PhD Studentship: Machine Learning and Computer Vision for Localisation, Classification and Quantification of Structural Damage

Inspection and maintenance are essential to ensure the serviceability and safety of bridges. Current inspection on site is often done manually on paper, and paper is the medium to exchange condition information between the involved stakeholders. After damage registration or information exchange, the data is processed digitally. The repeated digitalization is an error-prone process and leads to redundant work. To address these shortcomings, this research aims to devise a framework and deliver an augmented reality toolkit for identification and classification of damage in bridges using machine learning and computer vison techniques. To achieve this goal, the following steps will be carried out: i) data acquisition; ii) data processing and filtering; iii) CV method for localisation damage; iv) Deep learning for the segmentation of images and classification of damage type and severity v) development of a user-friendly augmented reality toolbox for on-site damage identification and classification based on the trained data set. This technology will enable linking the reconstructed bridge condition with a digitized BIM model and to support the complete lifecycle of built infrastructure. Finally, the expert toolkit will embed expert knowledge for decision making support related to repair and retrofitting demands and alternatives for the diagnosed bridge condition.

Application details

The position is available to start in October 2022.
Duration: 3 years
Closing date: open until filled

Entry requirements

Applicants should have, or expect to obtain, a 1st class or 2.1 honours degree in Civil Engineering. Students with experience in Matlab/Python are encouraged to apply.
Due to funding restrictions this position is only available for UK candidates.

How to apply

The project will be jointly supervised by Dr Jelena Ninić, University of Birmingham ([email protected] ) and Dr Georgia Thermou, The University of Nottingham ([email protected] ). Informal contact can be sent to Dr Jelena Ninić, ([email protected] ) and Dr Georgia Thermou, ([email protected] ) before submitting an online application. Please send a cover letter and a copy of your CV with your up to date relevant experience.

References:
- J Bush, J Ninic, G Thermou, J Bennetts, S Denton, L Tachtsi, P Hill; Point cloud registration for bridge defect tracking in as-built models; Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, CRC Press, 8, ISBN 9781003322641, 2022.
- J Bush, J Ninic, G Thermou, L Tachtsi, P Hill, S Denton, J Bennetts, Image registration for bridge defect growth tracking ; Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, CRC Press, 8, ISBN 9781003322641, 2022.
- J. Bush, T. Corradi, J. Ninic, G. Thermou, J. Bennetts “Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering”. 28th International Workshop on Intelligent Computing in Engineering EG-ICE, Berlin, Germany, 2021.