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Record W4221057167 · doi:10.1680/jgein.21.00088

Geosynthetic-reinforced embankment in cold regions: observations and numerical simulations

2022· article· en· W4221057167 on OpenAlexaff
Earl Marvin B. De Guzman, Marolo Alfaro, L. U. Arenson, Gérald Doré

Bibliographic record

VenueGeosynthetics International · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalUniversity of ManitobaBGC Engineering (Canada)
Fundersnot available
KeywordsLeveePermafrostGeotechnical engineeringFoundation (evidence)GeosyntheticsFinite element methodGeologyEnvironmental scienceEngineeringStructural engineering

Abstract

fetched live from OpenAlex

An embankment underlain by permafrost foundation was reinforced with wicking woven geotextiles at its side slopes to reduce slope displacements when the fill material that was compacted at frozen condition thaws during subsequent spring and summer seasons following winter construction. The embankment fill and permafrost foundation were instrumented with thermistor strings to monitor soil temperatures as well as ShapeAccelArrays installed at the mid-slope of the embankment to measure displacements. A numerical model was developed in a commercially-available finite element software to simulate the thermal and mechanical behaviour of this embankment and further understand the benefit of using woven geotextiles in cold regions. A sequentially-coupled approach was used where temperature-dependent mechanical properties were invoked based on a heat transfer analysis. Four years of monitored results from the reinforced embankment were used to calibrate the numerical model. An adjacent section of the embankment without the geotextiles was also modelled and results show a significant reduction in slope displacements. The monitored performance and the model results presented will improve the design guidelines for embankments in cold regions using geotextiles.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.985

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)0.0160.000

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 head0.050
GPT teacher head0.254
Teacher spread0.204 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

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