A novel method for the non-Gaussian wind pressure simulation based on autoregressive model and maximum entropy method

Abstract

The simulation of wind pressures with non-Gaussian characteristics is a prerequisite to apply the Monte Carlo method in the structural wind-resistant reliability analysis. Due to its simplicity and accuracy, the autoregressive (AR) model has been widely used for the non-Gaussian process simulation. However, AR-based simulation methods perform poorly for the wind pressures with strong non-Gaussian characteristics. Moreover, they cannot reproduce the bimodal characteristics of some hardening wind pressures. To improve the simulation accuracy, a novel non-Gaussian simulation approach is proposed in this study based on AR model and maximum entropy method. In this approach, a set of formulas to determine the first ten moments of the input process as a function of the corresponding moments of the output process in the AR model have been firstly derived. Then, the probability distribution of the input process is reconstructed by the enhanced maximum entropy method. Finally, the translation process theory is introduced to simulate the non-Gaussian wind pressure process combined with AR model. Numerical results show the proposed method has higher simulation accuracy for the wind pressures with strong non-Gaussianity compared with the conventional AR-based method. Additionally, it can reproduce the bimodal characteristic for the wind pressures with bimodal distribution.