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Record W3119912532 · doi:10.1139/cjce-2020-0452

Investigation of layered composite plates under acoustic emission using an appropriate finite element model

2021· article· en· W3119912532 on OpenAlexvenueno aff
Sanjay Sengupta, Parikshit Roy, Pijush Topdar, Aloke Kumar Datta

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodAcoustic emissionFlexural strengthMaterials scienceStructural engineeringWaveformTransverse planeComposite numberComposite materialAcousticsEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Structural damages generate acoustic emission (AE) in the media and cause extensional and flexural acoustic waves. Often in structures like plates, flexural mode is predominant. In this study, the flexural mode AE waveforms due to simulated damage are studied for multi-layered composite plates. A generalized refined 2D plate theory, which satisfies the transverse shear stress continuity at the layer interfaces, is proposed here for modelling the plates. This formulation is implemented through finite element method (FEM) where a four-node rectangular element, that satisfies C1 continuity, is used. Plates, having different thickness ratios, are studied through numerical examples using the model. Results are validated wherever applicable and some new results are obtained. The results indicate that the proposed model can simulate the flexural waveforms realistically for ‘very thin’ to ‘moderately thick’ plates. It is also found that the present model is as accurate as 3D FEM but it possesses much better computational efficiency.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.199
Teacher spread0.177 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

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