Bayesian Model Updating of a Five-Story Building Using Zero-Variance Sampling Method

Abstract

This study presents the Bayesian model updating and stochastic seismic response prediction of a reinforced concrete frame building with masonry infill panels. After the 2015 Gorkha earthquake, some of the authors visited the building and recorded ambient vibration data using a set of accelerometers. The seismic response of the building was also recorded during one of the moderate aftershocks, using a set of sensors at the basement and the roof. In this study, the ambient vibration data is used to calibrate a model and the earthquake data is used to validate it. Natural frequencies and mode shapes of the building are extracted through an output-only system identification process. An initial finite elementmodel of the building is developed using a recently proposed modeling framework for masonry-infilled RC frames. Bayesian model updating is then performed to update the stiffness of selected structural elements and evaluate their respective uncertainties, given the available data. A novel sampling approach, namely Zero-Variance MCMC, is implemented to address the computational challenges of stochastic simulation when estimating the joint posterior probability distribution of the model’s parameters. This sampling approach has been shown to drastically improve computational efficiency while preserving adequate accuracy. The calibrated model is used for the probabilistic prediction of the seismic response of the building to a moderate earthquake. This predicted response is shown to be in good agreement with the available recorded response of the building at the roof.