Severe rock mass damage of undercut and extraction level pillars at El Teniente Mine

Gomes, A; Rojas, Eduardo; Ulloa, Juan Carlos

doi:10.1002/geot.201600058

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…Since it is an operational parameter, the vulnerability to time-dependent degradation effects can be mitigated through good practices that ensure proper pillar volume preservation. It should be noted that 'cave front curvature' , which is a cave-back geometry proxy that also controls collapse potential (Pardo et al, 2012;Landeros, 2012;Gomes, et al, 2016), is implicitly related to 'time in abutment' as a concept of the overall advancement rate of the cave front in terms of production rate and therefore can also be controlled. In general, the analysis shows that operational-related features have the highest importance for modelling collapse events, in this case particularly the 'pillar area' feature.…”

Section: Discussionmentioning

confidence: 99%

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Cave mine pillar stability analysis using machine learning

Quevedo,

Sari,

McKinnon

2024

J. S. Afr. Inst. Min. Metall.

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The large scale of cave mines leads to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method, the effects of which are difficult to account for during the design stages. In this paper we present a case study of the application of a machine learning approach to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile. Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analysed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms, nor the calibration of associated controlling parameters. The proposed random forest model predicted pillar collapses with 80% accuracy despite limited samples to model from. The main contributing factors to collapses were found to be related to available pillar volume, cave front geometry, and time under abutment stress conditions. The effects and interactions of such factors were also studied, showing that careful and improved control over operational conditions can significantly reduce the likelihood of pillar collapses. These conclusions could not have been obtained from stress analysis alone, illustrating the complementary nature of conventional stress analysis and machine learning approaches.

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Section: Discussionmentioning

confidence: 99%

“…presence of major faults perpendicular to the direction of advancement of the caving). (Gomes, Rojas, and Ulloa, 2016;Cornejo 2014;Landeros, 2012). However, for this case study the factors involved, and their relative importance was not clear.…”

Section: Pillar Damage and Collapse Processmentioning

confidence: 99%

Cave mine pillar stability analysis using machine learning

Quevedo,

Sari,

McKinnon

2024

J. S. Afr. Inst. Min. Metall.

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“…These problems comprise stress related damage to pre-developed infrastructure (e.g. Fernandez et al 2010;Gomes et al 2016;Campbell et al 2020) and the occurrence of mining-induced seismicity and associated rockburst damage (e.g. Araneda & Sougarret 2008;Dahnér et al 2012).…”

Section: Specific Challenges Faced In Cave Miningmentioning

confidence: 99%

Raise caving: a novel mining method for (deep) mass mining

Ladinig¹,

Wimmer²,

Wagner³

2022

Caving 2022: Fifth International Conference on Block and Sublevel Caving

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Raise caving is a novel mining method, which is based on the raise mining method. Raises are central and utilised for different purposes. Here only those directly related to the extraction of the orebody will be discussed. The main objective of raises is to utilise them for creating de-stressing slots, large drawbells and large stopes in an efficient manner. Remote-controlled or automated machinery is operated in raises. Further objectives of raises comprise monitoring and preconditioning. Depending on the field of application two different variants of raise caving can be distinguished, namely a de-stressing variant and a block caving variant.In the de-stressing variant, narrow slots are created first with the objective of providing stress shadows for mining activities in the subsequent production phase. Creating the slots in the de-stressing phase is high stress mining. Hence, massive pillars are left between slots to control the stress situation for de-stress raise development and mining-induced seismicity. After the de-stressing phase is completed, large-scale mineral extraction commences in de-stressed zones. In the production phase large drawbells are developed from raises and large stopes are extracted. During stope blasting only the swell of each blast is mucked so that blasted rock mass provides temporary support to the stope walls. After blasting is completed, the stope is drawn empty and the hanging wall is allowed to cave and caved hanging wall rock mass fills up the stope successively. The massive pillars are extracted in the course of large-scale stoping in the production phase too.In the block caving, variant raises are utilised for drawbell development, undercutting, preconditioning and monitoring purposes. Large drawbells are developed from raises. As drawbells are developed in upward direction, the roof area of the drawbells is enlarged constantly until continuous caving is initiated. Due to the integration of several key activities the block caving variant of raise caving is also referred to as integrated raise caving. This paper highlights the background and motivation for raise caving and it describes both raise caving variants. Steps for the implementation of the methods are outlined and advantages of the individual methods are outlined briefly. Key issues for the implementation of raise caving are highlighted and discussed, and an outlook on currently ongoing research and development activities, which include in situ tests of the method and machinery, is provided. Dedicated accompanying papers provide more information on the ongoing research and development.

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“…The management of the environment and the goal of resource recovery can be achieved mainly through the use of loose and semi-dry tailings. These tailings are used to fill the voids created by the mining process, ensuring the recycling of residual ore and maintaining environmental sustainability 12 – 15 .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary patterns and microscopic mechanisms of strength in mine tailings backfilled with waste glass

Deng,

Gao,

Chen

et al. 2024

Sci Rep

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In order to promote the sustainable use of resources and reduce the waste of waste glass and tailings resources. The present study focuses on a fluorite mine as the research subject, utilizing coarse tailings, fine tailings, cement substitute-curing agent, and recycled waste glass as the primary raw materials. It investigates the changes in compressive strength of coarse tailing with varying sand- binder ratios and glass content at 3-day, 7-day, and 28-day intervals when the filling slurry concentration is set at 77% and the ratio of coarse tailings to fine tailings is maintained at 2:1. The findings indicate that there is minimal impact on the compressive strength of test blocks when using a sand binder ratio of 4:1 and a glass sand content below 10%. However, once the glass sand content exceeds 10%, a significant decline in compressive strength occurs. Scanning electron microscope (SEM) images reveal ettringite crystal formation in test blocks with both 0% and 25% glass sand content due to high levels of Na2O in the glass sand. This leads to internal expansion within test blocks resulting in reduced strength. Notably, when using a sand-binder ratio of 8:1 along with a glass sand content of 25%, early strength characteristics are observed for test blocks. Furthermore, incorporating glass sand has little influence on late-stage strength for backfill when employing either an 8:1 or 12:1 sand-binder ratio. Based on this experiment conducted under conditions including mass concentration of 77%, the optimal waste-glass-to-mine-tailings-filling-sand-binder-ratio is determined as 8:1with a corresponding glass content of 25%.

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Severe rock mass damage of undercut and extraction level pillars at El Teniente Mine

Cited by 6 publications

References 1 publication

Cave mine pillar stability analysis using machine learning

Cave mine pillar stability analysis using machine learning

Raise caving: a novel mining method for (deep) mass mining

Evolutionary patterns and microscopic mechanisms of strength in mine tailings backfilled with waste glass

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