
  • 28/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIProcedure for neural network trainingtime history of theacceleration recorded atsensor #Structural systemAmbient excitation1+−dtf 2−tf tf1−tf 1+tfTraining of the neuralnetwork model inundamaged condition2+tfTest of the trained neuralnetwork model on a new timehistory
  • 29/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIINeural network based damage detection strategy14 groups of networks have been created(one for each measurement point e one for each hour of measurements)14 (points) x24 (hours) = 336 neural network modelsTianjin Hangu1 (3) 2 (4) 3 (5) 7 (9) 9 (10) 11 (12) 13 (14)accelerometers
  • 30/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIDetection of anomaliesIf ∆e ≈ 0the structure is considered as undamagedIf ∆e is large an anomaly is detected
  • 31/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIDamaged areaError in the approximation of the accelerations in the undamaged sectionsTraining UndamagedDamage detectionTianjin Hangu
  • 32/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIBayesian model class selectionThe most plausible class can be obtained applying Bayes’ Theorem:( ) ( )( , ) |j jjp M D p D M p M∝M Mprior = costevidenceprovided by DThe various model can be compared by evaluating their evidenceThe chosen model has 3 hidden units:N hidden units 1 2 3 4 5evidence 20756 22603 24922 21944 23240
  • 33/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIBAYESIANMODELSELECTION
  • 34/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIError in the approximation of the undamaged conditionsdownriverupriver∆e at the various locationsData for training: January 1st 2008 (H1 to H24)Data for testing: January 17th 2008 (H1 to H24)Undamaged conditions
  • 35/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIError in the approximation of the damaged conditions∆e at the various locationsData for training: January 1st 2008 (H1 to H24)Data for testing: July 30th 2008 (H1 to H24Damaged conditions!
  • 36/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiPartIIDifference of the errorsThe difference of error in the approximation suggests the presence of structuralanomalies around sensor #10
  • 37/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiIntroductionPart IConclusionsPart IIThe ANCRIiST benchmark problemDescription of the bridge and available monitoring dataOutlineNeural network based damage detection strategyResults
  • 38/38PartIPartIIConclusionsIntroductionSTRUCTURAL IDENTIFICATION OF THE CABLE STAYED BRIDGEOF THE ANCRISST SHM BENCHMARK PROBLEMS. Arangio, S. Mannucci F. BontempiConclusionsIn the Performance based Design framework, monitoring is animportant tool for the verification of the accomplishment of theexpected performance during the entire life cycleDifferent approaches for processing monitoring data exist.A traditional approach (Enhanced Frequency DomainDecomposition) and a soft computing approach (neural networks)have been applied on the same data coming from the bridge of theANCRiSST SHM benchmark problem and both methods detectedthe occurrence of an anomaly.This work shows that the use of different methods is very important forthe cross validation of the obtained resultsThe current work is focused on the development of methods for thelocalization of the detected damageConclusions
  • POSTERIOR FOR α, βTRAINING: OPTIMIZATIONw = wMAP??( ) ( )DMEVDMEV ii 1−>INFERENCE OF NEW DATACHOOSE MODEL Mi-1?POSTERIOR FOR Miα, β = αMP, βMPDATA PRE- PROCESSINGOUTPUTNETWORK MODEL MiN HIDDEN = iN INPUT = kPOSTERIOR FOR wyesDATA POST PROCESSINGPROBABILISTIC MODELnoINPUTCHOOSE INITIAL α, βINITIALIZE WEIGHTS wRE-ESTIMATION OF α, βyesnoWγ ≈yesnoi= i+1is α1,…,αk‘very large’?k= k-1yesnoemail: stefania.arangio@uniroma1.itstefania.arangio@stronger2012.comThis research was partially supported by StroNGER s.r.l. fromthe fund “FILAS - POR FESR LAZIO 2007/2013 - Support forthe research spin off”.