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Record W4386440656 · doi:10.1061/jggefk.gteng-11239

Coefficient of At-Rest Earth Pressure of Cemented Nonplastic Mine Tailings

2023· article· en· W4386440656 on OpenAlexaff
Mohammadamin Jafari, Murray Grabinsky

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeotechnical engineeringTailingsRock mass classificationExcavationCompressive strengthCementLateral earth pressureGeologyCuring (chemistry)Mining engineeringInterlockingEngineeringMaterials scienceStructural engineeringComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Backfilling is critical to deep and high-stress mining in order to minimize stress redistributions in the host rock, which can lead to mining-induced seismicity and underground excavation collapse. Cemented paste backfill (CPB) is the most popular backfilling material in the mining industry; it is a mixture of mine tailings, binder, and water, which tightly fills the mined-out space, providing optimum regional ground control. This paper presents the results of a laboratory study of the evolution of the coefficient of at-rest earth pressure (K0) of a typical CPB material. The impact of parameters such as cement content and curing time on K0 and one-dimensional behavior of a CPB in stresses up to 1.8 MPa was studied. In general, K0 behavior is characterized by three regions: elastic, transitional, and posttransitional. Fitting functions are proposed to predict evolving K0 based on cement content, specimen curing time, and unconfined compressive strength (UCS) as the most common laboratory test in geomechanical engineering practices. Although the calibrated functions are specific to the material tested, the testing approach can be used to characterize other mines’ backfills.

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 score0.036
Threshold uncertainty score0.485

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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geotechnical and Geoenvironmental EngineeringSame topicTailings Management and PropertiesFrench-language works237,207