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Record W4386417384 · doi:10.1680/jgein.23.00056

Filtration behaviour of staple fibre geotextiles under unequal biaxial tensile strains

2023· article· en· W4386417384 on OpenAlexaff
Liang Tang, W. Francey, Junjie Zheng, X. Wang, Xianwu Luo

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeotextileMaterials scienceUltimate tensile strengthGeosyntheticsCloggingComposite materialTensile strainFiltration (mathematics)Permeability (electromagnetism)Geotechnical engineeringMembraneGeologyMathematicsChemistry

Abstract

fetched live from OpenAlex

Gradient ratio tests were conducted to investigate the filtration behaviour of staple fibre needle-punched nonwoven geotextiles subjected to unequal biaxial tensile strains. Three groups of biaxial tensile strains were designed, with the ratios of the strain in the machine direction to that in the cross-machine direction set to 1, 2, and 4, respectively. The strains in the machine direction in the three groups were the same, ranging from 10% to 30%. The tested filtration properties included the gradient ratio (GR), permeability of the soil-geotextile system, mass of soil loss, and permittivity of the pure geotextiles. Comparisons were made between the filtration properties of staple fibre (SN) geotextiles and continuous filament (CN) geotextiles. It is shown that for a certain strain ratio, the GR value at the time of test termination increases with increasing strain, and the permeability of the soil-geotextile system, soil loss, and permittivity of the pure geotextiles decrease with increasing strain. The soil loss and permittivity under equal biaxial tensile strains tend to be higher than those under unequal biaxial tensile strains. The CN geotextiles have better retention capability and more clogging potential for the tested soils than the SN geotextiles for a similar mass per unit area.

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)0.000
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 score0.033
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.234
Teacher spread0.217 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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