Application of Reliability Based Design and Assessment to Maintenance and Protection Decisions for Natural Gas Pipelines
Bibliographic record
Abstract
This paper describes a detailed assessment that was carried out to investigate the practical implications of using the Reliability Based Design and Assessment (RBDA) methodology, as described in Annex O of CSA Z662, as a basis for evaluating existing pipelines and making decisions on maintenance planning and damage prevention strategies. Two key pipeline failure threats are addressed, namely corrosion and equipment impact. The assessment was based on a number of test cases covering a wide range of diameters, grades, pressures, location classes and corrosion severities. The reliability levels associated with these cases were calculated as a function of time and compared to the reliability targets. Cases that did not meet the targets were re-analyzed with increasingly enhanced maintenance measures until the targets were met. Maintenance actions considered included higher maintenance frequencies and more stringent repair criteria for corrosion, and enhancements to such parameters as right-of-way patrol frequency and condition, public awareness programs and dig notification response for equipment impact. The results demonstrate that the reliability targets can be met through the implementation of reasonable and practical maintenance measures for the cases considered. The impact of using RBDA on the expected failure rates is discussed. In addition, the diameter and class ranges of pipelines requiring enhanced maintenance over the current norm are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".