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Record W3210242583 · doi:10.1139/cgj-2021-0120

One- and two-dimensional finite element modelling of thaw consolidation

2021· article· en· W3210242583 on OpenAlexaffvenue
Rajith Dayarathne, Bipul Hawlader, Ryan Phillips, Dilan Robert

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConsolidation (business)Geotechnical engineeringHydraulic conductivityVoid ratioFinite element methodPore water pressureGeologyEffective stressVoid (composites)Nonlinear systemSoil waterMaterials scienceSoil scienceEngineeringStructural engineeringComposite material

Abstract

fetched live from OpenAlex

Coupled thermo-hydro-mechanical finite element (FE) modelling of thaw consolidation is presented. One-dimensional FE analyses are performed for thaw consolidation of a soil column due to self-weight and with a combination of self-weight and surcharge, with the linear and nonlinear void ratio – effective stress – hydraulic conductivity relationships of thawed soil. The nonlinear behaviour of thawed soil is modelled using a modified Drucker–Prager cap model, while the hydraulic conductivity is varied with the void ratio. Finally, two-dimensional FE modelling of thaw consolidation around a warm pipeline buried in permafrost is performed. The rapid reduction of the void ratio with consolidation, especially at the low-stress level, results in a wide variation of hydraulic conductivity within the thawed zone. The significantly large hydraulic conductivity of soil elements along the curved thaw front, as compared to that of thaw consolidated soil, causes the flow of water along the thaw front, instead of a vertical flow, as assumed in previous 1D thaw consolidation modelling of buried pipelines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.241
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical Journal→Same topicClimate change and permafrost→French-language works237,207→