Peak factor estimation of non-Gaussian wind pressure on high-rise buildings

A vast quantity of measurements of wind-induced non-Gaussian effects on buildings call for the burgeoning development of more advanced extrema estimation approaches for non-Gaussian processes. In this study, a well-directed method for estimating the peak factor and modeling the extrema distribution for non-Gaussian processes is proposed. This method is characterized by using two fitted probability distributions of the parent non-Gaussian process to separately fulfill the estimations of the extrema on long-tail and short-tail sides. In this method, the Johnson transformation is adopted to be the probabilistic model for fitting the parent distribution of the non-Gaussian process due to its superior fitting goodness and universality. For each dataset, two Johnson transformations will be established by two parameter estimation methods to individually estimate the extrema on two sides. Then a Gumbel assumption is applied for conveniently determining the non-Gaussian peak factor. This method is validated through longduration wind pressure records measured on the model surfaces of a high-rise building in wind tunnel test. The results show that the proposed method is more accurate and robust than many existing ones in estimating peak factors for non-Gaussian wind pressures.