Historical Rates of Soil Side Corrosion for Use in Fitness-for-Service Evaluations of Buried Metallic Pipe
Bibliographic record
Abstract
Nuclear power plants and many other industries are required to periodically inspect their buried piping to determine its fitness-for-continued service (FFS). The FFS process requires that both the general corrosion rate and the rate of maximum penetration for localized corrosion (e.g., pitting) be estimated so that the remaining lifetime and/or time until the next inspection can be determined. Revision 1 to ASME Code Case N-806, “Evaluation of Metal Loss in Class 2 and 3 Metallic Piping Buried in a Back-Filled Trench” [1] provides 4 options for estimating the corrosion rates: a. Wall thickness measurements from the current examination and from one or more previous examinations of the same metal loss region. b. Repeat measurements at two or more times from another location that has a predicted metal loss rate greater than or equal to the rate of the metal loss region under evaluation. c. Repeat measurements using corrosion coupons, linear polarization probes, or electrical resistance probes d. Generic historical data Each of these methods has its uses and limitations, and it is generally preferable to consider results from 2 or more of the methods. This paper examines historical data gathered by the National Bureau of Standards (NBS, renamed in 1988 as the National Institute of Standards & Technology - NIST) at ∼ 70 locations around the US in the 1930s – 1950s. Maximum penetration and weight loss (general corrosion) data from each site were placed in one of four soil texture groups for both carbon steel and cast iron. A regression analysis was performed to determine the median rates and 80% and 95% probabilistic values. It was found that results within each soil texture group were relatively similar and that the corrosion rates in the first 3 years after burial tended to be much higher than rates in years 5–18. The coefficients of determination were determined to quantify differences within each soil texture group. It is proposed that the steady state rates provided herein are an option to be used as the Historical Rates for FFS evaluations as described in [1].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".