Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement using Data-Driven Approach
Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders' compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science.
S. A. Ponduru et al., "Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement using Data-Driven Approach," Materials, vol. 16, no. 2, article no. 654, MDPI, Jan 2023.
The definitive version is available at https://doi.org/10.3390/ma16020654
Electrical and Computer Engineering
Materials Science and Engineering
Keywords and Phrases
Analytical Model; Calcium Aluminate Cement; Compressive Strength; Phase Assemblage; XGBoost Model
International Standard Serial Number (ISSN)
Article - Journal
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Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
01 Jan 2023
National Science Foundation, Grant 1932690