Considering Roadway Traffic in Quantitative Risk Assessment for Natural Gas Pipelines
Bibliographic record
Abstract
Abstract Natural gas transmission pipeline systems can extend thousands of kilometers and traverse both populated and less populated areas. Traditionally, risk assessment of natural gas pipelines performed by TC Energy mostly focuses on evaluating the impact of pipeline failures to occupants of nearby structures. However, it is recognized that natural gas pipelines at road crossings or otherwise running in proximity to roads may also pose risks to motorists or other roadway users. Incidents of pipeline failures damaging road infrastructure, resulting in casualties, have been seen in the past. This paper reports work undertaken to investigate the risk assessment of natural gas transmission pipelines considering the consequence of pipeline failures to roadway users in addition to occupants of nearby structures. Differences between how pipeline failure consequences should be evaluated for motorists travelling at a high speed in vehicles versus occupants of non-moving structures were investigated. Relevant factors that are unique to consequence assessment for roads, such as vehicle speed and relative position between roads and pipelines, were identified, and the way in which these unique factors can be incorporated in risk assessment was studied. Based on the results from the study, TC Energy updated its System Wide Risk Assessment program to include consequence of pipeline failures associated with roads and roadway users. Data from a total of over 180,000 km of roadway segments across North America that are located in proximity to TC Energy’s over 90,000 km of natural gas transmission pipelines were analyzed. The societal risk with and without roads were evaluated and compared. The impact of considering roadway traffic in the risk assessment in different areas was assessed and the importance of proper consideration of roadway users in quantitative pipeline risk assessment was demonstrated. The methodology developed in the study is useful in advancing the overall accuracy of risk assessment for the pipeline industry, and particularly for pipeline operators in the US, provides a quantitative assessment tool to evaluate the risks associated with roadway driven Moderate Consequence Areas (MCA) and prioritize the integrity work accordingly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".