A Statistical Predictive Model to Prioritize Site Selection for Stress Corrosion Cracking Direct Assessment
Bibliographic record
Abstract
Pipeline stress-corrosion cracking (SCC) is an ongoing integrity concern for pipeline operators. A number of different strategies are currently employed to locate and mitigate SCC. Ultrasonic in-line inspection tools have proven capable of locating SCC, but reliability of these tools in gas pipelines remains in question. Rotating hydrotest programs are effectively employed by some companies but may not provide useful information as to the location of SCC along the pipeline. NACE Standard RP0204-2004 (SCC Direct Assessment Methodology) outlines factors to consider and methodologies to employ to predict where SCC is likely to occur, but even this document acknowledges that there are no well-established methods for predicting the presence of SCC with a high degree of certainty. Predictive modelling attempts to date have focused on establishing quantitative relationships between environmental factors and SCC formation and growth; these models have achieved varying degrees of success. A statistical approach to SCC predictive modelling has been developed. In contrast to previous models that attempted to determine direct correlations between environmental parameters and SCC, the new model statistically analyzed data from dig sites where SCC was and was not found. Regression techniques were used to create a multi-variable logistic regression model. The model was applied to the entire pipeline and verification digs were performed. The dig results indicated that the model was able to predict locations of SCC along the pipeline.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".