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Record W4205216330 · doi:10.3390/civileng3010005

Experimental Study on the Minimum Required Specimen Width to Maximum Particle Size Ratio in Direct Shear Tests

2022· article· en· W4205216330 on OpenAlexafffund
Akram Deiminiat, Li Li, Feitao Zeng

Bibliographic record

VenueCivilEng · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsDiameter ratioMaterials scienceAspect ratio (aeronautics)Particle sizeShear (geology)Composite materialGeology

Abstract

fetched live from OpenAlex

Conducting laboratory direct shear tests on granular materials is a common practice in geotechnical engineering. This is usually done by following the ASTM D3080/D3080M-11 (hereafter named ASTM), which stipulates a minimum required value of 10 for specimen width (W) to the maximum particle size (dmax) ratio. Recently, a literature review performed by the authors showed that the minimum required W/dmax ratio given in the ASTM is not large enough to eliminate the specimen size effect (SSE). The minimum required W/dmax ratio of ASTM needs to be revised. In this study, a critical analysis is first made on existing data in order to identify the minimum required W/dmax ratio. The analysis shows that more experimental data obtained on specimens having W/dmax ratios between 10 and 50 are particularly necessary. To complete this need, a series of direct shear tests were performed on specimens having different dmax by using three shear boxes of different dimensions. The results show once again that the minimum required W/dmax ratio of 10, defined in the ASTM, is not large enough to eliminate the SSE. Further analysis on these and existing experimental results indicates that the minimum required W/dmax ratio to remove the SSE of friction angles is about 60. These results along with the limitations of this study 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.567

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 head0.235
Teacher spread0.219 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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