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Record W2905812607 · doi:10.1115/1.4042388

Scale Effect in Ice Flexural Strength

2018· article· en· W2905812607 on OpenAlexafffund
Mohamed Aly, Rocky Taylor, Eleanor Bailey Dudley, Ian Turnbull

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
FundersHibernia Management and Development Company
KeywordsFlexural strengthSea iceGeologyGeotechnical engineeringMaterials scienceComposite materialClimatology

Abstract

fetched live from OpenAlex

Ice flexural strength is an important parameter in the assessment of ice loads on the hulls of ice-class ships, sloped offshore structures, and sloped bridge piers. While scale effects in compressive ice strength are well known, there has been debate as to the extent of scale effects in ice flexural strength. To investigate scale effects during flexural failure of both freshwater and saline ice, a comprehensive up-to-date database of beam flexural strength measurements has been compiled. The database includes 2073 freshwater ice beam tests with beam volumes between 0.00016 and 2.197 m3, and 2843 sea ice beam tests with volumes between 0.00048 and 59.87 m3. The data show a considerable decrease in flexural strength as the specimen size increases, when examined over a large range of scales. Empirical models of freshwater ice flexural strength as a function of beam volume, and of saline ice as function of beam and brine volumes have been developed using regression analysis. For freshwater ice, the scale-dependent flexural strength is given as: σf=839(V/V1)−0.13 For sea ice, the dependence of flexural strength has been modeled as: σ=1324(V/V1)−0.054e−4.969vb. Probabilistic models based on the empirical data were developed based on an analysis of the residuals, and can be used to enhance probabilistic analysis of ice loads where ice flexural strength is an input.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

Explore more

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