Machine learning enabled closed‐form models to predict strength of alkali‐activated systems
Machine learning enabled closed‐form models to predict strength of alkali‐activated systems
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机器学习使封闭式模型能够预测碱激活系统的强度
DOI：
10.1111/jace.18399
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发表时间：
2022
影响因子：
3.9
通讯作者：
Kumar, Aditya
中科院分区：
文献类型：
--
作者：
Han, Taihao;Gomaa, Eslam;Gheni, Ahmed;Huang, Jie;ElGawady, Mohamed;Kumar, Aditya
碱激发砂浆（AAM）是一种新兴的生态友好型建筑材料，可作为普通波特兰水泥（OPC）砂浆的补充。AAM的性质的预测-尽管非常需要补充实验-是困难的，这是由于其前体的物理化学性质的大量批次间变化（即，硅铝酸盐和活化剂溶液）。在这项研究中，采用机器学习（ML）模型，它表明，该模型一旦训练和优化，可以可靠地预测AAM的抗压强度仅从其初始的物理化学属性。当将铝硅酸盐的多个组成描述符组合成单一的复合化学结构描述符（即，网络比率和约束数量）;因此，减少了自由度。通过解释ML模型的结果-特别是AAM抗压强度的变量重要性-开发了一个简单，易于使用的封闭形式的分析模型。结果表明，分析模型产生的AAM的抗压强度的预测，而不会牺牲太多的准确性相比，ML模型。总的来说，这项研究的结果表明，路线图，结合ML模型中的复合化学结构描述符，可用于设计AAM，以实现目标的抗压强度。
Alkali‐activated mortar (AAM) is an emerging eco‐friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs—albeit much needed to complement experiments—is difficult, owing to substantive batch‐to‐batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the model—once trained and optimized—can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e.,network ratioandnumber of constraints); thus, reducing the degrees of freedom. Through interpretation of the ML model's outcomes—specifically the variable importance for the AAMs’ compressive strength—a simple, easy‐to‐use, closed‐form analytical model is developed. Results demonstrate that the analytical model yields predictions of compressive strength of AAMs without scarifying much accuracy compared to the ML model. Overall, this study's outcomes demonstrate a roadmap—incorporates composite chemostructural descriptors in ML models—that can be employed to design AAMs to achieve targeted compressive strength.