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Robustness of a Structural Health Monitoring System under Drop-weight Impact Loading in Composites

2010· article· en· W2554318111 on OpenAlex
Pierre-Claude Ostiguy, Kyle R Mulligan, Patrice Masson, Saïd Elkoun

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.

Bibliographic record

VenueAnnual Conference of the PHM Society · 2010
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceComposite numberStructural health monitoringRobustness (evolution)Composite materialTransducerDrop (telecommunication)Electrical impedanceAcousticsStructural engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this study, the robustness of a structural health monitoring system is tested on fiber glass composite coupons under impact testing using a drop-weight impact. The composite coupons are fitted with leadzirconate-titanate (PZT) transducers to induce Lamb waves into the specimens. Robustness of the structural health monitoring system is assessed. The electrical admittance defined by the inverse of the impedance is chosen as the robustness metric and is measured using an LCR analyzer prior to, and following an impact event. Detachment of the PZT transducer is monitored through comparison of the measured electrical admittances. An average minimum composite coupon thickness of 7 mm is defined for impacting fiber glass composite coupons with pre-attached PZT transducers. A 1.5 % drop of electrical admittance was observed for that thickness for one impact. The chosen metric is related to the capability of the structural health monitoring system to provide accurate damage detection results following an impact.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.336

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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