PhD in Artificial Intelligence for Geotechnical Engineering

Artificial intelligence is transforming civil engineering by providing new ways to model complex ground conditions and predict asset behaviour. This research theme focuses on the integration of machine learning, deep learning, and advanced data analytics with classical soil mechanics. By leveraging large datasets from laboratory testing, field instrumentation, and site investigations, intelligent models can capture non linear soil behaviour more accurately than traditional empirical relationships. Research within this theme addresses critical challenges such as slope stability, foundation design, and underground construction under changing environmental conditions. Candidates will explore how computer vision, neural networks, and evolutionary algorithms can optimize geotechnical design and enhance the resilience of infrastructure. This theme offers an opportunity to bridge the gap between computer science and geo engineering, developing intelligent tools that improve safety and sustainability in construction.

<p data-path-to-node="4"><strong data-index-in-node="0" data-path-to-node="4">Detailed description</strong></p> <p data-path-to-node="4">The geotechnical engineering sector faces unprecedented challenges from climate change, ageing infrastructure, and the demand for more efficient construction practices. Traditional geotechnical design heavily relies on simplified empirical models and analytical methods that often struggle to capture the inherent variability and highly non linear nature of soil and rock masses. This research theme investigates how artificial intelligence and advanced computational tools can overcome these limitations. By treating geotechnical engineering as a data rich discipline, I aim to develop next generation predictive models that learn directly from physical observations.</p> <p data-path-to-node="5">Research within this framework covers a wide spectrum of geotechnical assets, including tunnels, retaining walls, deep foundations, and earthworks. I am particularly interested in how multi scale data, ranging from micro structural laboratory measurements to macro scale regional geographic information systems, can be synthesized using deep learning architectures. This approach allows for real time updates to design parameters during construction, driving the development of smart infrastructure systems. Through this research theme, candidates will contribute to pioneering methods that replace conservative safety factors with probabilistic, AI driven risk assessments, ultimately leading to more sustainable and cost effective civil engineering solutions.</p> <p data-path-to-node="6"><strong data-index-in-node="0" data-path-to-node="6">Why this research is important</strong></p> <p data-path-to-node="6">Geotechnical engineering decisions carry significant financial and safety implications, as unforeseen ground conditions are a primary cause of project delays and structural failures worldwide. As infrastructure ages and environmental pressures increase, traditional engineering methods become less reliable. Artificial intelligence offers the ability to process vast, heterogeneous datasets to identify patterns that human analysis might miss. Implementing intelligent systems allows the construction industry to transition from reactive maintenance to proactive asset management. Furthermore, optimizing foundation and earthwork designs through machine learning directly reduces material consumption, such as concrete and steel, lowering the embodied carbon of major infrastructure assets and supporting global sustainability goals.</p> <p data-path-to-node="7"><strong data-index-in-node="0" data-path-to-node="7">Example PhD research topics</strong></p> <ul data-path-to-node="8"> <li> <p data-path-to-node="8,0,0">Machine learning for predicting regional landslide susceptibility</p> </li> <li> <p data-path-to-node="8,1,0">Deep learning architectures for automated interpretation of cone penetration testing data</p> </li> <li> <p data-path-to-node="8,2,0">Neural network modelling of non linear soil structure interaction for high speed railways</p> </li> <li> <p data-path-to-node="8,3,0">Computer vision algorithms for automated rock mass classification and fracture mapping</p> </li> <li> <p data-path-to-node="8,4,0">Optimizing underground excavation sequencing using reinforcement learning</p> </li> <li> <p data-path-to-node="8,5,0">Physics informed neural networks for predicting pore water pressure changes in embankments</p> </li> <li> <p data-path-to-node="8,6,0">Probabilistic assessment of foundation settlement using evolutionary optimization</p> </li> <li> <p data-path-to-node="8,7,0">Smart monitoring of retaining structures using anomaly detection algorithms</p> </li> </ul> <p data-path-to-node="9"><strong data-index-in-node="0" data-path-to-node="9">Methods and techniques</strong></p> <ul data-path-to-node="10"> <li> <p data-path-to-node="10,0,0">Deep neural networks and convolutional neural networks</p> </li> <li> <p data-path-to-node="10,1,0">Computer vision and image processing algorithms</p> </li> <li> <p data-path-to-node="10,2,0">Physics informed machine learning architectures</p> </li> <li> <p data-path-to-node="10,3,0">Random forests and support vector machines</p> </li> <li> <p data-path-to-node="10,4,0">Evolutionary algorithms and genetic programming</p> </li> <li> <p data-path-to-node="10,5,0">Big data analytics and cloud computing platforms</p> </li> <li> <p data-path-to-node="10,6,0">Advanced statistical analysis and Bayesian inference</p> </li> <li> <p data-path-to-node="10,7,0">Numerical modelling integrated with data driven algorithms</p> </li> </ul> <p data-path-to-node="11"><strong data-index-in-node="0" data-path-to-node="11">Suitable academic backgrounds</strong></p> <p data-path-to-node="11">Suitable candidates will hold an academic background in civil engineering, geotechnical engineering, engineering geology, computer science, data science, applied mathematics, or closely related engineering and scientific disciplines.</p> <p data-path-to-node="12"><strong data-index-in-node="0" data-path-to-node="12">FAQ</strong></p> <ul data-path-to-node="13"> <li> <p data-path-to-node="13,0,0">Can I propose my own PhD topic? Yes, the themes above are a guide only.</p> </li> <li> <p data-path-to-node="13,1,0">Can I bring my own funding? Yes, I welcome applicants who are funded through government scholarships, employers or self funding.</p> </li> <li> <p data-path-to-node="13,2,0">Do I need funding before contacting you? You should have at least identified your planned funder and commenced your application.</p> </li> <li> <p data-path-to-node="13,3,0">Can I study interdisciplinary topics? Yes. Many of my current research interests combine multiple disciplines.</p> </li> <li> <p data-path-to-node="13,4,0">When can I start? Start dates are in spring and autumn. Full details dates are available elsewhere on the University website.</p> </li> </ul> <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>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PhD Civil Engineering FT, </strong></em>in the research information section that the research degree you wish to be considered for is <em><strong>Artificial intelligence for geotechnical engineering</strong></em> as well as <a href="https://eps.leeds.ac.uk/civil-engineering/staff/1204/prof-david-p-connolly">Professor David Connolly</a> as your proposed supervisor and and in the finance section, please state clearly <em><strong>the funding that you are applying for, if you are self-funding or externally sponsored</strong></em>.</p> <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>Applications will be considered on an ongoing basis.  Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application.  We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p><strong>If you are applying for University or School Scholarships for 2026/27 entry, with external sponsorship or you are funding your own study, please ensure you provide your supporting documents 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 the grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <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><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>Scholarship opportunities open from October 2025</strong></p> <p><strong>UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/209-leeds-doctoral-scholarships-2024">Leeds Doctoral Scholarship</a> <strong>(closing date: 1 April 2026)</strong> and <a href="https://phd.leeds.ac.uk/funding/234-leeds-opportunity-research-scholarship-2022">Leeds Opportunity Research Scholarship</a> <strong>(closing date: 1 April 2026)</strong> are available to UK applicants.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a> is available to nationals of China <strong>(closing date: 7 January 2026)</strong>. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p>Please note that if you are successful in securing an academic offer for PhD study, this does not mean that you have been successful in securing an offer of funding.</p> <p>If you are applying for the Leeds Doctoral Scholarship, Leeds Opportunity Research Scholarship, China Scholarship Council-University of Leeds Scholarship or Leeds Marshall Scholarship, you will need to complete a separate application, specific to these scholarships, to be considered for funding.</p> <p>You will be responsible for paying the overtime fee in full in your writing up/overtime year (£340 in Session 2025/26), but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.</p> <p><strong>Important: </strong>Please note that that the award does not cover the costs associated with moving to the UK.  All such costs (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, 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>