An application of machine learning to the motion response prediction of floating assets

• URL: http://arxiv.org/abs/2506.15713v1
• Date: Sat, 31 May 2025 08:10:12 GMT
• Title: An application of machine learning to the motion response prediction of floating assets
• Authors: Michael T. M. B. Morris-Thomas, Marius Martens,
• Abstract summary: This study presents a supervised machine learning approach to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth.<n>We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver.<n>The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.

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