Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete

• Published in: Results in engineering Vol. 25; p. 104542
• Main Authors: Abdellatief, Mohamed, Murali, G., Dixit, Saurav
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2025; Elsevier
• Subjects: Extreme gradient boosting; Gaussian process regression; Low cement UHPC; Stepwise regression; Strength prediction; Gaussian process regression; Stepwise regression; Strength prediction; Low cement UHPC; Extreme gradient boosting

•Machine learning models showed strong predictive accuracy for UHPC compressive strength.•XGB outperformed RF, GB, and GPR with the highest R-value and the lowest RMSE.•Curing age, silica fume, and fiber content positively impact UHPC strength. Ultra-High-Performance Concrete (UHPC) is distinguished...