Nonlinear Model-Based System Identification of Lead–Rubber Bearings

Abstract

A nonlinear model-based system identification method is proposed, formulated, implemented, and applied to a three-span continuous base-isolated bridge. Transverse and rotational rigid-body motions of the bridge superstructure are formulated into two degrees-of-freedom dynamic governing equations. To model hysteretic behavior of lead–rubber bearings, the Menegotto–Pinto model is used. The proposed system identification procedures consist of two phases to address ill-conditioning issues. The uncertainty of parameters is considered, which results in probability distributions instead of single values for identified parameters. The proposed system identification is adopted in the comparison of experimental data sets. Hypothesis testing is used to determine the closeness of identification results. The proposed method is applied to quick-release field experiments on the bridge to investigate aging and temperature dependent effects in lead–rubber bearings.