Response‐only modal identification of structures using strong motion data

SUMMARY Dynamic characteristics of structures — viz. natural frequencies, damping ratios, and mode shapes — are central to earthquake-resistant design. These values identified from field measurements are useful for model validation and health-monitoring. Most system identification methods require input excitations motions to be measured and the structural response; however, the true input motions are seldom recordable. For example, when soil–structure interaction effects are non-negligible, neither the free-field motions nor the recorded responses of the foundations may be assumed as ‘input’. Even in the absence of soil–structure interaction, in many instances, the foundation responses are not recorded (or are recorded with a low signal-to-noise ratio). Unfortunately, existing output-only methods are limited to free vibration data, or weak stationary ambient excitations. However, it is well-known that the dynamic characteristics of most civil structures are amplitude-dependent; thus, parameters identified from low-amplitude responses do not match well with those from strong excitations, which arguably are more pertinent to seismic design. In this study, we present a new identification method through which a structure's dynamic characteristics can be extracted using only seismic response (output) signals. In this method, first, the response signals’ spatial time-frequency distributions are used for blindly identifying the classical mode shapes and the modal coordinate signals. Second, cross-relations among the modal coordinates are employed to determine the system's natural frequencies and damping ratios on the premise of linear behavior for the system. We use simulated (but realistic) data to verify the method, and also apply it to a real-life data set to demonstrate its utility. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Farzad Naeim,et al.  Evolutionary modal identification utilizing coupled shear–flexural response—implication for multistory buildings. Part II : Application , 2006 .

[2]  James L. Beck,et al.  Structural identification using linear models and earthquake records , 1980 .

[3]  Patrick Flandrin,et al.  Improving the readability of time-frequency and time-scale representations by the reassignment method , 1995, IEEE Trans. Signal Process..

[4]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[5]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[6]  Serdar Soyoz,et al.  Ambient and Forced Vibration Testing of a Reinforced Concrete Building before and after Its Seismic Retrofitting , 2013 .

[7]  Abdeldjalil Aïssa-El-Bey,et al.  Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain , 2007, IEEE Transactions on Signal Processing.

[8]  Abdelhak M. Zoubir,et al.  Blind separation of nonstationary sources , 2004, IEEE Signal Processing Letters.

[9]  E. Oja,et al.  Independent Component Analysis , 2013 .

[10]  Ying Lei,et al.  Identification, Model Updating, and Response Prediction of an Instrumented 15-Story Steel-Frame Building , 2006 .

[11]  Boualem Boashash,et al.  Note on the use of the Wigner distribution for time-frequency signal analysis , 1988, IEEE Trans. Acoust. Speech Signal Process..

[12]  Cédric Févotte,et al.  Two contributions to blind source separation using time-frequency distributions , 2004, IEEE Signal Processing Letters.

[13]  Seungjin Choi,et al.  Blind separation of second-order nonstationary and temporally colored sources , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[14]  Gene H. Golub,et al.  Matrix computations , 1983 .

[15]  J. M. Ueng,et al.  Parametric identification of asymmetric buildings from earthquake response records , 2005 .

[16]  Mihailo D. Trifunac,et al.  Impulse response analysis of the Van Nuys 7‐storey hotel during 11 earthquakes and earthquake damage detection , 2008 .

[17]  Anil K. Chopra,et al.  Dynamics of Structures: Theory and Applications to Earthquake Engineering , 1995 .

[18]  Gary C. Hart,et al.  System Identification in Structural Dynamics , 1977 .

[19]  A. Cichocki,et al.  Robust whitening procedure in blind source separation context , 2000 .

[20]  Rune Brincker,et al.  Modal identification of output-only systems using frequency domain decomposition , 2001 .

[21]  Boualem Boashash,et al.  Time-Frequency Signal Analysis and Processing: A Comprehensive Reference , 2015 .

[22]  Douglas L. Jones,et al.  A high resolution data-adaptive time-frequency representation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[23]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[24]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[25]  Dennis Gabor,et al.  Theory of communication , 1946 .

[26]  Shirley J. Dyke,et al.  Experimental validation of structural health monitoring for flexible bridge structures , 2005 .

[27]  John C. Wilson,et al.  Identification of base‐excited structures using output‐only parameter estimation , 2004 .

[28]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[29]  J. Wolf Dynamic soil-structure interaction , 1985 .

[30]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[31]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[32]  Adel Belouchrani,et al.  A one step time-frequency blind identification , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[33]  T. Kailath,et al.  A least-squares approach to blind channel identification , 1995, IEEE Trans. Signal Process..

[34]  K. Yuen Bayesian Methods for Structural Dynamics and Civil Engineering , 2010 .

[35]  Mihailo D. Trifunac,et al.  Apparent Periods of a Building. II: Time-Frequency Analysis , 2001 .

[36]  Keith Worden,et al.  Modal–Vibration‐Based Damage Identification , 2009 .

[37]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[38]  Maria I. Todorovska,et al.  Ambient vibration tests of a seven-story reinforced concrete building in Van Nuys, California, damaged by the 1994 Northridge earthquake , 2000 .

[39]  S. S. Ivanović,et al.  Apparent Periods of a Building. I: Fourier Analysis , 2001 .

[40]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

[41]  Boualem Boashash,et al.  Separating More Sources Than Sensors Using Time-Frequency Distributions , 2005, EURASIP J. Adv. Signal Process..

[42]  Jonathan P. Stewart,et al.  System identification for evaluating soil–structure interaction effects in buildings from strong motion recordings , 1998 .

[43]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[44]  Merit P. White Earthquake engineering researchComm. on Earthquake Engineering Research Natl. Acad. of Eng.; National Academy of Sciences, Washington, D. C., 1969, 313 pp., $3 , 1970 .

[45]  Joel P. Conte,et al.  Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table , 2010 .

[46]  E. I. Jury,et al.  Theory and application of the z-transform method , 1965 .

[47]  Budhaditya Hazra,et al.  Re-tuning tuned mass dampers using ambient vibration measurements , 2010 .

[48]  Moeness G. Amin,et al.  Blind source separation based on time-frequency signal representations , 1998, IEEE Trans. Signal Process..

[49]  Karim Abed-Meraim,et al.  Underdetermined Blind Audio Source Separation Using Modal Decomposition , 2007, EURASIP J. Audio Speech Music. Process..

[50]  Helmut Krawinkler,et al.  Van Nuys Hotel Building Testbed Report: Exercising Seismic Performance Assessment , 2005 .