Conventional and Linear Statistical Moments Applied in Extreme Value Analysis of Non-Gaussian Response

This work investigates numerically two different methods of moments applied to Hermite derived probability distribution model and variations of Weibull distribution fitted to the short-term time series peaks sample of stochastic response parameters of a simplified jack-up platform model which represents a source of high non-Gaussian responses. The main focus of the work is to compare the results of short-term extreme response statistics obtained by the so-called linear method of moments (L-moments) and the conventional method of moments using either Hermite or Weibull models as the distribution model for the peaks. A simplified mass-spring system representing a three-legged jack-up platform is initially employed in order to observe directly impacts of the linear method of moments (L-moments) in extreme analysis results. Afterward, the stochastic response of the three-legged jack-up platform is analyzed by means of 3D finite element model. Bias and statis-tical uncertainty in the estimated extreme statistics parameters are computed considering as the “theoretical ” estimates those evaluated by fitting a Gumbel to a sample of episodi-cal extreme values obtained from distinct short-term realizations (or simulations). Results show that the variability of the extreme results, as a function of the simulation length, determined by the linear method of moments (L-moments) is smaller than their corre-sponding ones derived from the conventional method of moments and the biases are more or less the same. [DOI: 10.1115/1.4028899