The analysis of time records, coming from seakeeping experiments in irregular waves, is used to determine the occurrence of extreme events. The common procedure used for data analysis is to assume that the statistics of record's peaks is following two or three parameters Weibull distribution. For particularly severe sea states, it can happen that the peaks assume a multi-modal distribution and the use of a Weibull distribution may lead to errors in the extreme value estimate. It is than possible to use multi-modal distributions, or to change the peaks extraction technique, adopting a certain threshold. Here, the determination of extreme values probability distribution parameters by genetic algorithm is applied to improve the methodology of extreme sea state prediction. A data analysis procedure is here proposed and tested on a time record coming from seakeeping model-scale experiments and on a set of wave heights record, in comparison with standard Weibull approach.

Extreme loads estimation using genetic algorithm approach

Mauro, F;Braidotti, L
;
2019-01-01

Abstract

The analysis of time records, coming from seakeeping experiments in irregular waves, is used to determine the occurrence of extreme events. The common procedure used for data analysis is to assume that the statistics of record's peaks is following two or three parameters Weibull distribution. For particularly severe sea states, it can happen that the peaks assume a multi-modal distribution and the use of a Weibull distribution may lead to errors in the extreme value estimate. It is than possible to use multi-modal distributions, or to change the peaks extraction technique, adopting a certain threshold. Here, the determination of extreme values probability distribution parameters by genetic algorithm is applied to improve the methodology of extreme sea state prediction. A data analysis procedure is here proposed and tested on a time record coming from seakeeping model-scale experiments and on a set of wave heights record, in comparison with standard Weibull approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2973019
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