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Record W2532487330 · doi:10.1115/omae2016-54151

Experimental and Numerical Studies of the Plastic Behavior of Large Structural Grillages Subjected to Ice Loads

2016· article· en· W2532487330 on OpenAlexafffund
Hyunmin Kim, John Dolny, Claude Daley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMemorial University of NewfoundlandSt. John’s Health Sciences CentreCanatec (Canada)
FundersNational Research Council CanadaSamsungMemorial University of NewfoundlandAtlantic Canada Opportunities AgencyU.S. Department of Energy
KeywordsStructural engineeringDeflection (physics)Finite element methodDeformation (meteorology)Materials scienceGeotechnical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Significant research efforts have been carried out to investigate the plastic behavior of grillage structures. However, most experimental work has used steel or other rigid indenters rather than real ice. The tests therefore showed certain structural response behavior that may occur differently if subjected to ice loads. In this study, ice samples were used to load the structure rather than rigid indenters. This allowed for investigation into structural deformation considering the failure of ice. Two large grillages were prepared and tested. The first grillage tests were intended to study the ultimate load-carrying capacity when subjected to central and symmetric loading. The second grillage was tested to study the influence of variable ice loading positions along a single frame. A finite element (FE) model was developed to analyze the experiments numerically. The load-deflection curves and deformation shapes measured by the MicroScribe® were used to validate the numerical results.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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