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Solutions to Monitor and Assess Resilience in Transport (SMART) Infrastructure

About This PhD Project

Project Description

The research will focus specifically on the vulnerability of bridge structures within the road network. This will require a pioneering approach which integrates existing bridge management databases with comprehensive regional information, producing a Bridge Management Decision Support Tool (BMDST). Two fully funded PhD studentships are open for application within this research project. The PhD topics will be focused on developing a digital road map for infrastructure in Northern Ireland. This will involve the structuring of existing data sets at the Department for Infrastructure (DfI) and establishing a framework which would facilitate a fully interoperable technology platform to allow for network wide performance analysis. This information will then be used to develop novel mathematical methodologies to identify relationships and correlations between structural condition, material properties, climate and traffic using a machine learning approach. An innovative risk consequence map will then inform future repair plans and budget allocation within DfI. The objective of this element is to, for the first time, lay the foundation of a risk consequence map across the network to understand the practical consequences of road closures, extended repair programs, or failure. Most importantly this will consider interdependency of assets within the network and the cascading effects due to service loss. Finally, new AI approaches will be used to develop accurate traffic models for assessment of behaviours in event of closure/failure. Using regional information on population density and socio-economic groupings, emergency service resources to determine service importance of structure.

Further detail:

Applications are invited from highly motivated individuals who want to influence how future systems are designed across the system stack: from hardware/software co-design, to operating systems and data processing engines.

Essential: Candidates should have the equivalent of a 2:1 Honours Degree, or UK 2:2 Honours Degree plus Masters with at least commendation (60% minimum), in relevant scientific or engineering discipline. Applicants from a data science, computer science or maths and physics background are particularly encouraged.

Desirable: Knowledge and/or experience of Python or R programming, spatial data analysis and AI approaches to data collection and analysis. Integration of real time sensor data to software systems.

Applications should include a cover letter and a curriculum vitae.

Fees and Maintenance
This studentship provides fees and a maintenance tax free stipend of £15,000 per annum.
Applications are invited from UK and EU residents.

Closing date:

30th April 2019

For further information on this research project and details of the application procedure, applicants should contact Dr Myra Lydon ()

Civil Engineering, School of Natural Built Environment, Queen’s University Belfast

or Prof Adele Marshall ()

School of Maths and Physics, Queen’s University Belfast

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