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Record W2056918981 · doi:10.1139/cgj-2014-0209

Capacity of dynamically installed anchors as assessed through field testing and three-dimensional large-deformation finite element analyses

2014· article· en· W2056918981 on OpenAlexvenueno aff
Colm O’Beirne, Conleth O’Loughlin, Dong Wang, Christophe Gaudin

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsEmbedmentFinite element methodGeotechnical engineeringDeformation (meteorology)Structural engineeringEnvelope (radar)Moment (physics)GeologyStructural loadEngineering

Abstract

fetched live from OpenAlex

The capacity of dynamically installed anchors in soft normally consolidated clay was examined experimentally through a series of field tests on a 1:20 reduced-scale anchor. The anchors were installed through free fall in water, achieving tip embedment of 1.5–2.6 times the anchor length, before being loaded under undrained conditions at various load inclinations. Vertical anchor capacities were between 2.4 and 4.1 times the anchor dry weight and were satisfactorily predicted using the American Petroleum Institute approach for driven piles. Anchor capacity under inclined loading increased as the load inclination approached horizontal; the field data indicated this increase to be up to 30% for the minimum achievable inclination of about 20° to the horizontal. Corresponding large-deformation finite element analyses showed a similar response, with the maximum capacity occurring at a load inclination between 30° and 45° to the horizontal. The finite element results demonstrate that, for the anchor geometry considered, an inclined load at the anchor padeye could be decomposed into ultimate vertical and moment loading at the anchor centroid. The establishment of a vertical and moment loading yield envelope for the geometry investigated forms the basis of a simple design procedure presented in the paper.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.445
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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

Citations71
Published2014
Admission routes1
Has abstractyes

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