CPTu-Based Spatial Variability Assessment of Thickened and Conventional Mine Tailings
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 150, Issue 10
Abstract
The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities (TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings, which have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) with an average offset of 1.5 m has been conducted to collect data. In addition to evaluating the spatial variability, the collected information is also used to assess the potential of machine learning (ML) for detrending when deriving random fields. Using a new proposed stationarity score, we find that an ML-based detrending outperforms traditional procedures for most scenarios. In terms of correlation lengths, we find similar ranges for thickened and conventional tailings (vertical: , horizontal ) and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature. Finally, by inspecting previous studies on natural soils, we find that the variability of mine tailings () resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional processes.
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Data Availability Statement
Some or all data, models, or code generated or used during this study are available from the corresponding author by request.
Acknowledgments
This material is based upon work supported by the National Science Foundation (NSF) under Grant No. CMMI 2145092. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. The PRONABEC program of the Peruvian government also provided complementary support. In addition, we would like to thank ConeTec, Newmont, and WSP-Golder for supporting the site characterization efforts. Finally, we thank Prof. Armin Stuedlein for sharing VBA codes we used to validate our implementations of calculations based on polynomial fittings and Prof. Jason Dejong for discussions when planning the CPTu campaign for the spatial variability characterization.
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© 2024 American Society of Civil Engineers.
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Received: May 27, 2023
Accepted: Feb 20, 2024
Published online: Jul 27, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 27, 2024
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