STRUCTURAL HEALTH MONITORING FOR CIVIL INFRASTRUCTURE –INFRASTRUCTURE
FROM INSTRUMENTATION TO DECISION SUPPORT
Anne S. KiremidjianDept. of Civil and Environmental Engineeringp g g
Stanford University
IWSHM 2011S t b 13 15 2011September 13‐15, 2011
This research is supported by the NSF CMMI Research Grant No. 0800932, the John A. Blume Fellowship, and the Samsung Scholarship.
Diversity of Civil InfrastructureDiversity of Civil Infrastructure
Why Monitor Civil Infrastructure?Gradual Long‐Term Deterioration
(from Dr. Hae Young Noh)
3/17
Why Monitor Civil Infrastructure? ‐Extreme Event DamageExtreme Event Damage
OutlineOutline
• Wireless/Wired Monitoring System DesignWireless/Wired Monitoring System Design
• Algorithmic Development
• Example Applications
• Decision Support System
• Conclusion
5
Structural Monitoring System Sensors &Structural Monitoring SystemControl Center
Sensors &
Network
Data management and archiving
Data StorageManager onManager on
site
Base Station
Decision Support & Emergency Response
S t
Data Analysis and Post‐processing System
System
Synchronization
Structure ‐level l i
Signal Processing
Spectral Analysis
Feature Extraction &analysis
Decision making
Feature Extraction & Damage Classification
The Smart Structural Monitoring System
Sensors Decision Support
• structure specific
System
specific• Damage specific
• Data
•model updating• decision making• warning
id Data collection/storage
• guidance• active control
DataAnalysis
&Sensor N t k&
Archiving
• physical modeling
Network
•Wired•Wireless
• signal processing• statistical analysis
7/17
•Wireless• Combined• Communication
Damage‐Specific Sensorsg p
Currently Available Still neededWired sensors Wireless/Wired CrackWired sensors Wireless/Wired
sensorsCrack
Fiber optic sensors Accelerometers Corrosion
High definition digital cameras
Strain gages Displacement
Laser GPS Materials specificLaser interferometers
GPS Materials specific
Laser scanner Tilt metersDeep penetration radar
Temperature
HumidityyCorrosionAnemometers
The Smart Structural Monitoring System
Sensors Decision Support
• structure specific
System
specific• Damage specific
• Data
•model updating• decision making• warning
id Data collection/storage
• guidance• active control
DataAnalysis
&Sensor N t k&
Archiving
• physical modeling
Network
•Wired•Wireless
• signal processing• statistical analysis
9/17
•Wireless• Combined• Communication
Sensor NetworksSensor Networks
• Wireless vs. wiredWireless vs. wired • Advantages of wireless systems
– ScalableScalable – Ease of installation– PortabilityPortability– Lower cost
• ChallengesChallenges– Potential signal loss– Communication barriersCommunication barriers
The Smart Structural Monitoring System
Multiple Sensor
Decision Support
• structure specific
System
specific• Damage specific
• Data
•model updating• decision making• warning
id Data collection/storage
• guidance• active control
Analysis & Data Sensor
N t kArchiving
• physical modeling
Network
•Wired•Wireless
• signal processing• statistical analysis
11/17
•Wireless• Combined• Communication
Types of Algorithmyp g
• Device control• Damage diagnosis– statistical pattern recognition methods
– Wake up – Database structure
pattern recognition methods– AR/ARMA/ARX
• Hypothesis testing
– Baseline collection• By time
• Gaussian Mixture Modeling‐ GMM
– Wavelet Based – Haar and Morelet wavelets
• By season• By temperature• By humidity
• Comparison of wavelet energies at high scales
• Gaussian Mixture Modeling– Synchronization
• Hardware – internal clock
– Rotation ‐ to drift ‐to damage
• Software – pre ‐& post synchronization
Damage Detection Using Statistical g gSignal Processing
• Main approach
– Use single sensor pre‐ and post‐ damage measurements
– Combine information from multiple sensors
– Computationally efficient ‐ local micro‐processingComputationally efficient local micro processing
– Independent of the sensor – can be used with acceleration, strain, etc.,
– Scalable with increased sensor density
– Reduces amount of transmitted data – power saving– Reduces amount of transmitted data – power saving
Steps in Damage Diagnosis
Statistics BasedARX
Collect data ARGMM
Extractfeatures
WaveletChange point detection
features
Classify damage
14/17
ExamplesExamples
• Using stationary vibration signals –Using stationary vibration signals – AR with information criteria testing
AR with Gaussian Mixture Model– AR with Gaussian Mixture Model
• Using non‐stationary vibration signals – e.g. th k tiearthquake motions
NEES – UNR Project‐¼ Scale Bridge TestAR & G i Mi t Al ithAR & Gaussian Mixture Algorithm
(Nair & Kiremidjian, 2006)
Decision Support SystemDecision Support System
Test Schedule – 4‐span Bridge Test at UNR
Baseline Signalsg
Minor cracks at base of
Major cracks at base of l & lli
column
Major cracks at base of l & lli
Concrete spalling and exposure or rebar
col. & spallingcol. & spalling
Rebar buckling/ breaking; concrete
i t fpouring out of core
Final Test – White Noise
Test Damage MeasureReno - Setup Day DC1 and DC2 baselineMild Shaking Day 1, White Noise 21.05Mild Shaking Day 2, White Noise 21 36.79Mild Shaking Day 3, White Noise 41 56.97Final Test Day White Noise Run 51 59 80Final Test Day, White Noise Run 51 59.80
C l iColumn Device
DM
Test Damage Measure
Reno - Setup Day DC1 and DC2
baseline
Mild Shaking Day 1 21 05Mild Shaking Day 1, White Noise
21.05
Mild Shaking Day 2, White Noise 21
36.79
Mild Shaking Day 3, White Noise 41
56.97
Final Test Day, White Noise Run 51
59.80
Example of Wavelet Damage Diagnosis(Noh et al., 2011)
• Feature fromFeature from wavelet energies of signal
• Used with non‐stationary signals –e.g. earthquake response motions
• Develop fragilities for rapid damage i di iindication
NEES 4 Story Steel FrameWavelet Based Algorithm g
• Scaled structural system tests Stanford/SUNYtests – Stanford/SUNY Buffalo
• Development of fragilityDevelopment of fragility functions in terms of structural response
t bt i blparameters obtainable from real time measurements – wavelet based fragilities
(Noh, Lignos, Nair and Kiremidjian
)2011)
The Smart Structural Monitoring System
• structure specific
SystemMultiple Sensor
Decision Support specific
• Damage specific
• Data
•model updating• decision making• warning
id Data collection/storage
• guidance• active control
Analysis & Data Sensor
N t kArchiving
• physical modeling
Network
•Wired•Wireless
23/17
• signal processing• statistical analysis
•Wireless• Combined• Communication
Decision Support SystemDecision Support System
• Visual representation of the• Visual representation of the structure
• Visual representation locations pof the wireless system
• Interface to wireless network
• System command and control center
• Display results of monitoringDisplay results of monitoring analyses
• Issue alerts
Components of a Decision Support System
• Monitor the sensor• Monitor the sensor
communications system• Serve as the
communications i t b tenvironment between
manager and system– Initial set‐up– Modifications of system– Modifications of system
parameters– Modifications of
monitoring settings• Serve as the information
delivery environment– Periodic queries
Following a major event– Following a major event
Decision Support System• Provide support for decision making for follow‐on actions
• Enable web services for – wide distribution of alerts and other information
– remote access by operators and other users.
C l iConclusion• Wireless monitoring systems – inevitable part of
h fthe future• Few applications in Europe, Asia and the US • Must combine structural with other monitoring systems, e.g.
B ildi i t l/ /li hti / it– Building environmental/energy/lighting/security monitoring
– Bridge, highway, tunnel, pipeline, transmission line, g , g y, , p p , ,etc. management systems
• Key to success is providing information and d ddecision support, not just data
27
Need for Full‐scale and Field Testing
• ObjectivesObjectives– Systematic damage at different levelsdifferent levels
– Different damage patternspatterns
– Different damage sequencesq
NIED – E‐Defense Test 5‐Story Full Scale RC Building, y g,August, 2011
AcknowledgementAcknowledgement• National Science Founcdation
• National Institute of Standards and Technologygy
• Students
Dr. E. Straser ‘98A Kotapalli ‘99
Dr. K. Nair ‘07Allen CheungA.Kotapalli 99
N. Mastroleon ‘00C Ch i i ‘03
Allen CheungDr. H‐ Y Noh ‘11
C. Charistis ‘03
THANK YOU!THANK YOU!
