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Record W2058635033 · doi:10.1139/t06-008

Characterization of a cemented sand with the pulse-velocity method

2006· article· en· W2058635033 on OpenAlexvenueno aff
Zahid Khan, Anwar Majid, Giovanni Cascante, D. Jean Hutchinson, Parsa Pezeshkpour

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCompressive strengthVoid ratioMaterials scienceGeotechnical engineeringCementWater contentComposite materialGeology

Abstract

fetched live from OpenAlex

The effect of variation in cement content, initial water content, void ratio, and curing time on wave velocity (low-strain property) and unconfined compressive strength (large-strain property) of a cemented sand is examined in this paper. The measured pulse velocity is compared with predictions made using empirical and analytical models, which are mostly based on the published results of resonant column tests. All specimens are made by mixing silica sand and gypsum cement (2.5–20% by weight) and tested under atmospheric pressure. The wave velocity reaches a maximum at optimum water content, and it is mostly affected by the number of cemented contacts; whereas compressive strength is governed not only by the number of contacts but also by the strength of contacts. Experimental relationships are developed for wave velocity and unconfined compressive strength as functions of cement content and void ratio. Available empirical models underpredict the wave velocity (60% on average), likely because of the effect of microfractures induced by confinement during the testing. Wave velocity is found to be a good indicator of cement content and unconfined compressive strength for the conditions of this study.Key words: wave velocity, low-strain stiffness, cemented sands, elastic moduli, unconfined compressive strength.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.490

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 integrity0.0000.001
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations44
Published2006
Admission routes1
Has abstractyes

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