A failure prediction model for corrosion in gas transmission pipelines
Bibliographic record
Abstract
Transmission pipelines comprise a major part of a gas network, conveying natural gas within jurisdictions, and across international boundaries. In the United States, more than 10,000 failure incidents have been reported in gas transmission pipelines in a 20-year period from 1996 to 2016 leading to a cumulative property damage of more than $748 million. Among different failure sources, corrosion is ranked as the most frequent one, corresponding to approximately a quarter of total failures. Though in-line inspection is counted as the most frequently applied corrosion monitoring technique for oil and gas pipelines, it imposes considerable costs due to the necessity of implementing frequent inspections using smart devices. For this reason, several failure prediction models have been developed to estimate the corrosion failure. However, the majorities of these prediction models rely solely on experimental tests or limited historical records which undermine the extent of their applicability and ignore pipeline environmental and geographical circumstances. The objective of this research is to develop failure prediction models for external corrosion in underground gas transmission pipelines by considering both conventional and environmental/geographical variables. For this objective, multiple regression analysis was performed on the accessible historical data reported for gas transmission pipelines. Two main climate regions of Great Plains and South East in the US were selected, and their corresponding failure prediction models were developed. Such development was based on a step by step procedure analyzing different scenarios. Considering diagnostic measures, null hypothesis and residual analysis, scenario 3 was selected as satisfactory. The validation tests of the developed models present a root mean square error (RMSE) of 0.04 and 0.07 and R-Sq of 0.93 and 0.75, respectively. The results of this research can be applied in maintenance planning of gas transmission pipeline to estimate the critical time in which a pipeline may encounter external corrosion failure, and to accordingly schedule the maintenance activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".