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Nizar Lajnef, Imen Zaabar, Shantanu Chakrabartty and Neeraj Buch Michigan State University Smart Sensing Technology for Infrastructure Monitoring Supported by FHWA and USDOT
Nizar Lajnef, Imen Zaabar,
Shantanu Chakrabartty and Neeraj Buch
Michigan State University
Smart Sensing Technology for
Infrastructure Monitoring
Supported by FHWA and USDOT
Moore’s Law and Structural Health Monitoring
• Economically viable to embed a million transistor IC in
every concrete brick, on a pound of titanium alloy, a
pound of steel, …
Chakrabartty, Feng, Aono, SPIE 2013.
1980 1985 1990 1995 2000 2005 2010 201510
-2
100
102
104
Year
Cost
($)
million silicon transistor
pavement concrete (lb)
structural concrete (lb)
structural steel (lb)
aluminum (lb)
titanium (lb)
passive RFID tag
Data Source: U.S. Geological Survey, ICKnowledge.
• Cost
• Size
• Power Source
• Maintenance – Maintenance free sensors
• Data meaning and interpretation
• Ease of installation and use
• Data type and format – Integration with existing
management systems
• Extreme events monitoring
Sensing Issues in Civil Structural Health Monitoring
Two Technologies
Long-term Tagging TechnologyEvents Detection and
Condition Monitoring Technology
Pavement Tagging Technology
PCC Mixture Design Inputs
Cement Content (lbs)
Type of cement
Supplementary Cementitious Materials (lbs)
Type of SCM
Coarse Aggregate (lbs)
Aggregate Geology
Coefficient of thermal expansion
Fine Aggregate (lbs)
Aggregate Geology
Water (lbs)
Admixture(s) (fl.oz)
Type of admixture(s)
Pavement Tagging Technology
Fresh Concrete Properties
Slump (inches)
Unit Weight (lb/ft3)
Concrete Temperature (OF)
Entrained Air (%)
Hardened Concrete Properties
Compressive Strength (psi)
Flexural Strength (psi)
Elastic Modulus (psi)
Measure CTE
Construction
Ambient Temperature at the time of concrete placement (OF)
Relative Humidity at the time of concrete placement (%)
Wind Speed (mph)
Curing material
Pavement Tagging Technology
Pavement Design
Slab thickness (inches)
Base thickness (inches)
Base type
Subbase thickness (inches)
Subbase type
Resilient Modulus of base (psi)
Resilient modulus of subbase (psi)
Modulus of subgrade reaction (psi/in)
Type of subgrade
Joint spacing
Joint sealant type
Dowel diameter
Dowel spacing
Dowel bar material
Pavement Tagging Technology
Pavement Tagging Technology
Pavement Tagging Technology
DEMO
Two Technologies
Long-term Tagging TechnologyEvents Detection and
Condition Monitoring Technology
• Sensors embedded inside “smart structures” that can
self-prognosticate damage and mechanical failure.
• Zero Maintenance Sensors: Operational life of sensors
comparable to the useful life of the structure – Powering is
one of the key challenges.
MSU PFG Technology
Sensor Size and Powering
Damage
S1
S2
S3
S5
S6
S4
Δ1(3)Δ1(2)
Δ2(2)
Δ3(2)
Δ6(1)
Δ5(1)
Δ4(1)
Δ3(1)
Δ2(1)
Δ1(1)
Time
Am
plit
ude
Δ6(i)
Δ5(i)
Δ4(i)
Δ3(i)
Δ2(i)
Strain Level
Δ1(i)
S6
S1
S2
S3
S4 S5
Cu
mul
ativ
e
Loa
din
g
Tim
e
Data compression Protocol
Material
Sensors
Damage area
Measured Strain Distributions
Time
shift
Measured Strain Distributions
Time
relative shift
Measured Strain Distributions
Time
Further from damaged area = less effect = smaller shift
Strain level * timeStrain level * time
Strain level * time
16
Piezo-floating-gate technology
Transducer Sensor
IC
Use Interface
physics for
sensing
computation
and storage
Eliminate power regulators, energy storage, data converters, RAMs and digital signal
processors. Use the physics of the device and the structure to perform computation
and storage (Use analog computation instead of digital).
source
drain
Inject
current
Floating
gate
pie
zo
(US Patents: 7,757,565 and 8,056,420)
• Piezoelectric ceramics and polymers can generate high-voltages for low
strain-levels but at ultra-low-driving currents.
piezoelectric
17
Comparison with other technologies
Process 0.5-μm standard CMOS
Size 1900μm x 1500μm
Maximum Current
consumption
110nA (7-channel level crossing monitoring)
90nA (3-channel impact monitoring)
10-2 10-3 10-4 10-5 10-6 W
MicaZ IRIS WISP Microstrain PFG
Mic
row
att
Ba
rrie
r
0 10 20 30 40 500
20
40
60
80
100
Time (ms)
Str
ain
()
0 10 20 30 40 500
2
4
6
8
10
Time (ms)
Vo
ltag
e (
V)
0 10 20 30 40 500
20
40
60
80
100
Time (ms)
Str
ain
()
0 10 20 30 40 500
2
4
6
8
10
Time (ms)
Vo
ltag
e (
V)
0 10 20 30 40 500
20
40
60
80
100
Time (ms)
Str
ain
()
0 10 20 30 40 500
2
4
6
8
10
Time (ms)
Vo
ltag
e (
V)
1 2 3 4 5 6 70
10
20
30
40
50
60
70
80
90
Memory cells (Programmed strain levels)
Cu
mu
lative
active
tim
e(N
um
be
r a
nd
du
ratio
n o
f e
ve
nts
)
Recorded data on the sensor
0 10 20 30 40 500
20
40
60
80
100
Time (ms)
Str
ain
()
0 10 20 30 40 500
2
4
6
8
10
Time (ms)
Vo
ltag
e (
V)
Data Recording Protocol on the sensor
Change Detection
Impact detection
Tamper - detection
Seismic MonitoringUsage Monitoring
Pavements
Long-term Usage
Monitoring
Intrusion Detection
Supply-chain Monitoring
Road-map: One sensor multiple Modalities
Challenges:
• Size
• Attachment to the host structure
• Location
• Meaning of data
• Data interpretation and
prognosis methods
PFG Chip
(a)(b) (c) (d)
(e)
Matching Element
Pavement Monitoring System
• At Turner-Fairbanks
Highway Research
Facility.
Manufacturing
Data Interpretation - Damage
-1
0
1
2
3
4
5
6
600 700 800 900 1000 1100 1200 1300 1400
Ou
tpu
t V
olt
age
Strain (με)
Cumulative Distributions Variation
10 000
20 000
30 000
40 000
50 000
60 000
70 000
80 000
90 000
100 000
120 000
140 000 -0.2
0
0.2
0.4
0.6
0.8
1
600 800 1000 1200 1400
Vo
ltag
e (V
)
Strain (µɛ)
Normalized Density Distributions
10,000 Cycles
140,000 Cycles
Data Interpretation - Damage
0
0.5
1
1.5 0
10
20
30
40
50
0
0.5
1
1.5
Number of Cycle (thousand)
Damage Index
Den
sity
Probability distribution of the damage
index versus the number of cyclic
loading events.
Data Interpretation - Damage
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.4
0.5
0.6
0.7
0.8
0.9
1
Normalized Time
Me
an
of th
e D
am
ag
e In
de
x D
istr
ibu
tio
n
Using the Sensnor
Evaluated Using the COD Gage
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
Normalized Time
Sta
nd
ard
De
via
tio
n o
f th
e D
am
ag
e In
de
x D
istr
ibu
tio
n
Example - Variation over time of the mean damage
index (from sensor) versus the damage index
evaluated using data from a COD gage.
Example - Variation over time of the standard
deviation of the damage index distribution.
Actual remaining lifePredicted remaining life using
the sensor
391 325
420 425
9350 7125
7022 11048
10980 23011
Predictions for example specimens
S
1
S
3
Single Edge Notched Beam Test
12.7 mm
The crack propagation phase during the test
Asphalt concrete sample:Length: 18′′ (457.2 mm)
Span length: 15′′ (381 mm)
Thickness: 6.5′′ (165.1 mm)
Width: 6′′ (152.4 mm)
- Damage states:Intact: a= 0 mm
Damage 1: a = 7/8'' (22.2 mm)
Damage 2: a = 1 1/4'' (31.75 mm )
Damage 3 (crack propagation): a = 1 3/4'' (44.45 mm)
Damage Detection Based on the FE Results
Damage Detection Based on the Experimental Results (0.2 mm, 5 Hz)
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 1
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 2
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 3
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 4
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 5
0%
5%
10%
15%
20%
25%
30%
Intact D1 D2 D3 D4
Volt
ag
e D
rop
pag
e (%
)
Damage
Gate 6
Looking into the Future
BIG DATA Cloud
Analysis Center
Self-powered Sensor Array
Reader
Future Maintenance Scheduling
3G/4G mobile
LAN
Self-powered Sensor Array
Reader
Self-powered Sensor Array
Reader
3G/4G mobile
3G/4G mobile
Highway Management and Planning Software
• Internet-of-Things and Big Data Integration.
• Vehicle-to-Infrastructure Communication
Nizar Lajnef, Imen Zaabar,
Shantanu Chakrabartty and Neeraj Buch
Michigan State University
Smart Sensing Technology for
Infrastructure Monitoring
Supported by FHWA and USDOT
