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LH-moment estimation for statistical analysis on the wave crest distributions of a deepwater spar platform model test

Lookup NU author(s): Professor Longbin Tao



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


The design of fixed and compliant offshore platforms requires the reliable estimation ofextreme values with small probabilities of exceedance based on an appropriate probabilitydistribution. The Weibull distribution is commonly utilised for the statistical analysis ofwave crests, including near-field wave run-ups. The parameters are estimated empiricallyfrom experimental or onsite measurements. In this paper, the data set of wave crests froma Spar model test was statistically analysed by using the method of LH-moments forparameter estimation of the Weibull distribution. The root-mean-square errors (RMSEs)and the error of LH-kurtosis were used to examine the goodness-of-fit. The results for thefirst four LH-moments, the estimated parameters, and the probability distributionsshowed that the level of the LH-moments has a significant influence. At higher levels, theestimation results gave a more focused representation of the upper part of the wave crestdistributions, which indicates consistency with the intention of the method of LHmoments.The low tail RMSE values of less than 2.5% demonstrated that a Weibull distributionmodel estimated by using high-level LH-moments can accurately represent theprobability distribution of large extreme wave crests for incident waves, wave run-ups, andmoon pool waves. Goodness-of-fit test on the basis of comparison of sampling LH-kurtosisand theoretical LH-kurtosis was recommended as a procedure for selecting an optimumlevel.

Publication metadata

Author(s): Xiao L, Lu H, Tao L, Yang L

Publication type: Article

Publication status: Published

Journal: Marine Structures

Year: 2017

Volume: 52

Pages: 15-33

Print publication date: 01/03/2017

Online publication date: 23/11/2016

Acceptance date: 15/11/2016

Date deposited: 18/01/2017

ISSN (print): 0951-8339

ISSN (electronic): 1873-4170

Publisher: Elsevier


DOI: 10.1016/j.marstruc.2016.11.001


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