Machine learning-based prediction of compressive strength and slump in high-performance concrete

The design of a concrete structure heavily depends on one of the most significant performance parameters: concrete strength. Reliable strength prediction can save design expenses and time, as well as reduce material waste from multiple mixing experiments. Because it has a very long service life, imp...

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Veröffentlicht in:	Multiscale and Multidisciplinary Modeling, Experiments and Design Jg. 8; H. 10; S. 463
Hauptverfasser:	Zhang, Ruikun, Meng, Chenyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.11.2025 Springer Nature B.V
Schlagworte:	Algorithms Cement Characterization and Evaluation of Materials Compressive strength Concrete mixes Concrete mixing Concrete properties Concrete structures Construction industry Engineering Fire resistance Machine learning Mathematical Applications in the Physical Sciences Mechanical Engineering Numerical and Computational Physics Optimization Original Paper Reinforced concrete Search algorithms Service life Simulation Soft computing Solid Mechanics Support vector machines CS XGBR Population-Based Vortex Search Algorithm Slump Honey Badger Algorithm High-performance concrete Escaping Bird Search for Constrained Optimization
ISSN:	2520-8160, 2520-8179
Online-Zugang:	Volltext
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