streicker bridge: the impact of monitoring on decision daniele zonta university of trento

zonta, glisic & adriaenssens • streicker bridge: the impact of monitoring on decision

Streicker Bridge: The impact of Streicker Bridge: The impact of monitoring on decisionmonitoring on decision

Daniele ZontaDaniele ZontaUniversity of TrentoUniversity of Trento

Branko Glisic, Sigrid Adrianssens Branko Glisic, Sigrid Adrianssens Princeton UniversityPrinceton University

SPIE Smart Structures / NDE • San Diego, Mar 15, 2012


impact of monitoring on decision?permanent monitoring of bridgespermanent monitoring of bridges is commonly presented

as a powerful tool supporting transportation agencies’ decisions

in real-life bridge owners are very skepticalvery skeptical

take decisions based on their experience or on common sense

often disregard the action suggestedthe action suggested by instrumental damage detection.

we propose a rational frameworkrational framework to quantitatively estimate the monitoring systems, taking into account their impact on decision making.


benefit of monitoring?

a reinforcement intervention improves capacity

monitoring does NOT change capacity nor load

monitoring is expensive

why should I spend my money on monitoring?


value of information (VoI)

VoI = C - C*operational cost w/o monitoringC =operational cost with monitoringC* =

recast the problem in the general framework of decision decision theorytheory [Pozzi et. al 2010, Pozzi & De Kiureghian 2011]

Value of InformationValue of Information: money saved every time the manager interrogates the monitoring system

maximum priceprice the rational agent is willing to paywilling to pay for the information from the monitoring system

implies the manager can undertake actions in reaction to actions in reaction to monitoringmonitoring response


Streicker Bridge at PU campus New pedestrian bridge being built at Princeton University campus over the

Washington Road Funded by Princeton alumnus John Harrison Streicker (*64), overall

design by Christian Menn, design details by Princeton alumni Ryan Woodward (*02) and Theodor Zoli (*88)

• Main span: deck-stiffened arch, deck=post-tensioned concrete, arch=weathering steel

• Approaching legs: curved post-tensioned concrete continuous girders supported on weathering steel columns


introducing 'Tom' fictitious character responsible of the imaginary Design

and Construction office in PU behaves in a rational manner aims at minimizing the operational

cost linear utility with cost no separation between direct cost to

the owner and indirect cost to the user concerned that a truck driving on

Washington Rd., could collide with the steel arch

"Tom"


possible states of the bridge

the bridge is still standing, but experienced severe damage at the steel arch structure; chance of collapse under design live load and under self-weight

the structure has either no damage or mere cosmetic damage, with no or negligible loss in capacity

SevereDamage

No damage


Tom's options

no special restriction is applied; bridge is open to pedestrian traffic; minimal repair or maintenance works con be carried out

both Streicker bridge and Washington Rd. are closed to pedestrian and vehicular traffic; access to the nearby area is restricted

Do Nothing

Close bridge


Tom's cost estimate

Daily Road User Cost (DRUC)that considers the value of time per day as a monetary term (Kansas DOT 1991, Herbsman et al. 1995)

estimated DRUC for Washington Road in $4660/day

estimated downtime: 1 month total downtime cost

CDT=4660 x 30 = $139,800

Close bridge


Tom's cost estimate

Pay nothing!!

Do Nothing No damageAND


Tom's cost estimate

Do Nothing AND SevereDamage

2 month DRUC $279,600

cost of fatality: k$ 3840chance of fatality: 15%

$576,000

cost of injury: k$ 52chance of injury: 50%

$26,000

total failure cost CF=$881,600


cost per state and action

CF

k$ 881.6 0Do Nothing

Close bridge

Damage No Damage

CDT

k$ 139.8CDT

k$ 139.8


decision tree w/o monitoring

DN

D

U

action state cost

0

CF

probability

P(D)

CDN = P(D) · CF

Do Nothing

Close Bridge

Damaged

Undamaged

DN D

U

action: state:LEGEND

expected loss

P(U)

CDT downtime cost

http://images.google.com/imgres?imgurl=http://green.gps.caltech.edu/pictures_images/UnderConstruction_1.jpg&imgrefurl=http://green.gps.caltech.edu/Sustainability_MenuFrame.html&usg=__O6XAnpO6RePLwvZ7dO31lEBWvPk=&h=1004&w=1004&sz=58&hl=en&start=70&itbs=1&tbnid=-x12yLUuZ1derM:&tbnh=149&tbnw=149&prev=/images%3Fq%3Dunder%2Bconstruction%26start%3D63%26hl%3Den%26sa%3DN%26ndsp%3D21%26tbs%3Disch:1




DN

D

U

action state cost

0

CF

probability

P(D)

CDN = P(D) · CF

Do Nothing

Close Bridge

Damaged

Undamaged

DN D

U


expected loss

P(U)

CDT downtime cost

C = min { P(D)·CF , CDT }

Optimal cost

decision criterion

CDT < CDN ?yn

DN





Tom's prior expectation

CF

k$ 881.6 0Do Nothing

Close bridge

DamageP(D)=30%

No DamageP(U)=70%

CDT

k$ 139.8CDT

k$ 139.8



DN

D

U

action state cost

0

k$881.6

probability

30%CDN = P(D) · CF= k$264.5

Do Nothing

Close Bridge

Damaged

Undamaged

DN D

U


expected loss70%

CDT = k$ 139.8downtime cost




Streicker Bridge, instrumentation

Half of main span equipped with sensors (assuming symmetry)

• Currently two fiber-optic sensing technologies used

• Discrete Fiber Bragg-Grating (FBG) long-gage sensing technology (average strain and temperature measurements)

• Truly distributed sensing technology based on Brillouin Optical Time Domain Analysis (BOTDA, average strain and temperature measurements)


5.182m 5.232m 5.232m 6.147m5.232m 5.232m6.147m

P3 P4 P5 P6 P7 P8 P9 P10

FBG Strain Sensor

BOTDA Distributed Strain & Temperature Sensor Junction Box

A;BC;D

E

A;B

C;D;E

C;D

E

A;BA;B

C;D;E

A;B

C;D

S N

P71.524m 1.524m2x0.487

S NP8

1.524m 1.524m2x0.741mS N

Close to P10

1.524m 2.178m 2.178m 1.524m

FBG Temp. SensorFBG Strain & Temp. Sensor

P9

S NP9

1.524m 1.524m1.249m1.249m

Sensor location in main span


5.182m 5.232m 5.232m 6.147m5.232m 5.232m6.147m

P3 P4 P5 P6 P7 P8 P9 P10BOTDA Distributed Strain & Temperature Sensor Junction Box

A;B

S N

P7

1.524m 1.524m2x0.487

FBG Strain & Temp. Sensor

Sensor location in main span


decision tree with monitoring

DN

D

U

action state cost

0

CF

posterior

probability

P(D|)

CDN = P(D|) · CF

Do Nothing

Close Bridge

Damaged

Undamaged

DN D

U


expected loss

P(U|)

CDT downtime cost




0

0.02

0.04

0.06

0.08

0.1

0.12

-1000 -500 0 500 1000 1500 2000 2500 3000

elongation [m]

PD

F( |

S)

Likelihoods and evidence

P(|D) · P(D)

P(|U) · P(U)

P()

S N

P71.524m 1.524m2x0.487



DN

D

U

action state cost

0

CF

posterior

probability

P(D|)

Do Nothing

Close Bridge

Damaged

Undamaged

DN D

U


expected loss

P(U|)

CDT downtime cost

C = min { P(D|)·CF , CDT }

Optimal cost

decision criterion

CDT < CDN ?yn

DN

CDN = P(D|) · CF






0

0.02

0.04

0.06

0.08

0.1

0.12

-1000 -500 0 500 1000 1500 2000 2500 3000

PD

F( |

S)

P(|D) · P(D)

P(|U) · P(U)

CDT=k$139.8

CDN = P(D|) · CF

c*()=min { P(D|)CF , CDT }

P()

DN



0

0.02

0.04

0.06

0.08

0.1

0.12

-1000 -500 0 500 1000 1500 2000 2500 3000

PD

F( |

S)

P(|D) · P(D)

P(|U) · P(U)

CDT=k$139.8

DN

DU

CDN = P(D|) · CF




CDT=k$139.8

CDN = P(D|) · CF


C*=∫ c*()PDF()d= k$ 84.6



VoI = C - C*

maximum priceprice Tom (the rational agent) is willing to willing to paypay for the information from the monitoring system

C=min { P(D)·CF , CDT }= k$ 139.8

C*=∫ c*()PDF()d= k$ 84.6

VoI = C - C*= k$ 55.2


conclusions an economic evaluation of the impact of SHM on

decision has been performed

utility of monitoring can be quantified using VoIVoI

VoI is the maximum priceprice the owner is willing to paywilling to pay for for the informationthe information from the monitoring system

implies the manager can undertake actions in reaction to monitoring response

depends on: prior probabilityprior probability of scenarios; consequenceconsequence of actions; reliability of monitoringreliability of monitoring system


Thank you for your attention!Acknowledgments

Turner Construction Company; R. Woodward and T. Zoli, HNTB Corporation; D. Lee and his team, A.G. Construction Corporation; S. Mancini and T.R. Wintermute, Vollers Excavating & Construction, Inc.; SMARTEC SA; Micron Optics, Inc.

the following personnel from Princeton University supported and helped realization of the project: G. Gettelfinger, J. P. Wallace, M. Hersey, S. Weber, P. Prucnal, Y. Deng, M. Fok; faculty and staff of CEE; Our students: M. Wachter, J. Hsu, G. Lederman, J. Chen, K. Liew, C. Chen, A. Halpern, D. Hubbell, M. Neal, D. Reynolds and D. Schiffner.

Matteo Pozzi, UC Berkeley Ivan Bartoli, 'Tom', Drexel University


Thanks. Questions?


general case

ci,kai

sk

scenarioac

tions

a1

aM

s1 sN

M available actions: from a1 to aM

N possible scenario: from s1 to sNcost per state and action

matrix



a1

action state cost

c1,1

probability

P(s1)

c1,k

s1

sN

sk

ai

aM

c1,N

...

ci,1

ci,k

s1

sN

sk

ci,N

...

cM,1

cM,k

s1

sN

sk

cM,N

...

P(sk)

P(sN)

P(s1)

P(sk)

P(sN)

P(s1)

P(sk)

P(sN)

C = min { ∑k P(sk)·ci,k }

...

i

decision criterion

∑k P(sk)·c1,k

∑k P(sk)·ci,k

∑k P(sk)·cM,k

expected cost

...

...

...

...



a1

state cost

c1,1

probability

P(s1|x)

c1,k

s1

sN

sk

ai

aM

...

c1,N

...

ci,1

ci,k

s1

sN

sk

ci,N

...

cM,1

cM,k

s1

sN

sk

cM,N

...

... C|x = min { ∑k P(sk|x)·ci,k }

...

i

decision criterion

∑k P(sk|x) ·c1,k

∑k P(sk|x)·ci,k

∑k P(sk|x)·cM,k

expected cost

outcome

X

P(sk|x)

P(sN|x)

P(s1|x)

P(sk|x)

P(sN|x)

P(s1|x)

P(sk|x)

P(sN|x)

action

...

...



VoI = C - C*

maximum price the rational agent is willing to pay for the information from the monitoring system

C = min { ∑k P(sk)·ci,k }

C* = ∫Dx min { ∑k P(sk)· PDF(x|sk)· ci,k }dxdepends on:

prior probability of scenariosprior probability of scenarios consequence of action reliability of monitoring system

streicker bridge: the impact of monitoring on decision daniele zonta university of trento

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