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Record W2048111971 · doi:10.1139/t10-020

Coupled two-dimensional finite element modelling of mine backfilling with cemented tailings

2010· article· en· W2048111971 on OpenAlexvenueno aff
Matthew Helinski, Martin Fahey, Andy Fourie

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsConsolidation (business)Geotechnical engineeringTailingsCementFinite element methodGeologyEngineeringMining engineeringMaterials scienceStructural engineeringMetallurgy

Abstract

fetched live from OpenAlex

Mine backfilling is a process whereby mine tailings mixed with small amounts of cement are placed hydraulically into mined-out voids (“stopes”) to stabilize the rockmass and allow full extraction of adjacent ore. A containment barricade is constructed to block the access point at the base of the stope, the design of which requires calculation of the total stress on the barricade during and following filling. For fine-grained backfill containing cement, the rate of development of stresses is governed by the rates of filling, consolidation, and cement hydration, each with its own timescale. As “consolidation” in backfill undergoing hydration can be dominated by “self-desiccation”, this mechanism must also be incorporated. Interaction between the backfill and the stope walls (“arching”) also has an influence. The paper describes a finite element (FE) model (“Minefill-2D”) that can model these interactions, although only in a two-dimensional (plane–strain or axisymmetric) fashion. It is shown that arching significantly influences the total stress distribution in a typical stope during filling, but only if and when effective stress develops. For cemented backfill, arching sometimes does not fully mobilize the cement bond strength, so that assessment of arching using traditional limit equilibrium methods is often not appropriate.

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 score0.078
Threshold uncertainty score0.698

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.187
Teacher spread0.173 · 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

Citations57
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicTailings Management and PropertiesFrench-language works237,207