Keywords: nonlinear dynamics, earthquake excitation, environmental conditions, condition assessment, damage detection, reduced order metamodels, nonlinear arx models, polynomial chaos expansion, system identification, uncertainty quantification.

The field of structural dynamics is inherently related to the modeling and identification of structural systems under varying loads, which are often impossible to quantify or measure. The task of correctly monitoring the characteristics of such vibrating systems is not a straightforward one due to complexities associated with a) the inefficiency of existing models to account for actual physical phenomena (modeling errors); b) potential nonlinear behavior, as well as lack of a priori knowledge on the loading agents and the properties of the system itself (system uncertainty/complexity); c) limited or incomplete sensory information; d) operation under varying loads and environmental conditions, which inevitably affect structural condition and response.

This paper overviews the state-of-the-art in appropriate modeling methodologies, usually relying on the use of stochastic processes, soft computing approaches and surrogate models, for efficiently simulating and tracking the behavior of large-scale systems under the influence of varying environmental or loading conditions, and uncertainties stemming from the properties of the structure itself and the sensing capabilities/limitations.

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