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Record W2089825837 · doi:10.1139/l99-086

Finite element analysis of buckling of corroded ship plates

2000· article· en· W2089825837 on OpenAlexfundvenueno aff
P A Slater, M.D. Pandey, A.N. Sherbourne

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBucklingCorrosionStructural engineeringFinite element methodMaterials scienceCritical loadBoundary value problemCompression (physics)Square (algebra)Composite materialEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

The paper is concerned with the assessment of ship plates in which corrosion has reduced their overall thickness making them vulnerable to buckling-related failures. The uniaxial buckling of simply supported square plates is studied for several corrosion patterns, which are allowed to progressively engulf the entire plate. The finite element method is employed for computing buckling loads of corroded plates with different boundary conditions. Numerical results are presented in the form of generic plots of buckling load versus volumetric metal loss for several corrosion patterns considered in the paper. The mechanics of buckling load variations by corrosion is discussed in detail. The spatial location of the corrosion patch appears to have a significant effect on the buckling strength. It is noted that the case of uniform corrosion is not the most detrimental case, rather corrosion confined in a corner or central area appears to be more critical.Key words: ship structures, square plate, buckling load, corrosion, uniaxial compression, finite element method.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.186
Teacher spread0.179 · 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 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

Citations11
Published2000
Admission routes2
Has abstractyes

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