Time-space identification of mechanical impacts and distributed random excitations on plates and membranes
Bibliographic record
Abstract
The identification of dynamic loads acting on structures is a key aspect of several engineering domains involving structure-borne sound and vibration problems, stress analysis, or even the study of fatigue-induced structural damages. This work is concerned with the reconstruction of localized transient and distributed random excitations on plates and membranes from their measured vibration response. In previous investigations by the authors, the virtual fields method, an identification approach based on the principle of virtual work, was employed to identify mechanical and acoustic loadings applied to a bending panel. However, vibration data were obtained using scanning laser Doppler vibrometry, which limits the application of the virtual fields method to stationary excitations in both space and frequency. In contrast, the deflectometry technique used here is an optical method that directly provides a full-field measurement of local slopes. With the addition of a high-speed camera, the measurements are resolved in both space and time, enabling the study of nonstationary excitations. Moreover, since the acquisition time is independent of the number of measurement points, high spatial density measurements can be performed in seconds. This paper reviews the principles of the virtual fields method for nonstationary excitations on plates and membranes. The deflectometry technique is then demonstrated and experimental reconstruction results on an aluminum panel are presented for two different load cases: impacting metal marbles (multiple unknown transient excitations) and a diffuse acoustic field excitation. Finally, the identification of acoustic and aerodynamic (turbulent boundary layer) excitations is considered using a membrane as the receiving structure.
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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) | 0.001 | 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".