Characterizing Rock Degradability as Basis for Slope Instability Mitigation for Open Pit Closure
Bibliographic record
Abstract
ABSTRACT: Worldwide, degradable, poor quality rock units frequently exist within the final perimeter walls of large open pits, sometimes en-masse and sometimes intercalated within otherwise competent rocktypes. Almost irrespective of location and origin, degradable zones pose a longevity instability problem and need to be dealt with as part of open pit mine closure planning, as with time and duration of exposure, major reductions in rock competence occur. This paper firstly outlines methods for assessing and characterizing degradable rock units as basis for quantitatively describing appropriate time-dependent geomechanical property change. It then discusses analytical and numerical evaluation approaches for assessing impact of progressive failure effects driven by degradability impact on pit wall stability. Conclusions relating to mitigation of degradation-induced instability effects are then discussed in a pit closure setting. 1. INTRODUCTION Worldwide, degradable poor quality rock units frequently exist within the final perimeter walls of large open pits. These frequently pose a long-term stability risk that needs to be addressed for closure. Often, the most such degradable rock units are present as alteration halos around the mined orebody. Other times they are present as distinct stratigraphic units within the country rock that the mined ore zone cuts through. In tropical and subtropical areas of the world, often deep saprolitic weathered zones exist that are highly degradable. Even in otherwise competent rocktypes, where little regolith exists due to glaciation, penetrative weathering may extend to great depth along faults and other major structural features, creating a degradable zone that needs to be dealt with in a pit wall. Sometimes palæoweathering zones exist, most notably along major unconformity contacts. Thick sequences of variably indurated Quaternary transported materials may also be exposed in pit slopes. In all these situations, susceptible rock units that will degrade with exposure may remain in final pit walls, thus potentially affecting closure slope stability. For some of the most susceptible rocktypes, such as that shown in Figure 1, significant strength weakening can occur in a matter of hours to days, as degradation is rapid; while, for other rock types the processes are much slower. In fact, in the most competent rocktypes, degradation may be so slow that physical rock breakdown may not become evident for centuries. In an open pit context, post closure, ongoing degradation of susceptible rocktypes, potentially can lead to subsequent instabilities. Predicting future stability influence is not straightforward, as assessing potential degradation rates and strength loss over long time scales is a significant challenge.
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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) | 0.000 | 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".