About the Project
Rail infrastructure deteriorates with time due to material fatigue, overloading, ground movement and environmental effects. This might affect the serviceability and structural integrity of rail assets, such as bridges, causing major socio-economic disruptions and occasionally life loss. Ageing masonry bridges comprise around the 50% of the UK and European rail stock and their structural assessment is particularly challenging. Traditional inspection practises and fragmented monitoring appear unable or impractical to ensure adequate maintenance on a national scale. Digitisation of civil infrastructure networks emerges today as an essential next step to address this challenge, offering opportunities to enhance our understanding of the structural deterioration mechanisms involved, and hence supporting decision-making for the maintenance of railway networks.
This PhD research will explore the benefits of combining advanced sensing technologies with data analytics to enable automated civil asset management tools for resilient infrastructure, focusing on ageing railway bridges. The student will work on the processing and interpretation of field monitoring and lab tests data from two ongoing deteriorating bridge monitoring projects. Data science techniques will be applied, combining information from fibre optic strain and temperature sensors, acoustic emission sensors and high-sensitivity accelerometers. The analysis will contribute towards the development of early-warning, sensors-based, deterioration monitoring systems that will integrate machine learning, cloud data management and statistics. The student will benefit from the close collaboration with researchers from ASTUTE and AiPT at Aston University, the University of Cambridge, the University of California, Berkeley, engineers from Network Rail and industry partners.
This PhD research combines elements from civil engineering, data science, systems engineering, information and communications technology, electronics and photonics. Applications from students with background or aspirations in any of these areas and with interest to develop their digital abilities are welcome to apply. Tailored training on Data Analytics and AI shall be provided.
The successful applicant should have been awarded, or expect to achieve, a Masters degree in a relevant subject with a 60% or higher weighted average, and/or a First or Upper Second Class Honours degree (or an equivalent qualification from an overseas institution) in Civil (structural) engineering, data/computer science, mathematics (statistics), electrical engineering, mechanical engineering, photonics, information and communications technology, or other relevant area . Preferred skill requirements include:
Essential – Experience in coding, data processing
Desirable – MSc or industry experience on data science, statistics or relevant area
Desirable – MSc or industry experience on sensing technologies (photonics or electronics)
Desirable – Experience in signal processing, pattern recognition techniques, statistics, neural networks, wavelets, cloud programming
Desirable – Experience in structural health monitoring or non-destructive testing
Optional – Experience in structural lab testing or structural analysis
Submitting an application
As part of the application, you will need to supply:
- A copy of your current CV
- Copies of your academic qualifications for your Bachelor degree, and Masters degree; this should include both certificates and transcripts, and must be translated in to English
- A research proposal statement*
- Two academic references
- Proof of your English Language proficiency
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, candidates should take this opportunity to detail how their knowledge and experience will benefit the project and should also be accompanied by a brief review of relevant research literature.
Please include the supervisor name, project title, and project reference in your Personal Statement.
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