Recent Advances in Structural Health Monitoring...Traffic Excitation Modeling NS Improved System...
Transcript of Recent Advances in Structural Health Monitoring...Traffic Excitation Modeling NS Improved System...
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Maria Q. FengProfessor, Dept. of Civil and Environmental Engineering
Director, Center for Advanced Monitoring and Damage Inspection University of California, Irvine
Recent Advances in Structural Health Monitoring
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Vulnerability of Our Civil Infrastructure
Courtesy of LA Time
Courtesy ofLA Times
Courtesy of A Worrall
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How can Structural Health Monitoring (SHM) Help?
Current Practice - Visual, Periodic Inspection
• Cannot detect invisible damage• Cannot timely detect problem
Future Practice - Incorporating SHM
• Use on-structure sensors for continuous monitoring to identify hot spots in real time
• Condition-based inspection focusing on hot spots
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Integration of Real-Time Global Monitoring with Targeted Local Inspection
• Use of On-Structure Sensors for Continuous Monitoring for Real-Time Assessment of Global Structural Integrity and Identification of Hot Spots
• Condition-Based NDE Inspection of Targeted Hot Spots
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Where Are We Now?
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We Need Better Sensors
Solar Penal
DC-5V
Piano wire
To recorder
+-
Bellows
L.V.D.T
Displacement Meter
Strain Gauge
Signal ConditionerTSA-20
+-
Wall
Soil Pressure Sensor
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Remote Control and Data Acquisition
Bridge Site
UCI Campus
Wireless Real-Time Data Acquisition System
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We Need Reliable Methods to Interpret Sensor Data
Server
Client
http://mfeng.eng.uci.edu
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Contents
• Innovative Sensors and NDE Devices– Fiber Optic Accelerometer– Wireless MEMS Accelerometer– Vision-Based Displacement Sensor– Distributed Fiber Optic Strain Sensor– Microwave Imaging NDE Device
• Health Diagnosis Methods and Experiments– Neural Networks and Application in Long-Term Monitoring– Traffic Excitation Modeling and Bayesian Updating– Extended Kalman Fitering and Shaking Table Tests– Nonlinear Damping Analysis and Shaking Table Tests– Estimation of Remaining Capacity
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Contents
• Innovative Sensors and NDE Devices– Fiber Optic Accelerometer– Wireless MEMS Accelerometer– Vision-Based Displacement Sensor– Distributed Fiber Optic Strain Sensor– Microwave Imaging NDE Device
• Health Diagnosis Methods and Experiments– Neural Networks and Application in Long-Term Monitoring– Traffic Excitation Modeling and Bayesian Updating– Extended Kalman Fitering and Shaking Table Tests– Nonlinear Damping Analysis and Shaking Table Tests– Estimation of Remaining Capacity
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Fiber Optic AccelerometerFiber Optic Accelerometer
Sensor Head
Light Control Unit
Signal Processing Unit
Sensor Head
Light Control Unit
Signal Processing Unit
Uniquely Suited for Monitoring Large-Scale Civil Infrastructure Systems in Harsh Environment
•Sensing Principle: moiré fringe
•Unique Feature:
High resolution and large measurement range, uniquely suited for accurate measurement of both ambient micro-vibration and strong motion.
Immunity to electromagnetic interference and lightning strikes, safe to use in explosion-prone environments.
Excellent low-frequency performance, suited for monitoring long-period structures.
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Beam
Columns
Base
Ch1
Ch2Ch3
Ch4
Ch5
FOAWireless Sensor
Shaking Table Test
0 5 10 15 20 25 30 35 40 45 50-1
-0.5
0
0.5
1
1.5Time signal of Sylmar 1.25x
Time (sec.)
Acc
eler
atio
n (g
)
Optical Fiber Sensor
0 5 10 15 20 25 30 35 40 45-1
-0.5
0
0.5
1
Time (sec.)
Acc
eler
atio
n (g
)
Reference Sensor (KINEMETRICS I6083)
0 2 4 6 8 10 12 14 16 18 200
0.5
1
1.5Power Spectrum Density of Sylmar 1.25x
Frequency (Hz)
Am
plitu
de
Optical Fiber Sensor
0 2 4 6 8 10 12 14 16 18 200
0.5
1
1.5
Frequency (Hz)
Am
plitu
de
Reference Sensor (KINEMETRICS I6083)
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Y- direction
Optical fiber accelerometer
Sokushinco.direction
Z- direction
Y- direction
Optical fiber accelerometer
Sokushinco.direction
Z- direction
Y- direction
Sokushinco.direction
Z- directionFOA
Servo Accelerometer
Continuous Monitoring of CalIT2 Building
210.0 210.2 210.4 210.6 210.8 211.0-0.4-0.3-0.2-0.10.00.10.20.30.4
Acce
lera
tion
* 10-3
(g)
Time (sec.)
Fiber Optic Accelerometer Servo Accelerometer
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ULTRA COMPACT LOW POWER
Wireless MEMS AccelerometerWireless MEMS Accelerometer
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RealReal--Time Damage Detection of Water PipelinesTime Damage Detection of Water Pipelines
CAP
Node B
Node ANode D
Node C
Valve
Water Input
62モ 62モ
62モ
31モ
9.5モ
62モ
Diameter of the pipe: 1 モ
CAP
Node B
Node ANode D
Node C
Valve
Water Input
62モ 62モ
62モ
31モ
9.5モ
62モ
Diameter of the pipe: 1 モ
DuraNode
DuraNode
Water Pipe Network
Water PipeNetwork
DuraNode
DuraNode
DuraNode
DuraNode
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Suitable for measuring Suitable for measuring longlong--period structuresperiod structures
Remote and nonRemote and non--contactcontact
VisionVision--Based Displacement SensorBased Displacement Sensor
Target panel with geometric pattern
Telescopic lens (3x optical zoom)
Digital camcorder (30x optical zoom) Notebook computer
IEEE1394
Image-processing software
Figure 7 Vision-Based Displacement Sensor System
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Remote Monitoring of Bridge ResponseRemote Monitoring of Bridge Response
Vision-based system
Displ. transducer and laser vibrometer
Ground
Laser vibrometer(Non-contact type)
Displ. transducer(Contact type)
Bridge
Wire
Vision-based system
Target panel Reflector
0 20 40 60 80 100 120 140 160 180
00.5
11.5
22.5
3Displ. Transducer
Laser Vibrometer
Image Processing
15 ton 30 ton 40 ton
Vision-based system
Laser vibrometer
Displ. transducer
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Distributed Fiber Optic Strain Sensor
• Principle: Brillouin loss•• Unique Feature:
– High spatial resolution (10cm) andmeasurement accuracy
• Applications: – Strain and temperature
measurement of large structures upto 40km;
– Crack detection up to 40 µm
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Detection of Fine Cracks
Crack A
Crack B
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Acoustic Emission Sensor
G-EFPI
PZT
G-EFPI
PZT
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by G-EFPI
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by PZT
Acoustic emission signal induced by pencil break
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by G-EFPI
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by PZT
Acoustic emission signal induced by pencil break
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by G-EFPI
-1 0 1 2 3 4 5-0.8
-0.4
0.0
0.4
0.8
Inte
nsity
(Vol
t.)
Time (msec.)
AE detected by PZT
Acoustic emission signal induced by pencil break
s
Gold deposited surface
E1E2
I
LD
PDMonochromatic laser
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Handheld Real-Time Microwave Imaging Device
Concrete Column
Steel RebarsFRP Jacket
Focusing Spot
Defocused Wave(weak interaction)
• Locate, in real time, invisible defects
• Portable & lightweight• Easy operation
requiring no training
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Inspection of FRP-Wrapped Concrete
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Contents
• Innovative Sensors and NDE Devices– Fiber Optic Accelerometer– Wireless MEMS Accelerometer– Vision-Based Displacement Sensor– Distributed Fiber Optic Strain Sensor– Microwave Imaging NDE Device
• Health Diagnosis Methods and Experiments– Neural Networks and Application in Long-Term Monitoring– Traffic Excitation Modeling and Bayesian Updating– Extended Kalman Fitering and Shaking Table Tests– Nonlinear Damping Analysis and Shaking Table Tests– Estimation of Remaining Capacity
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Structural Health Diagnosis
• Goal of SHM– Damage Detection– Damage Location– Damage Quantification– Damage Consequences
• Damage Signature–Structural Stiffness–Structural Damping
• What to Measure?–Structural Vibration (Ambient, Seismic Response, etc.)
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Neural Networks
Hidden layer
Input layer
Hidden layer
Output layer
Bridge Traffic Vibration Data
Frequencies & Mode Shapes
Experimental Modal Analysis
Neural Network
Assessment of structural
condition
Change of Stiffness from
Baseline
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Five-Year Continuous Monitoring
2002 2003 2004 2005 20062.65
2.7
2.75
2.8
2.85
2.9
Time (year)
Firs
t Mod
al F
requ
ency
(Hz) summer
winter
2002 2003 2004 2005 200694
95
96
97
Time (year)
Supe
rstru
ctur
e St
iffne
ss (%
)
summer winter
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Traffic Excitation Modeling
N S
Improved System Identification
Traffic Video
(a)
Reconstruct Traffic Excitation
Extract Traffic InformationVehicle arrival time, speed and type
0
5
10
15
200
0.20.4
0.60.8
1
0
0.2
0.4
s-t (sec)
xi-x1 (m)
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Bayesian Updating
Time (sec)
Prob
abili
ty
0 50 100 150 200 250 300 350
20
40
6080
P (k
N)
Time (sec)
(b)
(a)
50 100 150 200 250 300 350-0.4
-0.2
0
0.2
0.4(c)
Cha
nnel
4 (m
/s)
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Extended Kalman Filter for Seismic Damage Assessment
1 1/ oI I
Identified
• Use of seismic response and input
• Instantaneous and real-time identification
• Applicable to linear and nonlinear systems
• Baseline free
Ch-1Ch-2
Ch-3
Ch-4
Ch-5
Ch-6
Ch-7
Ch-8
Ch-9
Ch-10
Ch-11
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Shaking Table Tests of 3-Bent Concrete Bridge
Test Ground Motion Description PGA (g) Damage DescriptionWN-1 White Noise in TransverseT-13 Low Earthquake in Transverse 0.17 Bent-1 yieldsT-14 Moderate Earthquake in Transverse 0.32 Bent-3 yields
WN-2 White Noise in TransverseT-15 High Earthquake in Transverse 0.63 Bent-2 yields
WN-3 White Noise in TransverseT-19 Extreme Earthquake in Transverse 1.70 Bent-3 steel buckles
WN-4 White Noise in Transverse
1 1/ oP P
3 3/ oI I
2 2/ oI I
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Instantaneous Identification of Stiffness Change Using Seismic Response
T-13 T-14 T-15 T-19
0 20 400
0.2
0.4
0.6
0.8
1
0 20 400
0.2
0.4
0.6
0.8
1
Time (sec)
Stif
fnes
s R
educ
tion
0 20 400
0.2
0.4
0.6
0.8
1
0 20 400
0.2
0.4
0.6
0.8
1
0 20 400
0.2
0.4
0.6
0.8
1
0 20 400
0.2
0.4
0.6
0.8
1
T13T14T15T19
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Identification of Nonlinear Damping Using Ambient Response
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Comparison of Damage Assessment Methods
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Estimation of Remaining Capacity
Identification of Stiffness and Damping
White Noise Identification
Bayesian Updating
Pre-event Stiffness & Damping
Non-Linear Time History Analysis
Ductility
Nonlinear Time History Analysis under Design EQ
Capacity Estimation
Incr
emen
tal E
Q A
naly
sis
After T13 After T14 After T15 Bayesian Updated 53% 23% 0%
Simulated 75% 44% 3%
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Contents
• Innovative Sensors and NDE Devices– Fiber Optic Accelerometer– Wireless MEMS Accelerometer– Vision-Based Displacement Sensor– Distributed Fiber Optic Strain Sensor– Microwave Imaging NDE Device
• Health Diagnosis Methods and Experiments– Neural Networks and Application in Long-Term Monitoring– Traffic Excitation Modeling and Bayesian Updating– Extended Kalman Fitering and Shaking Table Tests– Nonlinear Damping Analysis and Shaking Table Tests– Estimation of Remaining Capacity
![Page 36: Recent Advances in Structural Health Monitoring...Traffic Excitation Modeling NS Improved System Identification Traffic Video (a) Reconstruct Traffic Excitation Extract Traffic Information](https://reader033.fdocuments.us/reader033/viewer/2022050513/5f9d34bab9180e2dfa040283/html5/thumbnails/36.jpg)
Concluding Remarks
Sensors Network• Low cost• Easy to install and maintain• Field durability and reliability• Power efficient
Health Diagnosis Methods• Baseline free• Real-time or near real-time assessment
Remaining Issues• Long-term monitoring data• Damage criteria• Prognostic as well as diagnostic methods