SUMMARYThis study presents an e ective method for identifying predictive models and the underlying modal parameters of linear structural systems using only measured output and excitation time histories obtained from dynamic testing. The system under examination is modelled as a ÿrst-order multi-input multi-output time-invariant system, and the structural model is realized using the Eigensystem Realization Algorithm together with the Observer=Kalman ÿlter IDentiÿcation algorithm. The identiÿed state-space model is further reÿned using a non-linear optimization technique based on sequential quadratic programming. The numerical examples show that the developed methodology performs very well even in the presence of inadequate instrumentation and measurement noise, and that the methodology is highly capable of creating realistic predictive models of structural systems, as well as estimating their underlying modal parameters.
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