Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Part 1–Workability

•Survey of 1,719 SCC Mixtures: Wide variability in properties, often outside guidelines.•Most of the reported SCC mixture proportions have a cement content of 251–510 kg/m3 (X¯ = 384), an SCM content of 11–230 kg/m3 (X¯ = 118).•Most of slump flow values (81 %) were in the range of 596–760 mm (X¯ = 6...

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Veröffentlicht in:	Construction & building materials Jg. 408; S. 133560
Hauptverfasser:	Safhi, Amine el Mahdi, Dabiri, Hamed, Soliman, Ahmed, Khayat, Kamal H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 08.12.2023
Schlagworte:	Machine learning Mixture design Self-consolidating concrete Survey of experimental data, Workabily XGBoost Self-consolidating concrete Mixture design Survey of experimental data, Workabily XGBoost Machine learning
ISSN:	0950-0618
Online-Zugang:	Volltext
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