Abstract

Cone penetration testing (CPT) is a widely used technique for soil characterisation. The introduction of the Global Industry Standard for Tailings Management (GISTM) in 2019 increased the necessity for understanding tailings properties (particularly shear strength), requiring better risk management and monitoring of tailings storage facilities (TSFs) to achieve zero fatalities. The complexity of tailings materials, influenced by ore processing, slurry deposition and water management techniques, causes greatly different geotechnical characteristics compared to other soils, requiring specialised monitoring equipment and in-depth investigation. One main aspect in interpretation of tailings properties is understanding the pore water pressure (PWP) within the deposited tailings layers which has often been overlooked in engineering studies. For example, in cases of interbedded layers of silt and sand, the downward drainage due to underdrain systems and lateral flow due to differences in lateral and vertical hydraulic conductivities can lead to the formation of distinct ‘perched’ phreatic surfaces in-between layers due to dissimilarity in material permeabilities. The use of commonly assumed linear PWP profiles in such cases can result in misinterpretations, hence, overestimation of tailings’ effective strengths. In these instances, instruments like vibrating wire piezometers (VWPs) have been widely used. However, VWPs provide limited data for understanding the true PWP profile. CPT with PWP measurements (CPTu) has therefore become a practicable technique for identifying internal PWP and distinguishing deposited layers. As such, a detailed approach to CPT data interpretation is required for interlayered tailings facilities with perched phreatic surfaces. This paper presents how CPTu techniques, combined with graphical interpretation, and VWP data can be used to understand complex internal profiles of interlayered TSFs.

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Published on 07/06/24
Submitted on 07/06/24

Volume From measurement to reliable in situ geotechnical site characterization – statistical data processing, 2024
DOI: 10.23967/isc.2024.143
Licence: CC BY-NC-SA license

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