Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems.
Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems.
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机器学习能够迅速预测多组分胶凝体系的水化动力学。
DOI：
10.1038/s41598-021-83582-6
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发表时间：
2021-02-16
影响因子：
4.6
通讯作者：
Kumar A
中科院分区：
文献类型：
--
作者：
Lapeyre J;Han T;Wiles B;Ma H;Huang J;Sant G;Kumar A
碳质（例如，石灰石）和铝硅酸盐（例如，煅烧粘土）矿物添加剂通常用于部分替代混凝土中的普通波特兰水泥以减轻其能量影响和碳足迹。这些矿物添加剂-取决于其物理化学特性-改变水泥的水化行为;这反过来又影响混凝土微观结构的演变以及其性能的发展（例如，压缩强度）。数值反应动力学模型，例如，相边界成核和生长模型（其部分基于理论推导的动力学机理，部分基于双折射）不能产生水泥水化动力学的先验预测;特别是在多组分体系中，其中水泥、水和矿物添加剂之间的化学相互作用同时发生。本文介绍了一种基于机器学习的方法，可以快速、高保真地预测水泥的时间依赖性水化动力学，包括普通水泥和多组分水泥（例如，二元和三元）体系，使用体系的物理化学特性作为输入。基于一个数据库，包括235个独特的系统，包括7个合成水泥和三种矿物添加剂与不同的物理化学属性的水化动力学配置文件的随机森林（RF）模型进行了严格的训练，以建立基本的组成反应性的相关性。RF模型随后利用这种训练：预测新的多组分系统中水泥的时间依赖性水化动力学;并制定满足用户强加的动力学标准的最佳混合物设计。
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.
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影响因子：
4.4
作者：
Chen, Xi;Ishwaran, Hemant
通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
Brand, Alexander S.;Bullard, Jeffiey W.
通讯作者：
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影响因子：
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作者：
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通讯作者：
Kumar, Aditya
DOI：
10.1061/(asce)mt.1943-5533.0002902
发表时间：
2019-11-01
影响因子：
3.2
作者：
Cook, Rachel;Lapeyre, Jonathan;Kumar, Aditya
通讯作者：
Kumar, Aditya