Buried Piping: Managing the Challenge
Bibliographic record
Abstract
Nuclear utilities have many kilometres of piping buried in a relatively small physical area resulting in what has been called a “spaghetti bowl”. Until recently, much of this piping has been neglected and considered “out of sight / out of mind” therefore given a low operational impact. However, current failures have raised the profile of buried piping maintenance with both utilities and regulators. Buried piping programs face many of the challenges familiar to well run maintenance programs, but these challenges are compounded for a number of reasons. This paper will discuss how Atomic Energy of Canada Limited (AECL) Nuclear Laboratories have partnered with utilities, service providers, CANDU Owners Group (COG), and the Electric Power Research Institute (EPRI) to provide support to the development and implementation of maintenance programs for buried piping. Initially, AECL developed station strategy manuals to establish a mechanism to ensure a proficient ongoing program. As part of this program, extensive data on the systems was collected using station records. This data was then used to produce risk informed assessments, with the help of EPRI’s BPWORKS™ software, and ultimately the selection of inspection locations. Lessons learned from this work have not only been integrated into the station’s buried piping program, but also incorporated into improvements to the EPRI BPWORKS software.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".