Supervisors - Dr. Stephen Suryasentana, University of Strathclyde and Dr. Craig Anderson, University of Glasgow
PhD Student - Emily Hird, University of Strathclyde
Status - In Progress
As offshore wind farms move into deeper waters, the cost of site investigation (SI) increases significantly. A rational way to reduce the cost of SI, while still maintaining the reliability of the ground parameters for foundation design, is critical for the continued growth of offshore wind energy. This project aims to use state-of-the-art Bayesian machine learning techniques to develop a statistical framework to integrate geophysical, geological and geotechnical data into an integrated ground model, which would allow improved ground evaluation using fewer, expensive geotechnical tests.