Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms
Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms
复制标题
DOI：
10.1016/j.conbuildmat.2022.127557
复制
发表时间：
2022-04-23
影响因子：
7.4
通讯作者：
Kumar, Aditya
中科院分区：
文献类型：
--
作者：
Bhat, Rohan;Han, Taihao;Kumar, Aditya
碱激发体系是一种环保、可持续的建筑材料，用于取代占全球碳足迹9%的普通波特兰水泥(OPC)。此外，与OPC相比，碱激发体系具有更高的早期强度和更好的耐腐蚀性。目前的分析和机器学习模型不能对由不同类型的富铝硅酸盐前驱体组成的碱激发体系的抗压强度进行高度可靠的预测，因为这些前驱体的化学成分和反应活性有很大的差异。在这项研究中，使用一个具有两个约束(即拓扑网络约束和热力学约束)的随机森林模型来预测由26个富铝硅酸盐前驱体和不同工艺参数制成的碱激发体系的抗压强度。结果表明，一旦对模型进行了严格的训练和优化，RF模型就可以先验地、高保真地预测抗压强度与富铝硅酸盐前驱体的物理化学性质、工艺参数和约束条件之间的关系。拓扑网络约束提供了富铝硅酸盐前驱体的化学结构性质和反应活性。而热力学约束估计了富铝硅酸盐前驱体在不同反应程度下的相组合。最后，论证了拓扑网络约束、相组合和抗压强度之间的关系。当网络拓扑约束为3.4时，碱激发体系的抗压强度最高。
Alkali-activated system is an environment-friendly, sustainable construction material utilized to replace ordinary Portland cement (OPC) that contributes to 9% of the global carbon footprint. Moreover, the alkali-activated system has exhibited superior strength at early ages and better corrosion resistance compared to OPC. The current state of analytical and machine learning models cannot produce highly reliable predictions of the compressive strength of alkali-activated systems made from different types of aluminosilicate-rich precursors owing to substantive variation in the chemical compositions and reactivity of these precursors. In this study, a random forest model with two constraints (i.e., topological network and thermodynamic constraints) is employed to predict the compressive strength of alkali-activated systems made from 26 aluminosilicate-rich precursors and distinct processing parameters. Results show that once the model is rigorously trained and optimized, the RF model can yield a priori, high-fidelity predictions of the compressive strength in relation to the physicochemical properties of aluminosilicate-rich precursors; processing parameters; and constraints. The topological network constraint provides the chemostructural properties and reactivity of the aluminosilicate-rich precursors. Whereas the thermodynamic constraint estimates the phase assemblages at different degrees of reaction of the aluminosilicate-rich precursors. Finally, the correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. When the topological network constraint equals 3.4, the alkali-activated systems can achieve their optimal compressive strength.