Download - Feng Handouts

7/28/2019 Feng Handouts

1/16

10/5/20

Structural Health Monitoring and

Diagnostic Technology

Maria Q. FengChancellors Professor

Department of Civil & Environmental Engineering

Director, Center for Advanced Monitoring and Damage Inspection

University of California, Irvine Courtesy of Business Week

Vulnerable Highway BridgesMore than a quarter of the bridges nationwide are structurally deficient.

Courtesy of LA Times, New York Times,, FHWA.ASCE

3

How Can Structural Health Monitoring Help?

4

Current Practice - Visual, Periodic Inspection

Cannot timely detect problems Difficult to detect invisible damage

Future Practice - Incorporating Structural Health Monitoring

Continuous monitoring to identify hot spots in real time Condition-based inspection focusing on hot spots

Integration of Real-Time Global Monitoringwith Targeted Local Inspection

Continuous Monitoring for Real-Time Assessment of GlobalStructural Integrity and Identification of Hot Spots

Condition-Based NDEInspection of Targeted

Hot Spots

Where Are We Now?


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We Need Better Sensors

SolarPenal

DC-5V

Pianowire

Torecorder

+-

Bellows

L.V.D.T

DisplacementMeter

StrainGauge

SignalConditioner

TSA-20

+-

Wall

Soil P ressureSensor

Accelerometer

We Need Tools to Interpret and Use the Data

Bridge Site

UCI Campus

Wireless Real-Time DataAcquisition System

Contents

9

Recent Advances in Sensors Moir fringe-based fiber optic accelerometer Wireless sensor network Vision-based remote displacement sensor Distributed fiber optic strain sensor Piezoelectric Impedance Sensor Microwave imaging system Integrated scour monitoring system

Diagnostics and Prognostics Tools Case Study #1: Post-event damage assessment

Case Study #2: Long-term structural health monitoring

Case Study #3: Integrated NDE of concrete bridge decks

Uniquely Suited for MonitoringLarge-Size Civil Infrastructure System

in Harsh Environment

Sensing Principle: moir fringe

Unique Features:High resolution and largemeasurementrange, uniquely suited foraccuratemeasurement of both ambient micro-vibration and strong motion.

Immunity to electromagneticinterference and lightning strikes,safe to use in explosion-proneenvironments.

Excellent low-frequency performance

suited for monitoring long-periodstructures.

Moir Fringe-Based Fiber Optic Accelerometer

10

fixedgrating

movinggrating

D

Intensity

y

time

Intensity

d

txts

d

txts

)(2cos)(

)(2sin)(

2

1

x(t) : displacementAcceler atio n

D/4Changing the direction

x(t)

11 12

Continuous Monitoring of CalIT2 Building

Y- direction

Optical fiber accelerometer

Sokus hinco.

Z-direction

Y- direction

Optical fiber accelerometer

Sokus hinco.

- direction

Y- direction

Sokus hinco.

- direction

Fiber Optic Accelerometer

Conventional Accelerometer


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Beam

Columns

Base

Ch1

Ch2Ch3

Ch4

Ch5

FOA

FOA

Seismic Shaking Table Test

13

Conventional

Acceler ometer Acc.

(g)

14

Measurement of Bridge Vibration

Hpwamyung Bridge, a prestressed concrete girder cable bridge

Main Span: 500m, Side Span: 115m x 2, including ramps: 1.039kmTo be completed in March, 2013

WirelessSensorNetwork

HwamyungCablestayedBridge:underConstruction

US:M.Shinozuka(UCI)

Korea: J.T.Kim,S.W.Shin(PKNU); C.B.Yun(KAIST)

US:M.Shinozuka(UCI)

Korea: J.T.Kim,S.W.Shin(PKNU); C.B.Yun(KAIST)

(onHwamyungBridgeinBusan,Korea)

MultiWirelessTechnologies: WiFi/Xstream/XBee/Eco

Supportvariousnetworktopologies

Supportrealtimemonitoringsystem

EstablishmentServerandWebbasedSHMmethod

MultiWirelessTechnologies: WiFi/Xstream/XBee/Eco

Supportvariousnetworktopologies

Supportrealtimemonitoringsystem

EstablishmentServerandWebbasedSHMmethod

WirelessCommunicationUnit(Roocas)WirelessCommunicationUnit(Roocas)

Busan

BLC02

Cable:4gophers Deck:7gophers Pylon:2gophers Total:13gophers

Gimhae

BRC17

WirelssTRX

SensingUnit

BaseStation

BRC05

BRC12

201 209 203 204 205 206 208 207

211 210

Daisychainconnectionupto120nodes

LownoiseMEMSaccelerometers

Supporttoconnectcommercialaccelerometers

Builtintilt/humidity/temperaturesensors

SensingUnit(Gopher)SensingUnit(Gopher)

Sensor Deployment

Cablenode

Gopher

Roocas

Gopher

Roocas

Gopher

RoocasAnemometer

Weblink:http://tchien.eng.uci.edu/Hawmyung/bridge.html

Measureddataaresaved/sent toUCIserver

WebloginusingPCandMobile

DataLoggingatUCI WebbasedMonitoring

RealTimeRemoteMonitoring:TemporaryConnection

RemoteMonitoringFramework

*HSDPA:HighSpeedDownlinkPacketAssess(3.5G)


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FEModelandModalExtraction

GimhaeBusan

115m270m115m

BLC02

0 100 200 300 400 500 600-0.1

-0.05

0

0.05

0.1

Time(s)

Acceleration(g)

0.279Hz

0.4134Hz

CableMonitoring(BLC02)

1st VerticalDirection 1st LateralDirection

Remote non-contact measurement by using low-cost video camera

Vision-Based Remote Displacement Sensor

20

Measurement wt and w/o Target Panel

21

OC Matching

Robust Against Obstacles and Lighting Change

yx IIif

otherwise

N

II

cxy

1tan

Robust Image Processing

Dark

0

12

345

6

7

8

910 1112 13

14

15

Light

Lighting Change

Light

Dark

22

Measured Displacement

- Excellent performance w/o target panel in the dark Time: 11:00 AM, 12/15/2009

Time: 5:15 PM, 12/15/2009

23 24

0

50

100

150

200

0 1 2 3 4 5

Frequency,Hz

PSD,mm2/H

0

50

100

150

200

0 1 2 3 4 5

Frequency,Hz

PSD,mm2/Hz

0

50

100

150

200

0 1 2 3 4 5

Frequency,Hz

PSD,mm2/Hz

0

50

100

150

200

0 1 2 3 4 5

Frequency,Hz

PSD,mm2/H

-50

-25

0

25

50

0 10 20 30 40

Time, sec

Displacement,mm

-50

-25

0

25

50

0 10 20 30 40

Time, sec

Displacement,mmVision-based system Vision-based system

Vision-based system Vision-based system

LVDT LVDT

Displacements by Vision-Based Sensor and LVDT

-50

-25

0

25

50

0 10 20 30 40

Time, sec

Displacement,mm

-50

-25

0

25

50

0 10 20 30 40

Time,sec

Displacement,mmLVDT LVDT

Great Hanshin-Awajiearthquake

Western Tottori earthquake

Seismic ShakingTable Test


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25

Remote Monitoring ofBridge Deformation

Distributed Fiber Optic Strain SensorPrinciple:

Brillouin scattering

Innovation: Stimulated Brillouinscattering - Increasingspatial resolution by 10times

Applications: Crack detection Long-distance

distributed strainmeasurement

26

Stimulated Brillouin ScatteringModulatedContinuous

(probe)

ModulatedContinuous

(pump)

[Hz] -B

Detection of Cracks in Fiber Reinforced Concrete

Opticalfiber

Position[m]

- 2. 0 - 1. 5 - 1. 0 - 0. 5 0 .0 0 .5 1. 0 1 .5 2. 0

Strain[10-6]

-200

0

200

400

600

800

1000

1200

Measurement

Analysis

Position [m]

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Strain[10-6]

-200

0

200

400

600

800

1000

1200

Measurement

Analysis

60 kN

Position [m]

- 2. 0 - 1.5 - 1. 0 - 0. 5 0. 0 0 .5 1. 0 1. 5 2. 0

Strain[10-6]

-200

0

200

400

600

800

1000

1200

Measurement

Analysis

70 kN

P/2 P/2

27

5 kN

70 kN

28

Long-Term Monitoring of Bridge

LevelSurvey

OFS

Deflections by optical fiber sensor vs. level survey

Position [m]0 5 10 15 20 25 30

Deflection[mm]

-8

-6

-4

-2

0

2

4

6

8

Measurementby OFS

Levelsurvey

MeasurementRegion

Opticalfiber

29

Loosened

Bolt

2.5 2.55 2.6 2.65 2.7 2.75

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

Freqeuncy (Hz)

RealImpedance(Ohm)

intact

loose 1/2 rotation

loose 1 rotation

retighten

PZT (5x5x0.05)

46

17

10

all dimensions in cm

Piezoelectric Impedance Sensor(C.B. Yun, KAIST, Korea)

Samseung Bridge

All dimensions in cm

Reference-Free Damage Detection Using Dual PZTs(H. Sohn, KAIST, Korea)


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PZT A PZT b

Signal AbS0 A0Signal Ab A0/S0 S0/A0

BB

PZT a PZT B

Signal Ba

BB

S0 A0Signal Ba A0/S0S0/A0

Difference

(Signal Ab- Ba)

damage

Welded zones between stiffener and web/flange Microwave ImagingMicrowave Image ofEf(xf,y f,zf) at rf (x f,y f,zf)

n m l k TkleRmneN

n

N

m V

N

l

N

k Tkl

jk

fTkl

Rmn

jk

objfRmnff dVe

Ie

IIE1 1 1 1 0

||

0

||

||4)(

||4)()(

00

rr

r

rr

rr

rrrr

Rr

R

R

RTsRTsRTs

RTsRTsRTs

RTsRTsRTs

TtTTff

I

I

I

EEE

EEE

EEE

IIIEM

L

MOMM

L

L

L 2

1

11,11,11,

11,11,11,

11,11,11,

21)(r

32

Use Antenna Array

Focusing

Fast Measurement

Detection of Concrete Voids

Switch Box Antenna Array

Network

Analyzer

33

Experimental vs. Numerical Results

x

y

0.2m

ReconstructedArea AntennaArray

Concrete Block

0.08 m

0.04 m

0.0

2m

0.02 m

0.3m

0.00 0.02 0.04 0.06 0.08

x (m)

-0.10

-0.08

-0.06

-0.04

-0.02

-0.00

0.02

0.04

0.06

0.08

0.10

y(m)

Experimental Numerical

0.00 0.02 0.04 0.06 0.08

x (m)

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

y(m)

Multi-Frequency Technique

- 100 - 80 - 60 - 40 - 20 0

X (mm)

-100

-80

-60

-40

-20

0

20

40

60

80

100

Y(mm)

- 100 - 80 - 60 - 40 - 20 0

X (mm)

-100

-80

-60

-40

-20

0

20

40

60

80

100

Y(mm)

Using Single-Frequency

(5.2GHz)

Using Multi-Frequency

(4.6, 4.8, 5.0, 5.2GHz)

Handheld Real-TimeMicrowave Imaging Device

Concrete Column

Steel RebarFRP Jacket

Focusing

Locate, in real time, invisible defects

Portable & lightweight

Easy operation requiring no training

36


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37

Inspection of FRP-Wrapped Concrete

Corroded rebar

Uncorroded rebar

Non-Destructive Detectionof Corrosion

38

Integrated Scour Monitoring System(KC Chang, NCEER, Taiwan)

39

Field Scour Monitoring System

40

Rechargeable battery

Wireless Sensing Network (WSN) Nodes(for pier to pier transmission)

IP Camera Server

Scour monitoring pipe

Field Bridge Scour Monitoring

41

WiFi

Freeway No.1 Bridge

HouLi Toll Station

Freeway No.3 Bridge

WiFi

FreewayNo.3 Bridge

FreewayNo.1 Bridge

HouLi Toll Station

Taichung, Taiwan

The measured data were transmitted through

Wi-Fi communication system which is end connected

with wired network, then the data was sent to the

remote server database in the office.

Laboratory Experiments

42

Verification on concepts of the field instrumentations

Simulation on varies sour conditions to verify the monitoring system

Experimental study on bridge collapse due to scouring and the associated numericalsimulations.

Experiment flume


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Experiment Measurements

43

Tilt meter

Accelerometer

Vibration sensors

Field Bridge Scour Monitoring Website

44

Information from

Central Weather Bureau

Location ofinstrumented bridges

Real-time

measured information

Predictive information

Graphic information(water level, flow speed

and scour depth)

Video Information

Numerical S imulationInformation

Update messages

Contents

45

Recent Advances in Sensors Moir fringe-based fiber optic accelerometer Wireless sensor network Vision-based remote displacement sensor Distributed fiber optic strain sensor Piezoelectric Impedance Sensor Microwave imaging system Integrated scour monitoring system

Diagnostics and Prognostics Tools Case Study #1: Post-event damage assessment

Case Study #2: Long-term structural health monitoring

Case Study #3: Integrated NDE of concrete bridge decks

Use of Sensor Data for Decision Support

Long-TermStructural Health Monitoring

Sensor Data from Instrumented Structure

Disaster MitigationSpeedy Recovery

Intelligent Maintenance

Post-EventDamage Assessment

Residual C apacity Estimation

46

Case Study #1 Post-Event Damage Assessment

Goals: To Detect, Locate, and Quantify Structural Damage

To Predict Remaining Capacity of Bridges

To Assist Decisions for Post-Event Disaster Mitigation and Speedy

Recovering

Damage Index: Structural Stiffness and Damping Identified from Structural Vibration

(Ambient, Seismic) Measurement

Approaches: A Variety of System Identification Techniques for Damage Assessment

Link Damage to Remaining Capacity

Realistic Experimental Validation

47

Damage Assessment Methods andExperimental Validation

Seismic shaking table tests of a 3-bentconcrete bridge

Progressive damage caused byincreasing ground motion intensity

Damage assessment by differentsystem identification techniques:

1. Bayesian UpdatingExtended Kalman FilterCentral Difference Filter

2. Optimization-Based ApproachesQuasi-NewtonEvolutionary Algorithm

3. Nonlinear Damping

4. Neural Networks 48


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Bayesian Upadating

Xis the states,Zthe observations, Uthe deterministic input, Wtheprocess noise, and Vthe measurement noise

Xk

f(Xk1

,Uk1

,Wk1

;)

Z

k h(X

k,U

k,V

k;)

f: state transfer function

h: observation function

p(Xk|Z

1:k)

p(Zk|X

k)p(X

k|Z

1:k1)

p(Zk|x

k)p(x

k|Z

1:k1)dx

k

By Bayesian Theorem, the recursive Bayesian filtering at time step k

p(Xk|Z

1:k)

p(Zk|X

k) p(X

k|x

k1)p(x

k1|Z

1:k1)dx

k1p(Z

k|x

k)p(x

k|x

k1)p(x

k1|Z

1:k1)dx

k1dx

k

a posteriori

knowledgeDeduction

byhDeduction

byfa priori

knowledge

49

1 2 L l T

i

K

i

A

Ki

D

Parameter Estimation

X

k f(X

k1,U

k1,W

k1;)

Z

k h(X

k

,Uk

,Vk

;)

F: state transfer function

H: observation function

fand h are augmented by parameter , element stiffness (Structural Condition)

For SHM purpose, we concerns about the changes ofand its distribution

Xk,

k T

F( Xk1

,k1

T

,Uk1

,Wk1

)

Zk

H( Xk,

k T

,Uk,V

k)

1 1 1( , , ; )k k k k X f X U W

1 1 1( , , )

k k k k f U W

F: k k1 Wk1

; I

H: ( , , ; )k k k k Z h X U V Propagate a distribution

through a nonlinear system

Xk,

k

T

Extended State

50

Extended Kalman Filter & Central Difference Filter

Extended Kalman Filter projection

Central Difference Filter projection

Xk,

k T

X

k1,

k1 T

Z

k

51

Seismic Shaking Table Test

52

Parameter Evolution(by Central Difference Filter)

53

WN-1

Identified Stiffness Reduction(by Central Difference Filter)

54

Identification results based on vibration data are consistent with observeddamage sequence based on embedded strain sensors:

Bent 1 yields Bent 3 yields Bent 2 yields Bent 3 buckles


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55

Identified Instantaneous Stiffness Reduction(by Extended Kalman Filter)

T-14

T-15

T-19

As P GA increases, the bridge column stiffness decreases.

T-13Hysteretic Loops in Low-Amplitude Vibration

Secant Stiffness

Largest Excursion Point

Pre- and Post-Event Equivalent Linear-Time-Invariant System56

Identification of Post-Event Stiffness

57

As PGAincreases,the bridgecolumnstiffnessdecreases.

Nonlinear Damping Analysis

58

As PGA increases, viscous dampingdecreases and friction damping increases.

Results by Different Identification Methods

59

Idealized elasto-plastic pushover curve of Bent-1

Use of Identified Stiffness for Capacity Estimation

60


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Use of Identified Stiffness for Capacity Estimation

61

DECISION: Open Partially Open Closed

Use of Sensor Data for Decision Support

Long-TermStructural Health Monitoring

Sensor Data from Instrumented Structure

Disaster MitigationSpeedy Recovery


Post-EventDamage Assessment

Residual C apacity Estimation

62

63

Case Study #2: Long-Term Structural Health Monitoring

64

Issues with Field Implementation

Soil-StructureInteractionAmplitude

Dependency

Modeling ofTraffic Excitation

Long-TermMonitoring Data

Vehicle-Structure

InteractionLong-Term

Monitoring Data

Bridge Doctor forDecision Support

0 10 20 30 40 50-20

-10

0

10

20

Time (sec)

Acceleration(cm/sec2)

0 10 20 30 40 50-10

-5

0

5

10

Time (sec)

Acceleration(cm

/sec2)

0 10 20 30 40 50-10

-5

0

5

10

Time (sec)

Acceleration(cm/sec2)

65Earthquake Records, Mw=4.9 Yucaipa Earthquake, June 16 2005

43 accelerometers

Scaled

Power

SpectralDensity Identifiedmodalfrequenciesanddampingratios

Yuca ipa SanClemente ChinoHills InglewoodMode f(Hz) (%) f (Hz) (%) f (Hz) (%) f (Hz) (%)

1 2.77 4.36 2.87 2.45 2.50 4.92 2.59 4.50

2 2.92 3.48 2.94 2.82 2.68 4.10 2.74 3.84

3 3.55 3.50 3.59 1.65 3.43 3.85 3.46 3.75

Recorded groundmotionatCalit2buildingsite\Earthquake Date Magnitude D istance(km) PGA(g)

Yucaipa Jun16,2005 4.9 90 0.017

S.Clemente Oct16,2005 4.9 135 0.005

ChinoHills Jul29,2008 5.4 35 0.074

I ngl ewo od May 17,2009 4.7 58 0.046

66


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67

The variations of modal parameters with the peak ground accelerations

2.4

2.6

2.8

3.0Frequency(Hz)

f1

2.5

2.7

2.9

3.1

f2

0 20 40 60 803.3

3.4

3.5

3.6

f3

PGA (cm/sec2)

2.0

3.0

4.0

5.0

6.0Damping ratio (%)

1

2.0

3.0

4.0

5.0

2

0 20 40 60 801.0

2.0

3.0

4.0

5.0

PGA (cm/sec2)

3

Identification of Stiffness by Neural Networks

Hidden layer

Input layer

Hidden layer

Output layer

Bridge Traffic Vibration Data

Frequencies &Mode Shapes

ExperimentalModal Analysis

Neural Network

Asses smen t o f

structural

Health

Change of

Stiffness from

Baseline68

Traffic Excitation Modelingby Integration of Vibration and Traffic Monitoring

N S

Bayesian Updating

Traffic Video

Image processing - Vehicle type, arrival time & speed

s-t(sec)xi-x1 (m)

Excitation Covariance

69

1cov[ ( ) ( )]Q Q

iF t F s

Five-Year Continuous Monitoring of J amboree Bridge

2002 2003 2004 2005 200694

95

96

97

Time (year)

Superstructure

summerwinter

Su

perstructureStiffness(%)

Stiffness Degradation

Temperature Effects

70

Eight-Year Continuous Monitoring of West St. On-Ramp

71* Vehicle-bridge interaction causes notable variation of natural frequencies

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Frequency[Hz]

Timeline

1st Mode Identified w/o Vehicle Interference 1st Mode Identified with Vehicle Interference

2nd Mode Identified w/o Vehicle Interference 2nd Mode Identified with Vehicle Interference

3rd Mode Identified w/o Vehicle Interference 3rd Mode Identified with Vehicle Interference

f3=-0.0262(# of years )+2.8404

f2=-0.0174(# of years )+2.4464

f1=-0.0202(# of years)+2.0185

Jan 2002 Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 Jan 2009

Winter02

Spring02

Summer02

Fall02

Winter03

Spring03

Summer03

Fall03

Winter04

Spring04

Summer04

Fall04

Winter05

Spring05

Summer05

Fall05

Winter06

Spring06

Summer06

Fall06

Winter07

Spring07

Summer07

Fall07

Winter08

Spring08

Summer08

Fall08

Winter09

Spring09

Vibration Tests at West St. Bridge

72


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TEST: 15mph, L1, BB-EB

-505

x10-3 CH1

-505

x10-3 CH2

-505

x10-3 CH3

-505

x10-3 CH4

-505

x10-3 CH5

-505

x10-3 CH9

-505

x10-3 CH10

0 10 20 30-505

x10-3 CH11

Time [sec]

Natural Frequencies from AVT

f1 =1.904Hzf2 =2.344Hzf3 =2.637Hz

0 1 2 3 4 5 60

0.5

1

1.5x 10

-5

Frequency in Hz

Amplitud

FDD using truck induced vibration

X:2.315Y:4.084e-006

X:2.655Y:1.245e-005

X:3.285Y:5.211e-006

Amplitude

0 10 20 30Time [sec]

Acceleration[g]

2.344

2.637

3.32

73

TEST: 15mph, L1, BB-EB

-505

x10-3 CH1

-505

x10-3 CH2

-505

x10-3 CH3

-505

x10-3 CH4

-505

x10-3 CH5

-505

x10-3 CH9

-505

x10-3 CH10

0 10 20 30-505

x10-3 CH11

Time [sec]

0 1 2 3 4 5 60

0.5

1

1.5

2

2.5x 10

-7

Amplit

ude

FDD using AVT data

Frequency in Hz

X:1.904Y:1.366e-007

X:2.344Y:2.011e-007

X:2.637Y:5.895e-008

0 1 2 3 4 5 60

0.5

1

1.5x 10

-5

Frequency in Hz

Amplitud

FDD using truck induced vibration

X:2.315Y:4.084e-006

X:2.655Y:1.245e-005

X:3.285Y:5.211e-006

Amplitude

1.904

2.344

2.637

2.344

2.637

3.32

0 10 20 30Time [sec]

Acceleration[g]

74

f = 1.904Hz f = 2.344Hz

f = 2.637Hz

f = 2.930Hz f = 6.055Hz

Identified TruckModal Shapes

Truck can now be modeled as aSDOF

To take vehicle-bridge dynamicinteraction into account

75

Truck Modal Shapes corresponding tobridge natural frequencies

Truck Modal Shapes corresponding tochassis natural frequencies76Compression Tension

Modeling of Vehicle-Bridge Dynamic Interaction

Bridge Doctor Software

Sensor Data from Instrumented Bridges

Post-Event

Structure Instrumented withSensors

Post-Event Disaster Mitigation& Speedy Recovery

Condition Assessment


Rapid DamageScreening

Detailed DamageAssessment

Long-Term

Remaining Capacity Estimation

Case Study #3: Integrated NDE ofConcrete Bridge Decks

(N. Gucunski, Rutgers University)

78

Electrochemical methods for corrosionassessment

Impact echo for delamination detection

Ground Penetrating Radar (GPR) inspection

Ultrasonic Surface Wave (USW) testing ofconcrete modulus


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Electrochemical Methods for Corrosion Detection

Electrical Resistivity

Half-Cell Potential

Principle of Half-Cell Potential Measurement

-414 mV

Current flow

Iso-potential lines

Half-cell probe(referenceelectrode)

Voltmeter

-500-600

-700

Cathode CathodeAnode

Half-Cell Potential Measurement and Map Principle of Electrical Resistivity Measurement

Current flow

Iso-potential lines

Wenner probe

Voltage U Resistivity=2dU/ICurrent I

d

Electrical Resistivity Measurement and Map

0 10 20 30 40 50 60

DISTANCE FROM WEST ABUTMENT (FEET)

Electrical resistivity (kohm*cm)

1

3

5

7

9

11

13

15

17

19

21

23

25

2729

31

33

35

37

39

41

43

45

47

DISTANCE

(FEET)

0 10 20

very high high moderate - low low

Delamination Detection by Impact Echo


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Impact Echo Validation with Cores

Distance fromwest abutment, ft

GPR Ground Coupled System

Air-Coupled (Horn) Antenna GPR System GPR Scan 2.6 GHz, Municipal Drive Bridge, Warren County, NJ

GPR Raw Scan and Condition Map

Deteriorated

Zone

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

DISTANCE FROM EAST ABUTMENT (FEET)

Top Rebar Amplitude (Normalized dB) - DEPTH CORRECTED

0

10

20

-22 -20 -18 -16 -14 -12 -10 -8

DebondedOverlayUltrasonic Surface Waves (USW) Method

IMPACTSOURCE

RECEIVERS

SS

1 2

Coherence

Phase

Phase velocity Shear modulus

Wavelength

Depth

DISPERSIONCURVE

YOUNG'SMODULUS

PROFILE

Wavelength considered

less than layer thickness


16/16

10/5/20

USWTesting

Using PSPA

Concrete Modulus from USW(Bridge R1, Iowa)

Youngs Modulus (ksi)

Distance from west abutment (ft)

Sustainable Transportation Infrastructure

Paradigm shift

Courtesy of Business Week

93

Periodic VisualInspection

ContinuousMonitoring +Condition-

Based

Inspection