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1
Emergence of Coputational Stochastic Systems
Response Monitoring for System Modeling and Calibration
Masanobu ShinozukaUniversity of California, Irvine
International Symposium on Stichastic Analysis for Risk Management (SARM 1 2010)
Tokyo International Foram,Tokyo, apan, December 23, 2010
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Response Monitoringng for Spatially Distributed
System Modeling and Calibration
Use of monitored response data for validation and calibration of large scale system performance and restoration models in order to minimize the uncertainty.
such as• Electric transmission network performance model
• Pipe ruptures in water distribution networks
• Tsunami wave propagation model
• Liquifaction induced displacement of caissons in port facilities
• Development seismic load factors in the LRF bridge desigof under earthquake and scour hazards
2
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The 2004 Indian Ocean Tsunami
Simulation of Tsunamis and Their Consequences
Simulation by Professor Koshimura,
Tohoku University, Japan
5
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Model Validation with Altimetry Data
6by Professor Koshimura, Tohoku University, Japan
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#
#
# #
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
##
$
$
$
%
River
Sylmar
Toluca
Harbor
Valley
Tarzana
Atwater
Olympic
Airport
Fairfax
Rinaldi
Century
Velasco
HynessG
HarborG
CasticG
Gramercy
St. John
Halldale
Glendale
Adelanto
Hollywood
Northridge
Wilmington
Victorvlle
Scattergood
ScattergoodG
Santa Monica Bay
LongBeach
SealBeach
Land.shp
Ocean.shp
Area.shp
1
2
Line.shp
Sub_station.shp
# Receiving Station
$ Generating Station(Thermal)
% Power Plant(Hydro)
Part of Western Electricity
Coordination
Council’s (WECC’s)
network covering 14
US western states,
2 Canadian provinces
and northern part of
Baja California
Los Angeles Department of Water and Power’s
Electric Power Transmission system
6,300 MW at a typical
peak hour for a
population of 3.7 million
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Seismic Hazard
Northridge(01/17/91 M=6.7)
San Fernando (02/09/71 M=6.6)
Sierra Madre (06/28/91 M=5.8)
Long Beach(03/10/33 M=6.4)
Whittier Narrows(01/10/87 M=5.9) http://www.scecdc.scec.org/labasin.html
Los Angeles Basin Seismicity (1932-1996)
To be represented by scenario earthquakes
San
Andrea
s
San Fernando
Malibu
Coast
Newport-Inglewood A/B
Elsinore
(Whittier)
Raymond
San Cayetano
Ventura(s)®)
(V)
Locations of Seismic Faults and
52 Receiving Stations in Study
Area
Seismic Exposure
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Bus Support
Buses Insulators
500kV/230kV Transformer Bus
Circuit Breakers Disconnect Switches
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One line diagram of a receiving station
230KV
345KV345KV
Nodes
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Models for Substation and Nodes
Simulation #1
Substation and Nodes
(230 kV) (230 kV) (500 kV)Line A Line B
Line DLine C
Line A Line B
Line DLine C
Line A Line B
Line DLine C
Bus 1 Bus 1 Bus 2 Bus 1 Bus 2
Circuit Breaker
Disconnect Switch
Bus
Trans. Line
Trans. Line
Trans. Line
Line(A)
Line(B)Trans. Line
Node 1
Node 2Node 3
Node 4Line C
Line DSubstation
Line A
Line B
Line DLine C
For Node 1
Bus 2
Disconnect Switch
BusCircuit BreakerBreaker and a Half
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Fragility Curve of Disconnect Switch
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Fragility Curve of Circuit Breakers
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Fragility Curve of Transformers
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Annual Probability of Exceedance for
Households without Power (enlarged view)
Risk Curve
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1616
Vulnerable components:
Transmission towers,
Transmission lines,
Substation equipments,
Power generation plants
IPFLOW
Code
Attenuation
Relationship
Annual
Occurrence
Probability
Fragility
Curves
Repair/
Restoration
Model
Inventory Data
IPFLOW
Code
Attenuation
Relationship
Annual
Occurrence
Probability
Fragility
Curves
Damageability (Fragility) Model of Vulnerable Components
Repair/
Restoration
Model
Ground Motion IntensityModel
Network Damage Model
Model of Power Flow in Damaged Network (IP FLOW)
Repair and Restoration Model
Scenario Earthquake Model
Data Source
USGS
USGS
Analytical, Empirical
and Experimental
Analytical and
Field Experiment
Considered Highly
Reliable
Experimental and
Empirical
Simulation of Seismic Performance of Power Systems
• Loss of connectivity
• Imbalance of power
• Abnormal voltage (node by node)
• Frequency change IPFLOW does not check
1demandtotal
supplytotalor05.1
demandtotal
supplytotal
1.0V
VV
intact
damagedintact
Failure
Criteria
Sequential modular tasks
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Repair/Replacement Curves
Half day 1day
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 6 12 18 24 30 36
Time (Hour)
Pro
ba
bilit
y o
f R
rep
lac
em
en
t
Circuit Breakers
Disconnect SwitchesTransformers
Buses
Assumed Models
Half day
1 day
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18Northridge/N=20/Tr,CB,DS,Bus,T1=12hrs T2=24 Hrs
LADWP’s Power Supply; Immediately after Earthquake
1-17-94 4:31AM
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LADWP’s Power Restoration; 6 Hours after Earthquake
Northridge/N=20/Tr,CB,DS,Bus,T1=12hrs T2=24 Hrs
1-17-94 10:00 AM
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LADWP’s Power Restoration; 12 Hours after Earthquake
Northridge/N=20/Tr,CB,DS,Bus,T1=12hrs T2=24 Hrs1-17-94 1:00 PM
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LADWP’s Power Restoration; 24 Hour after Earthquake
1-18-94 8:00 AM Northridge/N=20/Tr,CB,DS,Bus,T1=12hrs T2=24 Hrs
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Night Light Images Before and After
Northridge Earthquake: J.17,1994 Mw=6.7
PST 19:39, 01-15-1994 PST 20:16, 01-17-1994Images courtesy of the U.S. National Oceanic and Atmospheric
Administration & the Defense Meteorological Satellite Program
(NOAA/DMSP) 22
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A 62-inch water main
broke in Studio City,
Los Angeles at 10:40
p.m. September 5th,
2009.
Ventura Blvd,
Coldwater Canyon
Avenue and several
other studio city
streets were closed
for several days.
Source: L.A. Times
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•Conventional SCADA* for utility network systems to
sense and control operational perturbations in pressure
or voltage, flow rate, temperature, etc.
•Sensors are primarily installed at key components
of the network located at the nodes of the network.
• Sensor insatllation on network links such as pipes in
a water distribution system is sparse if any, and this
makes damage detection on pipes difficult.
•*Supervisory Control And Data Acquisition;.
Conventional SCADA*
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Next Generation SCADA* for
Prevention and mitigation of Water
System Infrastructure Disaster
Joint Venture Project under NIST** TIP
NIST Project Manager: Dr. Felix Wu
University of California, Irvine (UCI)
Orange County Sanitation District (OCSD)
Irvine Ranch Water District (IRWD)
Santa Ana Watershed Project Authority (SAWPA)
Earth Mechanics Inc (EMI)
*Supervisory Control and Data Acquisition
** National Inswtitute of Standards and Technology
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100 km7
0 k
m
A Water Distribution System
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AtEachNode,WaterHead
Gradient D is Measured
Where:
•H2 and H1 are the water head of a node at
the time t2 and t1
•D reveals the rapidness of water pressure
head drop
12
12
tt
HHD
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Contour of Water Head Gradient
P111 Damage
A B
32
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A B
C
D
Contour of Water Head Gradient
P111 & P24 Damage
33
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Vision-based sensing system to monitor dynamic
displacement of civil structures
ObjectiveReal-time displacement measurement of civil structures highly cost-effective
easy to implement
dynamic measurement resolution
simultaneous multi-points measurement
Contents System configuration and principle
Time synchronization for multipoint measurement
Laboratory and field tests
Robust object searching to measure displacement without target panel
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Field Test to Bridges
Yondai Bridge Samseung Bridge
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Field Test to a Steel Girder Bridge: Samseung Bridge
Test trucks
Camcorder + Laptop
Reflector
Target
Displ. LVDTtransducer
Laservibrometer
Test Bridge
wire
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Field Test to a Steel Girder Bridge: Samseung Bridge
0 20 40 60 80 100 120 140 160 180
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
ce
me
nt
[mm
]
Displ. Transducer
0 20 40 60 80 100 120 140 160 180
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
ce
me
nt
[mm
]Laser Vibrometer
0 20 40 60 80 100 120 140 160 180
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
ce
me
nt
[mm
]
Image Processing
15 ton 30 ton 40 ton
15 ton 30 ton 40 ton
15 ton 30 ton 40 ton
v = 3km/hr
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Field Test to a Steel Girder Bridge: Samseung Bridge
0 10 20 30 40 50 60 70
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
cem
ent [
mm
] Displ. Transducer
0 10 20 30 40 50 60 70
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
cem
ent [
mm
] Laser Vibrometer
0 10 20 30 40 50 60 70
0
0.5
1
1.5
2
2.5
3
Time [sec]
Dis
pla
cem
ent [
mm
]
Image Processing
15 tons 30 tons 40 tons
15 tons 30 tons 40 tons
15 tons 30 tons 40 tons
v = 50km/hr
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Field Test to a steel-box girder bridge:
Yondai Bridge
v=3km/hrv=20, 40km/hr
Measurement
location
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Field Test to a steel-box girder bridge:Yondai Bridge
0 20 40 60 80 100 120 140 160-2
-1
0
1
2
3
4
5
Time [sec]
Dis
pl.
[mm
]
LASER
0 20 40 60 80 100 120 140 160-2
-1
0
1
2
3
4
5
Time [sec]
Dis
pl.
[mm
]
IMAGE
30 tons
30 tons
40 tons
40 tons
v = 20km/hr
0 20 40 60 80 100 120 140 160-2
-1
0
1
2
3
4
5
Time [sec]
Dis
pl.
[mm
]
LASER
0 20 40 60 80 100 120 140 160-2
-1
0
1
2
3
4
5
Time [sec]
Dis
pl.
[mm
]
IMAGE
30 tons
30 tons
40 tons
40 tons
v = 40km/hr
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54
Field Test to a steel-box girder bridge:
Yondai Bridge
1 2 3 4 5 6 7 8 9 100
0.5
1
Frequency [Hz]
| FF
T(d
) |
Laser
1 2 3 4 5 6 7 8 9 100
0.5
1
Frequency [Hz]
| FF
T(d
) |
Image
1 2 3 4 5 6 7 8 9 100
0.5
1
Frequency [Hz]
| FF
T(d
) |
Laser
1 2 3 4 5 6 7 8 9 100
0.5
1
Frequency [Hz]
| FF
T(d
) |
Image
v = 20km/hr v = 40km/hr
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55
Field Test to a suspension bridge:
Target
Camcorder
Camcorder is located about 70
meters from the camcorder.
Target and accelerometer is
located at the mid span of the
bridge.
Accelerometer70m
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56
Field Test to a suspension bridge:
Test Result by Accelerometer
Test Result by Vision-based Sensing System
0 10 20 30 40 50 60 70 80-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06A
ccel
erat
ion
[g]
Time [sec]
0 5 10 15-20
-10
0
10
20
30
Frequency
Pow
er S
pect
rum
Mag
nitu
de (d
B)
1.83Hz
0 10 20 30 40 50 60 70 80-8
-6
-4
-2
0
2
4
Time [sec]
Dis
plac
emen
t [m
m]
0 5 10 15-20
-10
0
10
20
30
Frequency
Pow
er S
pect
rum
Mag
nitu
de (d
B)
1.82 Hz
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OC Matching
Robust Against Obstacles and Lighting Change
yx IIif
otherwise
N
II
cxy
1tan
Robust Object Recognition
- basis for measuring displacement without target panel
Dark
0
12
345
6
7
8
910
11 121314
15
Light
Lighting Change
Light
Dark
57
Orientation Code(OC)
OC Matching
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58Camcorders are placed approximately 300 meters away from the mid span
Field Test on a Long-span Bridge:Vincent Thomas Bridge
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Measurement w/ and w/o Target Panels
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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
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Location of Installed Anemometers on
Vincent Thomas Bridge
A1A2
A3
50 m
62 m
46 m 154 m 457 m 154 m 46 m
A – Anemometer
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Total 41 locations are considered along the deck of the bridge
and total 8 locations along the tower
Every other hanger and deck connections are considered along
the deck
Tower : connection between tower struts and tower legs
512 sec long wind velocity fluctuation records are used with a
time step of 0.25 sec
For simulation : N = 1024, M = 2048, sec/4 radu
Simulation of Spatially Correlated
Gaussian Wind Velocity Fluctuations
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Measured Wind Velocity Fluctuations
A1
A2
A3
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Gaussian Conditional Simulation
Anemometer # 2
Along the Deck
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Schematic Diagram for Aerodynamic
Forces on Bridge Deck
b : Buffeting se : Self-excited
tMtMtM
tDtDtD
tLtLtL
bse
bse
bse
Lift :
Drag :
Moment :
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Aerodynamic Forces on Bridge Deck
*
3
2*
2
*
1
2
*
3
2*
2
*
1
2
*
3
2*
2
*
1
2
2
1
2
1
2
1
AKU
BKA
U
hKABUtM
PKU
BKP
U
hKPBUtD
HKU
BKH
U
hKHBUtL
se
se
se
Self-excited force per unit length of the bridge deck (*Scanlan and Tomko, 1971)
Density of air B Width of the bridge deck
UBK Reduced frequency circular frequency of the bridge motion
,*
iH ,*
iP *
iA : (i = 1, 2, 3) aerodynamic coefficients
U Horizontal mean wind velocity
,h , ,h Vertical and rotational displacement, velocity of bridge deck
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Aerodynamic Forces on Bridge Deck
Buffeting force per unit length of the bridge deck
U
tw
d
dC
U
tuCBUtM
U
tuCBUtD
U
twC
d
dC
U
tuCBUtL
MMb
Db
DL
Lb
0210
2
1
210
2
1
002
102
1
2
2
2
,tu tw : Wind velocity fluctuations in horizontal and vertical directions
,0LC ,0DC 0MC : Dimensionless lift, drag and moment coefficients at a wind angle of 0°
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Buffeting Response of Vincent
Thomas Bridge
Spatially separated wind velocity field simulated : two independent stochastic vector processes
Three different methods were used to simulate the horizontal velocity fluctuation
The aerodynamic buffeting forces are calculated and applied at those 41 locations
Wind velocity fluctuation time history is simulated for 512 sec with a sampling duration of t = 0.25 sec
The dimensionless aerodynamic coefficients are
0,238.0,415.1,162.0,0,0 DMLDML CandCCCCC
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Simulated Lateral Deck Displacements
at the Center of the Mid Span
Peak Lateral Displacement : 0.67 cm, 0.59 cm, and 0.86 cm
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Conclusion
Use of visualized data remotely sensed and/or remotely monitoredis studied for validation and calibration of large scale system performance models for the purpose of minimizing their uncertainties.
For this purpose, satellite images are used for tsunami wave propagation modeling, and electric transmission network performance modeling.
Vision-based sensing technology is developed primarily for measuring displacement of civil structures, and the technology is verified by laboratory and field tests.
Recent research of on-line sensing and remote monitoring technology is demonstrated in conjunction with development of next generation SCADA for water distribution systems.
Quantitative evaluation of the reduction of uncertainty will be an important theme for future research.
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