Smart Structures and Systems

  • 제14권2호
  • /
  • Pages.85-104
  • /
  • 2014
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Statistics based localized damage detection using vibration response

  • Dorvash, Siavash (Civil and Environmental Engineering, Lehigh University) ;
  • Pakzad, Shamim N. (Civil and Environmental Engineering, Lehigh University) ;
  • LaCrosse, Elizabeth L. (Civil and Environmental Engineering, Lehigh University)
  • 투고 : 2012.09.04
  • 심사 : 2013.06.24
  • 발행 : 2014.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

키워드

과제정보

연구 과제 주관 기관 : National Science Foundation

참고문헌

  1. Alvandi, A. and Cremona, C. (2006), "Assessment of vibration-based damage", J. Sound Vib., 292, 179-202. https://doi.org/10.1016/j.jsv.2005.07.036
  2. Anon. (1992), ASM handbook, Volume 17, nondestructive evaluation and quality control, ASM International.
  3. Arici, Y. and Mosalam, K.M. (2003), "Modal analysis of a densely instrumented building using strong motion data", Proceedings of the International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, CA, June 6-9.
  4. Ball, R.J. and Almond, D.P. (1998), "Detection and measurement of impact damage in thick carbon fibre reinforced laminates by transient thermography", NDT & E Int., 31(3), 165-173. https://doi.org/10.1016/S0963-8695(97)00052-2
  5. Banks, H.T., Joyner, M.L., Wincheski, B. and Winfree, W.P. (2002), Real time computational algorithms for eddy-current based damage detection, CRSC-TR01-16, NCSU Inverse Problems 18 795-823.
  6. Bernal, D. (2002), "Load vectors for damage localization", J. Eng. Mech. - ASCE, 128(1), 7-14. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(7)
  7. Chang, M. and Pakzad, S.N. (2014), "Observer Kalman filter identification for output-only systems using Interactive Structural Modal Identification Toolsuite", J. Bridge Eng. ASCE, 19(5), 04014002. DOI: 10.1061/ (ASCE)BE.1943-5592.0000530.
  8. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Review paper: health monitoring of civil infrastructure", Struct. Health Monit., 2, 257-267. https://doi.org/10.1177/1475921703036169
  9. Chen, J. and Gupta, A.K. (2000), Parametric statistical change point analysis, Birkhauser, Boston
  10. Cho, S., Jo, H., Jang, S., Park, J., Jung, H.J., Yun, C.B., Spencer, Jr., B.F. and Seo, J. (2010), "Structural health monitoring of a cable-stayed bridge using smart sensor technology: data analysis", Smart Struct. Syst., 6(5-6), 461-480. https://doi.org/10.12989/sss.2010.6.5_6.461
  11. Cho, S., Yun, C.B., Lynch, J.P., Zimmerman, A.T., Spencer, Jr., B.F. and Nagayama, T. (2008), "Smart wireless sensor technology for structural health monitoring of civil structures", Int. J. Steel Struct., 8(4), 267-275
  12. Crossbow Technology, Inc. (2007), MICAz wireless measurement system, San Jose, CA.
  13. Deutsch, S. (1979), "A preliminary study of the fluid mechanics of liquid penetrant testing", J. Res. Natl. Bur. Stand., 84(4), 287-292 https://doi.org/10.6028/jres.084.012
  14. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Dig., 30, 91-105. https://doi.org/10.1177/058310249803000201
  15. Dorvash, S. and Pakzad, S.N. (2012), "Effects of measurement noise on modal parameter identification", Smart Mater. Struct., 21(6), 065008. https://doi.org/10.1088/0964-1726/21/6/065008
  16. Dorvash, S., Pakzad, S.N., Naito, C.J., Yen, B. and Hodgson, I.C. (2012), "Application of state of the art in measurement and data analysis techniques for vibration evaluation of a tall building", J. Struct. Infrastruct. Eng., 10(5), http://dx.doi.org/10.1080/15732479.2012.757795.
  17. Dorvash, S., Pakzad, S.N., Labuz, E.L ., Ricles, J.M. and Hodgson, I.C. (2014), Localized damage detection algorithm and implementation on a large-scale steel beamto-column moment connection, Earthquake Spectra In-Press. doi: http://dx.doi.org/10.1193/031613EQS069M
  18. Farrar, C.R., Allen, D.W., Ball, S., Masquelier, M.P. and Park, G. (2005), "Coupling sensing hardware with data interrogation software for structural health monitoring", Proceedings of the 6th International Symposium, Dynamic Problems of Mechanics, Ouro Preto, Brazil.
  19. Farrar, C.R., Baker, W.E., Bell, T.M., Cone, K.M., Darling, T.W., Duffey, T.A., Eklund, A. and Migliori, A. (1994), Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande, Los Alamos National Laboratory Report, LA 12767-MS.
  20. Gangone, M.V., Whelan, M.J. and Janoyan, K.D. (2011), "Diagnostic load testing and system identification using wireless sensors", Comput.-Aided Civil Environ. Eng., 26(7), 560-579. https://doi.org/10.1111/j.1467-8667.2010.00711.x
  21. Intel Corporation Research (2005), Intel Mote2 Overview, Version 3.0 Santa Clara, CA.
  22. Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J.A., Sim, S.H., Jung, H.J., Yun, C.B., Spencer, Jr., B.F. and Agha, G. (2010), "Structural health monitoring of a cable stayed bridge using smart sensor technology: deployment and evaluation", Smart Struct. Syst., 6(5-6), 439-459. https://doi.org/10.12989/sss.2010.6.5_6.439
  23. Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J.A., Sim, S.H., Jung, H.J., Yun, C.B., Spencer, Jr., B F. and Agha, G. (2010), "Structural health monitoring of a cable stayed bridge using smart sensor technology: deployment and evaluation", Smart Struct. Syst., 6(5-6), 439-459. https://doi.org/10.12989/sss.2010.6.5_6.439
  24. Kim, J., Swartz, R.A., Lynch, J.P., Lee, C.G. and Yun, C.B. (2010), "Rapid-to-deploy reconfigurable wireless
  25. Koh, C.G., See, L.M. and Balendra, T. (1995), "Damage detection of buildings: numerical and experimental studies", J. Struct. Eng. - ASCE, 121(8), 1155-1160. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:8(1155)
  26. Lu, K.C., Wang, Y., Lynch, J.P., Lin, P.Y., Loh, C.H. and Law, K.H. (2005), "Application of wireless sensors for structural health monitoring and control", Proceedings of the KKCNN Symposium on Civil Engineering, Taiwan.
  27. Lu, K.C., Wang, Y., Lynch, J.P., Loh, C.H., Chen, Y.J., Lin, P.Y. and Lee, Z.K. (2006), "Ambient vibration study of Gi-Lu cable-stay bridge: application of wireless sensing units", Proceedings of the SPIE 13th Annual Symposium on Smart Structural and Materials, San Diego, CA, USA.
  28. Lynch, J.P. and Loh, K.J. (2006), "A summary review of wireless sensors and sensor networks for structural health monitoring", Shock Vib. Dig., 38, 91-128. https://doi.org/10.1177/0583102406061499
  29. Lynch, J.P., Law, K.H., Kiremidjian, A.S., Carryer, E., Farrar, C.R., Sohn, H., Allen, D.W., Nadler, B. and Wait, J.R. (2004), "Design and performance validation of a wireless sensing unit for structural monitoring application", Struct. Eng. Mech., 17(3-4), 393-408. https://doi.org/10.12989/sem.2004.17.3_4.393
  30. Mallet, L., Staszewski, W.J. and Scarpa, F. (2004), "Structural health monitoring using scanning laser vibrometry: II. Lamb waves for damage detection", Smart Mater. Struct., 13(2), 261-269. https://doi.org/10.1088/0964-1726/13/2/003
  31. Morassi, A. and Rovere, N. (1997), "Localizing a notch in a steel frame from frequency measurements", J. Eng. Mech. - ASCE, 123(5), 422-432. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:5(422)
  32. Pakzad, S.N., Fenves, G.L., Kim, S. and Culler, D.E. (2008), "Design and implementation of scalable wireless sensor network for structural monitoring", J. Infrastruct. Eng. - ASCE, 14(1), 89- 101. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:1(89)
  33. Pakzad, S.N. (2008), Statistical approach to structural monitoring using scalable wireless sensor networks, PhD dissertation, University of California, Berkeley.
  34. PCB Piezotronics, Inc, (2005), Single axis capacitive accelerometer, series 3701, NY.
  35. Ratcliffe, C.P. (1997), "Damage detection using a modified laplacian operator on mode shape data", J. Sound Vib., 204(3), 5-517.
  36. Rice, J.A. and Spencer Jr., B.F. (2008), "Structural health monitoring sensor development for the Imote2 platform", Proceedings of the SPIE Smart Structures/NDE, San Diego, CA.
  37. Rice, J.A. and Spencer, B.F. (2009), Flexible smart sensor framework for autonomous full-scale structural health monitoring, NSEL Report Series, No. 18, University of Illinois at Urbana-Champaign.
  38. Sen, A. and Srivastava, M.S. (1975), "On tests for detecting change in mean", Annal. Statistics, 3, 98-108. https://doi.org/10.1214/aos/1176343001
  39. Shahidi, G., Nigro, M.B., Pakzad, S.N. and Pan, Y. (2014), "Local damage features using multivariate regression model", Struct. Infrastruct. E., DOI:10.1080/15732479.2014.949277, In Press.
  40. Sim, S.H., Jang, S.A., Spencer, Jr., B.F. and Song, J. (2008), "Reliability-based evaluation of the performance of the damage locating vector method", Probabilist. Eng. Mech., 23(4), 489-495. https://doi.org/10.1016/j.probengmech.2008.01.006
  41. Sim, S.H., Spencer, Jr., B.F. and Nagayama, T. (2011), "Multimetric sensing for structural damage detection", J. Eng. Mech. - ASCE, 137(1), 22-30. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000199
  42. Sohn, H. and Law, K.H. (1997), "A Bayesian probabilistic approach for structure damage detection", Earthq. Eng. Struct. D., 26, 1259-1281. https://doi.org/10.1002/(SICI)1096-9845(199712)26:12<1259::AID-EQE709>3.0.CO;2-3
  43. STMicroelectronics (2005), LIS3L02AS4 MEMS Inertial Sensor Geneva, Switzerland.
  44. Straser, E.G. and Kiremidjian, A.S. (1998), A modular, wireless damage monitoring system for structures, Technical Report 128, John A. Blume Earthquake Engineering Center, Stanford University, Stanford, CA.
  45. Trimm, M. (2003), "An overview of nondestructive evaluation methods", J. Fail. Anal., 3(3), 17-31.
  46. Wang, Y., Lynch, J.P. and Law, K.H. (2005), "Validation of an integrated network system for real-time wireless monitoring of civil structures", Proceedings of the 5th Int'l Ws. Structural Health Monitoring, Stanford, CA, September 12-14.
  47. Whelan, M.J. and Janoyan, K.D. (2009), "Design of a robust, high-rate wireless sensor network for static and dynamic structural monitoring", J. Intel. Mat. Syst. Str., 20(7), 849-863. https://doi.org/10.1177/1045389X08098768
  48. Yao, R. and Pakzad, S.N. (2014), "Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration", Smart Mater. Struct., 23(2), 025007. DOI: 10.1088/0964-1726/23/2/025007.
  49. Yoon, M.K., Heider, D., Gillespie Jr., J.W., Ratcliffe, C.P. and Crane, R.M. (2005), "Local damage detection using the two-dimensional gapped smoothing method", J. Sound Vib., 279(1-2), 119-139. https://doi.org/10.1016/j.jsv.2003.10.058
  50. Yu, Y., Ou, J. and Li, H. (2010), "Design, calibration and application of wireless sensors for structural global and local monitoring of civil infrastructures", Smart Struct. Syst., 6(5-6), 641-659. https://doi.org/10.12989/sss.2010.6.5_6.641
  51. Zilberstein V., Walrath K., Grundy D., Grundya, D., Schlickera, D., Goldfinea, N., Abramovicib, E. and Yentzerc, T. (2003), "MWM eddy-current arrays for crack initiation and growth monitoring", Int. J. Fatigue, 25(9-11), 1147-1155. https://doi.org/10.1016/j.ijfatigue.2003.08.010

피인용 문헌

  1. Nonlinear damage detection using higher statistical moments of structural responses vol.54, pp.2, 2015, https://doi.org/10.12989/sem.2015.54.2.221
  2. Truncated Physical Model for Dynamic Sensor Networks with Applications in High-Resolution Mobile Sensing and BIGDATA vol.142, pp.5, 2016, https://doi.org/10.1061/(ASCE)EM.1943-7889.0001022
  3. Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics vol.24, pp.4, 2017, https://doi.org/10.1002/stc.1881
  4. A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection vol.17, pp.6, 2016, https://doi.org/10.12989/sss.2016.17.6.957
  5. A hybrid structural health monitoring technique for detection of subtle structural damage vol.22, pp.5, 2014, https://doi.org/10.12989/sss.2018.22.5.587
  6. Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure vol.20, pp.11, 2020, https://doi.org/10.3390/s20113264