A study is presented comparing several identification approaches, both parametric and nonparametric, for developing reduced-order nonlinear models of full-scale nonlinear viscous dampers commonly used with large flexible bridges. Such models are useful for incorporation into large-scale computational models, as well as for use as part of structural health monitoring studies based on vibration signature analysis. The paper reports the analysis results from a large collection of experimental tests on a 1112 kN (250 kip) orifice viscous damper under a wide range of frequency and amplitude oscillations. A simplified parametric design model is used in the parametric phase, as well as two different nonparametric methods: the Restoring Force Method, and artificial neural networks. The variations of model parameters with the excitation and response characteristics are investigated, and the relative accuracy and fidelity of the modeling approaches are compared and evaluated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.