Time–frequency decomposition-assisted improved localization of proximity of damage using acoustic sensors
Why this work is in the frame
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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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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 | 0.000 |
| 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 it