Research data supporting Probabilistic selection and design of concrete using machine learning
Repository URI
Repository DOI
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Type
Dataset
Change log
Authors
Forsdyke, Jessica C https://orcid.org/0000-0001-8466-3917
Zviazhynski, Bahdan https://orcid.org/0000-0002-3862-8093
Conduit, Gareth
Lees, Janet https://orcid.org/0000-0002-8295-8321
Description
The first tab contains machine learning training data originating from the Tailored Reinforced Concrete Infrastructure research project [EP/N017668/1] contaning details of concrete mix designs as well as resulting concrete properties. Concrete properties include compressive strength, saturated-surface-dry density, carbonation coefficient and estimated economic cost. Units of all values are given.
The second tab contains predictions of these properties for blind mixes, obtained using the machine learning methodology outlined in the accompanying paper.
Version
Software / Usage instructions
Excel software required. No other limitations.
Keywords
Concrete, machine learning
Publisher
Sponsorship
Engineering and Physical Sciences Research Council (2275026)
Engineering and Physical Sciences Research Council (EP/N509620/1)
Royal Society (URF\R\201002)
Engineering and Physical Sciences Research Council (EP/N509620/1)
Royal Society (URF\R\201002)