Computer vision and machine learning for recognition of civil structural components and their damage analysis

In this project the PhD research student will investigate the strengths and weaknesses of the deep convolutional neural network for analysis of civil infrastructures, such as concrete bridges, highways, buildings, tunnels and stadiums, using videos and images. The student research work will be on finding contextual information that can be used for recognition/reidentification of the civil structural components (CSCs), such as columns, beams, slabs, arches, plates, shells, etc. This will be followed by analysis of unhealthy (defects such as spallation, exposed bar, corrosion, crack, etc) areas in the CSCs. The student will study, analyse and conduct experiments systematically to model and design new semi-supervised and/or unsupervised deep networks that outperforms the current-state-of-the-art algorithms on standard benchmarking and/or real-world databases. In this project, all databases used will be in accordance to their terms and conditions. Where applicable, appropriate favourable ethics approval will be obtained before any research project studentship offer is made. There is an expectation that the student will also research and apply relevant legal and ethical issues in data collections and analysis in this domain. These are active and important areas of research with many opportunities for innovation and collaboration. This project will provide an opportunity to pursue world-class research, provide experience of design and evaluation processes and an opportunity for substantial contribution to international publication in leading journals/conference/workshop proceedings/.

Two or three positions are available. Applicants can start as soon as possible.

Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants will require good general programming skills but will not need specific computing expertise in, for example, Computer Vision, Machine Learning and Video Analytics.

Applicants should have an enthusiasm for design and experimentation as well as a willingness to acquire new skills. Ideally, applicants will be self-motivated and have the ability to work both independently and as part of a team.

Attitude and Personality: Effective communication (oral and written) skills, presentation and training skills; Good interpersonal skills; Ability to work independently and as part of a team on research programmes; Ability to initiate, plan, organise, implement and deliver programmes of work; Willingness to learn new skills.

This opportunity is open to UK/EU and overseas/international students. The collaborative and presentation aspects of the research require good English language and communication skills.

For overseas PGR students joining us before August 2024 we would require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest. For PGR students joining us from August 2024 onwards, we require IELTS 6.5 with no less than 6.0 in any subtest.

Informal enquiries about the project are very welcome by email to the Project Lead, Dr B Mandal [Email Address Removed]

Full applications to: https://www.keele.ac.uk/study/postgraduateresearch/researchareas/computerscience/

Please quote FNS_BM December 23 on your application.