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M. Hossain
ASNT 20th Annual Research Symposium & Spring Conference
San Francisco, California
21-25 March, 2011
This project is sponsored by the U.S. Department of Commerce, NIST-TIP(Cooperative Agreement Number 70NANB9H9007)
Remote Detection of Stage II to Stage III Cracking in Steel Bridge Girder Material
Background
2
N
Safe area
S
da/dN
DK
stable cracking
unstable cracking
threshold
Paris Law:
da/dN=C(∆K)m
critical cracking level
Background
3
Acoustic emission (AE) techniques: High sensitivity and reliability Capability of locating and quantifying active cracks
Time, μS
Vol
tage
, mV
Objective
Bridge Prognostic System• Self-Powered • Wireless Sensor Network • Structural Bridge Health Prognosis
Current tasks• Understand mechanism of acoustic emission(AE) corresponding to
crack growth behavior in the steel bridge material• Data interpretation to identify structural damage and deterioration• Modeling to assess the remaining fatigue life
4
Experimental Procedure
5
AE-monitored fatigue tests:
Compact tension (CT) specimens, made of A572G50
MTS 810 hydraulic machine
Crack growth: clip gage, microscope and fiber light
AE sensors: R15I × 5, WDI × 3
Sensor Highway II-Remote Asset Integrity Monitor
12.0 in9.5 in
12.0 in
3.25 in
R15I WDI
Data Filtering and Reducing Procedures
6
Crack extension
Reversed yielding
Crack closure
Yielding
Crack opening
Stress-strain behavior at crack tip in a load cycle
Grating
Y-strain
Y-s
tres
s
loa
d
time
Pmin
Pmax
3
2 4
1 5
Pmean
1. Eliminate AE collected below 80% of peak load
Data Filtering and Reducing Procedures
7
Peak amplitude
Threshold
Rise time
Duration
Counts2. Friction emission tests to understand
characteristics of noise
3. Pencil lead break tests to understand characteristics of genuine hits
2 3
Data Filtering and Reducing Procedures
8
4. Swansong filter to minimize mechanical noise Ⅱ
Data Filtering and Reducing Procedures
9
5. Evaluate quality of filtered AE data
Results and Discussion
10
0 5,000 10,000 15,000 20,0000
0.5
1
1.5
2
2.5
3
3.5
4
Hit rate
Load cycles
Hit
rat
e
10% increase of cyclic loads
1. Sparse dataset
Results and Discussion
11
0 3,000 6,000 9,000 12,000 15,000 18,0000
2
4
6
8
10
12
0
50
100
150
200
250
300
350
Cumulative absolute energy
Maximum stress intensity
Load cycles
Cu
mu
lati
ve a
bso
lute
en
ergy
, aJ
Max
imu
m s
tres
s in
ten
sity
, MP
a√m
critical level
10% increase of cyclic loads
2. Determination of critical cracking level
Results and Discussion
12
2. Determination of critical cracking level
0 3,000 6,000 9,000 12,000 15,000 18,0000
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
0
50
100
150
200
250
300
350Cumulative signal strength
Maximum stress intensity
Load cycles
Cu
mu
lati
ve s
ign
al s
tren
gth
, V-T
Max
imu
m s
tres
s in
ten
sity
, Mp
a√m
critical level
10% increase of cyclic loads
Results and Discussion
13
2. Determination of critical cracking level
0 3,000 6,000 9,000 12,000 15,000 18,0000
5,000
10,000
15,000
20,000
25,000
30,000
35,000
Cumulative counts
Load cycles
Cu
mu
lati
ve c
oun
ts
critical level
10% increase of cyclic loads
Results and Discussion
14
2. Determination of critical cracking level
0 3000 6000 9000 12000 15000 180000
100
200
300
400
500
600
0
50
100
150
200
250
300
350Cumulative hits
Maximum stress in-tensity
Load cycles
Cu
mu
lati
ve h
its
Max
imu
m s
tres
s in
ten
sity
, MP
a√m
critical level
10% increase of cyclic loads
Results and Discussion
15
3. Prediction of fatigue life
Absolute energy of AE, U ∝ J(∆K), released energy due to crack growth
dU/dN=B(∆K)p
log(dU/dN)=plog(∆K)+log(B) Eq.(1)
Paris Law: da/dN=C(∆K)m; ∆K = ?
da/dN=D(dU/dN)q Eq.(2)
Applies to Stage crackingⅡ
Results and Discussion
16
3. Prediction of fatigue life
1.75 1.8 1.85 1.9 1.95 2 2.05-4
-3
-2
-1
0
1
2
f(x) = 5.74889754828652 x − 10.8645285573976
f(x) = 3.83380001661091 x − 9.95179643087376
crack growth rate, da/dNLinear (crack growth rate, da/dN)absolute energy rate, dU/dNLinear (absolute energy rate, dU/dN)
Stress intensity range, log(∆K)
Log
(da/
dN
), lo
g(d
U/d
N)
Results and Discussion
17
3. Prediction of fatigue life
0 10,000 20,000 30,000 40,0000
10
20
30
40
50
60
70
experimental crack
predicted crack
Load cycles
Cra
ck le
ngth
, mm
Results and Discussion
18
4. AE-detected cracking locations
crack path
Conclusions and Perspective
Summary:
AE absolute energy can provide warning signs for critical cracking in steel bridge material.
Absolute energy rate was found to most suitable feature.
Specific material constants in terms of both AE and crack growth behavior should be evaluated.
Robust data filtering techniques are required.
The combination of a Swansong II filter with a waveform-based approach was found to be appropriate.
Further work:
Mechanism of AE corresponding to crack growth in welded bridge elements
19
Acknowledgements
This work is performed under the support of the U.S. Department
of Commerce, National Institute of Standards and Technology,
Technology Innovation Program, Cooperative Agreement Number
70NANB9H9007. Special thanks to Jean-Louis Staudenmann.
South Carolina DOT for providing access to bridges and related
information for this project.
Valery Godinez, Adrian Pollock, Miguel Gonzalez (Mistras); Brian
Metrovich (Case Western Reserve Univ.); Fabio Matta (Univ. of
South Carolina).
20
Thanks for your time and attention
Annex1 Construction of a and dU/dN arrays for Fatigue Life Prediction
22
Array of a:
original a: ao
final a:
o generally, Kmax=F∙S∙(π∙afinal)1/2 = KIC ,
where F=f(geometry), S- applied stress, KIC – fracture toughness
o compact tension(CT) specimen-cantilever beam:
Kmax=Fp∙P/(t∙b1/2) = KIC ,
where Fp =fp (av/b), av=(afinal-1+ afinal)/2, P-applied load, t-thickness, b-width
o single edge(SE) specimen-freely supported beam:
Kmax=F∙Sg∙(π∙afinal)1/2 = KIC,
where F=f(av/b), Sg = 6M/(b2∙t), M-applied moment
a(i+1)=r∙ai , r ≈1.10,
Array of dU/dN:
original dU/dN: (du/dN)o
final dU/dN: (dU/dN)final=B(∆KIC )p , ∆K= KIC∙(1-R), where B, p-material constants, R-load ratio
(dU/dN)(i+1) / (dU/dN)i = B(∆K(i+1) / (∆K )i ) p = B(r) p/2 , r ≈1.10
CT SE