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Details of Grant 

EPSRC Reference: EP/R021090/1
Title: An integrated physics-based and data-driven approach to structural condition identification
Principal Investigator: Wang, Dr Y
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Civil and Environmental Engineering
Organisation: University of Surrey
Scheme: First Grant - Revised 2009
Starts: 01 January 2018 Ends: 31 December 2018 Value (£): 100,803
EPSRC Research Topic Classifications:
Structural Engineering
EPSRC Industrial Sector Classifications:
Construction Technical Consultancy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Dec 2017 Engineering Prioritisation Panel Meeting 6 December 2017 Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Organisation Website: http://www.surrey.ac.uk