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Record W2941636688 · doi:10.1520/jte20180547

Investigation of Macroscopic and Microscopic Behavior of Gravels Using Triaxial Compression Test with CT Scan

2019· article· en· W2941636688 on OpenAlexaff
Jingshan Jiang, Cheng Lin, Zhanlin Cheng, Yongzhen Zuo

Bibliographic record

VenueJournal of Testing and Evaluation · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTriaxial shear testGeotechnical engineeringMaterials scienceCompression (physics)Particle (ecology)Displacement (psychology)Stress (linguistics)Overburden pressureStress pathStress–strain curveMechanicsGeologyComposite materialDeformation (meteorology)PhysicsPlasticity

Abstract

fetched live from OpenAlex

Abstract A large number of studies have been conducted to investigate macroscopic responses of gravelly soils using large-scale triaxial compression tests; however, particle-level responses of gravelly soils under triaxial compression remain poorly explored. Here, we reported a series of large-scale triaxial compression tests on uniformly graded gravels subjected to a low to medium range of confining pressures (200–800 kPa). We further investigated the particle-level responses of the gravels using the combined triaxial compression test with computed tomography (CT) scan. A computer image analysis program was developed to analyze the CT images, providing visualized and quantitative particle displacement and rotation. Based on the experimental results, the stress-strain relationships at different confining pressures and the microscopic characteristics of the gravels (e.g., changes in internal structures) under different deviator stress levels were discussed. The changes in particle-level responses were linked to those in macroscopic responses. From both macroscopic and microscopic analyses, some insights into the underlying mechanism of the stress-strain relationships were provided.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.270
Teacher spread0.226 · 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 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

Citations6
Published2019
Admission routes1
Has abstractyes

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