Assessing Geohazard Probability of Pipeline Failure: Lessons and Improvements From the Last 10 Years
Bibliographic record
Abstract
Abstract Geohazards, consisting of geotechnical hazards where ground movements impact pipelines and hydrotechnical hazards where pipelines cross watercourses, can threaten pipeline integrity, causing leaks or ruptures. Given the vast geographies traversed by pipeline infrastructure, geohazard frequency can be high requiring triage of large inventories of identified geohazard sites. Since 2012, field screening probability of failure algorithms have been used to assess and prioritize geohazard threats to pipeline integrity. These algorithms were developed using empirical data from failure case histories, engineering judgement from geohazard professionals, and statistical rates of pipeline impact, exposure, and failure. When combined with consequences, the algorithms provide semi-quantitative risk assessments. The risk assessments are used to compare geohazard threats to other pipeline integrity threats to support cost-benefit decisions for pipeline operation. The algorithms have been applied to 243,000 sites on 440,000 km of oil and gas gathering, transmission, and distribution pipelines primarily in Canada and the United States. In this paper, lessons learned from applying probability of failure algorithms to geohazard sites over the past 10 years are shared. The algorithms have proved successful in that their use has allowed pipeline operators to focus their integrity management effort on higher probability of failure sites. Use of the algorithms also allows operators to reduce investment on low probability of failure geohazard crossings. For example, the probability of failure assessments provide justification for less frequent reinspection intervals of low probability of failure sites, while providing clear means of advocating for the need to mitigate and monitor high probability of failure geohazard sites. Over the past decade, recalibration of the algorithms has reduced conservatism in earlier versions. As well, advancements in data collection, storage, and quality assurance have been undertaken to improve accuracy. This paper describes the methods used to assess probability of failure for pipeline landslide and watercourse crossings. The use cases and limitations of the algorithms are also discussed.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".