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Record W1998819497 · doi:10.1155/2014/921815

Compressive Strength of Compacted Clay-Sand Mixes

2014· article· en· W1998819497 on OpenAlexafffund
Faseel S. Khan, Shahid Azam, Mavinakere Eshwaraiah Raghunandan, R. A. Clark

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Regina
FundersUniversity of Regina
KeywordsCompressive strengthMaterials scienceCompactionShearing (physics)Geotechnical engineeringComposite materialBrittlenessGeology

Abstract

fetched live from OpenAlex

The use of sand to improve the strength of natural clays provides a viable alternative for civil infrastructure construction involving earthwork. The main objective of this note was to investigate the compressive strength of compacted clay-sand mixes. A natural clay of high plasticity was mixed with 20% and 40% sand (SP) and their compaction and strength properties were determined. Results indicated that the investigated materials exhibited a brittle behaviour on the dry side of optimum and a ductile behaviour on the wet side of optimum. For each material, the compressive strength increased with an increase in density following a power law function. Conversely, the compressive strength increased with decreasing water content of the material following a similar function. Finally, the compressive strength decreased with an increase in sand content because of increased material heterogeneity and loss of sand grains from the sides during shearing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2014
Admission routes2
Has abstractyes

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