Developing FFS software for fitness-for-service assessment of equipment with hydrogen blistering damage based on API 579-1/ASME FFS-1
Bibliographic record
Abstract
Corrosion and metal degradation are inevitable phenomena in various industries, and using Standards that provide detailed assessment to evaluate the structural integrity of an in-service damaged component is absolutely essential. Among all existing Standards, API 579-1/ASME FFS-1 is a well-known assessment standard recognized as Fitness-For-Service (FFS) assessment and is employed in various industries to assess the structural integrity of in-service pressure vessels and storage tanks that may contain a flaw or damage. In this study, software for the FFS evaluation was developed according to Part 7 of the third edition of the AP1579-1/ASME FFS-1 and was written using C# programming language. This software is developed for low-strength ferritic steel pressurized components with hydrogen blistering (HB) damage, in order to facilitate decision making on run-repair-replace of an in-service damaged component.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".