Acoustic emission-based damage localization using wavelet-assisted deep learning
Bibliographic record
Abstract
Abstract Acoustic Emission (AE) has emerged as a popular damage detection and localization tool due to its high performance in identifying minor damage or crack. Due to the high sampling rate, AE sensors result in massive data during long-term monitoring of large-scale civil structures. Analyzing such big data and associated AE parameters (e.g., rise time, amplitude, counts, etc.) becomes time-consuming using traditional feature extraction methods. This paper proposes a 2D convolutional neural network (2D CNN)-based Artificial Intelligence (AI) algorithm combined with time–frequency decomposition techniques to extract the damage information from the measured AE data without using standalone AE parameters. In this paper, Empirical Mode Decomposition (EMD) is employed to extract the intrinsic mode functions (IMFs) from noisy raw AE measurements, where the IMFs serve as the key AE components of the data. Continuous Wavelet Transform (CWT) is then used to obtain the spectrograms of the AE components, serving as the “artificial images” to an AI network. These spectrograms are fed into 2D CNN algorithm to detect and identify the potential location of the damage. The proposed approach is validated using a suite of numerical and experimental studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".