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Record W2074821468 · doi:10.1115/omae2004-51109

Crack Identification on a Cross-Stiffened Plate Panel

2004· article· en· W2074821468 on OpenAlexaff
Agung Budipriyanto, A. S. J. Swamidas, M.R. Haddara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVibrationStructural engineeringIdentification (biology)Normal modeShell (structure)Mode (computer interface)Ambient vibrationLine (geometry)EngineeringCondition monitoringComputer scienceAcousticsFinite element methodMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Early identification of cracks in complex structures is desirable for the safety of operation and economy of maintenance of the structure. Monitoring of the vibration response of structures is a well-known technique for crack identification. As the complexity of the structure increases manual inspection becomes difficult and the use of on-line monitoring techniques for crack detection becomes more desirable. This paper discusses the use of a structure’s vibration response in the early detection of cracks. Analytical and experimental studies of the effect of cracks on the vibration response of an 1/20 scale aluminum model of the stiffened side shell panel of a tanker were carried out. The model was carefully designed to obtain natural frequencies and mode shapes similar to those of the prototype structure. The results of the analytical study were used to determine the best locations to place the sensors on the experimental model. Results of the experimental and analytical studies are reported.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.384

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 head0.042
GPT teacher head0.320
Teacher spread0.278 · 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 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

Citations2
Published2004
Admission routes1
Has abstractyes

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