Time–frequency decomposition-assisted improved localization of proximity of damage using acoustic sensors
Bibliographic record
Abstract
Abstract Nondestructive testing (NDT) technique has emerged as a valuable tool for detecting damage and evaluating the overall structural condition, leading to enhanced safety and optimized maintenance of large-scale structures. The acoustic emission (AE) approach is one of the powerful NDT techniques that can be suitable for damage detection due to its high sensitivity to localized damage. In this paper, an improved method based on empirical mode decomposition (EMD) and Shannon entropy ( E ) is proposed to localize the structural damage using AE sensors without considering any manual feature extraction of standalone AE parameters. EMD is first applied to eliminate the noise from the measured AE data and extract the key AE components, and then the E value of each AE component is estimated and used to identify the potential location of a crack in structural elements. The proposed method is validated using a suite of experimental studies and AE data obtained from a full-scale concrete dam located in Ontario, Canada. The results show the capability of the proposed method for identifying the approximate location of the damages and prove that the proposed method can be suitable for robust damage or crack localization.
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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 | 0.001 | 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".