Abstract
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 accurate a priori predictions of hydration kinetics and compressive strength of these binders.
Recommended Citation
S. Surehali et al., "On The Use Of Machine Learning And Data-Transformation Methods To Predict Hydration Kinetics And Strength Of Alkali-Activated Mine Tailings-Based Binders," Construction and Building Materials, vol. 419, article no. 135523, Elsevier, Mar 2024.
The definitive version is available at https://doi.org/10.1016/j.conbuildmat.2024.135523
Department(s)
Electrical and Computer Engineering
Second Department
Materials Science and Engineering
Keywords and Phrases
Compressive strength; Hydration kinetics; Machine learning; Mine tailings; Sustainability
International Standard Serial Number (ISSN)
0950-0618
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
15 Mar 2024
Comments
National Science Foundation, Grant DMR 2228782