Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

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 characteris...

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Autores principales:	Jonathan Lapeyre, Taihao Han, Brooke Wiles, Hongyan Ma, Jie Huang, Gaurav Sant, Aditya Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b8f2c49536b4819adcd2cd461f2d500
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spelling	oai:doaj.org-article:2b8f2c49536b4819adcd2cd461f2d5002021-12-02T14:03:57ZMachine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems10.1038/s41598-021-83582-62045-2322https://doaj.org/article/2b8f2c49536b4819adcd2cd461f2d5002021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83582-6https://doaj.org/toc/2045-2322Abstract 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.Jonathan LapeyreTaihao HanBrooke WilesHongyan MaJie HuangGaurav SantAditya KumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jonathan Lapeyre Taihao Han Brooke Wiles Hongyan Ma Jie Huang Gaurav Sant Aditya Kumar Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
description	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.
format	article
author	Jonathan Lapeyre Taihao Han Brooke Wiles Hongyan Ma Jie Huang Gaurav Sant Aditya Kumar
author_facet	Jonathan Lapeyre Taihao Han Brooke Wiles Hongyan Ma Jie Huang Gaurav Sant Aditya Kumar
author_sort	Jonathan Lapeyre
title	Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
title_short	Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
title_full	Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
title_fullStr	Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
title_full_unstemmed	Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
title_sort	machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b8f2c49536b4819adcd2cd461f2d500
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Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

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