An exponential model for fast simulation of multivariate non-Gaussian processes with application to structural wind engineering

• A new model based on binomial exponential function transformation is proposed.
• A set of nonlinear equations is derived and five steps are proposed to solve it.
• A numerical example for simulation, a large-span roof structure, is illustrated.
• The numerical results show that the proposed algorithm is efficient and accurate.

In order to generate the non-Gaussian loading excitations for time-domain analysis of structural response, an exponential function is used to express the relation between the non-Gaussian process and its underlying Gaussian process. Then, a set of nonlinear equations is derived to determine the coefficients of the exponential function. Based on the property of the joint density of a bivariate Gaussian vector, the relation between correlation functions is obtained. Also, the probability density function for the non-Gaussian process is provided. Therefore the exponential model is established. Further, an algorithm based on the exponential model is proposed for fast simulation of multivariate non-Gaussian processes. A numerical example, wind pressure field simulation of a large-span roof structure, indicates that non-Gaussian wind pressure time histories are generated quickly using the proposed algorithm. Moreover, the correlation functions, the power spectra, the cumulative distribution functions, and the probability histograms of the generated samples coincide well with the corresponding target curves. Hence the proposed algorithm is efficient and accurate.