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Record W2111978779 · doi:10.1155/2014/109403

A Hybrid Structural Health Monitoring System for the Detection and Localization of Damage in Composite Structures

2014· article· en· W2111978779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Sensors · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaTechnische Universiteit Delft
KeywordsStructural health monitoringComposite numberMaterials scienceLamb wavesTransducerPiezoelectricityPiezoelectric sensorStructural engineeringComposite materialCarbon fiber reinforced polymerComposite plateAerospaceFibre-reinforced plasticAcousticsSurface waveComputer scienceEngineeringAerospace engineeringTelecommunications

Abstract

fetched live from OpenAlex

A hybrid structural health monitoring (SHM) system, consisting of a piezoelectric transducer and fiber optic sensors (FOS) for generating and monitoring Lamb waves, was investigated to determine their potential for damage detection and localization in composite aerospace structures. As part of this study, the proposed hybrid SHM system, together with an in-house developed algorithm, was evaluated to detect and localize two types of damage: a through thickness damage (hole of 2 mm in diameter) and a surface damage (2 mm diameter bore hole with a depth of 0.65 mm) located on the backside of the plate. The experiments were performed using an aircraft representative composite plate skin, manufactured from carbon fiber reinforced polymer (CFRP).

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.

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 score0.090
Threshold uncertainty score0.242

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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