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Record W1749331562 · doi:10.1139/t2012-012

Prediction and follow-up of failure and fallouts in footwall drifts in the Kiirunavaara mine

2012· article· en· W1749331562 on OpenAlexvenueno aff
Catrin Edelbro, Jonny Sjöberg, Lars Malmgren, Christina Dahnér-Lindqvist

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersLuleå Tekniska Universitet
KeywordsCohesion (chemistry)GeologyBrittlenessGeotechnical engineeringStress fieldSofteningMining engineeringFinite element methodStructural engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

A likely result of changes in rock stresses due to progressing mining is an increased number of compressive stress–induced failures. This paper presents the results from numerical analysis and observations of stress-induced fallouts in footwall drifts in the Kiirunavaara underground mine. A brittle-plastic “cohesion-softening friction-hardening” (CSFH) material model was used for simulating brittle fallouts. To account for mining-induced stress changes, the local model stress boundary conditions were extracted from a global model. The rock mass properties were based on field observations in the footwall drifts as well as on results from laboratory testing. A multi-stage analysis was carried out to gradually change the stresses to simulate mining progress. A parametric study was conducted in which strength properties, location, and shape of the footwall drift were varied. Yielded elements and maximum shear strain were used as damage and fallout indicators. The modelling results were sensitive to the shape of the drift. The location of the predicted fallouts was in good agreement with the location of observed fallouts for the case in which the drift roof was simulated flatter than the theoretical cross section. The results indicate that the true shape of the drift is different from the planned one.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.193
Teacher spread0.180 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations19
Published2012
Admission routes1
Has abstractyes

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