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Record W3022708091 · doi:10.1680/jgein.20.00020

Thermal desiccation of geosynthetic clay liners under brine pond conditions

2020· article· en· W3022708091 on OpenAlexaff
Ali Ghavam-Nasiri, David Airey, R. Kerry Rowe, Abdelmalek Bouazza

Bibliographic record

VenueGeosynthetics International · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeosynthetic clay linerSubsoilWater contentBentoniteGeotechnical engineeringGeosyntheticsDesiccationPore water pressureShrinkageBrineMoistureGeomembraneGeologyEnvironmental scienceMaterials scienceComposite materialSoil scienceSoil waterChemistryHydraulic conductivityBotany

Abstract

fetched live from OpenAlex

Desiccation of geosynthetic clay liners (GCLs) in composite lining systems is investigated experimentally. Field conditions similar to those encountered in brine ponds (high surface temperature and low overburden pressure of 20 kPa) are simulated in two soil columns. The GCL is first allowed to hydrate from a sandy subsoil under isothermal conditions (44 days), then subjected to a thermal gradient applied through a heat source (78°C) on top of the geomembrane (39 days). Changes in water content, temperature of subsoil and movement of the surface during hydration and heating stages are measured. A sharp rise in moisture content is observed in the upper region of the subsoil immediately after the start of heating, which shortly dissipates. After 39 days of heating, the bentonite in the tested GCLs is dehydrated to around 8% gravimetric water content, down from over 100% after hydration. In addition, more than 3 mm of shrinkage of bentonite in the vertical direction is recorded. X-ray imaging after the tests reveals extensive desiccation cracking of bentonite. The study has confirmed the existence of a significant risk of desiccation of GCLs under thermal gradients and has generated data that are useful for validating models used for the prediction of GCL behaviour.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.991

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)0.0100.001

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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