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Record W2064992094 · doi:10.1520/gtj11388

Measurement of Energy and Strength of Sand at Critical State

2004· article· en· W2064992094 on OpenAlexaffabout
Balasingam Muhunthan, V. S. Pillai, D. Olcott

Bibliographic record

VenueGeotechnical Testing Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The energy input during shear deformation of sand is expended by three components: resistance against particle-to-particle frictional deformation, volumetric or nonrecoverable plastic deformation, and elastic deformation of the soil grains. These three components are identified for the shearing deformation of sand in triaxial compression using the energy balance principle. A new experimental technique to measure the elastic energy of sands is proposed. Shear resistance against particle-to-particle frictional deformation is then determined after applying corrections to the measured shear strength for plastic volumetric and elastic deformations. The shear resistance against particle-to-particle frictional deformation is shown to be the critical state strength of sand. The technique is illustrated with drained triaxial compression tests on Ottawa Sand. It has been found that the shear resistance against particle-to-particle friction deformation or the critical state strength attains a constant value at small strains. The conventional techniques to determine the critical state strength require it to be strained to large strains. At such large strains, it is not possible to achieve the critical state condition uniformly throughout the sample in a laboratory setup. This has led to many problems with the reliable measurement of the shear strength of sands at the critical state and the establishment of the critical state condition. The ability to measure the critical state strength at small strain levels overcomes such difficulties.

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 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 score0.686
Threshold uncertainty score0.566

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 integrity0.0000.000
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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

Citations4
Published2004
Admission routes2
Has abstractyes

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