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Research Papers: Structures and Safety Reliability

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

[+] Author and Article Information
Leonardo Sant'Anna do Nascimento

Bureau Veritas,
Rua Joaquim Palhares,
40, 7th Floor, Centro
Rio de Janeiro 20260-080, RJ, Brazil
e-mail: leonardo.santanna@br.bureauveritas.com

Luis Volnei Sudati Sagrilo

COPPE/UFRJ,
Av. Athos da Silveira Ramos,
149, Bloco B, 1 Andar, Ilha do Fundão,
Rio de Janeiro 21941-909, RJ, Brazil
e-mail: sagrilo@coc.ufrj.br

Gilberto Bruno Ellwanger

COPPE/UFRJ,
Av. Athos da Silveira Ramos,
149, Bloco B, 1 Andar, Ilha do Fundão,
Rio de Janeiro 21941-909, RJ, Brazil
e-mail: gbe@coc.ufrj.br

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received September 15, 2012; final manuscript received May 18, 2014; published online November 17, 2014. Assoc. Editor: Bernt J. Leira.

J. Offshore Mech. Arct. Eng 137(1), 011603 (Feb 01, 2015) (8 pages) Paper No: OMAE-12-1090; doi: 10.1115/1.4028899 History: Received September 15, 2012; Revised May 18, 2014; Online November 17, 2014

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 statistical uncertainty in the estimated extreme statistics parameters are computed considering as the “theoretical” estimates those evaluated by fitting a Gumbel to a sample of episodical 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 corresponding ones derived from the conventional method of moments and the biases are more or less the same.

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References

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Figures

Grahic Jump Location
Fig. 1

Distributions of the process, peaks, and extreme values of a time-series y(t)

Grahic Jump Location
Fig. 2

Jack-up model SDOF model: MPEV estimated extremes with Weibull-3P-PoT model for K = 3.00

Grahic Jump Location
Fig. 3

Jack-up model SDOF model: MPEV estimated extremes with Weibull-3P-PoT model for K = 1.00

Grahic Jump Location
Fig. 4

Jack-up model SDOF model: MPEV estimated extremes with Weibull-3P-PoT model for K = 0.25

Grahic Jump Location
Fig. 5

Simplified jack-up FE model

Grahic Jump Location
Fig. 6

3D jack-up model: estimated lateral displacement extreme MPV using Weibull-3P-PoT model (conventional versus L-moments)

Grahic Jump Location
Fig. 7

3D jack-up model: estimated base shear extreme MPV using Weibull-3P-PoT model (conventional versus L-moments)

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