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Record W4317033634 · doi:10.1139/cgj-2021-0643

Effect of particle size and particle size distribution on critical state loci of granular soils

2023· article· en· W4317033634 on OpenAlexafffundvenue
Mathan V. Manmatharajan, Sartaj Gill, Wei Liu, Edouardine-Pascale Ingabire, Alex Sy, Mason Ghafghazi

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsKlohn Crippen Berger (Canada)University of TorontoWSP (Canada)
FundersOntario Centres of Excellence
KeywordsParticle sizeSoil waterParticle-size distributionContext (archaeology)Particle (ecology)Granular materialGeotechnical engineeringMaterials scienceMineralogyMechanicsGeologySoil sciencePhysics

Abstract

fetched live from OpenAlex

The critical state soil mechanics captures a wide range of stress–strain behaviour in an understandable context. It provides a conceptual framework for predicting soil behaviour and that is why the critical state is a central part of most advanced constitutive models. This study aims at quantifying the effects of both particle size, and particle size distribution on the critical state loci. Two soils, a natural soil and a tailings, were selected and CSLs were identified for twelve uniform and well graded particle size distributions. Mineralogy and particle shapes were rigorously quantified to ensure other factors are not influencing the results. Particle size has a small influence on the CSL in the sand to gravel range, but silts can have a significantly different CSL. In both natural soil and tailings, particle size distribution appears to have a significant influence on the CSL in e − log p′space and little influence in q − p′ space. Well graded soils have lower CSLs compared to uniform ones, that are generally parallel to the CSLs of their dominant constituent, with the exception of convex distributions where progressively finer particles in larger proportions can form structures noticeably less compressible than any of their constituents.

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)0.002
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 score0.072
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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

Citations21
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207