IMAGE-BASED MODELING AND PREDICTION OF NON-STATIONARY GROUND MOTIONS

• Main Author: DAK HAZIRBABA, YILDIZ
• Format: Others
• Published: OpenSIUC 2015
• Subjects: GMRotI50; Ground motion simulation; Maximum direction; principal component analysis; wavelet transform; relevance vector machine; spectrogram; synthetic ground motion
• Online Access: https://opensiuc.lib.siu.edu/dissertations/1008; https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2012&context=dissertations

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 non-stationarities should be taken into account in simulations. This paper describes a nonparametric approach for modeling and prediction of non-stationary ground motions. Using Relevance Vector Machines, a regression model which takes as input a set of seismic predictors, and produces as output the expected evolutionary power spectral density, conditioned on the predictors. A demonstrative example is presented, where recorded and predicted ground motions are compared in time, frequency, and time-frequency domains. Analysis results indicate reasonable match between the recorded and predicted quantities.