October 22, 2014
Monitoring the Health of Structures for Quantifying and Achieving Resilience for Natural Hazards
Bilal M. Ayyub and Yunfeng Zhang
Department of Civil & Environmental Engineering
University of Maryland, College Park ba@umd.edu
and zyf@umd.edu

• After natural hazardous events, engineers are usually faced with many competing priorities in making safety and occupancy decisions about large inventories of building and bridge assets , which could be more effectively managed through automated inspection and computerized condition assessment .
Fractured EBF in Pacific Tower, from Bruneau et al. 2012
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

Club Tower building, completed in 2009. estimates of the peak inelastic demand in the active link were made through visible assessment of the active link yielded web metal.
Paint flaking of partially hidden EBF link & global view of
EBF braces obstructed by various utility runs. [Photos by
M. Bruneau and C Clifton]
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• The building remained plumb following the earthquake.
• The initial assessment of the structure by the owner's representative was that the structure had not sustained much damage (only one window had been broken).
• Only after the dry wall was removed, the extent of damage was revealed.
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Photos from Sabelli 2013 and Trembaly 1995


Clearly, ability of rapid structural condition assessment especially for many hidden locations after major hazardous events reduces the time to recovery and increases the resilience in disaster recovery
Rapid condition assessment
100%
50%
0
Q
0h1
Q
0h2 t f t f
+

T r1
Conventional inspection approach t f
+

T r2
5
Time

Performance
“as new”
A Poisson process with rate l leading to an incident occurrence
Target
Failure event definitions: f1. Brittle f2. Ductile f3. Graceful
Recovery event definitions: r1. E. better than new r2. E. as good as new r3. E. better than old r4. E. as good as old r5. As good as old r6. Worse than old
E. = Expeditiously f1 f2 f3 r1 r2 r3 r4 r5 r6
Robustness, i.e., residual performance ( Q r
)
Estimated performance with aging effects
Performance after recovery
Disruption duration

T d
Recovery duration

T r
Failure duration

T f
T r
= Time to recovery
T f
= Time to failure
T i
= Time to incident
0
0 t i t f t r
Resilience (
Recovery costs
R e
)

T i
T i

F

T f
Indirect impacts including loss of

R

T r
 
T f
 
T r
6
0
R e
Direct failure
> 0
Not to scale
Time
Impacts valuated
Failure ( F )
 f
 t t i fdt f
 t t i
Qdt
Recovery ( R )
 t
 t r f rdt t
 t r f
Qdt

Systems Performance
Buildings Space availability
Other structures: Highway bridges Throughput traffic
Facilities: Water treatment plants
Infrastructure: Water delivery
Water production capacity
Water available for consumption
Network: Electric power distribution Power delivered
Communities Economic output
Quality of life (consumption)
Units
Area per day
Count per day
Volume per day
Volume
Power per day
Dollars
Dollars
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Resilience ( R e
)

T i
T i

F

T f
 
T f


R

T r

T r
R e
< 1
Failure ( F )
 f
 t t i fdt f
 t t i
Qdt
Recovery ( R )
 t
 t r f rdt t
 t r f
Qdt
The failure-profile value (F) can be considered as a measure of robustness and redundancy; whereas the recovery-profile value (R) can be considered as a measure of resourcefulness and rapidity.
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 Measuring resilience based on its components
(MCEER):
 Robustness as the ability of the system and system elements to withstand external shocks without significant loss of performance
 Redundancy as the extent to which the system and other elements satisfy and sustain functions in the event of disturbance
 Resourcefulness as the ability to diagnose and prioritize problems and to initiate solutions by identifying and monitoring all resources, including economic, technical, and social information
 Rapidity as the ability to recover and contain losses and avoid future disruptions
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A Poisson process with rate l leading to an incident occurrence
Performance
“as new”
Target
Q f1
Q f0
Q f2
Failure event definitions: f1. Brittle f2. Ductile f3. Graceful
Recovery event definitions: r1. E. better than new r2. E. as good as new r3. E. better than old r4. E. as good as old r5. As good as old r6. Worse than old
E. = Expeditiously f1 f2 f3 r1 r2 r3 r4 r5 r6
Robustness, i.e., residual performance ( Q r
)
Estimated performance with aging effects
Performance after recovery
Disruption duration

T d
Recovery duration

T r
Failure duration

T f
0
0
T r
= Time to recovery
T f
= Time to failure
T i
= Time to incident t i t f
Recovery costs t r t r1 t r2
Indirect impacts including loss of performance
Direct failure impacts
Not to scale
Time
0 Impacts valuated


Structural health monitoring system should generate an alarm signal whenever the strain exceeds the pre-specified limit state (e.g., yielding, fracture or buckling).

Strain gage 1
Strain gage 2
Strain gage 1
30 in
(0.76 m)
RFID reader
Hybrid simulation test setup for system validation of WSCA on truss structure

-12000
-10000
Gage 1
Gage 2
-8000
-6000
H6W4-FEM
-4000
-2000
0
0
H9W2-FEM
H6W4
H9W2
50 100 150 200 250
Time (sec.)
300
Brittle Bar Size
Test #1
H6W4 H9W2
0.65% 1.10%
Test #2 0.61% 1.02%
Ave. of Test
0.63% 1.06%
350 values
400 450 500
Design value 0.55% 1.18%

Data acquisition system
RFID reader
BIM user interface stub column specimen with BT strain sensors

350
300
250
H6W4
200
150
H9W2
100
50
0
0 0.005
0.01
0.015
0.02
Axial strain
0.025
0.03
0.035



Resilience metrics is defined
For such seismically resilient structures with fuse members, automated wireless scanning of fuse zone for possible damages suffered during earthquakes or strong winds could be performed in a very efficient way and this practice would greatly accelerate condition assessment and thus enhance resilience through shorter and more accurate inspection.
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