On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

• Published in: Construction & building materials 2024-03, Vol.419, p.135523, Article 135523
• Main Authors: Surehali, Sahil, Han, Taihao, Huang, Jie, Kumar, Aditya, Neithalath, Narayanan
• Format: Article
• Language: English
• Subjects: Compressive strength; Hydration kinetics; Machine learning; Mine tailings; Sustainability
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2024.135523

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. [Display omitted] •Sustainable mine tailings (MT)-based binders (≥70% by mass) for infrastructure.•Data transformation enabled machine learning (ML) model for heat release rates.•Ability to use on small data sets without loss of accuracy.•Prediction of compressive strength, and strength-heat relations from ML models.