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Record W2131642819 · doi:10.1139/t10-078

Yield strength ratios, critical strength ratios, and brittleness of sandy soils from laboratory tests

2011· article· en· W2131642819 on OpenAlexvenueno aff
Abouzar Sadrekarimi, Scott M. Olson

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsVoid ratioBrittlenessGeotechnical engineeringShear (geology)Materials scienceShear strength (soil)Triaxial shear testDirect shear testComposite materialParticle sizeSoil waterGeologySoil science

Abstract

fetched live from OpenAlex

In this study, we performed 26 undrained triaxial compression and 32 constant-volume ring shear tests on two clean sands and one silty sand. We then used these results to evaluate the critical states, and shear strength ratios mobilized at yield and at critical state. We obtained yield strength ratios that ranged from 0.16 to 0.32 and from 0.20 to 0.35 in triaxial compression and ring shear, respectively. Critical strength ratios mobilized prior to particle damage ranged from 0.01 to 0.26 in triaxial compression and from 0.04 to 0.22 in ring shear. Particle damage and shear displacement increased the slopes of the critical-state lines during ring shear testing, and consequently the critical strength ratios incorporating particle damage decreased from 0.02 to 0.12. In addition, specimen brittleness (before particle damage) increases with initial void ratio and state parameter and is affected by initial fabric and particle shape. However, particle damage and crushing considerably increases sand brittleness, making it essentially independent of initial void ratio. A unique relation is found between sand brittleness and critical strength ratio independent of sand type, mode of shear, fabric, and particle damage, which indicates an upper bound critical strength ratio of about 0.3 for mildly contractive sands.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.201
Teacher spread0.186 · 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.

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

Citations46
Published2011
Admission routes1
Has abstractyes

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