Inverse identification of the gust loading of a high-rise tower with Bayesian statistics
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, SE, JAF) based on Maximum Likelihood
Estimates (MLE), Marcov-Chain Monte Carlo (MCMC) sampling and a Bayesian posterior (BP)
implementation to find parametric models different both aspects of aerodynamic admittance.
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, SE, JAF) based on Maximum Likelihood
Estimates (MLE), Marcov-Chain Monte Carlo (MCMC) sampling and a Bayesian posterior (BP)
implementation to find parametric models different both aspects of aerodynamic admittance.
TNO Identifier
977788
Source title
CWE Workshop 2022 Advanced modeling of stochastic Wind Effects and Vibrations
Pages
1-6
Files
To receive the publication files, please send an e-mail request to TNO Repository.