Funding

Self-funded

Project code

SCES4560220

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, to commence in October or February.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Jiye Chen. 

The work on this project will:

  • Collect/conduct camera images with multiscale damage, including macro and micro damage to build up the capability of artificially intelligent camera imaging algorithm (AIMCIA) to graphically recognise visible and invisible damage.
  • Develop the Image Benchmarks of Multiscale Damage (IBMD), including various common crack damage and corrosion damages into the image database, to underpin the AIMCIA.
  • Develop the proposed AIMCIA in terms of Convolutional Neural Networks with Multitask Learning technology for graphically recognising various types of multiscale damage.
  • Verify/adjust the AIMCIA and IBMD using selected images with common cracks and corrosion damages, especially damage mixed with dust particles, and invisible damage at the micro scale.
  • Validate the proposed AIMCIA using objects such as a marina seawater tank, a wing box or a blade joint in airplanes, selected by leading industrial partners.

This PhD project aims to develop a novel artificially intelligent camera imaging algorithm (AIMCIA) for the live monitoring of multiscale damage in engineering objects. A hybrid methodology with collection and conduction of camera images, image database creation and image algorithm development and analysis will be applied in this investigation. The challenging work is establishing the IBMD as well as developing the AIMCIA.

Image photogrammetry can be used to select and edit collected images and cloud data technology can be used to catalogue images and store them into the proposed IBMD. A deep learning technology based on the concept of Convolutional Neural Networks with Multitask Learning will be used to develop the proposed AIMCIA. It will be justified using selected images from the IBMD and validated by the objects selected by industrial partners.

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).

Entry requirements

You'll need an upper second class honours degree from an internationally recognised university 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.

Ideally, you should have a first degree in an appropriate subject, e.g. Computer Science or Computing. A Postgraduate qualification related to Computer Science, Computer image process or Computer Graphical recognition would be welcomed.

How to apply

We’d encourage you to contact Dr Jiye Chen (jiye.chen@port.ac.uk) 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. 

If you want to be considered for this self-funded PhD opportunity you must quote project code SCES4560220 when applying.