recent advances in structural health monitoring...traffic excitation modeling ns improved system...

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

Vulnerability of Our Civil Infrastructure

Courtesy of LA Time

Courtesy ofLA Times

Courtesy of A Worrall

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

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

Where Are We Now?

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

Remote Control and Data Acquisition

Bridge Site

UCI Campus

Wireless Real-Time Data Acquisition System

We Need Reliable Methods to Interpret Sensor Data

Server

Client

http://mfeng.eng.uci.edu

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

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.

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)

Y- direction

Optical fiber accelerometer

Sokushinco.direction

Z- direction

Y- direction

Optical fiber accelerometer


Z- direction

Y- 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

ULTRA COMPACT LOW POWER

Wireless MEMS AccelerometerWireless MEMS Accelerometer

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

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

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

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

Detection of Fine Cracks

Crack A

Crack B

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.)


-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


-1 0 1 2 3 4 5-0.8

-0.4

0.0

0.4

0.8

Inte

nsity

(Vol

t.)

Time (msec.)


-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


s

Gold deposited surface

E1E2

I

LD

PDMonochromatic laser

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

Inspection of FRP-Wrapped Concrete

Contents



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.)

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

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

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)

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)

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

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

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

Identification of Nonlinear Damping Using Ambient Response

Comparison of Damage Assessment Methods

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%

Contents



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

recent advances in structural health monitoring...traffic excitation modeling ns improved system...

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