REuse of Structural sTeel in cOnstRuction (RESTOR)
Lead Research Organisation:
University of Birmingham
Department Name: Civil Engineering
Abstract
Steel buildings form the vast majority of multi-storey and industrial buildings in the UK. More than 85% of the structural steel in existing buildings is recycled at the end of their service life whereas less than 15% is reused. However, steel recycling is energy-demanding and contributes to the UK iron and steel industry being the largest industrial sector in terms of both energy demands and greenhouse gas emissions. Thus, there is a genuine need for cutting-edge technical innovations embedded in the circular economy that maximizes sustainable, efficient and low-energy reuse, rather than energy-demanding recycling, of structural steel.
RESTOR is the first project of its kind to apply sophisticated non-destructive testing (NDT), machine learning optimization, and building information modelling to reuse structural steel in construction. At the end of the first lifespan of used steel members (e.g., beams, columns, braces), RESTOR will allow their material properties to be determined based on NDT measurements. RESTOR will optimize the repurposing of used steel members and validate their structural performance during their second lifespan. It will develop a new, validated and optimized state-of-the-art generative design tool that will create automated and optimized building configurations made of used steel members. The outputs of RESTOR will therefore enable sustainable delivery of the infrastructure projects planned as part of the post-COVID-19 economic recovery strategy.
RESTOR is the first project of its kind to apply sophisticated non-destructive testing (NDT), machine learning optimization, and building information modelling to reuse structural steel in construction. At the end of the first lifespan of used steel members (e.g., beams, columns, braces), RESTOR will allow their material properties to be determined based on NDT measurements. RESTOR will optimize the repurposing of used steel members and validate their structural performance during their second lifespan. It will develop a new, validated and optimized state-of-the-art generative design tool that will create automated and optimized building configurations made of used steel members. The outputs of RESTOR will therefore enable sustainable delivery of the infrastructure projects planned as part of the post-COVID-19 economic recovery strategy.
Organisations
Publications
Kookalani S
(2023)
Reinventing Steel with AI: Towards a Sustainable Future in Construction
Description | Invited talk - The Impact of AI on the Steel Construction Sector |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | About 50 practitioners from the construction industry as well as members of the British Constructional Steelwork Association attended a talk on the impact of AI on the steel construction sector. The talk sparked questions and discussion afterwards, and fueled interest in the project. |
Year(s) Of Engagement Activity | 2023 |
Description | Presentation to the British Constructional Steelwork Association |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | A comprehensive summary of RESTOR project was provided, with particular emphasis on work packages 1 and 2 (WP1 and WP2). The intended purpose of the presentation was to inform the tests planned as part of these WPs with industrial input in such a way that the test results will be impactful. The audience consisted of 40 practitioners actively involved in steel reuse initiatives. RESTOR team received valuable feedback from the attendees, which fed directly into refining the experimental programme to encompass real-life damage scenarios commonly observed in reclaimed steel members. |
Year(s) Of Engagement Activity | 2023 |