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Record W2002047516 · doi:10.1139/t07-083

Design of geosynthetic reinforcements for platforms subjected to localized sinkholes

2008· article· en· W2002047516 on OpenAlexvenueno aff
Pascal Villard, Laurent Briançon

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGeosyntheticsSinkholeReinforcementGeotechnical engineeringDisplacement (psychology)Deformation (meteorology)Finite element methodStructural engineeringEnhanced Data Rates for GSM EvolutionStress (linguistics)EngineeringGeologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Construction of road and railway platforms in areas subject to localized sinkholes requires the use of specific reinforcements, for example, geosynthetics. The current design method for these structures is based on the assumption that there is no displacement of the geosynthetic in the anchorage areas on either side of the cavity. A new analytical method is proposed that takes into account the displacements and deformation of the geosynthetic reinforcement in the anchorage areas and the increase in stress at the edge of the cavity. To validate this new analytical method, a full-scale experiment was carried out; the use of optical fibre sensors integrated into the geosynthetic sheet made it possible to accurately measure the strain of the geosynthetic reinforcement. Comparison of the results obtained by this new analytical method with measurements of a full-scale experiment and the results of a finite element model confirmed the relevance of these new developments.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.202
Teacher spread0.183 · 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

Citations61
Published2008
Admission routes1
Has abstractyes

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