Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-01T18:44:23.942Z Has data issue: false hasContentIssue false

Instantaneous identification of localized non-linearities in steel framed structures

Published online by Cambridge University Press:  15 September 2010

Pierre Argoul
Affiliation:
Université Paris-Est, UR Navier, École des Ponts ParisTech, 6 & 8 avenue Blaise Pascal, Champs-sur-Marne, 77455 Marne-la-Vallée Cedex 2, France
Rosario Ceravolo*
Affiliation:
Dip. Ingegneria Strutturale e Geotecnica, Politecnico di Torino, c. Duca degli Abruzzi 24, 10129 Torino, Italy
G. V. Demarie
Affiliation:
Dip. Ingegneria Strutturale e Geotecnica, Politecnico di Torino, c. Duca degli Abruzzi 24, 10129 Torino, Italy
D. Sabia
Affiliation:
Dip. Ingegneria Strutturale e Geotecnica, Politecnico di Torino, c. Duca degli Abruzzi 24, 10129 Torino, Italy
*
a Corresponding author: rosario.ceravolo@polito.it
Get access

Abstract

This paper discusses the characteristics of time-frequency estimators to be used in the identification of systems with localized non-linearities. The common idea underlying this research is that, for certain classes of structural response signals, the availability of a limited number of experimental data can be partially obviated by taking into account the “localisation” in time of the frequency components of the signals. Time-frequency techniques for structural identification are reported that extend the definition of instantaneous time-frequency estimators and Gabor instantaneous estimators were extracted from non-stationary vibration signals. In order to foresee their validity on the basis of measured data, methods were applied to seismic responses obtained from numerical tests conducted on steel frames. The results obtained made it possible to evaluate the characteristics of time-frequency identification techniques as well as their efficiency when applied to non-linear structures.

Type
Research Article
Copyright
© AFM, EDP Sciences 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Shi, G., Atluri, S.N., Static and dynamic analysis of space frames with non-linear flexible connections, Int. J. Num. Meth. Eng. 28 (1989) 26352650CrossRefGoogle Scholar
Moncarz, P.D., Gerstle, K.H., Steel frames with non-linear connections, J. Struct. Div. ASCE 107 (1981) 14271441Google Scholar
Mohebkhaha, A., Tasnimia, A.A., Moghadamb, H.A., Nonlinear analysis of masonry-infilled steel frames with openings using discrete element method, J. Constr. Steel Res. 64 (2008) 14631472CrossRefGoogle Scholar
N.M.M. Maia, J.M.M. Silva, Theoretical and Experimental Modal Analysis, Research Study Press LTD, Taunton, England, 1997
Hammond, J.K., White, P.R., The analysis of non-stationary signals using time-frequency methods, J. Sound Vib. 190 (1996) 419447CrossRefGoogle Scholar
Mallat, S., Papanicolaou, G., Zhang, Z., Adaptive covariance estimation of locally stationary processes, Ann. Stat. 28 (1998) 147Google Scholar
R. Ceravolo, Time-frequency analysis, in Encyclopedia of Structural Health Monitoring, C. Boller, F.-K. Chang & Y. Fujino (eds.), Wiley & Sons, 2008
Bonato, P., Ceravolo, R., Stefano, A. D., Molinari, F., Use of cross time-frequency estimators for the structural identification in non-stationary conditions and under unknown excitation, J. Sound Vib. 237 (2000) 775791CrossRefGoogle Scholar
Ceravolo, R., Use of instantaneous estimators for the evaluation of structural damping, J. Sound Vib. 274 (2004) 385401CrossRefGoogle Scholar
Erlicher, S., Argoul, P., Modal identification of linear non-proportionally damped systems by wavelet transform, Mech. Sys. Sig. Proc. 21 (2007) 13861421CrossRefGoogle Scholar
Collis, W.B., White, P.R., Hammond, J.K., Higher-order spectra: the bispectrum and trispectrum, Mechanical Systems and Signal Processing 12 (1998) 375394CrossRefGoogle Scholar
S.B. Kim, E.J. Powers, Estimation of Volterra kernels via higher-order statistical signal processing, Chapter 7 in Higher-Order Statistical Signal Processing B. Boashash, E.J. Powers, A. Zoubir (eds.), Longman/Wiley, Melbourne and New York, 1995
Demarie, G.V., Ceravolo, R., Stefano, A. D., Instantaneous identification of polynomial non-linearity based on Volterra series representation, Key Engineering Materials 293–294 (2005) 703710CrossRefGoogle Scholar
Worden, K., Manson, G., Tomlinson, G.R., A harmonic probing algorithm for the multi-input Volterra series, J. Sound Vib. 201 (1997) 6784CrossRefGoogle Scholar
Worden, K., Manson, G., Tomlinson, G.R., Random vibration of a multi-degree-of-freedom non-linear system using the Volterra series, J. Sound Vib. 226 (1999) 397405CrossRefGoogle Scholar
Thouverez, F., Jezequel, L., Identification of a localized non-linearity, Int. J. Non-Lin. Mech. 33 (1998) 935945CrossRefGoogle Scholar
Tawfiq, I., Vinh, T., Contribution to extension of modal analysis to non-linear structure using Volterra series, Mechanical Systems and Signal Processing 17 (2003) 379407CrossRefGoogle Scholar
Chatterjee, A., Vyas, N.S., Non-linear parameter estimation in multi-degree-of-freedom systems using multi-input Volterra series, Mechanical Systems and Signal Processing 18 (2004) 457489CrossRefGoogle Scholar
M. Schetzen, The Volterra/Wiener theories of non-linear systems, Krieger publishing company, Malabar, FL, 1980
W.J. Rugh, Nonlinear system theory. The Volterra/Wiener approach, 2002 (Web version:)
Vazquez Feijoo, J.A., Worden, K., Stanway, R., Associated linear equations for Volterra operators, Mechanical Systems and Signal Processing 19 (2005) 5769CrossRefGoogle Scholar
Carson, J.R., Notes on the theory of modulation, Proc. IEEE 10 (1922) 5764Google Scholar
Cloud, W.K., Hudson, D.E., Strong motion data from San Fernando, California earthquake of February 9, 1971, California Division of Mines and Geology Bull. 196 (1975) 273303Google Scholar