Wavelet packet transformation-based improved acoustic emission method for structural damage identification
Bibliographic record
Abstract
Abstract Acoustic emission (AE) technique has emerged as a sophisticated nondestructive testing technique that plays a crucial role in detecting and localizing damage in structures. This paper proposes a damage visualization approach by leveraging the classical signal decomposition capabilities of Wavelet Packet Transformation (WPT) and the classification abilities of the Gaussian Mixture Model (GMM). First, WPT decomposes AE signals acquired from the instrumented structure at different loading stages. The coordinates (e.g. x and y) of AE events identified by the localization model using denoised AE components obtained from WPT are then determined. The extracted coordinates are used in the GMM model to visualize the location of the damage during the intermediate and final loading stages. The proposed method is validated using a suite of lab-scale experimental studies of concrete beams. The study compares the outcomes of the proposed method with those obtained from a traditional digital image correlation (DIC) system for both intermediate and final stages of damage. The results indicate that the proposed framework effectively visualizes the locations of various types of damage, such as flexural and shear cracks, at an early stage compared to the DIC. This demonstrates the proposed method’s capability to be a reliable tool for early damage localization and visualization in concrete structures.
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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 | 0.001 |
| 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".