AI Article Synopsis

  • Hydration is essential in cement composites, but traditional methods to measure hydration heat have limitations.
  • This study introduces a machine learning model that predicts hydration heat for different types of cement composites, including ordinary Portland cement and fly ash mixes.
  • The optimized artificial neural network provides accurate predictions while minimizing the need for time-consuming testing, paving the way for more efficient research in cement properties.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10856311PMC
http://dx.doi.org/10.3390/ma17030715DOI Listing

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