benefits of structural health monitoring of bridges · what is structural health monitoring •use...
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Benefits of Structural Health
Monitoring of Bridges
Brent Phares
Bridge Engineering Center
Iowa State University
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What is Structural Health
Monitoring • Use quantitative information
– Sensors, measurements, images, etc.
• To make management decisions, to learn
about condition, or to establish behavior.
• But, why use SHM.
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Traditional Evaluation
1968 - National Bridge Inspection
Program initiated
Visual inspection
Conventional Calculations
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Basic Testing Concept • Record field measurements on structure subjected to
loads to identify actual structural response.
• Field data may be used to “calibrate” an analytical model
that closely represents the behavior observed in the
field.
• May be used for detecting damage or deterioration,
general structural evaluations, and for developing load
ratings.
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Objective: Short-Term SHM • Collect data, over a relatively short period
of time (several hours to one day), for the
purpose of obtaining quantitative
information that allows for:
– Improving qualitative assessments.
– Improving future designs.
– Determining “on-the-fly” modifications (e.g.,
during construction).
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Application #1 • Provide structural performance
recommendations for evaluation
– Load distribution
– Stress level
– Serviceability (deflection, shear keys)
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• Guidelines for evaluation – X-section strain compatibility
– Deflections within AASHTO limits
– W/ regard to “shear keys” transverse distribution satisfied AASHTO limits
• Decision: Delay replacement
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• Supplementary laboratory strength tests
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Application #2 • Determine if the excessive wind event and
associated bridge movement that was reported
was “accurate”.
• Install a monitoring system for remote alerting of
future events.
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• Short-term load test
– Identify dynamic modal characteristics
• Strain and acceleration
• Bridge/wind interaction possible
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• Long-term monitoring
– Girder strain and acceleration, wind speed
and direction
• Data loggers
• 24x7 data collection
• Alert system
– Cellular communications
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Application #3
• Evaluate effectiveness of
strengthening systems
– Strain profile
– Stiffness increase
– Change in load distribution
FRP Plates Bolted Angles
Post-tensioning
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• Added material (FRP, Steel)
– Short and long-term evaluations generally reveals that
strengthening mechanisms are effective
• Short and long-term increase in stiffness
• Minimal load distribution effect
CFRP PLATE
STRAIN GAGE
TB
EXT
T
hwebhCFRP
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Application #4 • Primary: Determine if load rating is reasonable
• Secondary: Validate evaluation process for subsequent permit trucks
900,000 lb permit load (~RF 0.5)
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• Tested with combinations of one and two loaded tandem axle dump trucks
• Learned about behavior
– Composite action
– End restraint
– Live load distribution • Improved load distribution characteristics used
in hand calculations; changed RF to 0.9
– Rating w/ validated analytical model • RF ~ 1.0 (permit truck)
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BRIDGELC
G1 G2 G4G3 G5 G6
-100
-50
0
50
100
150
200
250
0 100 200 300 400 500 600
Truck Position, ft
Mic
rostr
ain
G2
G3
G4
G5
-100
-50
0
50
100
150
200
250
0 100 200 300 400 500 600
Truck Position, ft
Mic
rostr
ain
G2
G3
G4
G5
Analytical: Experimental:
• Prediction validity for permit truck
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Application #5
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• Short term – eliminate panel overstress during
construction.
• Long term – monitor redistribution of loads in
hangers due to concrete creep.
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• Strain (force) correlated with target design
values during construction
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September 8-10, 2004
Application #6 Incremental Launch
of the U.S. 20 Iowa River Bridge
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Girders Assembled in Launching Pit
and Supported by Rollers
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Substructure Monitoring • General pier behavior
• (drilled shaft and driven
pile)
– Column base strain
– Column base translation
and tilt
– Cap beam tilt
At near and far column faces
near face
far face
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S
0
Str
ain
Launch Distance (ft)
Far face
Near face 0
+
-
• Largest day launch cumulative column stress measured
was 600 psi
• Residual stress at end of day launch
Monitoring Results - Substructure
S N
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Superstructure Monitoring • Girder load distribution
– Bending
• Cross-frame behavior
• Roller contact stresses
– Bottom flange
– Web
– Flange to web welds
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Monitoring Results - Superstructure
Roller
-1200
-1000
-800
-600
-400
-200
0
200
823 823.25 823.5 823.75 824Launch distance (ft)
Str
ain
-7634 με CL of Roller
at Pier 6
• Significant longitudinal flange strain measured > Fy
Longitudinal Strain
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Monitoring Results - Superstructure
• Significant vertical strain measured
Roller
-2400
-2000
-1600
-1200
-800
-400
0.00 1.50 3.00 4.50 6.00 7.50 9.00Distance above the bottom of the flange plate (in.)
Str
ain
1.125
-2037 με
-1838 με
Vertical Strain
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Member
Type
Design
Force
Calculated
Force (WB1)
Calculated
Force (WB5)
Upper
Chord
20 kips C 42.6 kips T 86.2 kips T
Diagonals 38 kips T
or C
56.2 kips T 172.1 kips T
Bottom
Chord
20 kips T
or C
31.1 kips T 39.7 kips C
Monitoring Results - Superstructure • Cross-frame behavior is complex and sensitive
-axial forces, biaxial bending, and torsion
• Measured values sometimes exceeded design values
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Objective: Long-Term SHM
• Assess (engineering based) the condition of a bridge continuously without human interaction over long periods of time (days, months, years)
– “Instantly” detect abnormalities.
– Assess load carrying capability.
– Active bridge management system.
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The Need
• Bridge owners are looking for autonomous systems that assess structural integrity/safety/etc.
– Enter structural health monitoring
• Research in this area has been going on for 20+ years…with very, very few successes
– Previous approaches relied upon dynamics
– And even fewer attempted field demonstrations
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System Attributes Desired by Owners
• Damage detection
– All types of damage at varying locations
– Autonomous data collection, reduction, evaluation, and storage
– Reporting in real-time and in understandable formats
• Load rating (including better capacity estimate) and remaining life estimates
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Our Measurement Metric of Choice
• Strain, strain, strain.
• In particular, strain due to the passage of live loads…specifically, five-axle “semi” trucks
– Heavy weights and large numbers of them on the highway system.
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Difficulties with Autonomous SHM
• Unknowns, unknowns, unknowns
– Vehicle weight and geometry
– Traffic density and position
– Dynamic impact and suspension system variability
• Conventional analysis techniques are difficult/impossible to reliably perform
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Data, Data, Data
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Generic System Architecture
Data
Rating/Capacity Model
Damage Detection
Decision for Bridge Rating
Remaining Life
Decision for Bridge
Inspection
Long-Term Management
Truck detection needed
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Truck Detection Methodology
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Raw Strain Data
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Case #1 Case #2
Case #3 Case #4 Case #5
Case #6 Case #7
Eliminate Concurrent Events: Side by Side
Eliminate Concurrent Events: Same lane
Desired:
Fundamental Situations
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North Lane South Lane
DL23 DL24 DL21 DL22
(DL13) (DL14) (DL11) (DL12)
① ② ③ ④ ⑤
1
d12 Lb +Lt
Deck bottom
sensor line 2
Deck bottom
sensor line 1
Truck and Lane Detection Basics
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Deck strains
Benefits of using strain rate
• Eliminate global strain
effects
• Clearly shows and exploits
the localized strain effects
of truck wheel loads
• Accentuates the behaviors
of interest
Five Peaks (Axles)
Five Peaks (Axles)
Deck Strain Rates
Axle Detection Using Deck Strain Rate
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Capabilities
• Can detect: event occurrence, determine passage lane, determine number of axles, estimated (~+/- 0.5’) axle spacing, etc.
• Eliminate instances where multiple trucks are on the bridge at the same time
• Can be applied to bridges of virtually any configuration.
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Video - I-80 Bridge: Truck #1
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Lane Detection Using Deck Strain Rates
5-axle truck #1 5-axle truck #2
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Lane Detection Using Deck Strain Rates
5-axle truck #1 5-axle truck #2
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Damage Detection
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Pattern Recognition
• Train the system to recognize and develop relationships between sensors that are indicative of typical/normal performance
• Deviations from trained relationships are indicators that something has changed But what pattern?
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Algorithm Fundamentals
• Footprint matching
– Each traffic event leaves a footprint with distinct strain record shape and magnitude(s)
– Need a combination of bridge engineering and statistical expertise to create meaningful relationships/patterns
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First Generation System
FINALLY! An autonomous temperature compensation algorithm that works.
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Capture the pseudo-static behavior
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Limitations of First Generation System
• Limits must be established manually
– Time consuming and subjective
• Notable “spread” in the match relationships
• Sometimes multiple match relationships exist
• Results need some further interpretation
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Second Generation System
• Develop and implement an autonomous truck parameter detection sub-system – Travel lane, concurrent vehicle presence, number of
axles, axle spacing, vehicle weight, and speed
– Allows decimation of data to reduce spread and potential for multiple relationships
• Systematic approach for – Establishing normal limits
– Direct interpretation that shows damage occurrence and location
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0 20 40 60 80 100 120 140-180
-160
-140
-120
-100
-80
-60
-40
-20
0
B-SG-BF-H (Max, )
C-S
G-C
B(1
)-V
(M
in, )
2axle r
2axle l
3axle r
3axle l
4axle r
4axle l
5axle r
5axle l
6axle r
6axle l
0 20 40 60 80 100-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
B-SG-BF-H (Max, )
C-S
G-C
B(1
)-V
(M
in, )
5axle r
0 20 40 60 80 100 120 140-200
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
B-SG-BF-H (max, )
C-S
G-C
B(1
)-V
(m
in, )
Data Selection
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, 0, 1,pred ij ij ij jS a a S
For the undamaged structure, the strain of one sensor can be used to predict the strain of another sensor (counter to most structural analyses)
20 40 60 80 100 120 14020
40
60
80
100
120
140
160
180
B-SG-BF-H (Range, )
C-S
G-C
B(1
)-V
(R
an
ge
n, )
5axle r
Model1 (heavy)
Model2 (all)
Model3 (light)
, ( 0 1 )ij i pred ij i ij ij jR S S S a a S
40 40
, ,1 1
( 1 40)i ij std ij stdj i
CRS R R i to
,
ij ij
ij std
ij
RR
3
3i i i
i i i
UCL
LCL
0 50 100 150 200 250 300-3
-2
-1
0
1
2UCL
LCL
CL
1.6753
-1.6753
Truck Groups
CR
S
C-SG-WG(1)-V Group Size:10 Level0
training
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Damage Detection and Localization
• Post-training (i.e., monitoring stage)
– Calculate residual matrix using the training stage prediction models
– Calculate CRS
– Plot data on the control chart
– Out of limit points indicate structural damage
0 50 100 150 200 250 300-3
-2
-1
0
1
2UCL
LCL
CL
Truck Groups
CR
S
C-SG-WG(1)-V Group Size:10 Level3
training
monitoring
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Second Generation Validation
• US30 bridge over the South Skunk River
– Two-girder, fracture critical bridge
– Multiple fatigue sensitive locations
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Specimen
Top Web Plate
Steel Strut
Stringer
Concrete Abutment Pedestal
Anchored Threaded Rods and Epoxy
Bottom Web Plate
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Typical Results
• Training data collected
– 3,653 heavy, right-lane, five-axle trucks
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Sensor 3 Sensor 4
Girder Bottom Flange Stringer Bottom Flange
Web-Gap Area 1 Web-Gap Area 2
0 10 20 30 40 50 60 70 80 90 100-6
-4
-2
0
2
4UCL=3.48
LCL=-1.79
CL=0.85
Specimen2-Sensor3 Undamaged
Truck Groups
Rsum
Training
Testing
0 10 20 30 40 50 60 70 80 90 100-3
-2
-1
0
1
2
UCL=1.43
LCL=-2.06
CL=-0.31
Specimen2-B-NG-BF-H Undamaged
Truck Groups
Rsum
Training
Testing
0 10 20 30 40 50 60 70 80 90 100-2
-1
0
1
2UCL=1.73
LCL=-1.58
CL=0.07
Specimen2-C-SG-CB(5)-V Undamaged
Truck Groups
Rsum
Training
Testing
0 10 20 30 40 50 60 70 80 90 100-4
-2
0
2
4
6
UCL=4.14
LCL=-3.8
CL=0.17
Specimen2-Sensor4 Undamaged
Truck Groups
Rsum
Training
Testing
0 10 20 30 40 50 60 70 80 90 100-4
-2
0
2
4
UCL=1.17
LCL=-2.21
CL=-0.52
Specimen2-D-NS-BF-H Undamaged
Truck Groups
Rsum
Training
Testing
0 10 20 30 40 50 60 70 80 90 100-3
-2
-1
0
1
2
3UCL=2.71
LCL=-2.09
CL=0.31
Specimen2-E-SG-CB(5)-V Undamaged
Truck Groups
Rsum
Training
Testing
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Fatigue Crack Induced
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Sensor 3 Sensor 4
Girder Bottom Flange Stringer Bottom Flange
Web-Gap Area 1 Web-Gap Area 2
0 10 20 30 40 50 60-4
-2
0
2
4
6 UCL=5.65
LCL=-2.05
CL=1.8
Specimen2-Sensor3 Damaged
Truck Groups
Rsum
0 10 20 30 40 50 60-15
-10
-5
0
5UCL=3.44
LCL=-3.86
CL=-0.21
Specimen2-Sensor4 Damaged
Truck Groups
Rsum
0 10 20 30 40 50 60-3
-2
-1
0
1
2
UCL=1.43
LCL=-2.06
CL=-0.31
Specimen2-B-NG-BF-H Damage1
Truck Groups
Rsum
0 10 20 30 40 50 60-3
-2
-1
0
1
2
3
UCL=1.17
LCL=-2.21
CL=-0.52
Specimen2-D-NS-BF-H Damage1
Truck Groups
Rsum
0 10 20 30 40 50 60-2
-1
0
1
2
3
UCL=1.73
LCL=-1.58
CL=0.07
Specimen2-C-SG-CB(5)-V Damage1
Truck Groups
Rsum
0 10 20 30 40 50 60-3
-2
-1
0
1
2
3UCL=2.71
LCL=-2.09
CL=0.31
Specimen2-E-SG-CB(5)-V Damage1
Truck Groups
Rsum
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Findings
• Damage of multiple types and multiple levels can be autonomously detected
• A correlation exists between damage level and damage indicator data (remaining life…)
• Unfortunately, algorithm has a higher than expected false-positive rate
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Third Generation Improvements
• More robust prediction model – orthogonal regression
-6
-4
-2
0
2
4
6
-6 -4 -2 0 2 4 6
Sen
sor
41
(m
icro
stra
in)
Sensor 25 (microstrain)
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Third Generation Improvements
• Damage detection model enhancement
𝒀 = 𝒌𝟏 𝜶𝟏 + 𝜶𝟑𝒙 + 𝒌𝟐 𝜶𝟐 + 𝜶𝟒𝒙
where:
𝑘1= 1 for training data
0 for post training data
and:
𝑘2 = 0 for training data
1 for post training data
𝒀 = 𝜸𝟏 + 𝜸𝟑𝒙
Full Model:
Reduced Model:
𝑭 =𝑹𝑺𝑺 𝒓𝒆𝒅𝒖𝒄𝒆𝒅 − 𝑹𝑺𝑺(𝒇𝒖𝒍𝒍) / 𝒅𝒇𝑹𝑺𝑺(𝒓𝒆𝒅𝒖𝒄𝒆𝒅) − 𝒅𝒇𝑹𝑺𝑺(𝒇𝒖𝒍𝒍)
𝑹𝑺𝑺(𝒇𝒖𝒍𝒍)/𝒅𝒇𝑹𝑺𝑺(𝒇𝒖𝒍𝒍) Model
similarity test:
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20
21
22
23
24
25
26
27
28
29
30
80 85 90 95 100 105 110 115 120
Sen
sor
19
(m
icro
stra
in)
Sensor 15 (microstrain)
Training Data Post-Training Data
Linear (Full Model) Linear (Reduced Model)
10
20
30
40
50
60
70
80 85 90 95 100 105 110 115 120
Sen
sor
44
(m
icro
stra
in)
Sensor 15 (microstrain)
Training Data Post-Training Data
Linear (Full Model) Linear (Reduced Model)
No damage
Damage!
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Third Generation
• False-positive rate appeared to drop
• All damage still detected
• Additional need: autonomous analysis of F-test results (currently being developed)
– Perhaps a return to control chart concepts!
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Fourth Generation
• Statistical strain range damage detection based upon quadruple redundant approach
• Four methods – Strain range
• Single truck
• Group size of ten
– Cross-prediction method
– FSHM
• Fully autonomous data collection, reduction, and analysis software.
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Strain Range Method Added
Single truck
Group of ten
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Load Rating
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Automated Load Rating Determination
• Use bridge response from unknown/partially known trucks.
• Use WIM data to statistically estimate the unknown truck features.
• Benefits: does not require bridge closure to perform load tests, load ratings can be estimated frequently (daily), allows for tracking changes with time.
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Hardwired strain gages
Wireless truck position indicator
Engineering based data interpretation
Structural modeling
Model analysis and optimization with field collected data Accurate
Assessment
Note: Automation of a proven approach
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Analytical Model Calibration
Load Rating using Calibrated Model
Select a Set of Strain Data with Maximum Strain of 90~100
Structural Health Monitoring System
Sample a Truck from WIM Database with Weight of 70~80 kips, Axle Spacing #3: X ± 2 ft, and Transverse Position of Lane Center ± 2 ft
Load Rating
Distribution
600 Runs
Next ‘Big’ Truck
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Other On-going Enhancements
• Capacity estimate from service level measurements.
• Remaining life estimation algorithm.
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Future Implementation
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Benefits of Using SHM
• No subjectivity in the data and/or
information.
• Can make decisions with greater
confidence.
• Data/information can be collected/used as
frequently as desired (must be cautious to
not get overwhelmed by data volume).
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Thank You!
Questions?