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Record W4396972884 · doi:10.1680/jgeot.23.00410

Aquathermal effect of anomalous pressure generation in consolidating minefill

2024· article· en· W4396972884 on OpenAlexaff
Gongda Lu, A. P. S. Selvadurai, Mohamed A. Meguid

Bibliographic record

VenueGéotechnique · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnvironmental scienceGeologyBusiness

Abstract

fetched live from OpenAlex

Recycling cement-amended tailings into subsurface cavities has delivered competitive socioeconomic revenue for underground mining. However, recent instances of anomalous thermal pressure in consolidating backfill have raised growing concerns over the current design philosophy. This study demonstrates that the excellent hydraulic sealing and considerable chemical energy inherent in cemented backfill can spontaneously generate substantial thermal pressurisation through constrained pore fluid expansion. An analytical solution is hence developed for characterising the non-isothermal behaviour of hydrating backfill based on classical thermo-poroelasticity. The influence of the preparation condition and mix design on the pressure evolution in the time domain and during spontaneous heat generation is then thoroughly examined, successfully pinpointing the causal mechanism responsible for typical thermal pressure anomalies. It is demonstrated that the anomalous pressure is attributable to both the conservative use of binder, which increases the intrinsic heat load, and the elevated initial temperature, which promotes water volume expansion. The fundamental influence of temperature-sensitive water expansivity on the path dependence of thermal pressurisation is also elucidated. This study thus contributes to a comprehensive understanding of the thermally correlated pressure anomalies that frequently occur in field operations. These critical findings would also hold practical implications for designing successful backfill solutions in challenging mine environments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.653

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)0.0010.000

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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 designBench or experimental
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

Citations6
Published2024
Admission routes1
Has abstractyes

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