Modal Identification through Ambient Vibration: Comparative Study
Publication: Journal of Engineering Mechanics
Volume 135, Issue 8
Abstract
An analytical comparison between three techniques for the identification of modal properties of structures when subjected to ambient vibrations is performed. The algorithms examined include the eigensystem realization algorithm with data correlations, the prediction error method through least squares, and the stochastic subspace identification (SSI) technique. Both analytical and experimental data from a four-storey building scaled at 1:3 are used to perform these evaluations. The level of noise added to the simulated data is varied to study the robustness of the techniques. All techniques are fully automated, allowing for assessments to be conducted through Monte Carlo simulations. The results indicate that the SSI technique provides the most accurate identification of natural frequencies and mode shapes even with high noise levels, all while requiring the least amount of experience for implementation.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers would like to acknowledge support in part from NSF Grant No. NSFCMS 02-45402.
References
Abdelghani, M., Verhaegen, M., Van Overchee, P., and De Moor, B. (1998). “Comparison study of subspace identification methods applied to flexible structures.” Mech. Syst. Signal Process., 12(5), 679–692.
Andersen, P. (1997). “Identification of civil engineering structures using ARMA models.” Ph.D. dissertation, Univ. of Aalborg, Aalborg, Denmark.
Bendat, J. S., and Piersol, A. G. (1980). Engineering applications of correlation and spectral analysis, Wiley, New York.
Brinker, R., De Stefano, A., and Piombo, B. (1996). “Ambient data to analyze the dynamic behavior of bridges: A first comparison between different techniques.” Proc., 14th Int. Modal Analysis Conf., SEM, Dearborn, Mich.
Caicedo, J. M., Dyke, S. J., and Mizumori, A. (2004). “Characterization of traffic excitation for numerical studies in structural health monitoring.” Proc., 17th Engineering Mechanics Conf., ASCE, Reston, Va.
Desforges, M. J., Cooper, J. E., and Wrigth, J. R. (1995). “Spectral and modal parameter estimation from output-only measurements.” Mech. Syst. Signal Process., 9(2), 169–186.
Dyke, S. J., Bernal, D., Beck, J., and Ventura, C. (2003). “Experimental phase of the structural health monitoring benchmark problem.” Proc., 16th Engineering Mechanics Conf., ASCE, Reston, Va.
Giraldo, D., Caicedo, J. M., and Dyke, S. J. (2003). “Experimental phase of the SHM benchmark studies: Damage detection using NExT and ERA.” Proc., 16th Engineering Mechanics Conf., ASCE, Reston, Va.
He, X., Moaveni, B., Conte, J. P., and Elgamal, A. (2006). “Comparative study of system identification techniques applied to new Caquinez Bridge.” Proc., 3rd Int. Conf. on Bridge Maintenance, Safety, and Management (IABMAS), Porto, Portugal.
Ibrahim, S. R., Brinker, R., and Asmussen, J. C. (1996). “Modal parameter identification from responses of general unknown random inputs.” Proc., Int. Modal Analysis Conf., SEM, Dearborn, Mich.
James, G. H., Carne, T. G., and Lauffer, J. P. (1993). “The natural excitation technique for modal parameter extraction from operating wind turbines.” Rep. No. SAND92-1666 (UC-261), Sandia National Laboratories, Albuquerque, N.M.
Johnson, E. A., Lam, H. F., Katafygiotis, L., and Beck, J. (2000). “A benchmark problem for structural health monitoring and damage detection.” Proc., 14th Engineering Mechanics Conf., ASCE, Reston, Va.
Juang, J. N., Cooper, J. E., and Wright, J. R. (1988). “An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification.” Control Theory Adv. Technol., 4(1), 5–14.
Juang, J. N., and Pappa, R. S. (1985). “An eigensystem realization algorithm for modal parameter identification and model reduction.” J. Guid. Control, 8(5), 620–627.
Kirkegaard, P. H., and Andersen, P. (1997). “State space identification of civil engineering structures from output measurements.” Proc., 15th Int. Modal Analysis Conf., SEM, Orlando, Fla.
Lanslots, J., Rodiers, B., and Peeters, B. (2004). “Automated pole-selection: Proof-of-concept and validation.” Proc., Int. Conf. on Noise and Vibration Engineering, ISMA, Leuven, Belgium.
Lau, J., Lanslots, J., Peeters, B., and Van der Auweraer, H. (2007). “Automatic modal analysis: Reality or myth?.” Proc., 25th Int. Modal Analysis Conf., SEM, Orlando.
Lew, J. S., Juang, J. N., and Longman, R. W. (1993). “Comparison of several system identification methods for flexible structures.” J. Sound Vib., 167(3), 461–480.
Ljung, L. (1999). System identification—Theory for the user, Prentice-Hall, Upper Saddle River, N.J.
Pappa, R. S., James, G. H., III, and Zimmerman, D. C. (1998). “Autonomous model identification of the space shuttle tail rudder.” J. Spacecr. Rockets, 35(2), 163–169.
Peeters, B., and Ventura, C. E. (2003). “Comparative study of modal analysis techniques for bridge dynamic characteristics.” Mech. Syst. Signal Process., 17(5), 965–988.
Wenzel, H., and Pichler, D. (2005). Ambient vibration monitoring, Wiley, New York.
Van Overchee, P., and De Moor, B. (1996). Subspace identification for linear systems: Theory, implementation and applications, Kluwer Academic, Dordrecht, The Netherlands.
Information & Authors
Information
Published In
Copyright
© 2009 ASCE.
History
Received: Nov 28, 2006
Accepted: Feb 9, 2009
Published online: Jul 15, 2009
Published in print: Aug 2009
Notes
Note. Associate Editor: Erik A. Johnson
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.