Unsupervised learning approach to current profile characterisation for extreme response analysis

Jasper Joseph Hunt, Michael Binsar Lubis, Mehrdad Kimiaei

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

Extreme response analysis of flexible offshore facilities under current loads is an engineering challenge mainly for deep and ultra-deep waters. In this study, an improved method for defining characteristic current profiles for application in extreme response analysis is presented. It utilises unsupervised dimensionality reduction algorithms including Principle Component Analysis (PCA) and AutoEncoders (AE) followed by application of clustering through K-Means Algorithm (KMA) for two different deep-water locations to identify the current profile corresponding to extreme response of a typical ROV umbilical line. Additionally, a combination of dimensionality reduction and clustering method, known as Embedded Clustering (EC), is also explored alongside various pre-processing techniques. The unsupervised methods presented are demonstrated as an effective approach to scaling the clustering approach to a higher-class resolution.

Original languageEnglish
Title of host publication14th ISOPE Pacific/Asia Offshore Mechanics Symposium, PACOMS 2020
PublisherInternational Society of Offshore and Polar Engineers
Pages185-192
Number of pages8
ISBN (Electronic)9781880653838
Publication statusPublished - 2020
Event14th ISOPE Pacific/Asia Offshore Mechanics Symposium, PACOMS 2020 - Dalian, China
Duration: 22 Nov 202025 Nov 2020

Publication series

Name14th ISOPE Pacific/Asia Offshore Mechanics Symposium, PACOMS 2020

Conference

Conference14th ISOPE Pacific/Asia Offshore Mechanics Symposium, PACOMS 2020
Country/TerritoryChina
CityDalian
Period22/11/2025/11/20

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