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Record W2089248477 · doi:10.1139/t04-062

Mechanical behavior of compacted composite clays

2004· article· en· W2089248477 on OpenAlexvenueno aff
Mohammad Kazem Jafari, Ali Shafiee

Bibliographic record

VenueCanadian Geotechnical Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersInternational Institute of Earthquake Engineering and Seismology
KeywordsGeotechnical engineeringComposite numberAggregate (composite)Materials sciencePore water pressureOverburden pressureMatrix (chemical analysis)Direct shear testShear strength (soil)Embankment damShear (geology)Composite materialGeologyLeveeSoil waterSoil science

Abstract

fetched live from OpenAlex

Composite clay is a mixture of clay, as the main body, and aggregates, which are floating within the clayey matrix. The undrained behavior of composite clay in its natural or compacted state, e.g., core material of embankment dams, has great importance for geotechnical engineers. An extensive test program was conducted on kaolin–gravel and kaolin–sand mixtures to investigate various effects of aggregates on the mechanical behavior of the mixtures during strain-controlled monotonic and cyclic loadings. Monotonic test results reveal that increasing the aggregate content leads to a gradual increase in shear strength. Meanwhile, when the aggregate content is raised, the pore pressure increases for both monotonic and cyclic loading. It is also found that the presence of aggregates within a cohesive matrix leads to formation of a heterogeneous matrix in the clayey part of composite clays. This heterogeneity in turn causes pore pressure to be increased with an increase in the aggregate content during both monotonic and cyclic loading. In addition, test results show that aggregate size does not influence mechanical behavior appreciably. The results of this experimental research program are presented and discussed in this paper.Key words: composite clay, aggregate, shear strength, pore pressure, heterogeneous matrix, embankment dam.

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: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.751

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations85
Published2004
Admission routes1
Has abstractyes

Explore more

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