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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 that 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) has been conducted to collect data. The results were processed using traditional and machine learning-based methods for data detrending when deriving random fields. In terms of correlation lengths, we find similar ranges for the thickened and conventional tailings 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. 

Cyclic liquefaction induced by seismic loading from earthquakes is a major concern for countries with active mining industries and moderate seismicity, such as Peru, Chile, and the USA. Cyclic liquefaction is more likely to induce flow failure as compared to other failure modes, and has been associated with a large portion of failures in seismic countries like Chile. Chile is a leading provider of copper and lithium to the global market, and the country’s ability to sustain such a large mining sector could be hindered by its ability to safely store its tailings. Mine tailings have been shown to have unique mechanistic responses dissimilar to those of traditional soils (i.e., sands and clays). Much of engineering practice relies on techniques and procedures developed from data derived from sands and clays. As such, our understanding of the cyclic behavior of mine tailings needs continued research interest to extract new insights into their unique behavior. This paper utilizes a recently developed database focused on the cyclic response of mine tailings to highlight some insights into their peculiar behavior. Specifically, their unique range of material properties and resulting liquefaction curves, the applicability of existing factors in the liquefaction assessment of these materials as compared to sands, and a comparison of in-situ and laboratory-derived cyclic resistances are showcased. 

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: δwv ¼ 0.2–0.6 m, horizontal δwh ¼ 1.5–4.5 m)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(δwh=δwv ¼ 2–21) resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional process 

This study assesses the robustness of a framework based on critical state soil mechanics (CSSM) principles in evaluating earthquake-induced liquefaction manifestation. The assessment is motivated by the contrasting procedures in evaluating static and cyclic liquefaction, where mechanical properties commonly inform the former, whereas the latter often relies on semiempirical-based methods. The framework discussed in this study considers as ingredients (1) laboratory-based mechanical properties that are an average representation of soil’s microstructure, (2) state inversion, (3) the link of state with cyclic resistance ratio (CRR), and (4) the seismic demand, represented by the cyclic stress ratio (CSR). The framework is assessed using ~5000 cone penetration tests (CPTus) conducted after the Canterbury earthquake sequence, where each CPTu is associated with liquefaction manifestation levels. The discussed framework is used to estimate safety factors, which are then combined with several liquefaction severity indexes (LSIs) to evaluate liquefaction manifestation in the context of a classification problem (i.e., “Yes” and “No”). The framework’s performance is assessed using machine learning by estimating receiver operating characteristic curves (ROC). Different state inversion procedures are also considered, and recommendations based on their performance are provided. In particular, a calibrated cavity expansion-based inversion for New Zealand is proposed. We find that the discussed framework offers comparable performance to state-of-practice procedures, even when general considerations for mechanical properties based on CSSM are made, which is encouraging. Moreover, by including mechanical properties, it can better inform extrapolations for regions without significant data and non-typical soils as long as adequate properties are considered. In this context, it shares conceptual similarities with non-ergodic approaches in earthquake engineering. 

Understanding the cyclic response of mine tailings is key for areas with moderate to high seismicity and an active mining industry (e.g. the United States, Peru, and Chile). However, assessing the cyclic response of mine tailings still relies on procedures and correlations developed for natural soils (i.e. sands and clays). This is due to information on the cyclic response of mine tailings being rather scarce compared to natural soils. Hence, it remains unclear if more efficient approaches can be implemented. This study presents an experimental database focused on the cyclic response of mine tailings compiled from various sources. The database is organized considering three classes, where all three contain cyclic simple shear (CSS) information. Class A also includes triaxial (Tx) and cone penetration testing (CPTu) information, Class B has Tx or CPTu information, and Class C contains no additional information beyond CSS. Most materials belong to Class A. It is worth noting that Class C (only cyclic information) is comparable with most databases for natural soils, hence highlighting the uniqueness of our database. In total, the database contains 129 CSS tests on 20 materials that represent a broad range of mine tailings. Thirteen materials belong to Class A, 5 to Class B, and 2 to Class C. In discussing the database, key information (e.g. the range of liquefaction resistance curves) is shared. In addition, potential assessments that can be conducted with the database are illustrated. The study closes by presenting the database organization and discussing potential uses. The database is available under the following DOI: https://doi.org/10.17603/ds2-1k0a-dt17