Peak factor estimation of non-Gaussian wind pressures based on a novel piecewise Johnson transformation model

In present study, a novel piecewise Johnson transformation model (PJTM) is proposed by conducting separation-mirror-expansion on a non-Gaussian process, aiming to enhance the accuracy and efficiency of the estimation of non-Gaussian peak factor. The parameters of the proposed PJTM are strictly derived from the analytical formula of the unbounded system (SU system) and the semi-analytical formula of the bounded system (SB system). Based on the proposed PJTM and the crossing rate theory, the algorithm of peak factor estimation, as well as the overall calculation procedure, is thus established with explicit formulas and applied to estimating the peak factors of sufficient long-time non-Gaussian wind pressures on a long-span roof. It is noted that the peak factors of the non-Gaussian wind pressures are also calculated by the commonly used Johnson transform model (JTM) and the traditional Hermite polynomial model (HPM). The results show that the peak factors of the non-Gaussian wind pressures estimated by the proposed PJTM agree well with the real values based on a sufficient long-time of data. In addition, the proposed PJTM has comparable accuracy but a broader applicability range than the HPM, and significantly superior accuracy and efficiency than those of JTM. The present study can provide valuable guidance for the wind-resistance design of long-span roofs.

Automated summary

A novel piecewise Johnson transformation model (PJTM) is proposed to enhance the accuracy and efficiency of the estimation of non-Gaussian peak factor. The proposed PJTM, derived from the analytical formula of the unbounded system (SU system) and the semi-analytical formula of the bounded system (SB system), is applied to estimate the peak factors of non-Gaussian wind pressures on a long-span roof. The results show that the proposed PJTM has superior accuracy and efficiency compared to the commonly used models.