MétaCan
Menu
Back to cohort
Record W4386291509 · doi:10.1680/jgein.23.00078

Investigating the effect of temperature and water freezing on the response of geogrid composite

2023· article· en· W4386291509 on OpenAlexaff
J. Jarjour, Mohamed A. Meguid

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeosyntheticsGeogridUltimate tensile strengthMaterials scienceComposite materialComposite numberGeotechnical engineeringMoistureWater contentReinforcementGeology

Abstract

fetched live from OpenAlex

Geosynthetics can be exposed to varying temperature and moisture conditions when embedded in soil, which can affect their mechanical properties. However, existing testing standards do not account for extreme environmental conditions such as low temperatures and ice formation. The aim of this study was to understand the effect of temperature variation and ice formation on the tensile properties of dry and wet geosynthetics by conducting single-rib tensile tests on a wicking geogrid composite in a temperature chamber. Tensile stress–strain curves were obtained at various temperatures for dry samples (−40°C to 40°C) and wet samples (0°C to −40°C). It was found that, with a decrease in temperature, the tensile strength and stiffness of dry and wet samples increased while the ultimate strain decreased. The freezing of water in wet samples also accelerated the rates of increase in ultimate strength and decrease in ultimate strain. The failure mode of the geogrid composite also changed with temperature, occurring at the middle junction at higher temperatures and closer to the end junctions with a fibrous appearance at lower temperatures. These findings provide insights into the significance of varying environmental conditions on geosynthetic properties.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGeosynthetics InternationalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207