Efficient Method for Approximating the Joint Extreme Value Distribution of Multivariate Stationary Gaussian Processes

Abstract

The joint extreme value distribution (JEVD) of multivariate random processes is important for evaluating the system reliability of a structure subjected to random vibrations. There are very limited analytical approaches for predicting the JEVD, and these approaches are only computationally viable for problems up to three dimensions. This paper presents an efficient approximate method, which is not limited to low-dimensional problems, for estimating the JEVD of multivariate stationary Gaussian processes. The proposed method modifies an existing method by using the Gauss-Legendre quadrature to evaluate the extreme value correlation coefficients under the bivariate Poisson assumption. Then, the JEVD is approximated using the Nataf transformation. The effectiveness of the proposed method is demonstrated via two numerical examples. The first example concerns the airgap problem of an offshore structure subjected to random waves, in which the extreme wave elevations are evaluated at six locations, and failure is defined as threshold exceedance for any location. The second example is a random vibration problem comprising a three degrees-of-freedom system. Previous studies focused on series system reliability; here three system reliabilities are examined, including series, parallel, and hybrid systems. The proposed method solves the problems efficiently, and it is found to provide an accurate prediction of the JEVD by comparison with Monte Carlo simulation.