An Insight to the RBI of Corrosion under Insulation (CUI) and Corrosion under Fireproofing (CUF)
Bibliographic record
Abstract
Abstract This paper covers the risk-based inspection (RBI) practices to address external corrosion under insulation (CUI) and corrosion under fireproofing (CUF). The paper discusses risk-based inspection practices of the external corrosion of carbon and low-alloy steels under insulation and fireproofing and the external chloride stress corrosion cracking (ECSCC) of austenitic and duplex stainless steels under insulation, and the external stress corrosion cracking of carbon steel under mineral wool insulation. The RBI methodology described consists of 4 steps, including unit level prioritization, challenging the need for insulation, data validation, and detailed RBI analysis. These steps work together to identify units at higher risk of CUI and CUF, develop an appropriate inspection plan to mitigate CUI and CUF, enhance personnel safety, and reduce unnecessary expenditures. The risk of CUI and CUF failure can be quantified by considering the likelihood of occurrence and severity of consequence.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".