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Record W4401479344 · doi:10.56952/arma-2024-1208

Towards Mine Closure: Assessing the Long-Term Stability of Open Pit Mine Slopes

2024· article· en· W4401479344 on OpenAlexaff
Erik Eberhardt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeomechanics and Mining Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClosure (psychology)Term (time)Open-pit miningMining engineeringStability (learning theory)GeologyEnvironmental scienceGeotechnical engineeringComputer scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT: The long-term stability of engineered slopes is becoming a critical focus point as the number of open pit mines anticipated to close in the coming years is increasing, and governments, regulators, and society are collectively placing more emphasis on sustainable management of mineral resources and land use (IISD, 2021). However, there are currently few guidelines on assessing long-term slope stability. Of central importance is the recognition that rock mass properties are not constants, and therefore, open pit slopes that are presently stable may not remain so in the future. Conventional engineering analyses generally assume the strength of a rock mass to be constant and, in doing so, fail to explain the temporal nature of rock slope behavior seen in monitoring data. Data shows that pit slope movements are intermittent, correlating with benching and seasonal precipitation patterns. These initiate episodic damaging events that, in the closure context, control strength degradation and impact long-term slope performance through progressive failure (Eberhardt et al. 2004). This talk will summarize the author's research over the last 20 years into progressive failure, its advancement of our mechanistic understanding of deep-seated rock slope failure, and recent results and guidance in applying it to open pit slope stability assessments and mitigation efforts to aid mine closure designs. Empirical data will be presented to show the evidence for progressive failure, with a focus placed on transient pore pressures driven by seasonal precipitation. Upon closure, changes to the slope geometry (i.e., benching) cease, but seasonal precipitation continues. Progressive failure posits that transient pore pressures in response to infiltration or groundwater recharge act to locally decrease effective stresses, promoting slip along non-persistent discontinuities, which in turn may cause the slip of adjacent fractures and/or the failure of intact rock bridges. Such repeated fluctuations in pore pressures and effective stresses thus are a key driver of progressive failure and can be equated to fatigue, where the rock slope experiences a slow weakening through repeated load cycles. Results from mine closure analyses will be presented, demonstrating how slope displacement monitoring and modeled groundwater fluctuations can be used to calibrate numerical models and establish the degree of criticality present in a slope. The modeling of seasonal variations further enables reference to be made to time in calculations that are otherwise limited to stress-strain behavior. This provides a means to assess displacement rate thresholds at which behavior change may occur for a given failure mode, which can be used to establish and constrain early warning alarm thresholds and trigger action response plans (TARPs). Examples will also be provided incorporating allowances for the development of a pit lake post-closure and for long-term stability improvement through engineered buttress designs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.281
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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