Computational modelling and risk assessment of heavy rainfall impacts on transport infrastructure

Description of the project

This highly exciting project help to address one of the most challenging engineering problems – adapting to climate change, and help the student to learning highly sought-after skills and knowledge for a wide range of career paths.

Mitigating and adapting to climate change has become one of the most important societal challenges today. We are seeing increasingly more extreme weather such as heavy rainfall events in the globe including the UK. Such events may cause hazards such as flooding, landslides, debris and failure of earthworks and structures. They cause significant damage and disruptions to our infrastructure. For Network Rail alone, the annual loss is estimated to be 100 million pounds.

Modelling the risk from these rainfall-related hazards is essential for increasing resilience. It underpins important applications that reduces risk or enables faster recovery. Current practice evaluates the main hazards posed by intense rainfall in segmented approaches using, for example, flood, mass movement or slope failure models. However, using separate segmented approaches may underestimate the combined risks of multiple, interacting hazards induced by an intense rainfall event.

Therefore, this project will integrate these different types of models to develop a multi-hazard risk assessment framework. This project aims to deliver tools that can be used to evaluate risk exposure of transport infrastructure using possible future scenarios that reflect Climate Change.

The project will couple numerical models of different types and scales, including the open-source model hipims, to simulate multiple hazards such as flood inundation, failure of earthwork, and debris flow. It will establish the relationship between the hazard information (such as depth and velocity of flood and debris flow and extent of earthwork failure) and risk metrics such as financial losses. The project will also explore how the new tools can be applied to facilitate climate adaptation measures.

Training and potential career paths

The project will be supervised by Dr Xilin Xia, Prof Andrew Quinn, and Dr Emma Ferranti. The supervisory team will equip the candidate with an inter-disciplinary knowledgebase and skillset required for this project. Training will be provided in skills relating to process-based numerical modelling, data management, statistical analysis, and stakeholder engagement. The skills developed will equip the candidate with the skills to follow multiple career pathways including academia or industry, such as consultancy, (re)insurance firms, government agencies and charities. The student will be supervised by a multi-disciplinary team that will support writing of peer-reviewed journals and attending academic conferences.

Funding notes

The studentship is for 3.5 years and is intended to start by October 2023. However earlier start date can also be considered. The studentship provides a tax-free stipend for the duration of the studentship plus tuition fees at the UK rate. International applicants from outside the UK will need to secure additional funding to cover the difference between international and UK fees.

Entry requirement

We are seeking an enthusiastic and highly motivated student with a keen interest in research. The applicant must have, or expect to achieve, at least a 2:1 honors degree or a distinction or high merit at MSc level (or international equivalent) in civil engineering, physical geography, environmental or earth sciences, mathematics, or a related subject. Preference will be given to candidates with relevant research experience. Experience in general coding/programming is essential.