An integrated physics-based and data-driven approach to structural condition identification

Infrastructure performance is important for a nation's economy and its people's quality of life. Inadequate infrastructure is estimated to cost the UK £2 million a day, in terms of maintenance and management. To manage and protect infrastructure efficiently and effectively, the proposed project aims to develop an integrated algorithm to create a reliable and effective approach for structural health monitoring, which can find different applications.

Metallic structures are widely employed in both transport and energy infrastructure. As load transferring elements, connections in such structures are vulnerable due to stress concentrations, with localised damage being particularly hard to detect even under regular inspections. Therefore, the case study of this research will focus on the monitoring of connection condition in bolted or riveted structures.

The project will commence with an experimental investigation of a steel beam with end bolt connections under different damage scenarios due to loosening/lack-of-fit. Monitoring data from strain gauges and accelerometers will be processed to determine the beam's dynamic features. A finite element model will also be constructed and calibrated using the experimental results. Last but not least, an integrated deep learning algorithm will be developed for structural condition identification. There are two innovations in the suggested approach. Firstly, it integrates physics-based and data-driven methods. Secondly, the exploitation of deep learning enables the identification and optimisation of non-linear features, due to the existence of multiple hidden layers.

Thus, the proposed project aims to make a novel contribution to structural health monitoring with diverse applications in different structural types.

Metallic structures are widely encountered all over the world, with applications ranging from transportation to power generation and distribution, both onshore and offshore. Connections are the most vulnerable elements in these structures and are often found to be primary contributors in mal-functions/failures. Therefore, connection condition assessment is of great significance to structural integrity and reliability. Provided that structural health monitoring (SHM) is targeted appropriately, the maintenance budget can be efficiently allocated to the more vulnerable and important elements/structures. Further, the analysis of SHM data enables early warning of structural failure, identification of structural condition, and evaluation of structural reliability. This can contribute towards enhanced infrastructure resilience to damage caused by natural hazards and/or climate change and benefit both operators and the general public.
The proposed methodology is a general approach to structural condition identification. By focusing on the appropriate monitoring parameters, it can be applied to condition identification of connections in other types of structures, for example, reinforced concrete and timber structures. This can add to the capabilities of both software and design/consulting companies seeking to expand/improve their asset management products/tools.
The project will have input from key collaborators from industry and academia that influence the development of UK asset management guidelines and standards. The active support of a major construction company and a national asset owner will facilitate an alignment of project outcomes with current practice and allow the uptake of relevant attributes in future standards related to the maintenance and management of infrastructure assets.