Examination of experimental variability in HFFB testing of a tall building under multi-directional winds

Furthermore, effects of experimental errors are incorporated by allowing the parameters in each function to vary randomly. Variability is evaluated by considering the second-order statistical moments of the parameters (standard deviations and correlations). At last, this paper introduces a data-driven Monte-Carlo approach to reconstruct the generalized PSD of the wind loading through model equations. Experimental variability is accounted for by exploiting information on the probability distributions of the model parameters. Verification of the reconstructed PSD functions against experiments suggests that the Monte-Carlo approach can effectively generate synthetic PSD curves.