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Record W2224627236 · doi:10.4271/2001-01-2628

Fatigue Damage Prediction for Bonded Composite Repairs Applied to Metallic Aircraft Structures

2001· article· en· W2224627236 on OpenAlexafffund
R.J. Clark, D.P. Romilly

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2001
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite numberMaterials scienceStructural engineeringComputer scienceComposite materialForensic engineeringEngineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">This paper describes the initial development of methods for predicting the fatigue life of bonded composite repairs applied to cracked aluminum aircraft structures. Bonded repairs offer great potential in stopping or slowing crack growth. The benefits of bonded repairs when compared to riveted repairs are significant, and include improvements in fatigue life, inspectability, and cost. The main barrier to the widespread use of this technology is the lack of methods for ensuring damage tolerance and durability [<span class="xref">1</span>–<span class="xref">3</span>].</div> <div class="htmlview paragraph">In this paper, approximate analytical equations are developed to characterize both disbonding and crack growth in a repair applied to a center-cracked plate. The equations are in a form suitable for use in probabilistic risk assessments and inclusion in industry codes and standards, and have been validated against a boundary element model. Comparisons of fatigue life predictions with results from published experiments illustrate the importance of cracked plate geometry and bending on the rate of cracking and disbonding.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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.

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

Citations3
Published2001
Admission routes2
Has abstractyes

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