Prediction of the compressive strength of concrete using selected machine learNing regression models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00356426" target="_blank" >RIV/68407700:21110/21:00356426 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Prediction of the compressive strength of concrete using selected machine learNing regression models
Original language description
Prediction of mechanical properties of cementitious composites is a topic of great concern as it could minimize the need for costly and laborious laboratory tests. In this paper, several machine learning models (Linear, Ridge, Lasso, and Support Vector Machine regression) are trained and evaluated on a publicly available dataset containing various concrete compositions and their compressive strength measured at different ages from casting. In this study, Support Vector Machine regression showed the highest accuracy when testing on the public dataset (mean absolute error 3.63 MPa). The trained models were also subsequently applied on additional more current data. Unfortunately, none of the models proved to be suitable which might be due to the low representativeness of the older public dataset for the currently used mixtures.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of PhD Workshop, Department of Concrete and Masonry Structures 2021
ISBN
978-80-01-06842-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
katedra betonových a zděných konstrukcí
Place of publication
Praha
Event location
Praha
Event date
May 21, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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