Bayesian Optimisation Framework for Robust and Adaptive Driveability Predictions for Offshore Wind Piles

Buckley, R. , Chen, Y., Sheil, B., Suryasentana, S., Randolph, M. and Doherty, J. (2023) Bayesian Optimisation Framework for Robust and Adaptive Driveability Predictions for Offshore Wind Piles. 4th International Symposium on Machine Learning and Big Data in Geoscience, Cork, Ireland, 29 August - 1 September 2023.

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Abstract

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Item Type:Conference or Workshop Item
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Buckley, Dr Roisin
Authors: Buckley, R., Chen, Y., Sheil, B., Suryasentana, S., Randolph, M., and Doherty, J.
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
314446iDrive: Intelligent Driveability Forecasting for Offshore Wind Turbine Monopile FoundationsRoisin BuckleyEngineering and Physical Sciences Research Council (EPSRC)R74903ENG - Infrastructure & Environment