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Record W2738139542 · doi:10.1139/cgj-2016-0607

Dynamic installation of a torpedo anchor in two-layered clays

2017· article· en· W2738139542 on OpenAlexvenueno aff
Young-Ho Kim, Muhammad Shazzad Hossain, J.K. Lee

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsEmbedmentGeotechnical engineeringGeologyPenetration (warfare)Parametric statisticsPenetration depthAnchoringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The behaviour of a torpedo anchor during dynamic installation in two-layered nonhomogeneous clay sediments was investigated through large-deformation finite-element (LDFE) analyses. Parametric analyses were undertaken varying the top layer thickness and strength ratio between two layers. Results show that the location of the demarcation point between the acceleration and deceleration phases of the anchor in the soil relative to the layer interface is the key factor directing the anchor behaviour in layered soils including the final embedment depth. Broadly speaking, the anchor behaviour in soft-over-stiff clay deposits is somewhat similar to that in single layer clay with strength increasing with depth. In stiff-over-soft clay deposits, the anchor penetrates deeper where the anchor deceleration phase (or the demarcation point) falls within the soft layer. Where the demarcation point lies within the top stiff layer, the anchor penetration depth decreases with increasing strength ratio, and the anchor penetration is terminated between the two layers for strength ratios ≥15. For assessing the anchor embedment depth in two-layered fine-grained ocean sediments in the field, an extended total energy–based method, along with a design expression, is proposed.

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 score0.052
Threshold uncertainty score0.997

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 science0.0010.000
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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