In this paper, we present a novel computational strategy for Bayesian probabilistic updating of building models with response functions extracted from ambient noise measurements using seismic interferometry.
The intrinsic building impulse response functions (IRFs) can be extracted from ambient excitation by deconvolving the motion recorded at different floors with respect to the measured ambient ground motion.
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A hierarchical Bayesian framework with Laplace priors is proposed for updating the finite element model.
A Markov chain Monte Carlo technique with adaptive random-walk steps is employed to sample the model parameters for uncertainty quantification.
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