Damage Detection of Beams by a Vibration Characteristic Tuning Technique Through an Optimal Design of Piezoelectric Layers
Bibliographic record
Abstract
An advanced technique for damage detection in beam structures, using a vibration characteristic tuning procedure is developed by an optimal design of piezoelectric materials. Piezoelectric sensors and actuators are mounted on the surface of the host beam to generate excitations for the tuning via a feedback process. The excitations induced by the piezoelectric effect are used to magnify the effect of the damage in the vibration characteristics of the damaged structure to realize an effective damage detection process. To describe the detection process, theoretical models of the cantilevered beams with and without tuning induced by piezoelectric effect are built first, while the damage is represented by a crack at the fixed end. From the established models, the natural frequencies of the tuned beams with and without the crack are obtained to study the sensitivity of the proposed technique. As a result of the tuning process, more obvious changes on the natural frequencies due to the existence of a crack are observed. The results also indicate a shorter distance from the piezoelectric actuators to the crack leads to a higher detection sensitivity. An optimal length of the piezoelectric actuators is also obtained for better detection.
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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 | 0.000 |
| 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".