STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION

KASPERKIEWICZ ( 1 )

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

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

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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Vickers indenterLVDT sensor

tested area

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D1 ≈ D2 ≈ 550μm

D1

D2

Cement Past – water-cement ratio: 0.60; loading level: 40 N

D.S.I.

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D1

D1 ≈ D2 ≈ 350μm

D2

aggregate

aggregate

air void

air void

Concrete; loading level: 45 N

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D

285437.1

DF HV=

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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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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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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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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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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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No.1

No-s: 1, 7 ... - aggregate

No.7

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No.3 – air void edge

No.3

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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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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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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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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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what about theidentification of its composition

?

it is possible to evaluatethe strength of the material;

KASPERKIEWICZ ( 22 )

Acoustic Signal

Sensor

AcousticEmission

Wave

IndentationNoise

Source

SoundWave

SoundWave

AE Signaldetection,recording,

etc.

AEMonitoring

System

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( 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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time: 5 s

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time: 2 s

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time: 2 s

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time: 0.5 s

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time: 0.3 s

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time: 0.14 s

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time: 0.003 s

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time: 0.4 ms

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time: 0.1 ms ( about 100 μs )

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( silica CP )

( no silica CP )

( stone aggregate )

KASPERKIEWICZ ( 34 )

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( signal transformation )

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Different possibilities of AE signal representations

Natural representation Fourier, (FT, FFT)

Windowed Fourier Wavelet analysis

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initial 440 ms

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time [ms]

time: 0.4 ms

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t[ms]

H (375kHz÷39kHz)M (46kHz÷18kHz)

L (6kHz÷4kHz)

NOISE

( Test No.: 5sR9 05 )

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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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aggregate

ITZcement paste

tests results database ( in Excel )

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( Machine Learning )

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

KASPERKIEWICZ ( 45 )

Machine Learning solutions:

See 5 (Quinlan)

WinMine (Microsoft)

?...

AQ algorithms (Michalski)

KASPERKIEWICZ ( 46 )

WinMine

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┌ 23.00 ≤ lzdM ≤ 233.50 ┐

AND

┌ sazM < 28.00 ┐

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

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.

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.

KASPERKIEWICZ ( 51 )

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.

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).

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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concepts in AQ

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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