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STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION

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. 1. MICROINDENTATION TESTS. - techniques, measuring setup, etc.

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STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION

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  1. KASPERKIEWICZ

  2. Institute of Fundamental Technological ResearchPolish Academy of Sciences (IPPT PAN) 00-049 Warszawa, Swietokrzyska 21 STRUCTURE IDENTIFICATION BY MICROINDENTATION AND ACOUSTIC EMISSION Janusz Kasperkiewicz KASPERKIEWICZ

  3. 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 COMPONENTSIDENTIFICATION 6. CONCLUSIONS KASPERKIEWICZ

  4. ( ~ a continuation of the Paisley 2003 paper - DSI setup, CP, concrete... ) ACOUSTIC EMISSION and AE SIGNALS PROCESSING MACHINE LEARNING IDENTIFICATION of the components KASPERKIEWICZ

  5. Vickers indenter LVDT sensor tested area KASPERKIEWICZ

  6. KASPERKIEWICZ

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

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

  9. D 1 . 85437 F = HV 2 D KASPERKIEWICZ

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

  11. cement paste with metakaolin KASPERKIEWICZ

  12. cement paste ... metakaolin effect KASPERKIEWICZ

  13. cement paste ... fly ash effect KASPERKIEWICZ

  14. 0 1 2 ... ... 19... ... 24 25 1pd ... 0pd 2pd ... ... 23pd 25pd ...24pd a set of 52 indentation imprints for example: upper imprints No-s: 1, 6÷9, 11÷18 - aggregate KASPERKIEWICZ

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

  16. No.3 No.3 – air void edge KASPERKIEWICZ

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

  18. HV – approx.: ( rock ) D . . = 7 00006 δ 1700 MPa 4300 ( test No.: 5sR9 ) 2700 5300 165 (No.1) 170 (No.6) F 1 . 85437 F = HV 2 D δ KASPERKIEWICZ

  19. HV – approx.: ( cement paste ) 650 1500 470 MPa 1000 KASPERKIEWICZ

  20. ( a sample under consideration ) 600 700 HV – approx.: 1400 500 MPa 1000 164 (No.0) KASPERKIEWICZ

  21. it is possible to evaluatethe strength of the material; what about theidentification of its composition? KASPERKIEWICZ

  22. Acoustic Signal Sensor AEMonitoringSystem AE Signaldetection,recording,etc. SoundWave SoundWave IndentationNoiseSource AcousticEmissionWave KASPERKIEWICZ

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

  24. time: 5 s KASPERKIEWICZ

  25. time: 2 s KASPERKIEWICZ

  26. time: 2 s KASPERKIEWICZ

  27. time: 0.5 s KASPERKIEWICZ

  28. time: 0.3 s KASPERKIEWICZ

  29. time: 0.14 s KASPERKIEWICZ

  30. time: 0.003 s KASPERKIEWICZ

  31. time: 0.4 ms KASPERKIEWICZ

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

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

  34. KASPERKIEWICZ

  35. ( signal transformation ) KASPERKIEWICZ

  36. Different possibilities of AE signal representations Natural representation Fourier, (FT, FFT) Windowed Fourier Wavelet analysis KASPERKIEWICZ

  37. initial 440 ms KASPERKIEWICZ

  38. time [ms] time: 0.4 ms KASPERKIEWICZ

  39. KASPERKIEWICZ

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

  41. lzdH - No. of events in range HlzdM - No. of events in range MlzdL – No. ... etc. senHsenMsenL sazHsazMsazL - ... amplitude in range L serial No. indent class (e.g. "a", "cp1", ...)material composition ... etc. KASPERKIEWICZ

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

  43. ( Machine Learning ) KASPERKIEWICZ

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

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

  46. WinMine KASPERKIEWICZ

  47. ┌ 23.00 ≤ lzdM ≤ 233.50 ┐ AND ┌ sazM < 28.00 ┐ KASPERKIEWICZ

  48. summary of the tests here there was no silica KASPERKIEWICZ

  49. Microindentationand 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

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