The Feasibility of Structural Health Monitoring Using the Fundamental Shear Horizontal Guided Wave in a Thin Aluminum Plate
Bibliographic record
Abstract
Structural health monitoring (SHM) is emerging as an essential tool for constant monitoring of safety-critical engineering components. Ultrasonic guided waves stand out because of their ability to propagate over long distances and because they can offer good estimates of location, severity, and type of damage. The unique properties of the fundamental shear horizontal guided wave (SH₀) mode have recently generated great interest among the SHM community. The aim of this paper is to demonstrate the feasibility of omnidirectional SH₀ SHM in a thin aluminum plate using a three-transducer sparse array. Descriptions of the transducer, the finite element model, and the imaging algorithm are presented. The image localization maps show a good agreement between the simulations and experimental results. The SH₀ SHM method proposed in this paper is shown to have a high resolution and to be able to locate defects within 5% of the true location. The short input signal as well the non-dispersive nature of SH₀ leads to high resolution in the reconstructed images. The defect diameter estimated using the full width at half maximum was 10 mm or twice the size of the true diameter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".