LSDC-RC-RAPID: An Improved Probabilistic Reconstruction Approach for Pipeline Corrosion Detection With UGWT
Bibliographic record
Abstract
Corrosion is an irreversible form of damage to pipe materials, which leads to the degradation of their mechanical and chemical properties. Corrosion damage reduces the lifespan of materials and, in some cases, can even lead to catastrophic failures. Therefore, it is essential to detect corrosion damages and develop effective preventative measures to maintain the structural integrity of the pipelines. In recent times, ultrasonic guided wave testing techniques have been employed to detect and monitor corrosion damage as they are useful for scanning large areas and conducting tomographic analyses. The obtained guided wave signals are then analyzed using the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) to obtain imaging of the damages. However, conventional RAPID assigns the same signal difference coefficient to all reconstruction points along a sensing path, which limits its ability to capture localized signal variations. In addition, it lacks a mechanism for evaluating signal reliability, making it prone to false positives caused by noise. Thus, this paper proposes an improved RAPID-based algorithm by employing a local signal difference coefficient (LSDC) and reliability coefficient (RC) to ensure that damage is accurately detected. The LSDC is employed to enhance the sensitivity to damage by capturing localized signal variations, while the RC is developed to weigh the signal reliability. The experimental results demonstrate that the proposed method accurately predicts corrosion locations. It maintained a strong overlap between the predicted and the actual defect locations, with an overlapping rate between 73.02% and 79.43% throughout all cycles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".