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Researchers at the University of ��«Ӱҵ will lead a £7.7m collaborative project, aiming to change how we monitor and maintain important parts of the UK’s infrastructure, such as bridges, telecoms masts and wind turbines.

Healthy infrastructure is critical to ensuring the continued functionality and growth of UK society and the economy. Unfortunately, monitoring and maintaining our buildings and transport network is expensive; in the UK, a backlog of maintenance works, identified in 2019, will cost £6.7bn*.

Considering bridges, inspection is usually carried out visually by human experts. Resources are stretched, so inspections cannot be carried out as often as desired, repairs aren’t made quickly and opportunities are missed to make cost effective decisions on maintenance and improvement. In a few extreme cases structural failure can result in fatalities.

The offshore wind (OW) sector is another area for concern. OW has driven down energy costs and increased power output, pioneering a global change to clean energy. The UK leads globally in OW energy, providing almost one third of the UK's annual electricity demand and helping meet the UK’s net-zero-by-2050 target. The drive for turbines in deeper water demands new ways of asset management, controlling and limiting operation/maintenance lifetime costs. As turbines increase in numbers, size, and capacity, these issues become even more important.

A collaborative team of researchers, led by the University of ��«Ӱҵ, has been awarded a £7.7m programme grant from the Engineering and Physical Sciences Research Council (EPSRC). The ROSEHIPS (Revolutionising Operational Safety and Economy for High-value Infrastructure using Population-based SHM) project will aim to solve the infrastructure asset management problem in the UK for maintaining our buildings and structures, such as bridges and transport networks, via transformative new research to automate health monitoring.

Instead of expensive scheduled inspections, diagnoses can be provided economically by permanently-installed sensors, collecting structural data continuously and interpreting it via computer algorithms.

The team in ��«Ӱҵ will work with partner institutions, the University of Cambridge, Queen’s University Belfast and the University of Exeter, combining sensor development, machine learning and civil engineering expertise, as well as with key industry partners, including Northern Ireland Department for Infrastructure, Translink, Arqiva, Cellnex (UK) and Siemens Gamesa.

Professor Keith Worden, from the University of ��«Ӱҵ’s Department of Mechanical Engineering said: “Population-Based Structural Health Monitoring (PBSHM) is a game-changing idea, emerging in the UK very recently. It has the potential to overcome current technological barriers and transform our ability to automatically infer the condition of a structure, or a network of structures, from sensor data.”

The EPSRC project will extend and exploit PBSHM, developing machine learning, sensing and digital twin technology for automated inference of health for structures in operation now, and drive new standards for safer, greener structures in future. 

Professor Worden continued: “This programme brings together the perfect team, mixing complementary skills in machine learning and advanced data analysis with expertise in new sensor systems and insight into complex infrastructure systems.”

Professor Mark Girolami from the University of Cambridge, added: “This research programme is set to make significant advances in the theory, methodology, application, successful deployment and adoption of PBSHM, in making our critical inter-connected infrastructure safe, resilient and more efficient.” 

The work will be underpinned by experiments using facilities such as the Structural Dynamics Laboratory for Verification and Validation (LVV) at the University of ��«Ӱҵ to monitor the dynamic response and ‘health’ of structures, such as traffic loading, a full scale or near full scale.

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