Funding

Self-funded

Project code

SCS10640529

Department

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 Chen, Dr Shikun Zhou and Dr Zhang Zhongi.

 

 

The work on this project will include:

  • Image benchmarks for damage and crack detection
  • MCNN- and GNN-based identification and characterisation of multiscale damage
  • Early-stage damage assessment and propagation monitoring
  • Inspection of inaccessible areas

 

 

The end users of engineering structures and mechanical equipment face significant challenges in inspecting multiscale damage in inaccessible areas, such as seawater tanks, during operation. This research aims to develop an Artificial Intelligence (AI)-enabled micro-camera imaging system capable of automatically detecting and assessing damage states in real time. Waterproof micro-cameras will be deployed to capture high-resolution images in harsh and confined environments. An advanced AI-based image analysis algorithm will be developed to automatically identify and classify damage states from the captured images.

The proposed system will utilise multitask convolutional neural networks (MCNNs) and graph neural networks (GNNs) to process image data dynamically and provide accurate information on crack initiation, damage evolution, and propagation. The key technologies underpinning this research include: (i) the development of comprehensive image benchmarks for damage and crack detection, including cases where damage is obscured by dirt or contamination; and (ii) MCNN- and GNN-based methodologies for the identification and characterisation of multiscale damage.

The expected outcome of this research is the first AI-driven micro-camera imaging monitoring system with integrated multiscale damage image benchmarks and advanced micro-damage detection capabilities. The system will enable reliable inspection of inaccessible areas, supporting early-stage damage assessment and propagation monitoring. Ultimately, this technology will help end users reduce maintenance costs, improve operational efficiency, and enhance safety.

 

 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only – eligibility criteria apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

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.

International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.

 

 

 

How to apply

We’d encourage you to contact Dr Jiye Chen ([email protected]) 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 SCS10640529.