Reliability analysis of gas pipelines considering spatial and temporal corrosion variability
Bibliographic record
Abstract
The accelerating deterioration rate, notably due to corrosion in buried gas pipelines, has prompted the development of a systematic methodology for assessing structural integrity. This study introduces a methodology to evaluate the system reliability of a corroded gas pipeline, incorporating the spatial and temporal variability in corrosion growth. To capture the spatial variability of the corrosion process, random field theory is applied, which illustrates the correlation between defect depth and length growth, while a stochastic process is utilized to model the temporal evolution of these defects. The methodology considers two primary failure modes, i.e., small leaks and bursts, and investigates how the parameters of the length scale in random fields, and the correlation strength between these fields influence the failure probability of the pipelines. System reliability is ultimately assessed based on the analysis of individual pipeline segments. The findings underscore the importance of considering spatial variability in estimating the reliability of gas pipelines. The proposed methodology offers a practical and more accurate approach to account for the spatial and temporal dynamics of corrosion, thereby enhancing the accuracy of reliability assessments for corroded gas 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".