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Record W2091925799 · doi:10.1680/gein.2010.17.4.193

An analytical method to predict the pullout response of geotextiles

2010· article· en· W2091925799 on OpenAlexaff
Lasantha Weerasekara, Dharma Wijewickreme

Bibliographic record

VenueGeosynthetics International · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeotextileGeotechnical engineeringGeosyntheticsDisplacement (psychology)Nonlinear systemMaterials scienceStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

ABSTRACT: Understanding the pullout mechanism of geotextiles is a key consideration in analyzing the stability of geotextile/reinforced-soil structures. The analytical models developed based on linear behavioral assumptions for the geotextile material stress–strain response have significant limitations in capturing the highly nonlinear pullout response observed in relatively extensible geotextiles. A new analytical model was developed combining the nonlinear responses of the geotextile and soil–geotextile interface characteristics. The interface behavior was modeled considering the changes in normal stress on the planar geotextile due to constrained dilation of soil and the subsequent frictional degradation behaviors at the interface. The analytical solution is useful in predicting the pullout resistances, strain, and mobilized frictional length along the geotextile for a given magnitude of displacement. The suitability of the analytical formulation was verified by predicting the experimentally observed performance of several geotextile pullout tests conducted with varying geotextile material and burial conditions. A simple chart and equation capable of predicting strain and mobilized frictional length, respectively, for a given magnitude of relative geotextile displacement are proposed, based on the research findings.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.298

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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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