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Enhancing the practicality of tools to estimate the whole life embodied carbon of building structures via machine-learning models

Pomponi, Francesco; Luque Anguita, Maria; Lange, Michal; D'Amico, Bernardino; Hart, Emma


Francesco Pomponi

Maria Luque Anguita

Michal Lange


The construction and operation of buildings account for significant environmental impacts, including greenhouse gas (GHG) emissions, energy demand, resource consumption and waste generation. While the operation of buildings is fairly well regulated and globally considered in the pathways to net-zero mid-century targets, a different picture emerges when looking at the other life cycle stages, which incur the so-called embodied impacts. These cover raw material extraction and product manufacturing through to construction and end of life activities. Only a handful of examples exist where such embodied carbon (EC) emissions are enshrined in law with most of the ongoing debate still around estimating and understanding where such emissions occur and how to mitigate them. Building structures account for a significant share of a building’s embodied emissions and they also are the building element with the longest service life, thus presenting potential lock-in challenges for choices made today. To support the ongoing global effort to mitigate embodied carbon and equip engineers and designers worldwide with easy-to-use and robust calculation tools, we describe a real- time decision-support tool to aid building design that leverages machine-learning (ML) methods from computer science to speed-up the computationally expensive process of finite element analysis (FEA) traditionally exploited in structural engineering. We demonstrate that replacing FEA calculations with a model learnt using ML from a large dataset offers real time decision support while guaranteeing the same level of confidence and accuracy that a traditional FEA-based method would offer at the design stage. The tool has been developed both as a standalone version and as a plugin for Trimble SketchUp to maximise its usability and diffusion. It offers results correlated with uncertainty analysis in the form of probability density functions to account for the inherent variability of input data that characterises early stages in the design process. This research contributes to the ongoing global efforts to decarbonising the built environment and offers an immediately implementable method and tool for doing so.


Pomponi, F., Luque Anguita, M., Lange, M., D'Amico, B., & Hart, E. (2021). Enhancing the practicality of tools to estimate the whole life embodied carbon of building structures via machine-learning models. Frontiers in Built Environment, 7, Article 745598.

Journal Article Type Article
Acceptance Date Sep 28, 2021
Online Publication Date Oct 15, 2021
Publication Date 2021
Deposit Date Sep 28, 2021
Publicly Available Date Sep 30, 2021
Journal Frontiers in Built Environment
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 7
Article Number 745598
Keywords Embodied carbon, Life Cycle Assesment, machine learning, SketchUp, tools, Sustainable buildings and cities
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Enhancing The Practicality Of Tools To Estimate The Whole Life Embodied Carbon Of Building Structures Via Machine-learning Models (2.3 Mb)

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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

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