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Past, Resent, and Future of Structural Health Assessment

  • Achintya HaldarEmail author
  • Ajoy Kumar Das
Conference paper

Abstract

Past, present, and future of structural health assessment (SHA) concepts and related areas, as envisioned by the authors, are briefly reviewed in this chapter. The growth in the related areas has been exponential covering several engineering disciplines. After presenting the basic concept, the authors discussed its growth from infancy, that is, hitting something with a hammer and listening to sound, to the use of most recent development of wireless sensors and the associated advanced signal processing algorithms. Available SHA methods are summarized in the first part of this chapter. The works conducted by the research team of the authors are emphasized. Later, some of the future challenges in SHA areas are identified. Since it is a relatively new multidisciplinary area, the education component is also highlighted at the end.

Keywords

Structural health assessment Kalman filter Substructure System identification Uncertainty analysis Sensors 

References

  1. 1.
    Aditya G, Chakraborty S (2008) Sensitivity based health monitoring of structures with static responses. Scientia Iranica 15(3):267–274Google Scholar
  2. 2.
    Anh TV (2009) Enhancements to the damage locating vector method for structural health monitoring. Ph. D. dissertation, National University of Singapore, SingaporeGoogle Scholar
  3. 3.
    Bernal D (2002) Load vectors for damage localization. J Eng Mech ASCE 128(1):7–14MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bray DE (2000) Historical review of technology development in NDE. In: 15th world conference on nondestructive testing, Roma, Italy, 15–21 Oct 2000Google Scholar
  5. 5.
    Carden EP, Fanning P (2004) Vibration based condition monitoring: a review. Struct Health Monit 3(4):355–377CrossRefGoogle Scholar
  6. 6.
    Ceravolo R (2009) Time–frequency analysis.  Chapter 26. In: Boller C, Chang F-K, Fuzino Y (eds) Encyclopedia of structural health monitoring. Wiley, ChichesterGoogle Scholar
  7. 7.
    Chang PC, Flatau A, Liu SC (2003) Review paper: health monitoring of civil infrastructure. Struct Health Monit 2(3):257–267CrossRefGoogle Scholar
  8. 8.
    Chase JG, Begoc V, Barroso LR (2005) Efficient structural health monitoring for benchmark structure using adaptive RLS filters. Comput Struct 83:639–647CrossRefGoogle Scholar
  9. 9.
    Chase JG, Spieth HA, Blome CF, Mandler JB (2005) LMS-based structural health monitoring of a non-linear rocking structure. Earthq Eng Struct Dyn 34:909–930CrossRefGoogle Scholar
  10. 10.
    Choi YM, Cho HN, Kim YB, Hwang YK (2001) Structural identification with unknown input excitation. KSCE J Civil Eng 5(3):207–213CrossRefGoogle Scholar
  11. 11.
    Das AK, Haldar A (2010) Structural integrity assessment under uncertainty for three dimensional offshore structures. Int J Terraspace Sci Eng (IJTSE) 2(2):101–111Google Scholar
  12. 12.
    Das AK, Haldar A (2010) Structural health assessment of truss-type bridges using noise-contaminated uncertain dynamic response information. Int J Eng under Uncertainity: Hazards, Assessment, and Mitigation 2(3–4):75–87Google Scholar
  13. 13.
    Das AK, Haldar A (2012) Health assessment of three dimensional large structural systems – a novel approach. Int J Life Cycle Reliab Saf Eng (in press)Google Scholar
  14. 14.
    Das AK, Haldar A, Chakraborty S (2012) Health assessment of large two dimensional structures using minimum information – recent advances. Adv Civil Eng. Article ID 582472. doi: 10.1155/2012/582472
  15. 15.
    Doebling SW, Farrar CR, Prime MB, Shevitz DW (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review, Los Alamos National Laboratory. Report no. LA-13070-MSGoogle Scholar
  16. 16.
    Fan W, Qiao P (2010) Vibration-based damage identification methods: a review and comparative study. Struct Health Monit 10(14):1–29Google Scholar
  17. 17.
    Garrido R, Rivero-Angeles FJ (2006) Hysteresis and parameter estimation of MDOF systems by a continuous-time least-squares method. J Earthq Eng 10(2):237–264Google Scholar
  18. 18.
    Ghanem R, Ferro G (2006) Health monitoring for strongly non-linear systems using the ensemble Kalman filter. Struct Control Health Monit 13:245–259CrossRefGoogle Scholar
  19. 19.
    Ghosh S, Roy D, Manohar CS (2007) New forms of extended Kalman filter via transversal linearization and applications to structural system identification. Comput Methods Appl Mech Eng 196:5063–5083CrossRefzbMATHGoogle Scholar
  20. 20.
    Haldar A (ed) (2012) Health assessment of engineered structures: bridges, buildings and other infrastructures. World Scientific Publishing CoGoogle Scholar
  21. 21.
    Haldar A, Das AK (2010) Prognosis of structural health – nondestructive methods. Int J Perform Eng. Special Issue on Prognostics and Health Management (PHM) 6(5):487–498Google Scholar
  22. 22.
    Hellier CJ (2003) Handbook of nondestructive evaluation. The McGraw-Hill Companies, Inc, New YorkGoogle Scholar
  23. 23.
    Hoshiya M, Saito E (1984) Structural identification by extended Kalman filter. J Eng Mech ASCE 110(12):1757–1770CrossRefGoogle Scholar
  24. 24.
    Huang NE, Huang K, Chiang W-L (2005) HHT-based bridge structural-health monitoring.  Chapter 12. In: Huang NE, Shen SS (eds) Hilbert-Huang transform and its applications. World Scientific Publishing Co. Pte. Ltd., Singapore
  25. 25.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Ibanez P (1973) Identification of dynamic parameters of linear and non-linear structural models from experimental data. Nucl Eng Design 25:30–41CrossRefGoogle Scholar
  27. 27.
    Katkhuda H (2004) In-service health assessment of real structures at the element level with unknown input and limited global responses. Ph. D. thesis, University of Arizona, Tucson, USAGoogle Scholar
  28. 28.
    Katkhuda H, Haldar A (2008) A novel health assessment technique with minimum information. Struct Control Health Monit 15(6):821–838CrossRefGoogle Scholar
  29. 29.
    Katkhuda H, Martinez-Flores R, Haldar A (2005) Health assessment at local level with unknown input excitation. J Struct Eng ASCE 131(6):956–965CrossRefGoogle Scholar
  30. 30.
    Kerschen G, Worden K, Vakakis AF, Golinval JC (2006) Past, present and future of nonlinear system identification in structural dynamics. Mech Syst Signal Process 20(3):505–592CrossRefGoogle Scholar
  31. 31.
    Koh CG, See LM (1994) Identification and uncertainty estimation of structural parameters. J Eng Mech 120(6):1219–1236CrossRefGoogle Scholar
  32. 32.
    Ling X, Haldar A (2004) Element level system identification with unknown input with Rayleigh damping. J Eng Mech ASCE 130(8):877–885CrossRefGoogle Scholar
  33. 33.
    Liu X, Escamilla-Ambrosio PJ, Lieven NAJ (2009) Extended Kalman filtering for the detection of damage in linear mechanical structures. J Sound Vib 325:1023–1046CrossRefGoogle Scholar
  34. 34.
    Martinez-Flores R, Haldar A (2007) Experimental verification of a structural health assessment method without excitation information. J Struct Eng 34(1):33–39Google Scholar
  35. 35.
    Martinez-Flores R, Katkhuda H, Haldar A (2008) A novel health assessment technique with minimum information: verification. Int J Perform Eng 4(2):121–140Google Scholar
  36. 36.
    Maybeck PS (1979) Stochastic models, estimation, and control theory. Academic, New YorkGoogle Scholar
  37. 37.
    McCaan D, Jones NP, Ellis JH (1998) Toward consideration of the value of information in structural performance assessment. Paper no. T216-6. Structural Engineering World Wide, CD-ROMGoogle Scholar
  38. 38.
    Montalvao M, Maia NMM, Ribeiro AMR (2006) A review of vibration-based structural health monitoring with special emphasis on composite materials. Shock Vib Dig 38(4):295–324CrossRefGoogle Scholar
  39. 39.
    Rytter A (1993) Vibration based inspection of civil engineering structures. Ph. D. dissertation, Department of Building Technology and Structural Engineering, Aalborg University, DenmarkGoogle Scholar
  40. 40.
    Sajjad S, Zaidi H, Zanardelli WG, Aviyente, S, Strangas EG (2007) Comparative study of time-frequency methods for the detection and categorization of intermittent fault in electrical devices. Diagnostics for electric machines, power electronics and drive, SDEMPED, IEEE symposium, 6–8 September, pp 39–45Google Scholar
  41. 41.
    Sanayei M, Imbaro GR, McClain JAS, Brown LC (1997) Structural model updating using experimental static measurements. J Struct Eng ASCE 123(6):792–798CrossRefGoogle Scholar
  42. 42.
    Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR, Czarnecki JJ (2004) A review of structural health monitoring literature: 1996–2001, Los Alamos National Laboratory, LA-13976-MSGoogle Scholar
  43. 43.
    Spencer BF Jr, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: opportunities and challenges. Struct Control Health Monit 11:349–368CrossRefGoogle Scholar
  44. 44.
    Vo PH, Haldar A (2003) Post processing of linear accelerometer data in system identification. J Struct Eng 30(2):123–130Google Scholar
  45. 45.
    Vo PH, Haldar A (2004) Health assessment of beams – theoretical and experimental investigation. J Struct Eng, Special Issue on Advances in Health Monitoring/Assessment of Structures Including Heritage and Monument Structures 31(1):23–30Google Scholar
  46. 46.
    Wang D, Haldar A (1994) An element level SI with unknown input information. J Eng Mech ASCE 120(1):159–176CrossRefGoogle Scholar
  47. 47.
    Wang D, Haldar A (1997) System identificatin with limited observations and without input. J Eng Mech ASCE 123(5):504–511Google Scholar
  48. 48.
    Welch G, Bishop G (2006) An introduction to the Kalman filter. Technical report. TR95-041 2006, Department of Computer Science, University of North Carolina at Chapel Hill, NCGoogle Scholar
  49. 49.
    Yadav SK, Banerjee S, Kundu T (2011) Effective damage sensitive feature extraction methods for crack detection using flaw scattered ultrasonic wave field signal. In: Proceedings of the 8th international workshop on structural health monitoring, Stanford, USA, 13–15 SeptemberGoogle Scholar
  50. 50.
    Yang JN, Huang H (2007) Sequential non-linear least-square estimation for damage identification of structures with unknown inputs and unknown outputs. Int J Non-Linear Mech 42:789–801MathSciNetCrossRefzbMATHGoogle Scholar
  51. 51.
    Yang JN, Huang H, Lin S (2006) Sequential non-linear least-square estimation for damage identification of structures. Int J Non-Linear Mech 41:124–140CrossRefzbMATHGoogle Scholar
  52. 52.
    Yang JN, Lei Y, Lin S, Huang N (2004) Hilbert-Huang based approach for structural damage detection. J Eng Mech 130(1):85–95CrossRefGoogle Scholar
  53. 53.
    Yang JN, Lin S (2005) Identification of parametric variations of structures based on least squares estimation and adaptive tracking technique. J Eng Mech 131(3):290–298MathSciNetCrossRefGoogle Scholar
  54. 54.
    Yang JN, Lin S, Huang HW, Zhou L (2006) An adaptive extended Kalman filter for structural damage identification. J Struct Control Health Monit 13:849–867CrossRefGoogle Scholar
  55. 55.
    Yang JN, Pan S, Lin S (2007) Least-squares estimation with unknown excitations for damage identification of structures. J Eng Mech 133(1):12–21CrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Civil Engineering and Engineering MechanicsUniversity of ArizonaTucsonUSA

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