Development of a learner model tool for predicting strength and embodied carbon for lightweight concrete production

Nukah, Promise D.; Abbey, Samuel J.; Booth, Colin A.

Abstract

The demand for sustainable concrete in meeting the net zero carbon target places a burden in optimizing concrete response to structural strength that satisfy acceptable embodied carbon. In most cases, a low carbon concrete is deficient in structural requirement and vice versa. This dilemma informs the need for a tool that can predict compressive strength as well as embodied carbon using the same input data. Since the use of alternative materials as cement replacement to enhance sustainability is emerging in the quest for a sustainable concrete, an optimal material that satisfy both conditions of structural integrity and sustainability is still lacking. Paucity of data in the emerging lightweight low carbon concrete using alternative materials, portends an upheave in the bias for prediction of the behaviour of lightweight low carbon concrete. This study therefore uses concrete data of lightweight low carbon concrete from laboratory experiment for the prediction of compressive strength and embodied carbon with their performance evaluated using eight machine leaning regression models. The results obtained indicates that the XG boost regression model exhibited excellent performance with a low Mean Squared Error (MSE) of 50.15, Mean absolute error(MAE) = 5.26, Mean absolute percentage error(MAPE) = 11.76 %, Explained variance score = 0.97, Root mean square error(RMSE) = 7.08 and a high R squared value of 0.96. The tool predicted compressive strength and embodied carbon for lightweight carbon concrete using multiple output regression such that the output can be limited to the yearly structural embodied carbon threshold to achieving 2050 net zero target. The developed tool when compared with concrete of similar mix ingredients performed more than 95 % in predicting concrete compressive strength and associated embodied carbon. In line with the inclusion of embodied carbon in carbon regulations of buildings in the UK as suggested by the professionals in the construction industry, the developed learner model and prediction tool has integrated concrete strength and embodied carbon to initiate a holistic approach to design and construction, balancing performance, cost, and environmental impact.

• Journal Article Type: Article
• Acceptance Date: Jul 28, 2024
• Online Publication Date: Aug 4, 2024
• Publication Date: Oct 15, 2024
• Deposit Date: Aug 5, 2024
• Publicly Available Date: Aug 5, 2024
• Journal: Journal of Building Engineering
• Electronic ISSN: 2352-7102
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 95
• Article Number: 110330
• DOI: https://doi.org/10.1016/j.jobe.2024.110330
• Keywords: Machine learning, Embodied carbon, Foundation structure, Artificial intelligence
• Public URL: https://uwe-repository.worktribe.com/output/12769242
• Additional Information: This article is maintained by: Elsevier; Article Title: Development of a learner model tool for predicting strength and embodied carbon for lightweight concrete production; Journal Title: Journal of Building Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jobe.2024.110330; Content Type: article; Copyright: © 2024 The Author(s). Published by Elsevier Ltd.