Sensitivity analysis of a transmission line model for damage characterization in complex structures
Bibliographic record
Abstract
With the goal to detect relatively small damage while minimizing signal processing burden, an approach in the medium frequency range (10 kHz - 50 kHz) is proposed for the characterization of a damage in a complex assembly structure and more specifically, a lap joint. The approach is based on the identification of the parameters of a reference transmission line model of a damaged lap joint structure through the experimental measurement of a reflection coefficient. The transmission line model of the lap joint is first presented, where symmetrical thickness variations on a beam are used to represent the lap joint region and a notch within this region. The cost function used in the model identification approach is then defined as the squared difference between simulated and measured reflection coefficients in a given frequency range. A sensitivity analysis is conducted using the Hessian of the cost function and simulation results are presented to demonstrate the sensitivity of the cost function to variations in the sought parameters, i.e. location and depth of the notch, in the frequency domain. Experimental results are then presented to assess the sensitivity of the cost function to the variation of the depth of the notch. These experimental results confirm the simulation results which indicate that the sensitivity of the cost function to the depth of the notch increases as this depth increases. Moreover, cross-sensitivity results indicate that the sensitivity of the cost function to the location of the notch also increases as the depth of the notch increases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".