Transcript on a possibility of structure identification by

KASPERKIEWICZ
(1)
Institute of Fundamental Technological Research
Polish Academy of Sciences (IPPT PAN)
00-049 Warszawa, Swietokrzyska 21
STRUCTURE IDENTIFICATION
BY MICROINDENTATION
AND ACOUSTIC EMISSION
Janusz Kasperkiewicz
KASPERKIEWICZ
(2)
1.
-
MICROINDENTATION TESTS
techniques, measuring setup, etc.
testing cement paste
testing concrete
2. ACOUSTIC EMISSION IN
MICROINDENTATION EXPERIMENTS
3. AE SIGNALS AND THEIR ANALYSIS
4. MACHINE LEARNING DATA PROCESSING
5. THE EXPERIMENT ON COMPONENTS
IDENTIFICATION
6. CONCLUSIONS
KASPERKIEWICZ
(3)
( ~ a continuation of the Paisley 2003
paper - DSI setup, CP, concrete... )
ACOUSTIC EMISSION
and AE SIGNALS PROCESSING
MACHINE LEARNING
IDENTIFICATION of the components
KASPERKIEWICZ
(4)
Vickers indenter
LVDT sensor
tested area
KASPERKIEWICZ
(5)
KASPERKIEWICZ
(6)
D.S.I.
D1 ≈ D2 ≈ 550μm
D1
D2
Cement Past – water-cement ratio: 0.60; loading level: 40 N
KASPERKIEWICZ
(7)
D1 ≈ D2 ≈ 350μm
aggregate
air void
D2
D1
air void
aggregate
Concrete; loading level: 45 N
KASPERKIEWICZ
(8)
D
HV
KASPERKIEWICZ
1
85437
F
.
=
D2
(9)
cement paste
(each point an average of about 10 indentations)
600
microhardenss, [MPa]
10N
500
20N
400
40N
300
200
100
0
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, [ - ]
KASPERKIEWICZ
( 10 )
cement paste with metakaolin
microhardness, [MPa]
600
10N
500
20N
400
40N
300
200
100
0
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, [ - ]
KASPERKIEWICZ
( 11 )
cement paste ...
microhardenss, [MPa]
600
10N
20N
500
40N
400
10N (Met)
40N (Met)
300
200
100
0
0.25
40N (Met)
metakaolin
effect
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
water-cement ratio, [ - ]
KASPERKIEWICZ
( 12 )
cement paste ...
microhardenss, [MPa]
600
10N
20N
40N
10N (Met)
40N (Met)
40N (Met)
FLW 20
FAS 20
FLK 20
FAS 35
FLW 35
FLK 35
500
400
300
200
100
0
0.25
fly ash effect
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
water-cement ratio, [ - ]
KASPERKIEWICZ
( 13 )
0 1 2 ...
... 19 ...
1pd ...
0pd
... 24 25
... 23pd 25pd
2pd ...
... 24pd
a set of 52
indentation imprints
for example: upper imprints No-s: 1, 6÷9, 11÷18 - aggregate
KASPERKIEWICZ
( 14 )
No.1
No.7
No-s: 1, 7 ... - aggregate
KASPERKIEWICZ
( 15 )
No.3
No.3 – air void edge
KASPERKIEWICZ
( 16 )
concrete ...
800
HV [MPa]
GB10
700
2
R = 0,9512
GB6
600
GB9
GB7 GB8
500
P II
400
300
R 2 = 0,8766
P4
iv-2003
vii-2002
200
Log. (iv-2003)
100
Log. (vii-2002)
fc28 [MPa]
0
0
10
20
30
40
50
60
(time effect observations)
KASPERKIEWICZ
( 17 )
HV – approx.:
( rock )
1700 MPa
4300
Load (N)5300 2700
50
( test No.: 5sR9 )
165 (No.1)
45
170 (No.6)
40
E2
165
E3
170
40
171
35
30
172
30
173
F
25
175
176
20
177
20
15
HV
10
180
181
E1
0
5
0.0
D2
182
D = 7.00006
.δ
10
0.1
0.2
0.3
Extension (mm)
0
0.00
1
85437
F
.
179
=
178
0.01
KASPERKIEWICZ
0.02
δ
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
( 18 )
HV – approx.:
( cement paste )
1500
50
650
1000
470 MPa
45
40
35
166
30
167
25
168
20
169
15
174
10
183
5
0
0.00
0.01
KASPERKIEWICZ
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
( 19 )
( a sample under consideration )
50
HV – approx.:
1400
1000
45
700 600
500 MPa
164 (No.0)
166
40
167
35
168
30
169
25
174
20
15
183
10
164
5
0
0.00
0.01
KASPERKIEWICZ
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
( 20 )
it is possible to evaluate
the strength of the material;
what about the
identification of its composition
?
KASPERKIEWICZ
( 21 )
Acoustic Signal
Sensor
Sound
Wave
KASPERKIEWICZ
Indentation
Noise
Source
AE
Monitoring
System
Sound
Wave
Acoustic
Emission
Wave
AE Signal
detection,
recording,
etc.
( 22 )
amplitude: -1.5 to +1.5 V
( signal from the Test No.: 5sR9 05 )
time: 0 to 5 s
KASPERKIEWICZ
( 23 )
time: 5 s
KASPERKIEWICZ
( 24 )
time: 2 s
KASPERKIEWICZ
( 25 )
time: 2 s
KASPERKIEWICZ
( 26 )
time: 0.5 s
KASPERKIEWICZ
( 27 )
time: 0.3 s
KASPERKIEWICZ
( 28 )
time: 0.14 s
KASPERKIEWICZ
( 29 )
time: 0.003 s
KASPERKIEWICZ
( 30 )
time: 0.4 ms
KASPERKIEWICZ
( 31 )
time: 0.1 ms
KASPERKIEWICZ
( about 100 μs )
( 32 )
( no silica CP )
( silica CP )
( stone aggregate )
KASPERKIEWICZ
( 33 )
KASPERKIEWICZ
( 34 )
( signal transformation )
KASPERKIEWICZ
( 35 )
Different possibilities of AE signal representations
Natural representation
Fourier, (FT, FFT)
Windowed Fourier
Wavelet analysis
KASPERKIEWICZ
( 36 )
initial 440 ms
KASPERKIEWICZ
( 37 )
time [ms]
time: 0.4 ms
KASPERKIEWICZ
( 38 )
KASPERKIEWICZ
( 39 )
( Test No.: 5sR9 05 )
H (375kHz÷39kHz)
M (46kHz÷18kHz)
NOISE
L
(6kHz÷4kHz)
t[ms]
KASPERKIEWICZ
( 40 )
lzdH - No. of events in range H
lzdM - No. of events in range M
lzdL – No. ... etc.
senH
senM
senL
sazH
sazM
sazL - ... amplitude in range L
serial No.
indent class (e.g. "a", "cp1", ...)
material composition
... etc.
KASPERKIEWICZ
( 41 )
tests results database
KASPERKIEWICZ
( in Excel )
( 42 )
( Machine Learning )
KASPERKIEWICZ
( 43 )
Rec. No. 113
air content
2.4%
Rec. No. 2
fc28
27 MPa
Rec. No. 1
air voids spacing 0.23
air content
6% mm
aggregate 2.4% ?
?
air fc28
content
... spacing
air voids
fc28
370.25
MPa mm
No
air aggregate
voidssilica
spacing 0.35 granite
mm
...
aggregate
basalt
Yes
... silica
silica
No
KASPERKIEWICZ
Rec. No. 219
air Rec.
content
7.3%
No. 116
fc28
45 MPa
Rec. No. 115
air content
4.5%
air voids
spacing 0.21 mm
Rec.
No. 114
MPa
airfc28
contentaggregate26
4.5%
?
air voids...
spacing26
0.25
MPamm
airfc28
content
4.5%
granite
airaggregate
voids spacing
mm
fc28
MPa
silica260.25
No
... spacing 0.25granite
airaggregate
voids
mm
...silica
aggregate
gravel No
No
...silica
silica
Yes
( 44 )
Machine Learning solutions:
 AQ algorithms (Michalski)
 See 5 (Quinlan)
 WinMine (Microsoft)
 ?...
KASPERKIEWICZ
( 45 )
WinMine
KASPERKIEWICZ
( 46 )
┌
23.00 ≤ lzdM ≤ 233.50
┐
AND
┌
KASPERKIEWICZ
sazM < 28.00
┐
( 47 )
summary of the tests
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
4/0/4
no silica
4
B20K_6_1
0 / 31
all correct
5
B40_1348
6
B50_6_1
(13)
13 / 0 / 13
no silica
7
850AD
(3)
3/0/3
no silica
8
B50K_8_1
28
0 / 24
9
R1_61103
20
0 / 32
10
S5_1
13
0 / 37
11
B20_810
total
KASPERKIEWICZ
(4)
19
{1×a, 1×cp, 1×cp1, 1×v}
no silica
{6×a, 4×cp, 2×cp1, 1×v}
{1×a, 0×cp, 1×cp1, 1×v}
here there was no silica
not analysed
169 (including erroneous 20)
mix with PFA
error of identification:
20 rec-s
( 48 )
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.
KASPERKIEWICZ
( 49 )
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.
KASPERKIEWICZ
( 50 )
KASPERKIEWICZ
( 51 )
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.
KASPERKIEWICZ
( 53 )
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 welldefined, relatively easy to implement).
KASPERKIEWICZ
( 54 )
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
KASPERKIEWICZ
(from Ryszard Michalski – George Mason Univ.)
( 55 )
concepts in AQ
KASPERKIEWICZ
( 56 )
parameters
run ambig
1
pos
trim
mini
wts
cpx
test criteria
e
default
default-criteria
# criterion tolerance
1 minsel 0.00
variables
#
type
1
con
2
con
3
con
4
con
5
con
6
con
7
con
8
con
9
con
levels
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
AQ19
cost
name
A.A
As.As
Type. | the target
AB.AB
XY.XY
No:label.
RR.RR
Ro.Ro
A:continuous.
W.W
As:continuous.
FD.FD
AB:continuous.
Der.Der
attribute
See5
XY:continuous.
Nob1-events
# A As AB XY RR Ro W FDRR:continuous.
Der
Ro:continuous.
149 5174 179 518 1281 3619
2695 78 1110 18
2382 30 100 1000 1000 5W:continuous.
1000 3 0 0
192 30 100 1000 1000 5 FD:continuous.
1000 3 0 0
......
Der:continuous.
Tob2-events
# A As AB XY RR Ro W FDType:Nob1,Qob1,Tob1,Tob2,Tob3,Tob4,Tob5,Tob9.
Der
914 36072 164 653 1522 2353 1592 197 1076
....
attributes excluded: Ro,Der.
Nob1-tevents
# A As AB XY RR Ro W FD Der1,1356,109,742,938,1385,1057,40,1063,2,Tob1
149 5174 179 518 1281 3619 2695 78 1110 18
2,1311,200,652,1833,5662,2685,30,1233,31,Tob1
2382 30 100 1000 1000 5 1000
3 0 0
····
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
KASPERKIEWICZ
aaaa
( 57 )
KASPERKIEWICZ
( 58 )