Keywords and Phrases
Hydration; Kinetics; Machine Learning; Ordinary Portland Cement (OPC); pBNG model; Thermodynamics
”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C3S) and belite (ß-C2S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling based the evolving understanding of hydration processes. However, there are remaining points of contention within literature, that could potentially take an additional decade to resolve. The dissertation work utilizes the numeric kinetic-based, phase boundary nucleation and growth (pBNG) model but also introduces ML models as a technique to predict the heat-evolution -- which, is related to other fresh properties, such as rheological, microstructural, and mechanical properties -- of a paste system by utilizing underlying nonlinear time-dependent composition-property relationships”--Abstract, page iv.
Smith, Jeffrey D.
O'Malley, Ronald J.
Okoronkwo, Monday Uchenna
Materials Science and Engineering
Ph. D. in Ceramic Engineering
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Elucidating the effect of water-to-cement ratio on the hydration of cement
- Influence of water activity on belite (ß-C₂S) hydration
- Mechanisms of tricalcium silicate hydration in the presence of polycarboxylate polymers
- nfluence of size-classified and slightly soluble mineral additives on hydration of tricalcium silicate
- Prediction of compressive strength of concrete: Critical comparison of performance of a hybrid machine learning model with standalone models
- Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
- Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems
xxii, 330 pages
© 2020 Rachel Elizabeth Cook, All rights reserved.
Dissertation - Open Access
Cook, Rachel, "Studying the effects of various process parameters on early age hydration of single- and multi-phase cementitious systems" (2020). Doctoral Dissertations. 2943.