PhD Studentship - Population-Based Indirect Damage Detection System for Railway Bridges

The UK’s railway bridge asset stock represents over 80% well-aged (>50 years) infrastructure, often carrying loads beyond their original design capacity, hence in urgent need of a reliable real-time damage identification system. The current practice of visual inspection of bridges can be subjective and exposes the workforce to hazardous job sites. In recent years, there have been significant efforts in instrumenting bridges and assessing the condition of the bridge using direct measurements. These methods are categorised as non-destructive testing techniques, but they can be costly considering the number of sensors required and the maintenance of the data acquisition system. Hence, the alternative of direct instrumentation of the structure, whilst effective, can be logistically expensive to implement for the entire network.

To address these challenges, the project aims to develop a novel, population-based indirect damage identification system, leveraging data collected on instrumented railway vehicles to autonomously assess bridge condition while passing over the structure at operational speed, providing a scalable and cost-effective alternative to traditional methods. The fundamental principle in indirect damage inspection is that damage causes changes in physical properties of the structure, which can lead to altering the vibration behaviour of the structure. The challenge in indirect damage inspection methods is to identify and extract these changes from the measurements recorded on the travelling vehicle while it is driving over a damaged bridge at operational speed.

Due to a lack of large, real-world datasets with ground truth labels, the application of data-driven approaches in the indirect damage identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g. self-supervised learning techniques, particularly suited to infrastructure applications where labelled data is scarce, enabling models to learn from the data itself without relying on extensive human annotation.

Supervisors: Dr Donya Hajializadeh, Dr Fernando Madrazo-Aguirre, Dr Sara Ahmed and Dr Dan Bompa

Entry requirements

Open to candidates who pay UK/home rate fees. See UKCISA for further information. Please note - international students are welcome to apply; however, the funding available will cover home fees only, and any difference would need to be met through self-funding.

Starting in October 2026. Later start dates may be possible, please contact Dr Donya Hajializadeh once the deadline passes.

You will need to meet the minimum entry requirements for our PhD programme.

We are looking for a highly motivated individual with a strong background in civil/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning.

The successful candidate will gain expertise at the intersection of structural health monitoring, railway engineering, and advanced artificial intelligence.

MEng in Civil/Structural/Mechanical/ Automotive Engineering with a UK equivalent 2:1 classification or above. Or MSc degree in Structural/Bridge/Rail/Mechanical/Automotive Engineering.

How to apply

Applications should be submitted via the Civil and Environmental Engineering PhD programme page via the ‘Apply’ button above.

In place of a research proposal, you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Funding

Fully and directly funded for this project only. Home Fees (only) and UKRI Standard Stipend (£21,805 for 2026/27 academic year) and RTSG (Research Training Support Grant) of £8k. Funding is for 39 months. 

Enquiries

Contact Dr Donya Hajializadeh 

Advert information