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Record W1977319932 · doi:10.1520/jai11573

Corrosion Risk Assessment of Aircraft Structures

2004· article· en· W1977319932 on OpenAlexaff
M. Liao, J.P. Komorowski

Bibliographic record

VenueJournal of ASTM International · 2004
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFuselageCorrosionStructural engineeringAerospaceService lifeStructural integrityRivetMaterials scienceAirframeDamage toleranceProbabilistic logicCorrosion fatigueForensic engineeringEngineeringComputer scienceComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

Abstract This paper presents a comprehensive summary of IAR/NRC research on risk assessment of aging aircraft structures in the presence of corrosion and fatigue. Extensive test data, especially for fuselage splices containing corrosion and multiple site fatigue damage (MSD) and coupons cut from service exposed aircraft lap joints, have been generated under previous projects. Based on these test data, an empirical stochastic crack growth model was first developed to analyze the probabilistic fatigue characteristics of the splices. Risk analysis was then performed on the fuselage splices using the computer codes PRISM (Bombardier Aerospace, Inc.) and PROF (United States Air Force). Holistic life assessment methodology (HLAM), which aims to quantify structural integrity in the entire life cycle by addressing the interaction effects of corrosion and fatigue, has advanced considerably during the past five years. Risk analysis based on HLAM was carried out on the fuselage splices. The analytical results, which were obtained at different stages, are compared with the test results. It is shown that corrosion in lap joints, even at the levels less than the typical maintenance limit (i.e., 10 % material loss), could significantly increase the risk level of structural failure.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.224

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 science0.0000.000
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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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