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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
Intelligent Maintenance
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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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
Intelligent Maintenance
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
7/28/2019 Feng Handouts
15/16
10/5/20
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
7/28/2019 Feng Handouts
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