Inspection of Buried Piping in Nuclear Power Plants: Field Observations and Challenges
Bibliographic record
Abstract
The issues over the integrity of buried piping in Nuclear Power Plants (NPPs) have received significant attention over the past few years. These piping systems have been in operation for over 30 years. Leaks from buried piping have the potential to raise safety, radiological, environmental, and financial concerns. Buried piping are subject to degradation mechanisms from the outside (soil side) as well as from the inside (fluid side) and they are primarily protected from external corrosion by applying coating on the pipe and then using cathodic protection to protect any bare areas or holidays in the coating. However, over a period of time the coating may lose its integrity and fail to provide the protection for which it was intended. As this happens, the amount of cathodic current needed for adequate protection increases. In some instances, the coating will disbond from the pipe and shield the cathodic protection from the pipe surface. Because of the economic, environmental, and safety consequences of a failure, NPPs embarked on inspection programs to determine the pipe’s condition and its suitability for continued service. This paper presents some of the observations made during the indirect and direct inspections of buried piping. In addition, the challenges encountered are reported.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".