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

Abstract

This work investigates numerically two different methods of moments applied to Hermite derived probability distribution model and variations of Weibull distribution fitted to the shortterm time series peaks sample of stochastic response parameters of a simplified jackup platform model which represents a source of high nonGaussian responses. The main focus of the work is to compare the results of shortterm extreme response statistics obtained by the socalled linear method of moments (Lmoments) and the conventional method of moments using either Hermite or Weibull models as the distribution model for the peaks. A simplified massspring system representing a threelegged jackup platform is initially employed in order to observe directly impacts of the linear method of moments (Lmoments) in extreme analysis results. Afterward, the stochastic response of the threelegged jackup platform is analyzed by means of 3D finite element model. Bias and statistical uncertainty in the estimated extreme statistics parameters are computed considering as the “theoreticalâ€‌ estimates those evaluated by fitting a Gumbel to a sample of episodical extreme values obtained from distinct shortterm 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 (Lmoments) is smaller than their corresponding ones derived from the conventional method of moments and the biases are more or less the same.