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Record W2888272957 · doi:10.1155/2018/9628141

Vibration‐Based, Nondestructive Methodology for Detecting Multiple Cracks in Bending‐Torsion Coupled Laminated Composite Beams

2018· article· en· W2888272957 on OpenAlexafffund
Stephen R. Borneman, Seyed M. Hashemi

Bibliographic record

VenueShock and Vibration · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaRyerson University
KeywordsStructural engineeringTorsion (gastropod)Nondestructive testingVibrationFinite element methodNatural frequencyComposite numberCantileverBendingTrigonometryMaterials scienceEngineeringComposite materialAcousticsMathematicsMathematical analysisPhysics

Abstract

fetched live from OpenAlex

Damage to composite structures occurs from impact, fatigue, or over stress and can be critical in the safe operation of wings or any structural member. This paper presents a method for detection of multiple cracks present in laminated composite bending‐torsion coupled cantilevered beams using natural frequency data, a type of Nondestructive testing (NDT). This methodology relies on both experimentally collected natural frequencies and frequencies calculated using a mathematical model. Precise natural frequencies are calculated using a new dynamic finite cracked element (DFCE) formulated within and based on dynamic trigonometric shape functions. An algorithm is devised based on the Adam–Cawley criterion and extended to laminated composites with multiple cracks. This method has shown exceptional convergence on the size and location of cracks using a number of modes of free vibration with and without error in measured frequencies.

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 score0.580
Threshold uncertainty score0.593

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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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