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Record W2126511095 · doi:10.1243/147509002320382149

Ultimate strength of ageing ships

2002· article· en· W2126511095 on OpenAlexfundno aff
Jeom Kee Paik, Anil K. Thayamballi

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment · 2002
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
FundersInnovation, Science and Economic Development Canada
KeywordsHullDamagesCrackingStructural engineeringStructural integritySize effect on structural strengthForensic engineeringUltimate tensile strengthFinite element methodAgeingStrength of materialsCorrosionComputer scienceEngineeringMaterials scienceMarine engineeringComposite material

Abstract

fetched live from OpenAlex

This paper is a summary of recent research and development in areas related to the ultimate strength of ageing ship hulls, undertaken by the authors. Some relevant methodologies for modelling the two age-related structural degradation factors, namely corrosion and fatigue cracking damages, are proposed and studied. The effects of corrosion and fatigue cracking damages on the ultimate strength of ship panels are investigated by non-linear finite element analyses and mechanical tests. The variations of ship hull ultimate strength as a function of age-related structural degradation are investigated by progressive collapse analyses using the idealized structural unit method (ISUM). Important insights and findings obtained by the research are noted and recommendations are made regarding future development work where appropriate. The development of procedures and criteria for the damage-tolerant design of ship structures is an eventual goal, and the overall outlook in this regard is promising.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.185
Teacher spread0.172 · 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 designObservational
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

Citations78
Published2002
Admission routes1
Has abstractyes

Explore more

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