Chung-Bang Yun - 東京大学park.itc.u-tokyo.ac.jp/tkdlab/ISARM2010/2010_Yun1.pdf · 2 1 •Introduction 2 •Vibration-based SHM-Long-term SHM systems for large civil infrastructure-Large

Chung-Bang YunProfessor, Department of Civil and Environmental Engineering

Director, Smart Infrastructure Technology Center

KAIST, Korea

International Symposium on Stochastic Analysis for Risk Management

Tokyo, Japan on Dec 23, 2010

2

1• Introduction

2

• Vibration-based SHM- Long-term SHM systems for large civil infrastructure

- Large-scale wireless sensor network system on a cable stayed bridge

- Kalman filtering technique for damage identification under earthquake

3

• Innovative Nondestructive Evaluation Techniques- Innovative sensing: OFS, Microwaves, Vision-based sensing

- Robotic trenchless rehabilitation of underground pipes

- Multifunctional wireless impedance sensor node

- Wireless power/data transmission for guided wave sensing

4• Concluding Remarks

4

Dedication to Prof. Shinozuka

Congratulation to Prof. Shinozukafor His 80th birthday, and

for His wonderful career as a professor and researcher leading the world technology developments for the mitigation and management of the natural and man-made disasters!!

Whole-hearted appreciationfor His great teaching and guidance to us:7 Korean PhD students & 10 visiting scholars

5

Identification of Linear Dynamic System(ASCE J. Eng. Mech. Div., 1982)

Identification of Nonlinear Dynamic System(J. Struct. Mech., 1980)

Bridge deck model and Wind forces

1 2 3 4

1 2 3 4

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

se

se

F t H h t H t H h t H t

Q t Ah t A t A h t A t

A fixed offshore tower and its model

Estimated parameters

1

0 0

1

0

1

0 0

1

0

( )

( )

( )

( )

a

a D

a

a M

J M M C

D M M C

K M M K

L M M C

ARMAX model &

Maximum likelihood method

0 0 0 {( ) }a D MM C K M C v v C v

Extended Kalman filter for

parameter estimation

Observed and Estimated responses in & ( )h t ( )t

Early Researches on System Identification

by Prof. Shinozuka and Yun (in late 1970)

I. Long-term SHM systems for large civil infrastructures

II. Large-scale wireless sensor network system on a cable stayed bridge

III. Kalman filtering techniques for damage identification under earthquake

7

Explore Sensor-Based Monitoring for

•Real-Time Structural Health Monitoring

Condition-Based Timely Maintenance

•Rapid & Remote Post-Event Damage Assessment

Effective Emergency Response

I. Long-term SHM Systems for Large Civil Infrastructures(M Feng at UCI)

•Soil-Structure Interaction

•Amplitude of Ground Motion

•Modeling of

Traffic Excitation•5-Year

Monitoring

•Vibration Test•Vehicle-Structure

Interaction•8-Year Monitoring

•Bridge Doctor

Software

8

Detection of Fatigue Damage in Steel Structures(M Shinozuka at UCI)

RepeaterReceiver

Non-Gaussian Conditional

Simulation along the Deck

A1A2

A3

Anemometer and

Transmitter

9

Non-Invasive Damage Detection of Underground Pipes(M Shinozuka at UCI)

(a) 2D contour map

(b) Visualized graphic

Based on Non-Invasive Acceleration Measurements of Pipe Motion rather than

Water Pressure in Pipe and Wireless Transmission of Data (NIST)

10

Advanced Diagnostics and Prognostics(M Feng at UCI)

Signal Processing and System ID Methods for Post-Event Damage Assessment

Validation by Seismic Shaking Table Tests

Field validation of state-of-the-art wireless smart sensor technology for a cable-stayed bridge in KoreaConstruction of an international SHM test-bedParticipants :

Korea : H.J. Jung & C.B. Yun (KAIST),

H.K. Kim (SNU), J.J. Lee (Sejong Univ.)

J.W. Seo (Hyundai Institute of Const. Tech.)

US : B.F. Spencer, Jr. & G. Agha (UIUC)

Japan : T. Nagayama & Y. Fujino (U. of Tokyo)

US-Korea-Japan Collaborative Project (2009-2011)

II. Bridge SHM Test-bed Using Large-scale Wireless Sensor Network(CB Yun, HJ Jung at KAIST; BF Spencer at UIUC; & T Nagayama at UT)

Daejeon (KAIST)

Seoul

Jindo Island

Jindo Bridges

Jeju Island

12

External Input

Connector

SHM-A Sensor Board

(bottom)

Basic

Connect

or

Programmable

Filter w/ ADC

Quickfilter

QF4A512

SHM-A Sensor Board

(top)

Accelerometer

ST Microelectronics

LIS344ALH

OP AMP

TI OPA 4344

Humidity & Temp.

Sensor

SHT11

Light Sensor

TAOS 2561

Basic

Connector

SHM-A Board (rev. 4.0)

External Antenna

(Antenova 2.4GHz)

iMote2

Battery Board

(IBB2400CA)

w/ 3 AAA

Batteries

Assembled iMote2

Multi-scale SHM-A Board

Hierarchical Data/Information Processing

Data acquisition

Outcome forwarding

processing

Energy Harvester

Wireless Smart Sensor: Hardware & Software

Data & Power Management

Effective Power

ManagementSnoozeAlarm

Data Inundation

MitigationThresholdSentry

Continuous /Autonomous

OperationAutoMonitor

13

Cable : 8

Deck : 22

Pylon : 3

Total : 33

Cable : 7

Deck : 26

Pylon : 3

Wind: 1

Total : 37

70 wireless sensor nodes

(207 Acceleration Ch. & 3 Wind Ch.)

Nodes on pylon top(powered by solar cell)Deck Nodes Nodes on pylons Nodes on cables

Nodes on cables(powered by solar cell) Wind-Sentry

Amemometerinterfaced with

Wind-Sentry

Various Types of Sensor Nodes

Sensor Deployments

14

Output-only Modal Analysis Combined Mode Shapes

Click Mouse!! (Left: Skip, Right: Change Phase of Mode Shape)










DV1

DV2

DV3

DV6

DV9

Output-only Modal Identification

0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

Frequency (Hz)

Syste

m O

rder

0

20

40

60

80

The 1

st

Sin

gula

r V

alu

e

DL1 DV1 DV2 NC1 DV3 DV4 DV5NC2

DV6 DT1DV7

DV8 PB1NC3

DV9


DV1

SSI: 0.4380Hz

FDD: 0.4492Hz

FEM: 0.4293Hz


DV2

SSI: 0.6439Hz

FDD: 0.6445Hz

FEM: 0.6375Hz


DT1

SSI: 1.8410Hz

FDD: Not found

FEM: 1.9592Hz

DV3

SSI: 1.0364Hz

FDD: 1.0352Hz

FEM: 0.9958Hz


Jindo-side

Subnetwork

15

Jindo Haenam70m 70m344m

f 7x139f 7x109f 7x73f 7x151

JC4(C-JE1)

JC6(C-JE2)

JC9(C-JE4)

JC13(C-JE7)

JC15(C-JE8)

HC15(C-HE7)

HC13(C-HE6)

HC9(C-HE4)

HC6(C-HE2)

HC4(C-HE1)

4 parallel cables

4 parallel cables

15

Using high mode frequencies

Cable

Tension force (tonf)

Routine inspection Installed WSSNsin 2009 (Avg.)In 2007 In 2008

HC4 262.7 268.4 274.0 (2.04)*

HC6 304.6 304.1 294.7 (-3.19)*

HC9 86.9 88.5 89.3 (0.90)*

HC13 164.0 165.1 170.2 (3.00)*

HC15 219.9 220.0 224.9 (2.18)*

JC4 245.1 250.7 254.0 (1.30)*

JC6 282.0 277.5 274.5 (-1.09)*

JC9 85.5 86.6 88.5 (2.15)*

JC13 148.3 150.7 154.3 (2.33)*

JC15 214.1 216.5 216.8 (0.14)*

*Differences (%) compared with tensions in 2008

0 5 10 15 20

50

100

150

200

250

300

350

400

450

500

Measurements

Tensio

n F

orc

e (

Tonf)

Estimated Tension from Haenam-side Cables

HC4

HC6

HC9

HC13

HC15

0 5 10 15 20 25

50

100

150

200

250

300

350

400

450

500

Measurements

Tensio

n F

orc

e (

Tonf)

Estimated Tension from Jindo-side Cables

JC4

JC6

JC9

JC13

JC15

2 2 22

2 44 4

n

eff eff

f T EI na bn

n mL mL

24 effT mL aLinear regression

to find a and b

Cable Tension Force Estimation

Vibration-based Tension Force Estimation

Estimated Tension Forces

16

*

1

m

j jj

a=

= b Sa s;踐踐D D鉗・鉗鉗顏顏

r pS

r p

HPC based on Cosine Similarity FE Model Updating Using GA

jq

js

is

j ja s

i i

a si

V*b

jV

iq

Two Single Vectors

*b

js

is

iV j

V

g

is

g

iq

g

iV

i jq q=

Grouped Vector with

Same Cosine Similarity

2*

j j jV a= -b s * /T T

j j j ja = s b s s

Least Square

Estimation:

( )2

*

* * * * 2

* *1 (1 cos )

( )( )

T

jT T

j jT T

j j

V q

踐 = - = - 顏

s bb b b b

s s b b

Always smaller

than

0 5 10 15 20 25 30 35 40 45 50

0.090.1

0.2

0.3

0.4

0.5 0.6

Generation

Norm

aliz

ed O

bje

ctive F

unction V

alu

e

Objective Function

No Group:27P

Conv. Group:11P

HPC1:18P

HPC2:25P

Average Adjustment Rate

No Group: 50.5%, HPC1(18P)=38.6%

fG1 fG5 fG10 fG15 fG20 fG25PJ PH

0.5

1

1.5

2

Parameters

Pupdate

d /

Pin

itia

l

No Group:27P

HPC1:18P

FE Model Updating with Hierarchical Parameter Clustering

No Grouping: 27P

10 16 1 25 5 21 9 17 6 20 13 23 3 11 15 7 19 4 22 8 12 14 18 2 24 27 260

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Parameters

Cosin

e D

ista

nce

Hierarchical Clustering (Threshold Distance = 0.0038053)

Hierarchical Parameter

Clustering (HPC1:18P)

17

- Modified Takeda Model: Non-analytic Form – Identification of , , &

Stiffness Degradation : Pinching : Strength Deterioration :

yM

Elasticcurvature

Inelasticcurvature

60m 60m

20m

① ② ③ ④ ⑤

⑥ y

xz

Ground acceleration (pga=0.4g)

M ( , )x xM Mf ( , )x xM Mf

M

fff1( )EI

2( )EI

"

3( )EI

'

3( )EI

*( , )p pM Mf

0.00 2.00 4.00 6.00Time(sec)

-5.0E+0

0.0E+0

5.0E+0

Exc

it ati o

n(m

/ sec

2)

gu pL

yfyf f

III. Extended Kalman Filter for Damage Identification of Bridge Pier

(KJ Lee at Daelim Co., CB Yun at KAIST)

18

Observation Equations for Acceleration Response Measurement

State Vector and Parameter Vector

θ( ) { ( ) ( ) ( ) ( )}yk M k k k k

1 1 1[ ( ) ( ) ( ) ( ) ( ) (k) θ( )]T

k l l lq k q k q k q k q k q k

1 ( , ; , )k k k k kG f k w

Y (Χ ; )k k kh k v

Extended Kalman Filter Formulation

Two Step Approach

Sequential Prediction Error Method for Parameters, θ(k)

Extended Kalman Filter for State, X(k)1Yk 1Yk

1/ 1 / 1 1 1ˆˆ [ ( )]k k k k k k k kX X K Y Y

1 1 1 1 1 1/ 1ˆ ˆ[ ( , )]k k k k k k k k kB Y Y X

1ˆk

1Xk

19

Parameter Identification by Extended Kalman Filter

Estimated nonlinear parameters

Nonlinear Parameters My(KNm) α β γ

Exact Values 1200.0 0.01000 1.0000 0.2000

Initial guesses 600.0 0.00500 0.5000 0.1000

w/ 2-modes 1156.8 0.0096 0.4956 0.1674

w/ 5-mode3 1174.2 0.0096 0.4894 0.1789

w/ 12-modes 1167.8 0.0096 0.5509 0.1789

Parameter estimation Estimated M-φ

5-modes 12-modes

-4.0E-4 0.0E+0 4.0E-4Curvature

-2.0E+6

0.0E+0

2.0E+6

Mom

ent (K

N*m

)

Exact

w/ SMEKF

-4.0E-4 0.0E+0 4.0E-4Curvature

-2.0E+6

0.0E+0

2.0E+6

Mom

ent (K

N*m

)

Exact

w/ SMEKF

Estimated M-φ relationship by SMEKF Estimated responses

2.00 3.00 4.00 5.00 6.00Time (sec)

-5.0E+0

0.0E+0

5.0E+0

Acce

lerati

on (m

/sec^2

)

Exact

w/ SMEKF

2.00 3.00 4.00 5.00 6.00Time (sec)

-5.0E+0

0.0E+0

5.0E+0

Accel

eration

(m/se

c^2)

Exact

w/ SMEKF

at Node 3

at Node 6

I. Innovative sensing: OFS, Microwaves, Vision-based sensing

II. Robotic trenchless rehabilitation of underground pipes

III. Multifunctional wireless impedance sensor node

IV. Wireless power/data transmission for guided wave sensing

I. Innovative Sensors and NDE Technologies(M Feng at UCI)

Microwave Imaging System

Distributed Fiber Optic Strain SensorMoiré Fringe–based Fiber Optic Accelerometers

C

r

ac

kA

C

r

B

X [mm]

0 50 100 150 200

Y [m

m]

0

50

100

150

200

250

300

0

20

40

60

80

100

120

140

160

Strain []

Non-Contact Vision-Based Disp. Sensors

• Spin carbon fibers on internal wall of pipes 11 times faster than

manual application

• Reduce cost by 15 times

• Eliminate environmental impact

Robotics Trenchless Rehabilitation of Underground Pipes(M Feng at UCI)

2 1

31

( )( ) [ (1 )]

( ) ( )

s

sAZ ZZ i

ZC

• Electromechanical Impedance

PZT

Impedance Analyzer PC

GPIB Interface

Card & Cable

- +

PZT PZT

MFC

Thermo-

coupler

3.05 3.1 3.15

x 104

-2

-1

0

1

2

3

4

5

6

7

8

Frequency [Hz]

Re

al (

Z(

) )

Test #1 at 22.6oC

Test #338 at 10.3oC

Max. CC=0.9858

CC=0.9900

3.05 3.1 3.15

x 104

85

90

95

100

105

110

115

120

125

130

135

140

Frequency [Hz]

Re

al (

Z(

) )

Test #1 at 22.6oC

Test #338 at 10.3oCCC=-0.0986

0 200 400 600 800 1000 1200 1400 1600

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

Test No.

Max

. CC

w/ E

FS

0 200 400 600 800 1000 1200 1400 16005

10

15

20

25

30

35

Tem

pera

ture

(o C)

thr1=0.942

thr2=0.884

thr3=0.8072mm cut

4mm cut

8mm cut

0 200 400 600 800 1000 1200 1400 1600

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Test No.

Max

. CC

w/o

EFS

0 200 400 600 800 1000 1200 1400 1600

10

15

20

25

30

35

Tem

pera

ture

(o C)

Intact 2mm cut 4mm cut 8mm cut

Temperature

Compensation

30 35 40

0

10

20

30

Frequency [kHz]

Re

al (

Z(

) )

Intact 2mm 4mm 8mm

II. Multifunctional Wireless Impedance Sensor Node(J Min, CB Yun at KAIST, S Park at SKKU)

125 130 1356

7

8

9

10

11

12

Real part (V/I

)

Frequency (kHz)

BaselineDamage

Baselines Damage

Effective Frequency Shift (EFS)

Temperature Compensation Technique

1

1CC { ( ( ) )( ( ) )}/max max

N

i i i X Y

i

x x y yN

Low-Cost Wireless Impedance Sensor Node

Wireless Impedance Sensor Node (WISN)

* Impedance Analyzer (HP4294A) : $41,000

Impedance Sensor Node (KAIST, UTO)

Control Room

Internet

Base station

Wireless

PZT Sensors

Pattern Recognitionfor Damage Diagnosis

Temperature Compensation

Sensor Self-Diagnosis

Power Supply &Battery Power Monitoring

Multi-Functional

WISNMulti-Channel

Sensing

($300)

($300)

Intelligent Damage Diagnosis Using Neural Network

• It is important to select a proper frequency range sensitive to the expected damage type.

• The sensitive frequency range varies to the type of the damage.

• Damage type and severity are to be determined.

Correlation Coefficients at Multiple Frequency Ranges

Loose Bolts Cracks1

0.95

0.90

0.80

0.75

0.70

Loose Bolt #1

Loose Bolts #1&2

Cut #1

Cut #2

1

0.95

0.90

0.80

0.75

0.70

Unknown

Health Status

of Structures

Damage Classification &

Quantification

Damage

Features (CC)

for Sub-freq. Ranges

Classify

DamagesTraining

NN

Data Base

FR1 FR2 FR3 FR4 FR5 FR6 FR7 FR8 FR9

Full Frequency Range(10-100 kHz)

CC = 0.9911

(2 Cuts)

Impedance Signals

26

Multiple Damage Diagnosis on Bridge

0 50 100 150 200 250 3000.3

0.4

0.5

0.6

0.7

0.8

0.9

1

290 Training Patterns

(5 Cases)

Loose Bolt and Crack Detection on a Bridge (Korea Expressway Corp.)

0 10 20 30 40 50 60 700.4

0.5

0.6

0.7

0.8

0.9

1

No. of Samples

Te

mp

era

ture

Co

mp

en

sa

ted

C.C

.

Loose Bolt #1

Retighten Bolt #1

Cut #1

Cut #1 & Loose Bolt #1

Cut #1 & Loose Bolt #1&2

Cut #1&2 & Loose Bolt #1&2

Single Freq. Range (45-50 kHz) Multiple Freq. Ranges (40-80 kHz)

PZT

(PIC151 Type,

50*50*1 mm)

Thermo-coupler

1 2 3 4 5 6 70

0.5

1

1.5

2

Bolt(Real)

Bolt(Estimated)

Crack(Real)

Crack(Estimated)

Excluded Cases

in NN Training

NN Verification : 7 Test Cases

Damage Type & Severity

CC Values in 8 Frequency Ranges

DamagesOutput 1

TypeOutput 2Severity

No Damage [0 0] [0 0]

Loose Bolt Only [1 0] [N 0]

Crack Only [0 1] [0 ƩW/L]

Multiple Damages(Loose Bolt and Crack)

[1 1] [N ƩW/L]

* N : No. of Loose Bolts; * W/L : Normalized Crack Length

Training

for NN

Autonomous Frequency Range

Selection and Damage Diagnosis

Damage Cases #3 & #6

were excluded

from NN training

27Structural Dynamics Laboratory, Dept. of Civil & Environmental Engineering

SHM System for Han River Railroad Bridge, Seoul, Korea(Demonstration Project)

12 Imote2 Wireless Sensors10 Wireless Impedance Sensors

Antenna

Solar Panel

WISN

PZT

20 FBG Strain Sensors

Solar Panel

Antenna

Sensor Node

16 Accelerometers

28

Wireless power and

data transmission

Wireless node for power and data transmission

PZT transducerLaser Diode

Photodiode

Active Sensor

Signal Generator Laser Diode

Signal Analyzer Photodiode

Power Demand: 40-80mW Power Demand: 600mW

Make the sensor node as “simple” as possible

Microprocessor

RF Transmitter

A/D Converter

Wave Generator

Memory

D/A Converter

Active Sensor

Battery

Microprocessor

RF Transmitter

A/D Converter

Memory

Passive Sensor

Battery

III. Wireless Power/Data Transmission for Guided Wave Sensing

(HJ Park, H Sohn, CB Yun at KAIST)

29

Wireless Power

Transmission

Wireless Data

Transmission

High Power Laser

TransmissionDriver

SignalGenerator

PhotodiodeOscilloscope

PhotodiodeReceiving

Driver

PhotovoltaicPanel

LaserDiode

TransmissionDriver

LaserDiode

ReceivingDriver

PZT

Schematics of Wireless Guided Wave Generation and Sensing System

1 i

p r o

V (ω )Z

( C C ) V (ω )

Impedance measurement using a simple self-sensing circuit.

Oscilloscope

NI-DAQ PXI DC-to-DC converter

Rx driver

Laser diode

Tx driver

Photodiode

PZT transducer

30

1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5

x 104

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Frequency (Hz)

Real o

f N

orm

alized

E/M

Im

ped

an

ce

Impedance analyzer

Laser-based wireless system

Results of Wireless E/M Impedance Sensing

30 – 35 KHz

* Maximum amplitudes have been normalized for comparison.

2 2.05 2.1 2.15 2.2 2.25 2.3 2.35 2.4 2.45 2.5

x 104

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Frequency (Hz)

Real o

f N

orm

alize

d E

/M Im

ped

an

ce

Impedance analyzer


20 – 25 KHz

10 – 15 KHz

3 3.05 3.1 3.15 3.2 3.25 3.3 3.35 3.4 3.45 3.5

x 104

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Frequency (Hz)

Real o

f N

orm

alize

d E

/M Im

ped

an

ce

Impedance analyzer


1.31 1.315 1.32 1.325

x 104

-0.2

0

0.2

0.4

0.6

0.8

1

2.275 2.28 2.285 2.29 2.295 2.3

x 104

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

3.055 3.06 3.065 3.07 3.075 3.08 3.085 3.09

x 104

-0.2

0

0.2

0.4

0.6

0.8

1

Recent research and application activities on the vibration-based

SHM and innovative NDE techniques for civil infrastructures at

KAIST and UCI are introduced:

Vibration-based SHM

Innovative NDE Techniques

- Long-term SHM systems for large civil infrastructures

- Large-scale wireless sensor network system on a cable stayed bridge

- Kalman filtering technique for damage identification under earthquake

- Innovative sensing: OFS, Microwaves, Vision-based sensing

- Robotic trenchless rehabilitation of underground pipes

- Multifunctional wireless impedance sensor node for steel structure

- Wireless power/data transmission for guided wave sensing

Best wishes to Prof. Shinozuka and his family

in Many Years to Come!!

Merry Christmas and Happy New Yearto All the Participants!!

Documents

Chung-Bang Yun - 東京大学park.itc.u-tokyo.ac.jp/tkdlab/ISARM2010/2010_Yun1.pdf · 2 1 •Introduction 2 •Vibration-based SHM-Long-term SHM systems for large civil infrastructure-Large