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Record W2107668159 · doi:10.1139/cgj-2012-0203

Granular anchors under vertical loading – axial pull

2012· article· en· W2107668159 on OpenAlexvenueno aff
V. Sivakumar, Brendan C. O’Kelly, M. R. Madhav, Catherine Moorhead, Barry Rankin

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGranular materialAnchoringGeotechnical engineeringDisplacement (psychology)Structural engineeringFailure mode and effects analysisBase (topology)Ultimate loadEngineeringGeologyMaterials scienceFinite element method

Abstract

fetched live from OpenAlex

Granular anchors are a relatively new concept in ground engineering with relatively little known regarding their load–displacement behaviour, failure modes, ultimate pullout capacity, and also potential applications. A granular anchor consists of three main components: a base plate, tendon, and compacted granular backfill. The tendon is used to transmit the applied load to the base plate, which compresses the granular material to form the anchor. A study of the load–displacement response and ultimate pullout capacity of granular anchors constructed in intact lodgement till and made ground deposits is reported in this paper. Parallel tests were also performed on cast in situ concrete anchors, which are traditionally used for anchoring purposes. A new method of analysis for the determination of the ultimate pullout capacity of granular anchors is presented and verified experimentally, with the dominant mode of failure controlled by the column length (L) to diameter (D) ratio. Granular anchors with L/D > 7 principally failed by bulging whereas short granular anchors failed on shaft resistance, with the latter mobilizing similar pullout capacities as conventional concrete anchors.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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