Assessing Susceptibility of Insulation Materials to IGSCC under Hot and Wet Conditions: A Novel Test Method
Bibliographic record
Abstract
Abstract Intergranular stress corrosion cracking (IGSCC) in above-ground carbon steel pipelines has been identified as a distinct mode of failure, different from the typical neutral pH and high pH SCC observed in buried pipelines. This form of SCC occurs under specific conditions, including operating temperatures ranging from 70°C to 190°C, exposure to wet mineral wool insulation, and insulation installations dating from post-2003. The affected carbon steel grades have included API 5L or CSA Z245.1 with specified minimum yield strength (SMYS) of 52 ksi or higher, as well as A106 Gr B, A234 WPB, and A516 Gr 70. Both field and laboratory tests have been conducted in the past to investigate this phenomenon. However, the underlying mechanism remains unclear. This paper presents a summary of the results obtained from various analytical methods, including chemical analyses, cyclic potentiodynamic polarization (CPP) testing, field crack inspections, U-bend autoclave tests, and a novel wet/dry cyclic test. It outlines the gaps in our understanding and highlights key findings that contribute to a better understanding of SCC in this specific context. Additionally, this paper discusses the development of test methods for screening susceptible materials, which is crucial for preventing failures related to intergranular SCC in above-ground carbon steel pipelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".