Project Lead: Professor Mohamed Rouainia
Institution: Newcastle University.
Partners: Ryder Geotechnical Limited.
Amount: £ 29k
Scheme: Ideas Translation.
Theme/Area: Low Carbon Energy Generation/Onshore/Offshore Wind and Subsea.
Brief Description: Recent advances in machine learning (ML) and artificial intelligence (AI) offer significant potential to address engineering challenges through data-driven methods. This project aims to develop a prototype ML tool to predict offshore trencher speeds, a critical parameter for subsea cable installation in offshore wind developments, as a step towards commercial deployment. Trencher performance is influenced by soil conditions, trench depth, and machine characteristics, and accurate prediction will improve operational efficiency, reduce project duration, and lower costs. The project leverages regional expertise in offshore geotechnics, subsea trenching, and data innovation to support accelerated offshore wind deployment.