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KASPERKIEWICZ ( 1 ) KASPERKIEWICZ ( 2 ) Institute of Fundamental Technological Research Polish Academy of Sciences (IPPT PAN) 00-049 Warszawa, Swietokrzyska.

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Presentation on theme: "KASPERKIEWICZ ( 1 ) KASPERKIEWICZ ( 2 ) Institute of Fundamental Technological Research Polish Academy of Sciences (IPPT PAN) 00-049 Warszawa, Swietokrzyska."— Presentation transcript:

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2 KASPERKIEWICZ ( 1 )

3 KASPERKIEWICZ ( 2 ) Institute of Fundamental Technological Research Polish Academy of Sciences (IPPT PAN) Warszawa, Swietokrzyska 21 Janusz Kasperkiewicz STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION

4 KASPERKIEWICZ ( 3 ) 2. ACOUSTIC EMISSION IN MICROINDENTATION EXPERIMENTS 5. THE EXPERIMENT ON COMPONENTS IDENTIFICATION 6. CONCLUSIONS 3. AE SIGNALS AND THEIR ANALYSIS MACHINE LEARNING DATA PROCESSING - testing cement paste 1. MICROINDENTATION TESTS - techniques, measuring setup, etc. - testing concrete

5 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... )

6 KASPERKIEWICZ ( 5 ) Vickers indenter LVDT sensor tested area

7 KASPERKIEWICZ ( 6 )

8 KASPERKIEWICZ ( 7 ) D1 ≈ D2 ≈ 550μm D1 D2 Cement Past – water-cement ratio: 0.60; loading level: 40 N D.S.I.

9 KASPERKIEWICZ ( 8 ) D1 D1 ≈ D2 ≈ 350μm D2 aggregate air void Concrete; loading level: 45 N

10 KASPERKIEWICZ ( 9 ) D D F HV =

11 KASPERKIEWICZ ( 10 ) cement paste (each point an average of about 10 indentations)

12 KASPERKIEWICZ ( 11 ) cement paste with metakaolin

13 KASPERKIEWICZ ( 12 ) metakaolin effect cement paste...

14 KASPERKIEWICZ ( 13 ) fly ash effect cement paste...

15 KASPERKIEWICZ ( 14 ) pd 25pd... 24pd 1pd... 0pd 2pd... a set of 52 indentation imprints for example: upper imprints No-s: 1, 6÷9, 11÷18 - aggregate

16 KASPERKIEWICZ ( 15 ) No.1 No-s: 1, aggregate No.7

17 KASPERKIEWICZ ( 16 ) No.3 – air void edge No.3

18 KASPERKIEWICZ ( 17 ) (time effect observations) concrete...

19 KASPERKIEWICZ ( 18 ) ( test No.: 5sR9 ) δ F D F HV = D δ=. 165 (No.1) 170 (No.6) MPa HV – approx.: ( rock )

20 KASPERKIEWICZ ( 19 ) MPa HV – approx.:( cement paste )

21 KASPERKIEWICZ ( 20 ) 164 (No.0) MPa HV – approx.: ( a sample under consideration )

22 KASPERKIEWICZ ( 21 ) what about the identification of its composition ? it is possible to evaluate the strength of the material;

23 KASPERKIEWICZ ( 22 ) Acoustic Signal Sensor Acoustic Emission Wave Indentation Noise Source Sound Wave AE Signal detection, recording, etc. AE Monitoring System

24 KASPERKIEWICZ ( 23 ) ( signal from the Test No.: 5sR9 05 ) time: 0 to 5 s amplitude: -1.5 to +1.5 V

25 KASPERKIEWICZ ( 24 ) time: 5 s

26 KASPERKIEWICZ ( 25 ) time: 2 s

27 KASPERKIEWICZ ( 26 ) time: 2 s

28 KASPERKIEWICZ ( 27 ) time: 0.5 s

29 KASPERKIEWICZ ( 28 ) time: 0.3 s

30 KASPERKIEWICZ ( 29 ) time: 0.14 s

31 KASPERKIEWICZ ( 30 ) time: s

32 KASPERKIEWICZ ( 31 ) time: 0.4 ms

33 KASPERKIEWICZ ( 32 ) time: 0.1 ms ( about 100 μs )

34 KASPERKIEWICZ ( 33 ) ( silica CP ) ( no silica CP ) ( stone aggregate )

35 KASPERKIEWICZ ( 34 )

36 KASPERKIEWICZ ( 35 ) ( signal transformation )

37 KASPERKIEWICZ ( 36 ) Different possibilities of AE signal representations Natural representationFourier, (FT, FFT) Windowed FourierWavelet analysis

38 KASPERKIEWICZ ( 37 ) initial 440 ms

39 KASPERKIEWICZ ( 38 ) time [ms] time: 0.4 ms

40 KASPERKIEWICZ ( 39 )

41 KASPERKIEWICZ ( 40 ) t[ms] H (375kHz÷39kHz) M (46kHz÷18kHz) L (6kHz÷4kHz) NOISE ( Test No.: 5sR9 05 )

42 KASPERKIEWICZ ( 41 ) 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.

43 KASPERKIEWICZ ( 42 ) aggregate ITZ cement paste tests results database( in Excel )

44 KASPERKIEWICZ ( 43 ) ( Machine Learning )

45 KASPERKIEWICZ ( 44 ) Rec. No. 219 air content 7.3% fc28 45 MPa air voids spacing 0.21 mm aggregate ?... silica No Rec. No. 219 air content 7.3% fc28 45 MPa air voids spacing 0.21 mm aggregate ?... silica No Rec. No. 116 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate granite... silica No Rec. No. 116 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate granite... silica No Rec. No. 115 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate granite... silica No Rec. No. 115 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate granite... silica No Rec. No. 114 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate gravel... silica Yes Rec. No. 114 air content 4.5% fc28 26 MPa air voids spacing 0.25 mm aggregate gravel... silica Yes Rec. No. 113 air content 2.4% fc28 27 MPa air voids spacing 0.23 mm aggregate ?... silica No Rec. No. 113 air content 2.4% fc28 27 MPa air voids spacing 0.23 mm aggregate ?... silica No Rec. No. 2 air content 6% fc28 ? air voids spacing 0.25 mm aggregate granite... silica Yes Rec. No. 2 air content 6% fc28 ? air voids spacing 0.25 mm aggregate granite... silica Yes Rec. No. 1 air content 2.4% fc28 37 MPa air voids spacing 0.35 mm aggregate basalt... silica No Rec. No. 1 air content 2.4% fc28 37 MPa air voids spacing 0.35 mm aggregate basalt... silica No positive examples negative examples

46 KASPERKIEWICZ ( 45 ) Machine Learning solutions: See 5 (Quinlan) WinMine (Microsoft) ?... AQ algorithms (Michalski)

47 KASPERKIEWICZ ( 46 ) WinMine

48 KASPERKIEWICZ ( 47 ) ┌ ≤ lzdM ≤ ┐ AND ┌ sazM < ┐

49 KASPERKIEWICZ ( 48 ) mix symbol No of EA readings recognized as Silica; (cases Not recognized!) errors / unrecognized / errors in 493 rec-s comments / 13all correct /16as above 3B20_6_1(4)4 / 0 / 4no silica {1×a, 1×cp, 1×cp1, 1×v} 4B20K_6_1190 / 31all correct 5B40_1348no silica 6B50_6_1(13)13 / 0 / 13no silica {6×a, 4×cp, 2×cp1, 1×v} 7850AD(3)3 / 0 / 3no silica {1×a, 0×cp, 1×cp1, 1×v} 8B50K_8_1280 / 24 9R1_ / 32 10S5_1130 / 37 11B20_810not analysedmix with PFA total169 (including erroneous 20)error of identification: 20 rec-s summary of the tests here there was no silica

50 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.

51 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.

52 KASPERKIEWICZ ( 51 )

53 KASPERKIEWICZ ( 52 )

54 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.

55 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).

56 KASPERKIEWICZ ( 55 ) Example of an Attributional Rule Consider a rule: If x 1 ≤ x 2, x 3 ≠ x 4, and x 3 is red or blue, then decision is A (1) If variables x i, 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] <= [x 1 ≤ x 2 ] & [x 3 ≠ x 4 ] & [x 3 = red v blue] (2) To provide a user with more information about the rule, AQ adds annotations to the rule: [Decision = A] if [x 1 ≤ x 2 : 3899, 266] & [x 3 ≠ x 4 : 803, 19] & [x 3 = 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.)

57 KASPERKIEWICZ ( 56 ) concepts in AQ

58 KASPERKIEWICZ ( 57 ) AQ19 See5

59 KASPERKIEWICZ ( 58 )

60 KASPERKIEWICZ ( 59 )


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