Abstract Summary
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 validation and reliable model estimation studies based on real-data. In this paper, monitoring 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), Markov-Chain Monte Carlo (MCMC) sampling and a Bayesian posterior (BP) implementation to find parametric models for both aspects of aerodynamic admittance. Based on measurements of the high-rise tower "New Orleans," located in the center of Rotterdam, the mentioned aspects of dynamic wind load effects and the corresponding wind-induced vibrations are analyzed in detail. Available pressure data allow for the coherence analysis for a floor level. Overall accelerations are used to identify the joint acceptance functions, representing the wind load coherence in the context of the structural model properties. All analytes are performed in the frequency domain. The determined models are also used to propose a concept of time-domain analyses, taking into account the coherent composition of the load components. Present results show valuable results of coherence structure based on the pressure measurements. As such results are rare for on-site measurements, it is of particular interest to compare the model estimations (based on Bayesian inference) to known prediction models. Further comparisons will be presented with respect to the overall structural response and the causing dynamic load process. The inversely determined joint-acceptance function allows for a model comparison between frequency and time-based analyses (using correspondingly generated artificial time series of wind forces). Finally, the accuracy of the predictions is also discussed on the basis of the identified uncertainties using the Bayesian statistical concepts.
