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Record W2289499819 · doi:10.1680/ecsmge.60678.vol7.604

Numerical study of the use of actively-tensioned polymeric strips for reinforced soil walls

2015· article· en· W2289499819 on OpenAlexaff
Ivan Puig Damians, Richard J. Bathurst, Juan Lima, A. Lloret, Alejandro Josa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsReinforcementPrecast concreteStructural engineeringSTRIPSTension (geology)Mechanically stabilized earthGeotechnical engineeringRetaining wallEngineeringMaterials scienceUltimate tensile strengthComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Polymeric strip reinforced soil walls require a practical method to fix the reinforcement tail-ends in the soil backfill during construction to ensure adequate anchorage and to avoid any slack along the linear reinforcement elements. A number of different techniques are currently used depending on the reinforcement type, arrangement and the contractor. This paper reports the results of a series of numerical simulations that were carried out on an idealized 6 m-high wall with precast partial height facing panels. A different reinforcement pre-tension load was applied to the layers in each simulation case. The results of numerical simulations include the reinforcement axial load distribution, vertical facing load and end-of-construction facing alignment plotted as a function of the reinforcement layer depth location and applied pre-tension load. The results demonstrate that the magnitude of pre-tensioning has a significant effect on wall performance for walls subjected to operational (working stress) conditions at end of construction.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.058
GPT teacher head0.230
Teacher spread0.171 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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