PhD Studentship in Advanced Numerical Modelling and Predictive Maintenance of Buried Infrastructure

Buried infrastructure underpins essential urban services, yet its deterioration often remains hidden until significant damage or service disruption occurs. Ageing pipelines face increasing pressures from climate change, groundwater fluctuations and nearby construction. Their integrity is closely linked to the surrounding ground. Pipe defects can initiate water leakage and internal soil erosion, leading to loss of support, subsidence and potentially sinkhole formation. Conversely, ground movement can impose additional stresses on pipelines, accelerating damage and leakage. Understanding and predicting this dynamic, coupled deterioration process is essential for timely and effective maintenance.

This PhD project will develop a hybrid physics-based and data-driven framework for assessing the condition and predicting the performance of buried infrastructure, with a focus on water pipelines and associated ground instability. The central research question is how advanced computational models and monitoring data can be combined to identify infrastructure deterioration and predict its progression towards failure.

The project will innovate computational modelling techniques to simulate interactions between structure damage, water leakage, soil erosion and ground deformation. Building on these physical models, the research will investigate AI-assisted surrogate modelling and data assimilation to connect numerical predictions with monitoring observations, potentially including distributed fibre-optic strain measurements, hydraulic data and ground displacement. The anticipated outcome is a validated computational framework linking deterioration mechanisms, monitoring-informed prediction and maintenance decision-making. The project will support earlier intervention, reduced water losses and service disruption, and greater resilience of buried infrastructure to urban ground hazards.

The successful candidate will be based in the School of Engineering at the University of Birmingham, which hosts the National Buried Infrastructure Facility (NBIF, www.birmingham.ac.uk/nbif). They will have access to advice and support from a multidisciplinary team of experts in numerical modelling, AI-solutions, physical sensing for buried infrastructure, alongside documented experimental datasets obtained using a wide range of instrumentation and measurement techniques, including fibre-optic sensing, laser scanning, and force and strain measurements.

This project is expected to start before/in September 2027.

Requirements: The successful candidate will be motivated and enthusiastic about working across disciplines to address challenges in buried infrastructure resilience. Applicants should have:

• A First- or Upper Second-class Honours degree, or an equivalent qualification, in civil engineering, geotechnical engineering, mechanical engineering, applied mathematics, computer science or a closely related discipline. A relevant master’s degree and/or research or industry experience would be an advantage.
• Strong analytical and mathematical skills, with an interest in computational mechanics and numerical modelling.
• Experience in programming or scientific computing using languages such as Python, and C++.
• Good written and verbal communication skills.

Funding notes:

Funding for this project covers tuition fees (UK/home-student rate), UKRI minimum annual stipend (currently £21,805/annum) for the full duration of the 3.5-year programme. International students are welcome to apply if you are willing to cover the tuition difference between home and international rates through self-funding or external sources. Support will be provided for applications for external funding, such as Commonwealth fellowships or funding from the Schlumberger Foundation and the China Scholarship Council.

Interested candidates should email Dr Ruofeng Feng at r.feng@bham.ac.uk via the above 'Apply' button using the subject line “PhD studentship + [Your name] + [Current institution] + [Current programme of study]”. Please include the names and contact details of two referees in the email. Attach the following documents:

• Your CV.
• A supporting statement (maximum two pages).
• Academic transcripts for awarded and in-progress university qualifications.

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