Image based modeling and prediction of nonstationary ground motions

Abstract

Nonlinear dynamic analysis is a required step in seismic performance evaluation of many structures. Performing such an analysis requires input ground motions, which are often obtained through simulations, due to the lack of sufficient records representing a given scenario. As seismic ground motions are characterized by time-varying amplitude and frequency content, and the response of nonlinear structures is sensitive to the temporal variations in the seismic energy input, ground motion nonstationarities should be taken into account in simulations. This paper describes a nonparametric approach for modeling and prediction of nonstationary ground motions. Using the Relevance Vector Machine, a nonparametric regression model which takes as input a set of seismic predictors, and produces as output a wavelet image showing the expected time–frequency distribution of energy, conditioned on the predictors, is developed. Demonstrative examples comparing the recorded and predicted ground motions in time, frequency, and time–frequency domains are presented.