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Record W4388410202 · doi:10.2991/978-94-6463-258-3_50

Slope Stability in Open Pits with Thin Weak Layers

2023· book-chapter· en· W4388410202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAtlantis highlights in engineering/Atlantis Highlights in Engineering · 2023
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsStability (learning theory)Materials scienceGeologyComposite materialGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

Open pit mining projects usually face particular situations while dealing with slope stability analysis.More than any other projects, mining projects build a considerable number of slopes, which are more likely to behave differently among them, due to the change in orientation in the open pit.One of the situations that represent a considerable problem is the presence of a weak layer, which could be easily analyzed by using the limit equilibrium methods.Nonetheless, there are some practical measures that mining engineers take in order to improve the slope stability condition of mining slopes involving a weak layer, which consist in creating a disturbance in the rock medium surrounding the weak layer by blasting a strip of the rock mass.That modification of the rock medium is not easy to analyze with traditional limit equilibrium methods, because there is not a constitutive model to properly characterize that portion of the disturbed medium.This paper presents a first approach to analyze the stability in open pit mining slopes in the presence of a weak layer, both, before and after blasting to create a disturbance of the medium.This approach considers the effect of the rock mass disturbance, by using a combination between data coming from inclinometer monitoring in the slopes and numerical simulations with finite elements, which allows to monitor the slope stability during the rock mass disturbance.The disturbed rock mass, known as "bimrock", is then characterized and included into the slope stability models to obtain the factor of safety considering the disturbance of the medium.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.202
Teacher spread0.189 · 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