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Record W2034880698 · doi:10.1139/l06-029

Crack identification in a cross-stiffened plate system using the root mean square of time domain responses

2006· article· en· W2034880698 on OpenAlexfundvenueno aff
Agung Budipriyanto, M.R. Haddara, A. S. J. Swamidas

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVibrationStructural engineeringRoot mean squareFlangeFrequency domainTime domainFrame (networking)Intersection (aeronautics)Shell (structure)EngineeringAcousticsComputer sciencePhysicsMechanical engineering

Abstract

fetched live from OpenAlex

On-line identification of cracks occurring in large structures has attracted the attention of many researchers. Identification becomes difficult when there is no easy access available for investigators to employ conventional nondestructive evaluation techniques. In this paper, a scheme is presented for identifying the crack location and its extent using vibration response. Numerical studies were carried out on a 1/20th-scale model of the side shell of the structure of a ship. The first natural frequency of the numerical model was 584.3 Hz. The numerical and physical models were excited by a random force having a dominant spectral frequency of 2 Hz. Cracks under investigation occurred at the connection between a horizontal and bulkheads and at the intersection between horizontals and a web frame. A scheme using the root mean square of vibration response is presented for identifying cracks occurring in the flange and web of the horizontal. It is demonstrated that the scheme can identify the crack location and size.Key words: side shell of a ship model, pre-resonance excitation regime, vibration-based monitoring.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

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