Abstract
The production of ordinary Portland cement (OPC), the most broadly utilized man-made material, has been scrutinized due to its contributions to global anthropogenic CO2 emissions. Thus -- to mitigate CO2 emissions -- mineral additives have been promulgated as partial replacements for OPC. However, additives -- depending on their physiochemical characteristics -- can exert varying effects on OPC's hydration kinetics. Therefore -- in regards to more complex systems -- it is infeasible for semi-empirical kinetic models to reveal the underlying nonlinear composition-property (i.e., reactivity) relationships. In the past decade or so, machine learning (ML) has arisen as a promising, holistic approach to predict the properties of heterogeneous materials, even without an across-the-board comprehension of the underlying composition-properties correlations. This paper describes the use of a Random Forests (RF) model to enable high-fidelity predictions of time-dependent hydration kinetics of OPC-based systems -- more specifically [OPC + mineral additive(s)] systems -- using the system's physiochemical attributes as inputs. Results show that the RF model can also be used to formulate mixture designs that satisfy user-imposed kinetics-related criteria. Lastly, the presented results can be expanded to formulate mixture designs that satisfy target kinetic criteria, even without knowledge of the underlying kinetic mechanisms.
Recommended Citation
R. Cook et al., "Machine Learning for High-Fidelity Prediction of Cement Hydration Kinetics in Blended Systems," Materials and Design, vol. 208, article no. 109920, Elsevier, Oct 2021.
The definitive version is available at https://doi.org/10.1016/j.matdes.2021.109920
Department(s)
Civil, Architectural and Environmental Engineering
Second Department
Electrical and Computer Engineering
Third Department
Materials Science and Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Hydration; Machine Learning; Mineral Additives; Portland Cement; Random Forests
International Standard Serial Number (ISSN)
0261-3069; 0264-1275
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2021 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Oct 2021
Included in
Electrical and Computer Engineering Commons, Materials Science and Engineering Commons, Structural Engineering Commons
Comments
The authors acknowledge financial support for this research provided by the UM system; the Federal Highway Administration (Award no: 693JJ31950021); the Leonard Wood Institute (LWI:W911NF-07-2-0062) and the National Science Foundation (NSFCMMI:1661609 and 1932690; and NSF-DMR: 2034856).