Assessment of the Impacts of Climatic Factors and Infrastructure Characteristics on Gas Pipeline Failures
Bibliographic record
Abstract
The increasing reliance on natural gas as a transitional energy source has underscored the importance of ensuring the safety and reliability of gas pipelines. This study examines failure patterns in gas transmission pipelines in the US, considering both infrastructure characteristics and climatic factors. Initial analyses of the spatial and temporal characteristics of pipeline incidents is performed based on the kernel density estimation (KDE) approach, and Moran’s I. Detailed analysis of the influence of various factors, including concurrent and antecedent climatic factors, on pipeline failures is achieved through negative binomial (NB) and random parameters negative binomial (RPNB) models, developed for both underground and aboveground pipelines. The RPNB model, which appears superior to the NB model for both underground and aboveground pipelines—as evidenced by the Akaike information criterion and Bayes information criterion—captures unobserved heterogeneity, enabling a more nuanced representation of complex, real-world dynamics. Marginal effect analysis based on the RPNB models provides a quantitative assessment of how specific factors influence pipeline incident probabilities. Precipitation and soil moisture emerged as the most influential climatic factors for underground pipeline failure, and precipitation was also found to be the primary factor affecting aboveground pipeline failure. Additionally, it was found that temperature-related factors potentially contributed to the failure of gas pipelines. The results provide useful insights regarding pipeline failure and controlling factors and will form the basis for additional detailed investigations and advancements in pipeline design, maintenance, and decision-making.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".