Faster RSTRENG: A More Efficient Effective Area Method Algorithm for Corrosion Assessment
Bibliographic record
Abstract
Abstract Corrosion is one of the major threats to the safety and structural integrity of oil and gas transmission pipelines. The corrosion threat is usually managed by regular in-line inspection (ILI). The effective area method (RSTRENG) is the most popular corrosion assessment model to convert the measured corrosion size to predicted burst pressure. Given a detailed corrosion measurement profile, the effective area method involves an iterative process to find the minimum burst pressure. As stated in ASME B31G, “for a corroded profile defined by n measurements of depth of corrosion including the end points at nominally full wall thickness, n!/2(n − 2)! iterations are required to examine all possible combinations of local metal loss with respect to surrounding remaining material,” the widely used effective area algorithm has at least an order of n-square time complexity (O(n2)). As n increases, the computation time increases nonlinearly. This paper reviewed the traditional RSTRENG algorithm first, and demonstrated that it is not necessary to always loop through all the combinations and check the corresponding burst pressure one by one. Because some combinations with shallower and shorter corrosion size are certainly not the final critical combination corresponding to the minimum burst pressure. A more efficient algorithm (Faster RSTRENG) is proposed and presented in this paper, which can reduce the algorithm computation time significantly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".