Experimental identification of structural uncertainty - An assessment of conventional and non-conventional stochastic identification techniques

Engineering Structures

Volumn 53, Issue , 2013, Pages 112-121

  Michaelides, P G ^a   Fassois, S D ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Structural dynamics; Structural identification; System identification; Uncertainty; Variability

Indexed keywords

EXPERIMENTAL ASSESSMENT; EXPERIMENTAL IDENTIFICATION; OPERATING CONDITION; STOCHASTIC IDENTIFICATION; STRUCTURAL IDENTIFICATION; STRUCTURAL UNCERTAINTY; UNCERTAINTY; VARIABILITY;

EXPERIMENTS; IDENTIFICATION (CONTROL SYSTEMS); STRUCTURAL DYNAMICS;

UNCERTAINTY ANALYSIS;

IDENTIFICATION METHOD; NUMERICAL MODEL; STOCHASTICITY; STRUCTURAL ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 84877802173     PISSN: 01410296     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engstruct.2013.03.033     Document Type: Article

References (40)

1. Der Kiureghian A., Ditlevsen O. Aleatory or epistemic? Does it matter?. Struct Safety 2009, 31:105-112.
2. Lopez I., Sarigul-Klijn N. A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: challenges and opportunities. Prog Aerospace Sci 2010, 46:247-273.
3. Moens D., Vandepitte D. A survey of non-probabilistic uncertainty treatment in finite element analysis. Comput Methods Appl Mech Eng 2005, 194:1527-1555.
4. Möller B., Beer M. Engineering computation under uncertainty - capabilities of non-traditional models. Comput Struct 2008, 86:1024-1041.
5. Doebling S.W., Farrar C.R. Estimation of statistical distribution for modal parameters identified from averaged frequency response function data. J Vib Control 2001, 7(4):603-624.
6. Araújo J.M., Awruch A.M. On stochastic finite elements for structural analysis. Comput Struct 1994, 52:461-469.
7. Arici Y., Mosalam K.M. Statistical significance of modal parameters of bridge systems identified from strong motion data. Earthq Eng Struct Dynam 2005, 34:1323-1341.
8. Worden K. Confidence bounds for frequency response functions from time series models. Mech Syst Signal Process 1998, 12(4):559-569.
9. Anderson M.G., Zhou N., Pierre J.W., Wies R.W. Bootstrap-based confidence interval estimates for electromechanical modes from multiple output analysis of measured ambient data. IEEE Trans Power Syst 2005, 20(2):943-950.
10. Farrar CR, Doebling SW, Cornwell PJ. A comparison study of modal parameter confidence intervals computed using the Monte Carlo and Bootstrap techniques. In: Proceedings of the 16th international modal analysis conference; 1998. p. 936-44.
11. Andersen P. Identification of civil engineering structures using vector ARMA models. Ph.D. thesis, Department of Civil Engineering, Aalborg University, Aalborg, Denmark; 1997.
12. Andersen P, Brincker R. Estimation of modal parameters and their uncertainties. In: Proceedings of the 17th international modal analysis conference; 1999. p. 323-9.
13. Vuerinckx R, Pintelon R, Schoukens J, Rolain Y. Obtaining accurate confidence regions for the estimated zeros and poles in system identification problems. In: Proceedings of the 37th IEEE conference on decision & control, Tampa, Florida, USA; 1998. p. 4464-9.
14. Pintelon R., Guillaume P., Schoukens J. Uncertainty calculation in (operational) modal analysis. Mech Syst Signal Process 2007, 21:2359-2373.
15. Adhikari S., Friswell M.I., Lonkar K., Sarkar A. Experimental case studies for uncertainty quantification in structural dynamics. Probab Eng Mech 2009, 24:473-492.
16. Adhikari S., Phani A.S. Random eigenvalue problems in structural dynamics: experimental investigations. AIAA J 2010, 48(6):1085-1097.
17. Stefanou G. The stichastic finite element method: past, present and future. Comput Methods Appl Mech Eng 2009, 198:1031-1051.
18. Fonseca J.R., Friswell M.I., Mottershead J.E., Lees A.W. Uncertainty identification by the maximum likelihood method. J Sound Vib 2005, 288:587-599.
19. Soize C. A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics. J Sound Vib 2005, 288:623-652.
20. Shuëller G.I. On the treatment of uncertainties in structural mechanics and anaysis. Comput Strucut 2007, 85:235-243.
21. Govers Y., Link M. Stochastic model updating - covariance matrix adjustment from uncertain experimental modal data. Mech Syst Signal Process 2010, 24:696-706.
22. Bakhary N., Hao H., Deeks A.J. Damage detection using artificial neural networks with consideration of uncertainties. Eng Struct 2007, 29:2806-2815.
23. de Lima B.S.L.P., Ebecken N.F.F. A comparison of models for uncertainty analysis by the finite element method. Finite Elem Anal Des 2000, 34(2):211-232.
24. Manson G. Calculating frequency response functions for uncertain systems using complex affine analysis. J Sound Vib 2005, 288:487-521.
25. Möller B., Reuter U. Prediction of uncertain structural responses using fuzzy time series. Comput Struct 2008, 86:1123-1139.
26. Shuëller G.I., Pradlwarter H.J. Uncertain linear systems in dynamics: retrospective and recent developments by stochastic approaches. Eng Struct 2009, 31:2507-2517.
27. Ben-Haim Y. Uncertainty, probability and information-gaps. Reliab Eng Syst Safety 2004, 85:249-266.
28. Grigoriu M. Evaluation of Karhunen-Loève, spectral, and sampling representations for stochastic processes. J Eng Mech 2006, 132(2):179-189.
29. Govers Y, Boswald M, Fullekrug U, Goge D, Link M. Analysis of sources and quantification of uncertainty in experimental modal data. In: Proceedings of ISMA06, Leuven, Belgium; 2006.
30. Bae H., Grandhi R.V., Canfield R.A. Epistemic uncertainty quantification techniques including evidence theory for large-scale structures. Comput Struct 2004, 82:1101-1112.
31. Hios JD, Fassois SD. Identification of a global model describing the temperature effects on the dynamics of a smart composite beam. In: Proceedings of ISMA06, Leuven, Belgium, Paper ID 230; 2006.
32. Sakellariou JS, Fassois SD. A functional pooling framework for the identification of systems under multiple operating conditions. In: Proceedings of the 15th mediterranean control conference, Athens, Greece; 2007.
33. Florakis K.A., Fassois S.D., Hemez F.M. MIMO LMS-ARMAX identification of vibrating structures - Part II: A critical assessment. Mech Syst Signal Process 2001, 15(4):737-758.
34. Ljung L. System identification: theory for the user 1996, Prentice-Hall Inc., N.J.
35. Söderström T., Stoica P. System identification 1989, Prentice-Hall International, U.K.
36. The MathWorks. System identification toolbox, version 5; 2001. <>. http://www.mathworks.com.
37. Fassois S.D. Parametric identification of vibrating structures. The encyclopedia of vibration 2001, 673-685. Academic Press. S.G. Braun, D.J. Ewins, S.S. Rao (Eds.).
38. The MathWorks. Signal processing toolbox, version 5; 2001. <>. http://www.mathworks.com.
39. Fassois S.D., Sakellariou J.S. Time-series methods for fault detection and identification in vibrating structures. Phil Trans Roy Soc Ser A 2007, 365:411-448.
40. He J., Fu Z.F. Modal analysis 2001, Butterworth-Heinemann, Oxford.