Project code



School of Civil Engineering and Surveying

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Civil Engineering and Surveying and will be supervised by Dr Jiye ChenDr Mehdi Rouholamin and Dr Philip Benson from the School of the Environment, Geography and Geosciences.

The work on this project could involve:

  • Establish 2D and 3D slope numerical models of selected failed natural or engineering slope sites  
  • Create a soil rainfall-moisture decohesion law  
  • Explore correlation between stability parameters and critical slope material decohesion scale which initiates failure propagation. 

Natural and engineering slope failure cause extensive human suffering and financial losses worldwide. Slope failure mechanisms are slope material cohesion lost due to heavy rainfall. There is an urgent need to establish robust approaches to predict slope failure because global climate change frequently brings heavy rainfall. This PhD research will develop a predictive method to simulate natural and engineering slope failure propagation, study slope stability parameters of selected slope sites and explore correlation between stability parameters and critical slope material decohesion scale which initiates failure propagation. Research outcome will be used to increase resilience and enable planning to mitigate extensive human and financial losses. The proposed predictive method will be developed based on the advanced extended cohesive damage model (ECDM) within the framework of nonlinear computational damage mechanic’s approach. The ECDM was recently developed as a continuous cohesive damage element by the principal investigator in the University of Portsmouth (UoP). The ECDM uses fully condensed equilibrium equations with standard FEM degree freedoms for highly efficient approximating discontinuity. The ECDM has been successfully applied in predicting progressive failure, including a railway embankment slope failure under heavy rainfall. This research will collaborate with established industrial partners, e.g., British Geological Survey (BGS) and NetworkRail in selecting slope sites, collect required geotechnical and geological data for studies in this project, and to explore industrial applications. Except the developed ECDM, the supervision team at the UoP has established basic framework of soil shear failure mechanisms under variable soil saturation degree and rainfall intensity conditions to support investigation of slope failure propagation. This PhD  will also benefit from BGS and NetworkRail by their professional experience and practical skills. The outcome from this research will directly support industrial applications led by BGS and NetworkRail in the future.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

BEng or MSc or MEng in Civil Structural Engineering or Geotechnical Engineering, required knowledge and skills include soil and rock mechanics, numerical modelling analysis and nonlinear fracture mechanics.

How to apply

We encourage you to contact Dr Jiye Chen ( to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Civil Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: SCES7660423.