Improved acoustic source localization method for crack identification in structures
Bibliographic record
Abstract
• An improved AE crack localization method is proposed. • Empirical mode decomposition is first-ever integrated with a source localization model. • The proposed method is free of the use of bulk AE parameters. • The proposed method is verified using a suite of experimental studies with different damage types. Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".