Robustness of a Structural Health Monitoring System under Drop-weight Impact Loading in Composites
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".