Pipeline Defect Detection Using Artificial Intelligence-Based Active Acoustic Sensing
Bibliographic record
Abstract
Abstract This study presents an innovative AI-enhanced active acoustic sensing approach for continuous pipeline defect detection, overcoming limitations of conventional in-line inspection methods that include sparse inspection frequencies and extensive data analysis requirements. The proposed method introduces acoustic energy to detect circumferential notch-type defects. Numerical simulations are performed using Finite Element Analysis (FEA), and experiments performed using axial piezoelectric transducers and acoustic emission sensors. Due to the rapid attenuation at typical inspection frequencies, we excite the system close to natural resonance frequencies, improving defect detection accuracy. Our approach incorporates power spectral analysis and root mean square error (RMSE) computations against baseline references, with Z-score normalization addressing signal variations. The neural network model at the core of this AI methodology significantly boosts defect detection accuracy, proving its efficacy and reliability for enhancing pipeline structural health monitoring (SHM) and integrity management.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".