Parameter identification of large structural systems in time domain

Abstract

Though many methods of system identification are currently available for parameter estimation of structural systems, the challenge lies in the numerical difficulty in convergence when the number of unknowns is large. In this study, the genetic algorithms (GA) approach is adopted, which has several advantages over classical system identification techniques. Nevertheless, if applied directly, this approach requires tremendous computational time when dealing with structural systems large in both unknowns and degrees of freedom. A method is proposed herein to alleviate this problem by conducting a GA search in modal domains of a much smaller dimension than the physical domain. The objective function is defined based on the estimated modal response in time domain and the corresponding modal response transformed from the measured response. With some modification, this method also works well even with incomplete response measurement. Numerical examples of structural systems of up to 50 degrees of freedom are presented. Effects of measurement noise are considered.