Bayesian Statistics to determine Aerodynamic Admittance based on measurements at a high-rise Building
conference paper
For the modeling of stochastic structural responses due to wind gusts, it is reasonable to make elementary assumptions for the aerodynamic admittance. However, there are still a large variety of corresponding models, and there is a lack of validation and reliable model estimation studies based on real data. In this paper, real data from a high-rise tower in Rotterdam are used to identify the aerodynamic admittance (size-effect and joint-acceptance functions, SEF and JAF) using inverse modeling based on Maximum Likelihood Estimates (MLE), Markov-Chain Monte Carlo (MCMC) sampling, and a Bayesian posterior (BP) implementation to find parametric models for both aspects of the aerodynamic admittance. © Published under licence by IOP Publishing Ltd.
Topics
TNO Identifier
997152
ISSN
17426588
Publisher
Institute of Physics
Source title
XII International Conference on Structural Dynamics
Pages
1-10
Files
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