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Record W6901654617 · doi:10.60692/me315-j1e80

Matrix damage detection in laminated composite structures by Discrete Wavelet Transform

2019· article· en· W6901654617 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsWaveletVibrationFinite element methodMatrix (chemical analysis)Wavelet transformDiscrete wavelet transformModalComposite numberSIGNAL (programming language)

Abstract

fetched live from OpenAlex

The present paper seeks to enhance the potential damage detention and location of the discrete wavelet transform (DWT) in a thin laminated composite damaged by a signal derived from vibration modes. The DWT-1D is used to spatially identify (x-y plane) the damage location. The matrix damage level occurs during the impulsive load application over the analyzed structure. The finite element used is a Serendipity-type element with a rectangular shape, eight nodes and five mechanical degrees-of-freedom per node, and is formulated by First-order shear Deformation Theory (FSDT). The dynamic equation of motion including internal damage is solved by the Newmark implicit integration method, considering small deformations. The results have demonstrated that discrete mother wavelets applied in dynamic modal signal processing are capable of detecting a small damage magnitude in the matrix level. The wavelet coefficients reached great amplitudes in the damaged area.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

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