Damage identification in beams using empirical mode decomposition
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".