System Identification for Base Isolated Buildings

Abstract eng:
A procedure for nonlinear structural system identification for base isolated buildings is proposed herein. The study looks for an identification algorithm capable of addressing the nonlinear behavior of this type of systems to strong motion, thus helping to improve the accuracy on the response and dynamic properties estimation when undergoing different amplitude excitations. A variety of relevant techniques developed in the literature are reviewed, including Power Spectral Density (PSD), Multivariate Output Space State Error with moving window (MOESP-MW), Recursive Prediction Error Method (RPEM), Bayesian processors (UKF) and Particle Filter methods (PF). The effectiveness of each method is evaluated and compared through simulated data from a set of MIMO models, including Bouc-Wen and Bilinear hysteresis accounting for the nonlinear behavior of base isolation. Also time history acceleration recordings of more than 90 seismic events recorded on an instrumented isolated confined masonry four-story building, between 1993 and 2014, are used to perform identification. Limitations of the algorithms and models, associated to an increasing complexity on the identification problem are discussed throughout the paper. Results indicate that Particle Filter provides a more complete and appropriate tool to deal with the identification process for a base isolated building, given their good performance on non-stationary state and parameter estimation.

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