Review of Current Developments on High Strength Pipeline Steels for HIC Inducing Service
Bibliographic record
Abstract
Nowadays, an increasing number of oil and gas transmission pipes are constructed with high-strength low alloy steels (HSLA); however, many of these pipelines suffer from different types of hydrogen damages, such as hydrogen-induced cracking (HIC). So many research efforts are being carried out to reduce the detrimental effects of hydrogen damage in HSLA steel pipes. The thermomechanical control process (TMCP) is a microstructural control technique that is able to eliminate the conventional heat treatment after hot rolling. Recent research demonstrated that TMCP provides high HIC resistance without adding high amounts of alloying elements or expensive heat treatments. However, once these HSLA steel pipes are put into service, they experience HIC damage, and the prediction of its kinetics is a necessary condition to perform Fitness-For-Service assessments. To develop a reliable predictive model for the kinetics of HIC, the relations among the microstructural features, environmental parameters, and mechanical properties have to be fully understood. This paper presents a review of the key metallurgical and processing factors to develop HSLA steel pipes, as well as a review of the phenomenological and empirical models of HIC kinetics in order to identify specific research directions for further investigations aimed to establish a reliable and sound model of HIC kinetics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".