STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION
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Transcript of STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION
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KASPERKIEWICZ ( 1 )
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KASPERKIEWICZ ( 2 )
Institute of Fundamental Technological ResearchPolish Academy of Sciences (IPPT PAN) 00-049 Warszawa, Swietokrzyska 21
Janusz Kasperkiewicz
STRUCTURE IDENTIFICATION BY MICROINDENTATION
AND ACOUSTIC EMISSION
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KASPERKIEWICZ ( 3 )
2. ACOUSTIC EMISSION IN MICROINDENTATION EXPERIMENTS
5. THE EXPERIMENT ON COMPONENTS IDENTIFICATION
6. CONCLUSIONS
3. AE SIGNALS AND THEIR ANALYSIS
4. MACHINE LEARNING DATA PROCESSING
- testing cement paste
1. MICROINDENTATION TESTS - techniques, measuring setup, etc. - testing concrete
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KASPERKIEWICZ ( 4 )
ACOUSTIC EMISSION and AE SIGNALS PROCESSING
IDENTIFICATION of the components
MACHINE LEARNING
( ~ a continuation of the Paisley 2003 paper - DSI setup, CP, concrete... )
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KASPERKIEWICZ ( 5 )
Vickers indenterLVDT sensor
tested area
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KASPERKIEWICZ ( 6 )
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KASPERKIEWICZ ( 7 )
D1 ≈ D2 ≈ 550μm
D1
D2
Cement Past – water-cement ratio: 0.60; loading level: 40 N
D.S.I.
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KASPERKIEWICZ ( 8 )
D1
D1 ≈ D2 ≈ 350μm
D2
aggregate
aggregate
air void
air void
Concrete; loading level: 45 N
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KASPERKIEWICZ ( 9 )
D
285437.1
DF HV=
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KASPERKIEWICZ ( 10 )
0
100
200
300
400
500
600
0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75
water-cement ratio, [ - ]
mic
roh
ard
en
ss
, [M
Pa
]
10N
20N
40N
cement paste
(each point an average of about 10 indentations)
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KASPERKIEWICZ ( 11 )
0
100
200
300
400
500
600
0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75
water-cement ratio, [ - ]
mic
roh
ard
nes
s, [
MP
a]
10N
20N
40N
cement paste with metakaolin
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KASPERKIEWICZ ( 12 )
0
100
200
300
400
500
600
0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75
water-cement ratio, [ - ]
mic
roh
ard
enss
, [M
Pa]
10N
20N
40N
10N (Met)
40N (Met)
40N (Met)
metakaolin effect
cement paste ...
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KASPERKIEWICZ ( 13 )
0
100
200
300
400
500
600
0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75
water-cement ratio, [ - ]
mic
roh
ard
enss
, [M
Pa]
10N20N40N10N (Met)40N (Met)40N (Met)FLW 20FAS 20FLK 20FAS 35FLW 35FLK 35
fly ash effect
cement paste ...
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KASPERKIEWICZ ( 14 )
0 1 2 ... ... 24 25
... 23pd 25pd
... 24pd
1pd ...
0pd 2pd ...a set of 52 indentation
imprints
for example: upper imprints No-s: 1, 6÷9, 11÷18 - aggregate
... 19 ...
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KASPERKIEWICZ ( 15 )
No.1
No-s: 1, 7 ... - aggregate
No.7
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KASPERKIEWICZ ( 16 )
No.3 – air void edge
No.3
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KASPERKIEWICZ ( 17 )
R2 = 0,9512
R2 = 0,8766
0
100
200
300
400
500
600
700
800
0 10 20 30 40 50 60
iv-2003
vii-2002
Log. (iv-2003)
Log. (vii-2002)fc
28 [MPa]
HV [MPa]
P4P II
GB6 GB7 GB8
GB9GB10
(time effect observations)
concrete ...
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KASPERKIEWICZ ( 18 )
Load (N)
0
10
20
30
40
Extension (mm)0.0 0.1 0.2 0.3
E3
E2
E1
( test No.: 5sR9 )
δ
F
285437.1
DF HV=
00006.7D δ = .
0
5
10
15
20
25
30
35
40
45
50
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
165
170
171
172
173
175
176
177
178
179
180
181
182
165 (No.1)
170 (No.6)
5300
43002700
1700 MPaHV – approx.: ( rock )
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KASPERKIEWICZ ( 19 )
0
5
10
15
20
25
30
35
40
45
50
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
166
167
168
169
174
183
15001000
650470 MPa
HV – approx.: ( cement paste )
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KASPERKIEWICZ ( 20 )
0
5
10
15
20
25
30
35
40
45
50
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
166
167
168
169
174
183
164
164 (No.0)
14001000
700 600500 MPaHV – approx.:
( a sample under consideration )
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KASPERKIEWICZ ( 21 )
what about theidentification of its composition
?
it is possible to evaluatethe strength of the material;
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KASPERKIEWICZ ( 22 )
Acoustic Signal
Sensor
AcousticEmission
Wave
IndentationNoise
Source
SoundWave
SoundWave
AE Signaldetection,recording,
etc.
AEMonitoring
System
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KASPERKIEWICZ ( 23 )
( signal from the Test No.: 5sR9 05 )
time: 0 to 5 s
am
plitu
de:
-1.5
to +
1.5
V
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KASPERKIEWICZ ( 24 )
time: 5 s
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KASPERKIEWICZ ( 25 )
time: 2 s
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KASPERKIEWICZ ( 26 )
time: 2 s
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KASPERKIEWICZ ( 27 )
time: 0.5 s
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KASPERKIEWICZ ( 28 )
time: 0.3 s
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KASPERKIEWICZ ( 29 )
time: 0.14 s
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KASPERKIEWICZ ( 30 )
time: 0.003 s
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KASPERKIEWICZ ( 31 )
time: 0.4 ms
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KASPERKIEWICZ ( 32 )
time: 0.1 ms ( about 100 μs )
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KASPERKIEWICZ ( 33 )
( silica CP )
( no silica CP )
( stone aggregate )
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KASPERKIEWICZ ( 34 )
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KASPERKIEWICZ ( 35 )
( signal transformation )
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KASPERKIEWICZ ( 36 )
Different possibilities of AE signal representations
Natural representation Fourier, (FT, FFT)
Windowed Fourier Wavelet analysis
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KASPERKIEWICZ ( 37 )
initial 440 ms
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KASPERKIEWICZ ( 38 )
time [ms]
time: 0.4 ms
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KASPERKIEWICZ ( 39 )
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KASPERKIEWICZ ( 40 )
t[ms]
H (375kHz÷39kHz)M (46kHz÷18kHz)
L (6kHz÷4kHz)
NOISE
( Test No.: 5sR9 05 )
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KASPERKIEWICZ ( 41 )
lzdH - No. of events in range HlzdM - No. of events in range MlzdL – No. ... etc.senHsenMsenLsazHsazMsazL - ... amplitude in range L
serial No.indent class (e.g. "a", "cp1", ...)material composition... etc.
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KASPERKIEWICZ ( 42 )
aggregate
ITZcement paste
tests results database ( in Excel )
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KASPERKIEWICZ ( 43 )
( Machine Learning )
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KASPERKIEWICZ ( 44 )
Rec. No. 219
air content 7.3%fc28 45 MPaair voids spacing 0.21 mmaggregate ?...silica No
Rec. No. 219
air content 7.3%fc28 45 MPaair voids spacing 0.21 mmaggregate ?...silica No
Rec. No. 116
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate granite...silica No
Rec. No. 116
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate granite...silica No
Rec. No. 115
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate granite...silica No
Rec. No. 115
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate granite...silica No
Rec. No. 114
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate gravel...silica Yes
Rec. No. 114
air content 4.5%fc28 26 MPaair voids spacing 0.25 mmaggregate gravel...silica Yes
Rec. No. 113
air content 2.4%fc28 27 MPaair voids spacing 0.23 mmaggregate ?...silica No
Rec. No. 113
air content 2.4%fc28 27 MPaair voids spacing 0.23 mmaggregate ?...silica No
Rec. No. 2
air content 6%fc28 ?air voids spacing 0.25 mmaggregate granite...silica Yes
Rec. No. 2
air content 6%fc28 ?air voids spacing 0.25 mmaggregate granite...silica Yes
Rec. No. 1
air content 2.4%fc28 37 MPaair voids spacing 0.35 mmaggregate basalt...silica No
Rec. No. 1
air content 2.4%fc28 37 MPaair voids spacing 0.35 mmaggregate basalt...silica No
positive examples
positive examples
negative examples
negative examples
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KASPERKIEWICZ ( 45 )
Machine Learning solutions:
See 5 (Quinlan)
WinMine (Microsoft)
?...
AQ algorithms (Michalski)
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KASPERKIEWICZ ( 46 )
WinMine
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KASPERKIEWICZ ( 47 )
┌ 23.00 ≤ lzdM ≤ 233.50 ┐
AND
┌ sazM < 28.00 ┐
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KASPERKIEWICZ ( 48 )
mix symbol
No of EA readings recognized as Silica; (cases Not recognized!)
errors / unrecognized/ errors in 493 rec-s
comments
1 101103 37 0 / 13 all correct
2 221103 32 0 /16 as above
3 B20_6_1 (4) 4 / 0 / 4 no silica{1×a, 1×cp, 1×cp1, 1×v}
4 B20K_6_1
19 0 / 31 all correct
5 B40_1348
no silica
6 B50_6_1 (13) 13 / 0 / 13 no silica{6×a, 4×cp, 2×cp1, 1×v}
7 850AD (3) 3 / 0 / 3 no silica {1×a, 0×cp, 1×cp1, 1×v}
8 B50K_8_1
28 0 / 24
9 R1_61103
20 0 / 32
10 S5_1 13 0 / 37
11 B20_810 not analysed mix with PFA
total 169 (including erroneous 20)
error of identification: 20 rec-s
summary of the tests
here there was no silica
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KASPERKIEWICZ ( 49 )
Microindentation and AE (Acoustic Emission) observations make possible identification of structural characteristics of concrete materials.
In particular possible was an indirect identification of a silica additive presence in hardened concrete.
It is expected that the same approach could be used to discriminate signals in aggregate grains (stone) from those and in cement paste or mortar.
The procedure involves AE signal transformation followed by machine learning rules detection processing, resulting in hypotheses formulated in everyday language.
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KASPERKIEWICZ ( 50 )
The experiments should be continued, aimed - e.g. – to establishing what are optimal settings of AE data acquisition system, structural points better identification, selection of the proper procedure timing, etc.
The proposed procedure may by important for hardened concrete diagnostics, perhaps also in case of certain forensic analysis situations, when the problem is to find out whether a silica fume was actually used as a component of a given concrete mix or not.
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KASPERKIEWICZ ( 51 )
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KASPERKIEWICZ ( 53 )
If x1 ≤ x2, x3 ≠ x4, and x3 is red or blue, then decision is A
if x1, x2, x3 are N-valued each then the knowledge above demands:
N=2 a decision tree with 26 nodes and 20 leaves, or 12 conventional decision rules;
N=5 a decision tree with 190 nodes and 810 leaves, or 600 conventional decision rules.
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KASPERKIEWICZ ( 54 )
natural induction system (Michalski, 2001), based on a knowledge representation language that would facilitate natural induction, (using structures and operators approximately corresponding to natural language concepts, syntactically and semantically well-defined, relatively easy to implement).
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KASPERKIEWICZ ( 55 )
Example of an Attributional Rule
• Consider a rule:
If x1 ≤ x2 , x3 ≠ x4, and x3 is red or blue, then decision is A (1)
• If variables xi, i=1,2,3,4 are five-valued, then representing (1) would require a decision
tree with 810 leaves and 190 nodes, or 600 conventional rules
• A logically equivalent attributional calculus rule is:
[Decision = A] <= [x1 ≤ x2] & [x3 ≠ x4] & [x3 = red v blue] (2)
• To provide a user with more information about the rule, AQ adds annotations to the rule:
[Decision = A] if [x1 ≤ x2: 3899, 266] & [x3 ≠ x4: 803, 19] & [x3= red or blue: 780, 40]
t=750, u=700, n=14, f=4, q=.9 where t - the total number of examples covered by the rule (rule coverage) u - the number of examples covered only by this rule, and not by any other rule associated with Decision=A n - the number of negative examples covered by the rule (“negative coverage’) q - the rule quality combining the coverage and training accuracy gain f - the number of examples in the training set matched flexibly
(from Ryszard Michalski – George Mason Univ.)
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KASPERKIEWICZ ( 56 )
concepts in AQ
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KASPERKIEWICZ ( 57 )
parameters run ambig trim wts test criteria 1 pos mini cpx e default default-criteria # criterion tolerance 1 minsel 0.00 variables # type levels cost name 1 con 1 1 A.A 2 con 1 1 As.As 3 con 1 1 AB.AB 4 con 1 1 XY.XY 5 con 1 1 RR.RR 6 con 1 1 Ro.Ro 7 con 1 1 W.W 8 con 1 1 FD.FD 9 con 1 1 Der.Der Nob1-events # A As AB XY RR Ro W FD Der 149 5174 179 518 1281 3619 2695 78 1110 18 2382 30 100 1000 1000 5 1000 3 0 0 192 30 100 1000 1000 5 1000 3 0 0 ...... Tob2-events # A As AB XY RR Ro W FD Der 914 36072 164 653 1522 2353 1592 197 1076 .... Nob1-tevents # A As AB XY RR Ro W FD Der 149 5174 179 518 1281 3619 2695 78 1110 18 2382 30 100 1000 1000 5 1000 3 0 0 ····
AQ19
Type. | the target attribute No:label. A:continuous. As:continuous. AB:continuous. XY:continuous. RR:continuous. Ro:continuous. W:continuous. FD:continuous. Der:continuous. Type:Nob1,Qob1,Tob1,Tob2,Tob3,Tob4,Tob5,Tob9. attributes excluded: Ro,Der.
Type. | the target attribute No:label. A:continuous. As:continuous. AB:continuous. XY:continuous. RR:continuous. Ro:continuous. W:continuous. FD:continuous. Der:continuous. Type:Nob1,Qob1,Tob1,Tob2,Tob3,Tob4,Tob5,Tob9. attributes excluded: Ro,Der. 1,1356,109,742,938,1385,1057,40,1063,2,Tob1
2,1311,200,652,1833,5662,2685,30,1233,31,Tob1 3,6751,102,668,1147,1305,1342,96,1088,2,Tob1 4,1112,108,802,929,1247,1000,34,1063,1,Tob1 5,137,182,750,667,4714,1004,9,0,9,Qob1 6,30,100,1000,1000,5,1000,3,0,0,Qob1 .... 2502,2126,582,727,6000,10149,2922,20,1076,178,Tob1 2503,556,172,570,2000,3869,1635,22,1089,11,Tob1 2504,15,226,1000,2000,6,1000,0,0,0,Tob1 aaaa
1,1356,109,742,938,1385,1057,40,1063,2,Tob1 2,1311,200,652,1833,5662,2685,30,1233,31,Tob1 3,6751,102,668,1147,1305,1342,96,1088,2,Tob1 4,1112,108,802,929,1247,1000,34,1063,1,Tob1 5,137,182,750,667,4714,1004,9,0,9,Qob1 6,30,100,1000,1000,5,1000,3,0,0,Qob1 .... 2502,2126,582,727,6000,10149,2922,20,1076,178,Tob1 2503,556,172,570,2000,3869,1635,22,1089,11,Tob1 2504,15,226,1000,2000,6,1000,0,0,0,Tob1 aaaa
See5
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KASPERKIEWICZ ( 58 )