Damage identification in beams using empirical mode decomposition
Why this work is in the frame
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Bibliographic record
Abstract
Damage detection of beam-type components, which are often vital elements in many structures, is crucial for the prevention of failure of the entire structure and potential catastrophic consequences. In this article, the effectiveness of a damage index, referred to as the EMD energy damage index, for damage detection of beams is demonstrated through a set of numerical and experimental investigations. The proposed damage index utilizes the empirical mode decomposition for health assessment of the system based on its vibrational data. In the numerical study, finite element simulation of a cantilevered steel beam with a transverse notch was analyzed and various notch sizes, located at different locations along the beam, were investigated. In the experimental investigation, which used the same beam as in the numerical study, five notch sizes at the mid-span of the beam were examined. In both the numerical and experimental studies, the free vibration of the beam was acquired via piezoceramic sensors adjacent to the notch and then processed by the proposed methodology for evaluating the EMD energy damage index. This was motivated as the preliminary stage of our investigation with the notion of detecting the presence of a crack in a welded joint. The results were encouraging and proved the capability of the EMD energy damage index for detection and quantification of notches in beams and therefore can be regarded as an effective tool for structural health monitoring purposes. The results were also compared with a method based on changes in the beam natural frequencies. The effect of the boundary conditions on the EMD energy damage index was also experimentally studied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it