Condition-Based Optimal Maintenance Decision Modeling for Corroding Natural Gas Pipelines
Bibliographic record
Abstract
This paper investigates the optimal timing of the first inspection for newly-built onshore underground natural gas pipelines with respect to external metal-loss corrosion by considering the generation of corrosion defects over time and time-dependent growth of individual defects. The non-homogeneous Poisson process is used to model the generation of new defects and the homogeneous gamma process is used to model the growth of individual defects. A realistic maintenance strategy that is consistent with the industry practice and accounts for the probability of detection (PoD) and sizing errors of the inspection tool is incorporated in the investigation. Both the direct and indirect costs of failure are considered. A simulation-based approach is developed to numerically evaluate the expected cost rate at a given inspection interval. The optimal inspection interval is determined based on either the cost criterion or the safety criterion. An example gas pipeline is used to examine the impact of the cost of failure, PoD, and the excavation and repair criteria on the optimal inspection interval through parametric analyses. The results of investigation will assist engineers in making the optimal maintenance decision for corroding natural gas pipelines and facilitate the reliability-based corrosion management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".