Machine learning to predict properties of fresh and hardened alkali-activated concrete
Machine learning to predict properties of fresh and hardened alkali-activated concrete
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DOI：
10.1016/j.cemconcomp.2020.103863
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发表时间：
2021-01-01
影响因子：
10.5
通讯作者：
Kumar, Aditya
中科院分区：
文献类型：
--
作者：
Gomaa, Eslam;Han, Taihao;Kumar, Aditya
碱激发混凝土（AAC）被广泛认为是波特兰水泥混凝土的可持续替代品。然而，由于铝硅酸盐组成的广泛异质性，再加上经典材料科学方法无法解开潜在的组成-性质联系，因此对AAC性质的可靠预测仍然不可行。本文提出了一种随机森林（RF）模型来预测两个性能的粉煤灰为基础的活性炭是重要的，从遵守的观点-坍落度流动;和抗压强度-在有关的理化属性，养护条件和搅拌程序的混凝土。结果表明，RF模型-一旦经过精心训练，并且在其超参数经过严格优化之后-能够对新AAC的两种特性进行高保真预测。该模型还用于定量评估理化属性和工艺参数对AAC性能的影响。本工作的结果为活性炭的性能优化提供了一条途径。
Alkali-activated concrete (AAC) is widely considered to be a sustainable alternative to Portland cement concrete. However, on account of extensive heterogeneity in composition of the aluminosilicates, coupled with the failure of classical materials science approaches to unravel the underlying composition-property linkages, reliable prediction of AAC's properties has remained infeasible. This paper presents a random forest (RF) model to predict two properties of fly ash-based AACs that are important from compliance standpoint - slump flow; and compressive strength - in relation to physiochemical attributes, curing conditions, and mixing procedures of the concretes. Results show that the RF model - once meticulously trained, and after its hyperparameters are rigorously optimized - is able to produce high fidelity predictions of both properties of new AACs. The model is also used to quantitatively assess the influence of physiochemical attributes and process parameters on the AAC's properties. Outcomes of this work present a pathway to optimization of AACs' properties.