Abstract

Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria.

Department(s)

Civil, Architectural and Environmental Engineering

Second Department

Electrical and Computer Engineering

Third Department

Materials Science and Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

Comments

Financial support for this research was 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 (NSF-CMMI: 1661609 and 1932690).

International Standard Serial Number (ISSN)

2045-2322

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2021 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

16 Feb 2021

PubMed ID

33594212

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