Big Data Analysis from Bridges: Developing Structural Health Monitoring Instrumentation
Most bridges worldwide lack Structural Health Monitoring instrumentation. Part of the reason is that older bridges pre-date the development of appropriate instrumentation; and part of the reason is that bridge owners do not have the staff/resource to analyse the “big data” emanating from an instrumentation system.
Long span bridges in critical locations worldwide provide a lifeline to communities and commerce. Many of these bridges built in the last 50 years were built down to a price with a promise of a 120-year life span. Now the engineering community is finding that these bridges are approaching the premature end of their operational life. Sophisticated monitoring technology did not exist at the time of their construction, so the majority of bridges have no monitoring systems on board. Of course, the mathematical and computational capacity to process large volumes of data were little understood and did not exist either.
This initial focus of this cutting-edge project will be on collecting data from the new Queensferry Crossing (the world’s longest 3 tower cable stayed bridge). It will involve using the University of Edinburgh’s Terra-correlator and super computer to interrogate “big data” to identify anomalies in performance and behaviour. The plan is to extend this to other bridges worldwide, especially the Memorial Bridge in New Hampshire.
The project will build on the excellent theses of Edinburgh graduates. These theses will be available to you.
You will liaise with industry and Transport Scotland.
The project will also involve liaising with professors at the University of Edinburgh. Day-to-day liaison will be with Prof Mike Forde & Dr Robert De Bold (Research Associate). We expect to liaise with Dr Erin Bell, University of New Hampshire, USA (via Skype).
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)
FTE Category A staff submitted: 91.80
Research output data provided by the Research Excellence Framework (REF)
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