SCC In-Field Inspection Technology Evaluation and its Application to EMAT Based Pipeline Integrity Management Programs
Bibliographic record
Abstract
Most recently, as a complement to the ongoing efforts to monitor and document improvements in EMAT ILI technology, PRCI conducted an extensive study of NDE inspection technologies for characterizing SCC in pipelines using various in-ditch technologies and methods. The test pipes used for the study were cut outs from an operating pipeline where SCC features were identified and sized using EMAT ILI technologies. These are now sized with the NDE study and correlated with EMAT data to support an improvement of EMAT technology in characterizing SCC features. More importantly, the test pipes were burst tested to failure, with post failure analysis completed to fully characterize the crack features, including detailed length and depth measurements. This complete data set provides a comprehensive view of the current capabilities of NDE inspection technologies and EMAT ILI technologies to detect and characterize SCC and crack-like features. In this paper, the approach used for the evaluation of in-ditch NDE and EMAT ILI technologies is presented first. The in-ditch NDE technologies used for evaluation which were commonly used for SCC characterization are then described. SCC characterization results from in-ditch NDE and EMAT ILI are summarized and compared to those directly measured from fracture surfaces exposed by burst tests. The findings and its application to pipeline integrity management programs are discussed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".