Doctoral Dissertations

Rachel Cook

Keywords and Phrases

Hydration; Kinetics; Machine Learning; Ordinary Portland Cement (OPC); pBNG model; Thermodynamics

Abstract

”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.

Advisor(s)

Kumar, Aditya
Ma, Hongyan

Committee Member(s)

Smith, Jeffrey D.
O'Malley, Ronald J.
Okoronkwo, Monday Uchenna

Department(s)

Materials Science and Engineering

Degree Name

Ph. D. in Ceramic Engineering

Comments

Research funding was provided by MRC (MRC Young Investigator Seed Funding) at Missouri S&T; by the University of Missouri Research Board (UMRB); by the National Science Foundation (CMMI: 1661609 and 1932690); by the UM system; by the Federal Highway Administration (Award no: 693JJ31950021); by the Leonard Wood Institute (LWI); and by the Materials Research Center (MRC) at Missouri S&T.

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2020

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

Pagination

xxii, 330 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2020 Rachel Elizabeth Cook, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11780

Electronic OCLC #

1240361914

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