System identification of linear MDOF structures under ambient excitation

This paper introduces the eigenspace structural identification technique for tall buildings subjected to ambient excitations that are stationary and where only the response time histories are measured. Based on the forward innovation model of the Kalman filter sequence, the actual response can be constructed as a function of the measured response time history with contamination of either displacement or velocity. The response time history is decomposed into subspace matrices using QR decomposition and Quotient Singular Value Decomposition (QSVD) techniques. These are then substituted into the least-square formulation to obtain the solution which is non-unique. Similarity transformation is applied to arrive at the desired solution employing the fact that eigenvalues of self-similar systems are identical. The advantages of this eigenspace technique are that it is non-iterative, initial estimates of the parameters to the identified are not required, well-established numerical algorithm of the decomposition techniques employed are available, and the method can handle MDOF systems efficiently. Copyright © 1999 John Wiley & Sons, Ltd.

[1]  P. Young An instrumental variable method for real-time identification of a noisy process , 1970 .

[2]  R. Vaicaitis,et al.  Digital Generation of Random Forces for Large-Scale Experiments , 1976 .

[3]  Chung Bang Yun,et al.  Identification of Linear Structural Dynamic Systems , 1982 .

[4]  K. Kleinschmidt,et al.  Book Reviews : INDUSTRIAL NOISE AND VIBRATION CONTROL J.D. Irwin and E.R. Graf Prentice-Hall, Inc., Englewood Cliffs, NJ, 1979 , 1980 .

[5]  M. Hoshiya,et al.  Structural Identification by Extended Kalman Filter , 1984 .

[6]  M. S. Ahmed,et al.  Fast GLS algorithm for parameter estimation , 1984, Autom..

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

[8]  R. Hartley Stochastic Modelling and Control , 1985 .

[9]  Morteza A. M. Torkamani,et al.  Stiffness identification of a tall building during construction period using ambient tests , 1988 .

[10]  Masanobu Shinozuka,et al.  Fundamentals of system identification in structural dynamics , 1989 .

[11]  H. G. Natke,et al.  Real‐Time System Identification of Degrading Structures , 1990 .

[12]  L. M. See,et al.  Estimation of structural parameters in time domain: A substructure approach , 1991 .

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

[14]  Achintya Haldar,et al.  Element-level system identification with unknown input , 1994 .

[15]  R. Ghanem,et al.  Structural-System Identification. I: Theory , 1995 .

[16]  K. Hjelmstad,et al.  Time-domain parameter estimation algorithm for structures. I: Computational aspects , 1995 .

[17]  Nicholas P. Jones,et al.  System-identification procedure for system and input parameters in ambient vibration surveys , 1995 .