Reference : R190306
PhD Studentship (3.5 years)
Supervisor : Dr Yuqing Zhang & Dr Sergey Sergeyev
Key words : traffic speed deflectometer, laser signal processing, road structural analysis, road performance prediction, intelligent road infrastructure
Applications are invited for a studentship in the Aston Institute of Urban Technology and the Environment (ASTUTE), funded by the School of Engineering and Applied Science.
The successful applicant will join a cohort of graduate students working on projects across the broader Smart Cities field, and as part of the PhD will receive training and experience in collaborative research, relevant to industry and Smart City planners.
The position is available to start in October 2019
This studentship includes a fee bursary to cover the home / EU fees rate, plus a maintenance allowance of £15,009 in 2019 / 20 (subject to eligibility).
Applicants from outside the EU may apply for this studentship but will need to pay the difference between the Home / EU’ and the Overseas’ tuition fees, currently this is £12,573 in 2019 / 20.
As part of the application you will be required to confirm that you have access to this additional funding.
Traffic Speed Deflectometer (TSD) is being used by highway agencies and construction industries for evaluating structural health conditions of the national road network and making decisions for road maintenance schedule.
The challenge exists around how to interpret and reduce the variation of the deflection data in order to accurately evaluate the road structural conditions and predict the road premature deteriorations (e.
g., moisture, potholes) and the long-term distress (e.g., rutting, cracking), to assist road asset maintenance and management.
The proposed research aims to develop an autonomous road health warning system by re-processing the laser signals and adopting principles of engineering mechanics and machine learning methodologies.
The system will be able to accurately quantify the road structural conditions, achieve advance warning of the developing deteriorations and predict the residual life of the road infrastructures.
The implementation of this autonomous health warning system will permit preventative maintenance and rational rehabilitation for the national road networks in smart cities, leading to the longest road service life, the least maintenance costs and the least delay costs for the road users.
The successful applicant should have a first class or upper second class honours degree or equivalent qualification in Civil Engineering, Engineering Mechanics or Electronic Engineering.
Preferred skill requirements include knowledge / experience of Pavement Structures and Materials, Signal Processing or Machine Learning.
We would particularly like to encourage applications from women seeking to progress their academic careers. Aston University is committed to the principles of the Athena SWAN Charter, recognised recently by a prestigious Silver Award to EAS, and we pride ourselves on our vibrant, friendly and supportive working environment and family atmosphere.
For formal enquiries about this project contact Dr Yuqing Zhang by email at y.zhang10 aston.ac.uk.
Submitting an application
Details of how to submit your application, and the necessary supporting documents can be found here.
The application must be accompanied by a research proposal statement. An original proposal is not required as the initial scope of the project has been defined, however candidates should take this opportunity to explain how their knowledge and experience will benefit the project and should demonstrate their familiarity with some relevant research literature.
If you require further information about the application process please contact the Postgraduate Admissions team at seasres aston.ac.uk
Email details to a friend