RESEARCH ARTICLE
Derivation of the Probability Distribution of Extreme Values of Offshore Structural Response by Efficient Time Simulation Method
M.K. Abu Husain*, 1, N.I. Mohd Zaki1, G. Najafian2
Article Information
Identifiers and Pagination:
Year: 2013Volume: 7
First Page: 261
Last Page: 272
Publisher ID: TOCIEJ-7-261
DOI: 10.2174/1874149501307010261
Article History:
Received Date: 9/9/2013Revision Received Date: 14/11/2013
Acceptance Date: 18/11/2013
Electronic publication date: 13/12/2013
Collection year: 2013
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. To this end, the conventional simulation technique (CTS) is frequently used for predicting the probability distribution of the extreme values of response. However, this technique suffers from excessive sampling variability and hence a large num-ber of simulated response extreme values (hundreds of simulated response records) are required to reduce the sampling variability to acceptable levels. A more efficient method (ETS) was recently introduced which takes advantage of the cor-relation between the extreme values of surface elevation and their corresponding response extreme values. The method has proved to be very efficient for high-intensity sea states; however, the correlation and hence the efficiency and accura-cy of the technique reduces for sea states of lower intensity. In this paper, a more efficient version of the ETS technique is introduced which takes advantage of the correlation between the extreme values of the nonlinear response and their corre-sponding linear response values.
