Queen’s University Belfast researchers are taking part in a new £7.7 million initiative focused at improving the monitoring and maintenance of UK infrastructure such as bridges, cellphone masts, and wind turbines.
Healthy infrastructure is vital to the UK economy and society, yet it is costly to monitor and maintain. The ROSEHIPS (Revolutionising Operational Safety and Economy for High-Value Infrastructure Using Population-based SHM) initiative attempts to tackle the UK’s infrastructure asset management problem by automating health monitoring for infrastructure such as bridges. Diagnoses can be offered by permanently installed sensors that collect structural data continuously and evaluate it using computer algorithms, rather than expensive regular inspections.
Experts from Queen’s University have joined a collaborative research team that will collaborate with the Universities of Sheffield, Cambridge, and Exeter, as well as important industry partners such as Northern Ireland’s Department for Infrastructure, Translink, Arqiva, Cellnex (UK), and Siemens Gamesa. The researchers will concentrate on building unique sensing, which will be customised for infrastructure and will aid in the real-world execution of the research.
Dr. David Hester and Professor Su Taylor of Queen’s School of Natural and Built Environment have expertise in bridge structural health monitoring, while Professor Roger Woods of the School of Electronics, Electrical Engineering and Computer Science has competence in sensor and embedded AI.
Dr Hester says, “Our initial work developing innovative sensing solutions and our considerable body of bridge monitoring experience has provided a critical practical platform for this project. Through cutting-edge research, experts at Queen’s are continuing to develop solutions to real world problems on our doorstop, which has a positive impact right across the globe.”
The project will expand and utilise Population Based Structural Health Monitoring (PBSHM), which allows data from one structure to be utilised to infer the state of another structure. “Population-Based Structural Health Monitoring is a game-changing idea, emerging in the UK very recently,” says Professor Keith Worden, from the University of Sheffield’s Department of Mechanical Engineering. “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 project will also develop machine learning, sensing, and digital twin technology for automated health inference for existing structures, as well as set new criteria for future structures that are safer and greener.