Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Rheological properties

Rheological properties are critical for assessing self-consolidating concrete (SCC)’s performance and application. However, predicting these properties accurately, specifically plastic viscosity and yield stress, faces challenges due to inconsistent data, small sample sizes, and measurement inaccura...

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Veröffentlicht in:Powder technology Jg. 438; S. 119623
Hauptverfasser: Safhi, Amine el Mahdi, Dabiri, Hamed, Soliman, Ahmed, Khayat, Kamal H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2024
Schlagworte:
algorithms
cement
concrete
Machine learning
Plastic viscosity
prediction
Rheological properties
rheology
rheometers
Self-consolidating concrete
technology
viscosity
Yield stress
Self-consolidating concrete
Plastic viscosity
Yield stress
Machine learning
Rheological properties
ISSN:0032-5910, 1873-328X
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Zusammenfassung:Rheological properties are critical for assessing self-consolidating concrete (SCC)’s performance and application. However, predicting these properties accurately, specifically plastic viscosity and yield stress, faces challenges due to inconsistent data, small sample sizes, and measurement inaccuracies, with the type of rheometer significantly impacting results. This study meticulously analyzes 348 mixtures from 19 peer-reviewed sources, focusing on experiments that detail rheometer types to understand variability in rheological properties. Twelve variables, including cement content and water-to-powder ratio, were identified as key to SCC's rheology. Utilizing these, an XGBoost model demonstrated exceptional accuracy (R2 of 0.99), markedly better than traditional methods. This advance not only aids in SCC design but also showcases the potential of machine learning in construction materials research, suggesting a new direction for material property prediction and innovation in construction. [Display omitted] •Analyzed 306 mixes from 16 papers on concrete rheology.•Found rheometer type impacts measurements notably.•XGBoost model shows high accuracy (R2 of 0.99) in predictions.
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ISSN:0032-5910
1873-328X
DOI:10.1016/j.powtec.2024.119623