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, reasonable assumptions for the aerodynamic admittance are elementary. However, still a large variety of corresponding models exists and it lacks of validation and reliable model estimation studies based on real-data. In this paper, real-data from a high-rise tower in Rotterdam is used to inversely identify the aerodynamic admittance (size-effect and joint-acceptance functions, SEF, JAF) based on Maximum Likelihood Estimates (MLE), Marcov-Chain Monte Carlo (MCMC) sampling and a Bayesian posterior (BP) implementation to find parametric models for both aspects of aerodynamic admittance.
Topics
TNO Identifier
993974
Source title
Eurodyn 2023, XII Conference on Structural Dynamics, 2-5 July, Delft, The Netherlands
Pages
1-10