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.

碱活化混凝土（AAC）被广泛认为是硅酸盐水泥混凝土的可持续替代品。然而，由于铝硅酸盐的组成中存在广泛的异质性，再加上经典的材料科学方法无法揭示潜在的组成-性能联系，对AAC性能的可靠预测仍然不可行。本文提出了一个随机森林（RF）模型来预测基于粉煤灰的AAC的两个特性，从合规性的角度来看它们很重要–坍落度；和抗压强度–与混凝土的物理化学特性，固化条件和混合程序有关。结果表明，RF模型-经过精心训练，并对其超参数进行了严格的优化后–能够对新AAC的两种特性产生高保真度的预测。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。这项工作的成果为优化AAC的性能提供了一条途径。