A novel methodology for structural health assessment of steel-framed structures.
Project ID: 2531ad1495
(You will need this ID for your application)
Research Theme: Engineering
UCL Lead department: Civil, Environmental and Geomatic Engineering (CEGE)
Lead Supervisor: Luke Lapira
Project Summary:
This research project aims to develop a new methodology for assessing the in-situ health of steel-framed structures.
Why this research is important: The construction industry is responsible for nearly 40% of global CO2 emissions and represents one of the greatest challenges to achieving net-zero targets. The challenge is compounded in light of increasing demands on buildings and infrastructure, due to rising load requirements and the need for more floor space. While such needs can be met by demolishing and reconstructing to new specifications, this approach comes at significant carbon cost. Research suggests that rehabilitating existing buildings and infrastructure to meet new requirements is a more carbon-efficient solution.
What you will be doing: A published proof-of-concept (doi:10.1098/rsta.2022.0033) demonstrated how measuring the response of a structural column to an external, non-destructive load—termed probing—and pairing this with a machine learning model can predict its strength and health. This project extends the concept to entire steel-framed structures. You will conduct experiments on scale models and subsequently develop validated finite element models to study the response of steel-framed structures to probing. These results will support the development of a machine learning model that can predict the overall strength and health of steel frames. Ultimately, you will develop practical guidance for implementing this methodology in full-scale structures.
Who you will be working with: The primary supervisor for the project is Dr Luke Lapira, a key contributor to the development of this probing technique for structural health assessment, alongside researchers from other universities. Dr Lapira brings expertise in structural mechanics, computational structural analysis, and practical consulting experience to the team.
Who we are looking for: The ideal candidate will have a degree in structural engineering and a strong interest in computational analysis. Familiarity with 3D modelling software and Python coding is beneficial but not required.