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- Title
Prediction of Hydration Heat for Diverse Cementitious Composites through a Machine Learning-Based Approach.
- Authors
Lu, Liqun; Li, Yingze; Wang, Yuncheng; Wang, Fengjuan; Lu, Zeyu; Liu, Zhiyong; Jiang, Jinyang
- 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.
- Subjects
PORTLAND cement; HEAT of hydration; CEMENT composites; SPECIFIC heat; FLY ash; CEMENT industries
- Publication
Materials (1996-1944), 2024, Vol 17, Issue 3, p715
- ISSN
1996-1944
- Publication type
Article
- DOI
10.3390/ma17030715