PhD in Transport Engineering

Railways and highways form the backbone of national economies, yet they are ageing, increasingly heavily used and exposed to growing environmental pressures. This theme investigates how emerging digital technologies can be applied to detect defects, predict deterioration and support more efficient asset management decisions across large transport networks. Particular emphasis is placed on computer vision for automated inspection, geospatial artificial intelligence for network scale monitoring, predictive maintenance strategies for railways and highways, and the development of digital twins that replicate transport systems in virtual environments. Structural health monitoring of transport assets, including bridges, track and pavements, is also a central focus, exploring how sensor data and machine learning can be combined to understand structural behaviour in real time. Research within this theme suits candidates interested in bridging civil engineering with data science, remote sensing and artificial intelligence to build smarter, safer and more resilient transport systems.

<p>Transport networks are among the most heavily used and economically critical assets in any country, yet their scale makes comprehensive monitoring and maintenance extremely challenging. Traditional inspection methods rely heavily on manual surveys, which are time consuming, costly and often limited in coverage. This research theme investigates how computer vision, geospatial artificial intelligence and advanced sensing technologies can be used to monitor transport assets more efficiently and at greater scale. By combining imagery from vehicles, drones and satellites with machine learning algorithms, it becomes possible to automatically identify defects such as cracking, rutting, corrosion and track geometry faults across entire networks rather than isolated sites.</p> <p dir="ltr">Research within this theme also explores predictive maintenance, where historical and real time condition data are used to forecast when and where transport assets are likely to deteriorate or fail. This shifts maintenance planning from reactive or scheduled approaches towards condition based strategies that reduce cost and improve reliability. A further area of interest is the development of digital twins for transport systems, where physical infrastructure is represented virtually and continuously updated using sensor and inspection data. These digital replicas allow engineers to simulate future scenarios, test maintenance strategies and support decision making without disrupting live operations. Structural health monitoring underpins much of this work, using distributed sensing, vibration analysis and data driven models to assess the ongoing performance of bridges, track and pavements under increasing traffic and environmental loading.</p> <h3 dir="ltr">Why this research is important</h3> <p dir="ltr">Railways and highways underpin national economies, connecting people to employment, services and opportunities. As transport networks age and usage intensifies, the risk of asset failure, service disruption and safety incidents grows, while budgets for inspection and maintenance remain constrained. Manual inspection cannot realistically keep pace with the scale of modern networks, creating a need for automated, data driven approaches that can monitor infrastructure continuously and at low cost. Artificial intelligence and geospatial technologies offer the ability to detect early signs of deterioration before they develop into costly failures, supporting safer operations and more efficient use of limited maintenance budgets. Digital twins and predictive maintenance further allow transport operators to move from reactive repair towards proactive asset management, extending asset life, reducing carbon intensive reconstruction and improving the resilience of transport systems to increasing traffic demand and environmental change.</p> <h3 dir="ltr">Example PhD research topics</h3> <ul dir="ltr"> <li>Computer vision for automated detection of pavement and track defects</li> <li>Geospatial artificial intelligence for network scale transport asset management</li> <li>Predictive maintenance modelling for railway infrastructure</li> <li>Predictive maintenance modelling for highway infrastructure</li> <li>Digital twin development for railway or highway systems</li> <li>Structural health monitoring of bridges using sensor and vision data</li> <li>Satellite and drone based monitoring of transport corridors</li> <li>Machine learning for track geometry and ride quality prediction</li> <li>Data fusion of multi source sensing for transport asset condition assessment</li> </ul> <h3 dir="ltr">Methods and techniques</h3> <ul dir="ltr"> <li>Computer vision and image processing algorithms</li> <li>Deep learning and convolutional neural networks</li> <li>Geospatial data analysis and geographic information systems</li> <li>Digital twin development and simulation</li> <li>Structural health monitoring and vibration analysis</li> <li>Predictive analytics and condition based forecasting</li> <li>Sensor data fusion and signal processing</li> <li>Remote sensing using satellite, drone and vehicle based platforms</li> </ul> <h3 dir="ltr">Suitable academic backgrounds</h3> <p dir="ltr">Suitable candidates will hold an academic background in civil engineering, transport engineering, computer science, geomatics, data science, remote sensing, applied mathematics or a closely related engineering or scientific discipline.</p> <h3 dir="ltr">FAQ</h3> <ol dir="ltr"> <li>Can I propose my own PhD topic? Yes, the themes above are a guide only.</li> <li>Can I bring my own funding? Yes, I welcome applicants who are funded through government scholarships, employers or self funding.</li> <li>Do I need funding before contacting you? You should have at least identified your planned funder and commenced your application.</li> <li>Can I study interdisciplinary topics? Yes. Many of my current research interests combine multiple disciplines.</li> <li>When can I start? Start dates are in spring and autumn. Full details dates are available elsewhere on the University website.</li> </ol> <h3>Join an International Research Group</h3> <p dir="ltr">My research group includes PhD students and researchers from a range of countries working across Artificial Intelligence, climate resilience, satellite monitoring, transport engineering and geotechnical engineering. Although every student develops their own specialist project, many topics overlap, creating opportunities to exchange ideas, collaborate and learn from one another. You will also become part of the wider Civil Engineering postgraduate research community at the University of Leeds.</p> <p data-path-to-node="14"><strong data-index-in-node="0" data-path-to-node="14">Useful links</strong></p> <ul data-path-to-node="15"> <li> <p data-path-to-node="15,0,0"><a href="https://eps.leeds.ac.uk/civil-engineering/staff/1204/prof-david-p-connolly">University Profile</a></p> </li> <li> <p data-path-to-node="15,1,0"><a href="https://www.linkedin.com/in/drdavidconnolly/">LinkedIn</a></p> </li> <li> <p data-path-to-node="15,2,0"><a href="https://scholar.google.com/citations?user=jzkpu9IAAAAJ&hl=en">Google Scholar</a></p> </li> <li> <p data-path-to-node="15,3,0"><a href="https://www.researchgate.net/profile/David-Connolly-7">ResearchGate</a></p> </li> <li> <p data-path-to-node="15,4,0"><a href="https://orcid.org/0000-0002-3950-8704">ORCID</a></p> </li> <li> <p data-path-to-node="15,5,0"><a href="https://www.scopus.com/authid/detail.uri?authorId=35098220800">Scopus</a></p> </li> </ul>

<p>To apply for this project you will need to make a formal application for research degree study through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For <strong>‘Appli</strong><strong>cation type’</strong> please select <strong>‘Research Degrees – Research Postgraduate’</strong>. <br /> •    The <strong>admission year</strong> for this project is <strong>2027/28 Academic Year</strong>. <br /> •    You will need to select your <strong>‘Planned Course of Study’</strong> from a drop-down menu. For this project, scroll down and select ‘<strong>PhD Civil Engineering FT</strong>’. <br /> •    The <strong>project start date</strong> for this project is<strong> </strong>from<strong> 1 October 2027</strong>, please use this as your <strong>Proposed Start Date of Research</strong>. <br /> •    Please state clearly in the research information section that the research degree you wish to be considered for is <strong>PhD in Transport Engineering</strong> as well as <a href="https://eps.leeds.ac.uk/civil-engineering/staff/1204/dr-david-p-connolly">Professor David Connolly</a><strong> </strong>as your proposed supervisor.</p> <p>We will assess applications continuously as we receive them. We welcome and strongly encourage any potential applicants to contact the supervisor(s) for an informal discussion, prior to applying, and recommend submitting your application early. </p> <p><strong>Please note that you must provide the following documents in support of your application at the point you submit your application:</strong></p> <ul> <li>Full transcripts of all degree study or if in final year of study, full transcripts to date including grading scheme</li> <li>CV</li> <li>A personal statement outlining your interest in the project, why you have chosen it and how your skills map onto the requirements of the project.</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>This is a non funded PhD project. Applicants are expected to be self funded or to secure external funding.</p> <p>You can find out more about our Funding Opportunities on our <a href="https://phd.leeds.ac.uk/">Postgraduate Research Opportunities</a> website.</p> <p><strong>Important:</strong> Please note that all costs associated with your arrival at Leeds (visa, Immigration Health Surcharge, flights etc) would have to be met by yourself, or you will need to find an alternative funding source.</p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p> <p>For further information about this project, please contact Professor David Connolly by email to <a href="mailto:D.Connolly@leeds.ac.uk">D.Connolly@leeds.ac.uk</a></p>