Rapid evaluation of embodied carbon of buildings’ lifecycle using artificial intelligence

Al-Habaibeh, A ORCID: https://orcid.org/0000-0002-9867-6011, Manu, E ORCID: https://orcid.org/0000-0002-9002-3681, Clement, T, Shakmak, B ORCID: https://orcid.org/0000-0003-4534-9196, Selvam, J ORCID: https://orcid.org/0009-0008-6532-7326 and Lin, T-H, 2024. Rapid evaluation of embodied carbon of buildings’ lifecycle using artificial intelligence. In: Riffat, S, ed., Sustainable Energy Technologies: proceedings of the 21st International Conference on Sustainable Energy Technologies, 12 to 14th August 2024, Shanghai, China. University of Nottingham, pp. 1-9. ISBN 9780853583561

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Abstract

The global drive to reduce carbon emissions and the footprint of the construction industry and its effect on climate change has led to construction organisations integrating life cycle carbon assessment into their activities. One of the key areas for enhancing sustainability is the reduction of embodied carbon in the materials. This comprises the carbon associated with intrinsic features of the materials themselves, the transportation, and installation-related emissions. There is also the carbon emission associated with the maintenance, and operation of the built assets during their life cycles. To enable the rapid evaluation of the whole-life carbon, i.e. carbon emission during design, construction, operational and end-of-life phase of buildings, this study presents a novel approach of integrating artificial intelligence (AI) into existing practices with focus on embodied carbon. This novel approach comprises the integration of data using AI to provide quick guidance for designers during the early design stage of the process to reduce the life-cycle carbon impact. This approach will exploit a wide range of datasets from up-to-date Carbon databases and previous construction projects and their carbon footprint. This will allow a reasonable estimation of the carbon footprint of different design choices for clients and designers, allowing them to collaborate in rapidly reaching an optimum configuration and material selection for their buildings. Current practice involves manual input of carbon data into carbon estimation software to enable the output. With the integration of fuzzy logic and deep-learning neural networks, the new proposed process will contribute to time savings and enhanced decision-making. The paper begins with a literature review of the importance of monitoring carbon emissions in the construction industry, followed by a discussion of CarboniCa Software, an in-house carbon assessment package used by a major UK construction organisation. And finally, an analysis of how AI capability will support the software for rapid evaluation of whole life carbon will be presented. This application highlights the importance of AI towards a more efficient and sustainable future.

Item Type:	Chapter in book
Description:	Paper presented at SET2024: 21st International Conference on Sustainable Energy Technologies 12 to 14th August 2024, Shanghai, China.
Creators:	Al-Habaibeh, A., Manu, E., Clement, T., Shakmak, B., Selvam, J. and Lin, T.-H.
Publisher:	University of Nottingham
Date:	12 December 2024
Volume:	1
ISBN:	9780853583561
Identifiers:	Number Type 2334992 Other
Rights:	© 2024 Copyright University of Nottingham & WSSET
Divisions:	Schools > School of Architecture, Design and the Built Environment
Record created by:	Laura Borcherds
Date Added:	20 Feb 2025 15:55
Last Modified:	24 Feb 2025 12:02
URI:	https://irep.ntu.ac.uk/id/eprint/53098

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