Prediction of Hydration Heat for Diverse Cementitious Composites through a Machine Learning-Based Approach.
Materials (Basel)
; 17(3)2024 Feb 02.
Article
en En
| MEDLINE
| ID: mdl-38591570
ABSTRACT
Hydration plays a crucial role in cement composites, but the traditional methods for measuring hydration heat face several limitations. In this study, we propose a machine learning-based approach to predict hydration heat at specific time points for three types of cement composites ordinary Portland cement pastes, fly ash cement pastes, and fly ash-metakaolin cement composites. By adjusting the model architecture and analyzing the datasets, we demonstrate that the optimized artificial neural network model not only performs well during the learning process but also accurately predicts hydration heat for various cement composites from an extra dataset. This approach offers a more efficient way to measure hydration heat for cement composites, reducing the need for labor- and time-intensive sample preparation and testing. Furthermore, it opens up possibilities for applying similar machine learning approaches to predict other properties of cement composites, contributing to efficient cement research and production.
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Base de datos:
MEDLINE
Idioma:
En
Revista:
Materials (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
China