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Record W2566564853 · doi:10.3390/ma10010032

Monitoring the Damage State of Fiber Reinforced Composites Using an FBG Network for Failure Prediction

2017· article· en· W2566564853 on OpenAlexaff
Esat Selim Kocaman, Erdem Akay, Çağatay Yılmaz, Halit S. Türkmen, I. B. Misirlioglu, Afzal Suleman, Mehmet Yıldız

Bibliographic record

VenueMaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Victoria
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsMaterials scienceComposite materialFiber Bragg gratingEpoxyFracture (geology)Strain gaugeStructural health monitoringStrain (injury)Glass fiberOptical fiberComputer scienceWavelength

Abstract

fetched live from OpenAlex

A structural health monitoring (SHM) study of biaxial glass fibre-reinforced epoxy matrix composites under a constant, high strain uniaxial fatigue loading is performed using fibre Bragg grating (FBG) optical sensors embedded in composites at various locations to monitor the evolution of local strains, thereby understanding the damage mechanisms. Concurrently, the temperature changes of the samples during the fatigue test have also been monitored at the same locations. Close to fracture, significant variations in local temperatures and strains are observed, and it is shown that the variations in temperature and strain can be used to predict imminent fracture. It is noted that the latter information cannot be obtained using external strain gages, which underlines the importance of the tracking of local strains internally.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.383

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 head0.024
GPT teacher head0.263
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 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

Citations28
Published2017
Admission routes1
Has abstractyes

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