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Record W3006304511 · doi:10.1139/cgj-2019-0406

Experimental study of one-dimensional compression creep in crushed dry coral sand

2020· article· en· W3006304511 on OpenAlexvenueno aff
Wang Jiabo, Pengxian Fan, Mingyang Wang, Lu Dong, Linjian Ma, Lei Gao

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsBreakageGeotechnical engineeringCoralDeformation (meteorology)GeologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Understanding the time-dependent deformation behaviour of backfill coral sand is important to the long-term stability of engineering facilities built on reefs and reclaimed land. A series of one-dimensional compression tests (with no lateral strain) were carried out on crushed coral sand with a variety of grading and relative densities (50%, 70%, and 90%) sampled from the South China Sea. Axial pressure was applied in stepped loading form: 100, 200, 400, 800, and 1600 kPa. Each level loading was applied for 3 days and then completely unloaded until the deformation was stable, after which the next loading level was applied. The test results indicate: (i) the deformation of coral sand is much larger than silica sand and involves a larger proportion of time-dependent and plastic deformation; (ii) the total deformation of coral sand and proportion of irreversible deformation decreases as the relative density increases; (iii) coral sands of better grading tend to deform less in total and have larger proportions of elastic and time-dependent deformation; and (iv) the grading of coral sand changes during the deformation process due to particle breakage. Based on the test results, the relationships between particle breakage and pressure, relative density, and grading, as well as the grain-scale mechanism of the deformation, are discussed.

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 score0.011
Threshold uncertainty score0.839

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 integrity0.0000.001
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 head0.021
GPT teacher head0.219
Teacher spread0.198 · 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

Citations44
Published2020
Admission routes1
Has abstractyes

Explore more

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