Unsupervised learning approach to current profile characterisation for extreme response analysis

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.