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Record W2600354472 · doi:10.5539/jsd.v10n2p238

Study on the Impact of Strain Rate and Loading Speed on Geogrid-Reinforced Soil

2017· article· en· W2600354472 on OpenAlexvenueno aff
Mohammadehsan Zarringol, Mohammadreza Zarringol

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGeogridGeotechnical engineeringMaterials scienceShear stressShear strength (soil)Clay soilEnvironmental scienceGeologyComposite materialSoil waterSoil scienceReinforcement

Abstract

fetched live from OpenAlex

During the past decades, reinforced soil has normally been constructed by coarse grained soil. Recently, low quality and locally accessible materials have been successfully used in reinforced soil due to economic observations. Loading speed is one of the effective factors in soil-geosynthetic interaction. In order to determine the impact of this factor, we carried out a pullout test on the samples with dimensions of 30×30×17 cm under four strain rates of 0.75, 1.25, 1.75 and 2.25 mm/min and three vertical stress rates of 20, 50 and 80 KN/m2. The results of this study indicated that the mobilization of geosynthetic strength in contact area depends on the amount of vertical stress. The increased vertical stress results in the increased shear strength in clay-geogrid contact area. Furthermore, the increased strain rate results in the reduced shear strength.

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 score0.006

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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