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Record W2158587473 · doi:10.1139/t08-109

Pre- and post-cyclic behavior of mixed clayey soils

2009· article· en· W2158587473 on OpenAlexvenueno aff
Abbas Soroush, Hossein Soltani-Jigheh

Bibliographic record

VenueCanadian Geotechnical Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersAmirkabir University of Technology
KeywordsConsolidation (business)Soil waterGeotechnical engineeringShearing (physics)Clay soilPore water pressureGeologyMonotonic functionCyclic stressMaterials scienceSoil scienceComposite materialMathematics

Abstract

fetched live from OpenAlex

Soil structures are often comprised or supported by soils that are mixtures of cohesive and granular geomaterials. These soils are termed as intermediate or mixed soils and are widely found in natural deposits and man-made soil structures. The objective of this paper is to study the behavior of mixed clayey soils under monotonic, cyclic, and post-cyclic monotonic loading, with the main focus on the last of these three. For this purpose, a number of strain-controlled monotonic, cyclic, and post-cyclic triaxial compression tests were carried out on “clay–sand” and “clay–gravel” mixtures in undrained conditions. The effects of sand (or gravel) contents, number of cycles, cyclic strain amplitude, consolidation pressure, and grain size on the mechanical behavior of the mixtures are evaluated. The major findings from the laboratory study are: (i) the addition of granular materials to the clayey soils increases excess pore-water pressure during strain-controlled monotonic undrained shearing and cyclic loading and (ii) during post-cyclic monotonic shearing, mixed clayey soils behave similarly to overconsolidated soils; the higher the granular material is, the higher the overconsolidation ratio would be. Based on the research results, an algorithm for estimating post-cyclic mechanical parameters of mixed clayey soils, without conducting cyclic and post-cyclic tests, is suggested.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.775

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations82
Published2009
Admission routes1
Has abstractyes

Explore more

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