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Record W3209342881 · doi:10.1680/jgein.21.00032a

Effect of temperature on the mechanical properties of two polymeric geogrid materials

2021· article· en· W3209342881 on OpenAlexaff
Romaric Léo Esteban Desbrousses, Mohamed A. Meguid, S. Bhat

Bibliographic record

VenueGeosynthetics International · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeogridUltimate tensile strengthGeosyntheticsMaterials scienceComposite materialBrittlenessReinforcementPolypropyleneComposite numberGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Understanding the tensile behavior of geosynthetic reinforcement materials at different temperatures is essential for the design of reinforced soil structures in seasonally cold regions. This study describes a series of tensile tests performed on two polypropylene geogrid materials, namely a biaxial geogrid and a geogrid composite. A total of 84 tests were performed in an environmental chamber with temperatures as low as −30°C and as high as +40°C. The response of each material is examined over the range of investigated temperatures to evaluate the effect of temperature changes on the tensile strength of the two geogrid materials. The response of the biaxial geogrid is found to be sensitive to temperature variations, with samples tested at low temperatures exhibiting brittle behavior characterized by high rupture strength and small ultimate strain while samples tested at elevated temperatures displayed ductile behavior with large elongation at failure and comparatively small rupture strength. A similar response was found for the geogrid composite, however, the rupture strength seemed to be less sensitive to temperature changes. The modes of failure observed at each temperature are examined based on photographic evidence taken during the experiments.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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 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

Citations32
Published2021
Admission routes1
Has abstractyes

Explore more

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