emergence of coputational stochastic systemswebpark1671.sakura.ne.jp/isarm2010/tokyo122310.pdf ·...

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

2

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

5

The 2004 Indian Ocean Tsunami

Simulation of Tsunamis and Their Consequences

Simulation by Professor Koshimura,

Tohoku University, Japan

5

6

Model Validation with Altimetry Data

6by Professor Koshimura, Tohoku University, Japan

7

#

#

# #

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

##

$

$

$

%

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

8

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

9

Bus Support

Buses Insulators

500kV/230kV Transformer Bus

Circuit Breakers Disconnect Switches

10

One line diagram of a receiving station

230KV

345KV345KV

Nodes

11

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

12

Fragility Curve of Disconnect Switch

13

Fragility Curve of Circuit Breakers

14

Fragility Curve of Transformers

15

Annual Probability of Exceedance for

Households without Power (enlarged view)

Risk Curve

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

17

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

18Northridge/N=20/Tr,CB,DS,Bus,T1=12hrs T2=24 Hrs

LADWP’s Power Supply; Immediately after Earthquake

1-17-94 4:31AM

19

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

20

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

21

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

22

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

24

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

26

•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*

27

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

28

100 km7

0 k

m

A Water Distribution System

30

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

32

Contour of Water Head Gradient

P111 Damage

A B

32

33

A B

C

D

Contour of Water Head Gradient

P111 & P24 Damage

33

40

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

48

Field Test to Bridges

Yondai Bridge Samseung Bridge

49

Field Test to a Steel Girder Bridge: Samseung Bridge

Test trucks

Camcorder + Laptop

Reflector

Target

Displ. LVDTtransducer

Laservibrometer

Test Bridge

wire

50


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



v = 3km/hr

51


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



v = 50km/hr

52

Field Test to a steel-box girder bridge:

Yondai Bridge

v=3km/hrv=20, 40km/hr

Measurement

location

53

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

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

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

57

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

58Camcorders are placed approximately 300 meters away from the mid span

Field Test on a Long-span Bridge:Vincent Thomas Bridge

59

Measurement w/ and w/o Target Panels

60

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

63

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

64

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

65

Measured Wind Velocity Fluctuations

A1

A2

A3

66

Gaussian Conditional Simulation

Anemometer # 2

Along the Deck

67

Schematic Diagram for Aerodynamic

Forces on Bridge Deck

b : Buffeting se : Self-excited

tMtMtM

tDtDtD

tLtLtL

bse

bse

bse

Lift :

Drag :

Moment :

68

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

69

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°

70

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

71

Simulated Lateral Deck Displacements

at the Center of the Mid Span

Peak Lateral Displacement : 0.67 cm, 0.59 cm, and 0.86 cm

72

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.

72

emergence of coputational stochastic systemswebpark1671.sakura.ne.jp/isarm2010/tokyo122310.pdf ·...

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