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dc.contributor.authorPomponi, Francesco
dc.contributor.authorAnguita, Maria Luque
dc.contributor.authorLange, Michal
dc.contributor.authorD’Amico, Bernardino
dc.contributor.authorHart, Emma
dc.date.accessioned2021-10-29T05:13:59Z
dc.date.available2021-10-29T05:13:59Z
dc.date.issued2021-10-15
dc.date.submitted2021-07-22
dc.identifier.issn2297-3362
dc.identifier.other745598
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330029
dc.description.abstract<jats:p>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.</jats:p>
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectBuilt Environment
dc.subjectembodied carbon
dc.subjectlife cycle assesment
dc.subjectmachine learning
dc.subjectsketchup
dc.subjecttools
dc.subjectsustainable buildings and cities
dc.titleEnhancing the Practicality of Tools to Estimate the Whole Life Embodied Carbon of Building Structures via Machine Learning Models
dc.typeArticle
dc.date.updated2021-10-29T05:13:58Z
prism.publicationNameFrontiers in Built Environment
prism.volume7
dc.identifier.doi10.17863/CAM.77473
dcterms.dateAccepted2021-09-28
rioxxterms.versionofrecord10.3389/fbuil.2021.745598
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2297-3362
cam.issuedOnline2021-10-15


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