We aim to revolutionize the safety assessment of deteriorated and aged existing masonry structures. The current procedure for this is based on visual inspection and limited localized non-destructive or semi-destructive testing techniques. These techniques are not only time consuming and costly, but also only provide information about the local properties of the structure and are subjected to human and methodological errors.
In this project, the successful candidate will combine machine learning and Multiphysics computational models to develop novel tools that allow automation of the detection of deterioration signs and of their influence on the safety of existing structures. The develop tools can be used by the construction industry to assess the ageing and safety of existing masonry and historical constructions. In order to ensure the applicability of the software for the industrial sector, the candidate will interact with several construction companies and industrial laboratories during the project.
We are seeking an enthusiastic and highly motivated home/UK student with good interpersonal skills and a keen interest in research and machine learning. You must have, or expect to achieve, at least a 2:1 honors degree or a distinction or high merit at BSc and MSc levels (or international equivalent) in Civil Engineering, mathematics or a related subject. Preference will be given to candidates with educational and/or research experience in engineering. Experience in general coding/programming is essential. The candidate will be expected to have good interpersonal skills. For further details and discussion about the position, please contact Dr Bahman Ghiassi ([Email Address Removed]).