Consortium to develop carbon analytics AI system
Winvic, University of the West of England, Edgetrix and Costain will use £800,000 funding to develop system
Winvic, University of the West of England, Edgetrix and Costain will use £800,000 funding to develop system
A consortium comprising Winvic, University of the West of England, Edgetrix and Costain will receive £800,000 in funding to develop the first embodied carbon analytics AI system of its kind in the next two years.
The project entitled ‘AI System for Predicting Embodied Carbon in Construction’ (ASPEC) will predict the carbon output on building and infrastructure projects based on BIM designs, materials carbon data and lessons learnt on past projects. Its aim is in line with article eight – Carbon Capture – on the government’s 10 Point Plan for a green industrial revolution, which was launched last week.
While construction organisations continue to make advances in how they address both embodied and operational carbon emissions, current calculation methods for the former are onerous; with no design support calculation solution, there is no efficient way for design teams to proactively drive down the embodied carbon and carbon footprint of projects.
The revolutionary technology will therefore be crucial in propelling constructors and material manufacturing firms to meet the UK government target removing 10MT of carbon dioxide by 2030. Furthermore, a 50 per cent reduction in carbon emissions has been set through the Construction 2025 strategy and the target for 2050 is to bring all greenhouse gas emissions to net zero.
In the UK alone, building stocks are responsible for 40 per cent of the UK's total greenhouse gas emissions.
Buildings and construction together account for 36 per cent of global final energy use and 39 per cent of energy-related carbon dioxide (CO2) emissions (UNEP, 2018)
The proposed ASPEC therefore has three components, to meet the tangible needs of the industry:
Tim Reeve, Winvic’s Technical Director and project lead, said ollowing the announcement of government targets paired with BREEAM sustainability requirements, construction organisations and clients have worked hard to reduce emissions, but the task isn’t simple.
"That’s why we believe that utilising the most up-to-date AI and advanced big data analytics techniques in a way that has never been done before will be transformational for Winvic’s green agenda and pave the way for significant changes across the whole industry," he said.
"Having the ability to optimise schemes and see embodied carbon costs as real-time design and material changes are applied will naturally lead to quantifiable reductions in greenhouse gas emissions, and having technology that automatically estimates the embodied carbon cost of any digitally designed project will make the delivery of many project tasks much faster.
"It’s very exciting to be working on ASPEC – our third Innovate UK funded initiative – with Costain, UWE and Edgetrix and together we aim to deliver these two primary benefits of the project to the industry as early as Autumn 2022.”
Dr Lukman Akanbi, Associate Professor at UWE Bristol - which is aiming to be carbon neutral by 2030 - said it hopes to contribute technical know-how and expertise in Big Data Analytics and AI to help move the UK one-step closer towards the realisation of her clean growth strategy and 2050 net-zero carbon target.
Muktar Tijani at Edgetrix, said the ASPEC project provides a simpler, more accurate and innovative approach to predicting embodied carbon in construction projects. "Edgetrix is delighted to be part of this project and are committed to helping organisations commence their Net-Zero journey through our expertise in carbon-cutting and modern tech-solutions."
Winvic and its project partners will develop a Common Data Environment (CDE) to eradicate the challenges that arise from having multiple, siloed datasets.
The huge amounts of data that are generated from different phases of construction projects will be documented and stored in one system; it is this data nucleus that will facilitate the machine learning technologies, providing industry professionals with the insights to make more eco-friendly decisions on materials used.