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Record W2158757591 · doi:10.1139/cgj-2012-0255

Numerical analysis of strengthening by rockfill embankments on an upstream tailings dam

2013· article· en· W2158757591 on OpenAlexvenueno aff
Linda Ormann, Muhammad Auchar Zardari, Hans Mattsson, Annika Bjelkevik, Sven Knutsson

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
FundersLuleå Tekniska UniversitetKungliga Tekniska HögskolanChalmers Tekniska Högskola
KeywordsBermConsolidation (business)Geotechnical engineeringTailingsTailings damLeveeGeologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

The consolidation process could be slow in an upstream tailings dam; therefore, the stability can reduce due to an increase in excess pore pressures when the dam is raised. The safety of the dam can be enhanced by constructing rockfill berms on the downstream side. This paper presents a case study on the strengthening of an upstream tailings dam with rockfill berms. The finite element analyses were performed for modelling the staged construction of the dam and for optimizing the volume of the rockfill berms. The dam was raised in 11 stages; each stage consisting of a raising phase and a consolidation phase. The study shows that the slope stability of the dam reduced due to an increase of excess pore pressures during the raising phase. The stability of the dam was successfully improved by utilizing rockfill berms as supports on the downstream side. A technique has been presented to minimize the volume of the rockfill berms so that the required stability can be achieved at minimum cost. This paper shows that the finite element method can be a useful tool for modelling the consolidation behaviour of an upstream tailings dam and minimizing the volume of the rockfill berms that may be needed to maintain the stability of the dam during staged construction.

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 categoriesInsufficient payload (model declined to judge)
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 score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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.

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

Citations33
Published2013
Admission routes1
Has abstractyes

Explore more

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