With increasingly evident impacts of climate change, there is a growing need to improve how unstable coastal cliffs are characterised and monitored. Along the south coast of the UK, cliff failures are occurring under unprecedented conditions. In many cases, understanding and managing these hazards is limited by the inability to safely observe the condition of highly unstable slopes over time.
Conventional geotechnical investigation methods rely on intrusive testing and embedded sensors, which can be unsafe or impractical at actively failing cliffs. As a result, internal changes associated with drying, cracking, or progressive damage often remain unobserved, particularly under extreme conditions where changes can develop gradually before culminating in sudden failure.
This PhD project spans geotechnical engineering and applied particle physics to explore the use of muon tomography as a non-invasive method for subsurface characterisation and long-term monitoring of unstable cliffs. Muons are naturally occurring atmospheric particles capable of passing through tens of metres of rock or soil. By measuring how muons are attenuated or scattered, it is possible to infer internal density and structural anomalies without the need for drilling or embedding sensors.
Student activities
You will conduct lab experiments using muon detectors to study how different soil/rock samples influence measured responses. These experiments will examine sensitivity to material type, moisture, and internal structure and how changes can be detected over time. Building on the lab studies, you will use multi-detector muon tracking and machine learning, techniques routinely used in experimental particle physics, to explore how different materials and states can be distinguished beyond density, including sensitivity to atomic number and mineralogical traits. Lessons from the lab will be applied at an unstable coastal cliff in southern England, where you will test and refine these approaches in a real-world setting.
Student benefits
This studentship will offer training across geotechnical engineering, particle physics, and data science, providing hands-on experience with cutting-edge sensing technologies and field deployment. The student will develop skills in experimentation, machine learning, and interpretation of environmental data while collaborating with academic, industry, and public-sector partners. The project offers exposure to interdisciplinary research and real-world problem-solving, building expertise applicable across a wide range of challenges. The student will also interact with project partners from the World Heritage Organisation, local council authorities, and an industry partner with expertise in muon imaging.
Contacts:
Lead supervisor:
Tiago Gaspar | Department of Civil and Environmental Engineering | t.gaspar@imperial.ac.uk
Co-Supervisor:
Nicholas Wardle | Department of Physics | n.wardle09@imperial.ac.uk