PhD in Civil/Structural/Mechanical Engineering

The UK’s railway bridge asset stock represents over 80% well-aged (>50 years) infrastructure, often carrying loads beyond their original design capacity, hence in urgent need of a reliable real-time damage identification system./nThe project aims to develop a novel, population-based indirect damage identification system, leveraging data collected on instrumented railway vehicles to autonomously assess bridge condition while passing over the structure at operational speed, providing a scalable and cost-effective alternative to traditional methods./nThe project will explore the application of the next generation of deep learning algorithms, e.g. self-supervised learning techniques, particularly suited to infrastructure applications where labelled data is scarce.