Effect of Saturation Condition on the Mechanical Performance of Mine Backfill Material
Bibliographic record
Abstract
ABSTRACT: Backfilling has increasingly been used to fill the mined cavities in underground mine operations, to reuse tailings in underground mines. Influence factors of mine backfills’ mechanical performance have been widely studied over the past few decades to minimize operating costs and superior mechanical support for underground exploration. However, additional work is required to consider more realistic conditions of in-situ backfill stope, such as the drainage of initially saturated backfill and the interference of surrounding underground water, which may lead to the variation of backfill stope saturation condition. A series of triaxial compression tests under various confining pressures for compacted backfill samples were carried out at designed matric suction values (0kPa, 50 kPa, 200 kPa, 400 kPa, and 600 kPa) to predict the influence of post-desiccation and post-saturation on in-situ backfill stope. Overall, with the increase of matric suction, the unsaturated specimens were found to have a higher peak deviator stress than saturated specimens for any given confining pressure and thus led to improved mechanical performance. The cohesion and friction angles show a linear relationship concerning the matric suction. 1. INTRODUCTION Mine tailings, deposited after minerals have been extracted, are the largest source of waste in mine processing. Approximately 14 billion tons of tailings were produced globally by the mining industry in 2010 (Behera et al. 2021; Jones and Boger 2012; Naganathan et al. 2012; Zhao et al. 2018). Tailings have been traditionally deposited into tailings storage facilities associated with severe environmental, geotechnical, and economic concerns (Dold 2014; Franks et al. 2011; Liu et al. 2016). Backfill technology has increasingly been used to fill the mined cavities in underground mine operations to reuse tailings in underground mines (Fall et al. 2010; Sivakugan et al. 2006; Zhang et al. 2012). Backfill material generally consists of tailings, none or a low proportion of cementitious binders to form a high-density slurry with none setting character to be gravity transferred or pumped to underground mine voids. Backfill recycles processed tailings into underground mined voids, which reduces the volume that needs to be surface-disposed. Using backfill thus mitigates the potential environmental impacts associated with tailings disposal and assists waste management (Fall et al. 2010; Kesimal et al. 2004; Tang et al. 2021; Zhao et al. 2021). These refilled backfills can then perform as both the support system and working platform for further ore extraction (Grabinsky et al. 2021; Ouattara et al. 2017; Sivakugan et al. 2015). Over the past few decades, backfill technology has been increasingly applied to revive mined cavities in underground mine operations, owing to its low operating costs as well as its superior mechanical performance compared with other backfilling methods (Fall and Pokharel 2010; Xu et al. 2018; Zhao et al. 2019).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".