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Record W2249554614 · doi:10.1139/t2012-075

Summary of collapsible behaviour of artificially structured loess in oedometer and triaxial wetting tests

2012· article· en· W2249554614 on OpenAlexvenueno aff
Mingjing Jiang, Haijun Hu, Fang Liu

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsOedometer testWettingGeotechnical engineeringOverburden pressureLoessTriaxial shear testVoid ratioGeologyMaterials scienceComposite materialSoil waterSoil scienceShear (geology)Geomorphology

Abstract

fetched live from OpenAlex

This paper summarizes experimental studies on wetting-induced collapsibility in loess using single-oedometer, double-oedometer, and triaxial wetting tests. Artificially structural loess samples with interparticle bonding calcite (CaCO 3 ) and a large void ratio were tested in the laboratory to avoid sampling disturbance of natural loess. The comparison between the single- and double-oedometer tests confirms that the wetting-induced deformation is independent of the sequence of wetting and loading. The conventional triaxial apparatus was enhanced for investigating the collapse deformation in response to different water content increments when subjected to different stress levels. The wetting-induced strain subjected to high confining pressure develops in two steps. It increases with increasing water content and reaches a relatively stable plateau, and then increases rapidly again until a final stable state is reached. The initial collapse surface was found by plotting the wetting-induced strain vectors observed in triaxial wetting tests. The wetting-induced strain is negligible when a specimen is wetted at a stress point inside this surface, while it becomes significant when wetted beyond this surface.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.554

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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 designObservational
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

Citations115
Published2012
Admission routes1
Has abstractyes

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