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Record W4293074138 · doi:10.11159/iccste22.158

Detecting Cracks in Isotropic Plates Using Contact Acoustic Nonlinearity

2022· article· en· W4293074138 on OpenAlexvenueno aff
Reza Soleimanpour, Sayed Mohamad Soleimani

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersKuwait Foundation for the Advancement of Sciences
KeywordsIsotropyAcousticsMaterials scienceNonlinear systemComposite materialOpticsPhysics

Abstract

fetched live from OpenAlex

This paper investigates using second harmonic of symmetric Lamb wave (S0) for detecting cracks in aluminum plates induced by contact acoustic nonlinearity (CAN) experimentally. The generation of nonlinear guided waves around the crack is studied in an aluminum plate. The effects of crack on generation of second harmonic of guided waves are investigated. The data is acquired in time domain and is processed using a signal processing approach consists of several applications in frequency and time-frequency domain. The results show that crack induces CAN which can be observed in form of higher harmonic of guided waves in frequency and time-frequency domain. Also it is shown that crack induces wave distortion with small magnitudes which can be observed in time domain data. However, the magnitude of wave distortion is not large enough to confirm the existence of crack in aluminum plates. The results show that nonlinear wave technique does not require base line data and can successfully detect cracks in aluminum plates.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.528

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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