On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders
On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders
复制标题
用机器学习和数据变换方法预测碱激发尾矿基胶凝材料的水化动力学和强度
DOI：
10.1016/j.conbuildmat.2024.135523
复制
发表时间：
2024-02-21
影响因子：
7.4
通讯作者：
Neithalath，Narayanan
中科院分区：
文献类型：
--
作者：
Surehali，Sahil;Han，Taihao;Neithalath，Narayanan
作为采矿业副产品的尾矿产量不断增加，对环境和健康构成重大危害，因此需要一种具有成本效益和可持续的办法来处理或再利用尾矿。本研究建议使用MT作为建筑粘合剂的主要成分（质量≥70%），从而确保其有效的升级回收，并大大减少与使用普通波特兰水泥（OPC）相关的环境影响。评估了mt基粘结剂的早期水化动力学和抗压强度，重点阐明了碱活化参数和矿渣或水泥用量作为次要成分的影响。研究揭示了不同年龄阶段累积放热量与抗压强度的相关性；这些相关性可以用来估计基于水化动力学的抗压强度。此外，本研究提出了一个随机森林（RF）模型，结合快速傅立叶和直接余弦变换技术，克服了与数据库有限的体积和多样性相关的局限性，从而能够高保真地预测与混合物设计相关的基于mt的粘合剂的时间依赖性水化动力学和抗压强度。总体而言，本研究展示了将尾矿作为低碳建筑粘合剂的主要成分进行可持续升级的方法；并提出了基于分析和机器学习的方法来准确预测这些粘合剂的水化动力学和抗压强度的优先级。
The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement that are used as minor constituents. This study reveals correlations between cumulative heat release and compressive strengths at different ages; these correlations can be leveraged to estimate the compressive strength based on hydration kinetics. Furthermore, this study presents a random forest (RF) model—in conjunction with fast Fourier and direct cosine transformation techniques to overcome the limitations associated with limited volume and diversity of the database—to enable high-fidelity predictions of time-dependent hydration kinetics and compressive strength of MT-based binders in relation to mixture design. Overall, this study demonstrates a sustainable approach to upcycle mine tailings as the primary component in low-carbon construction binders; and presents both analytical and machine learning-based approaches for accuratea prioripredictions of hydration kinetics and compressive strength of these binders.