Reliability Based Design and Assessment for Location-Specific Failure Threats With Application to Natural Gas Pipelines
Bibliographic record
Abstract
The acceptance criteria used in reliability based design and assessment are defined as a set of reliability targets (where reliability is defined as 1.0 minus the probability of failure). Because of the linear nature of pipeline systems, reliability targets are defined on a per kilometer-year basis. Such targets are directly applicable to failure causes (or limit states) that are equally likely to occur anywhere along a segment of the pipeline (e.g., equipment impact or yielding/rupture of defect-free pipe under internal pressure). They are, however, not directly applicable for design and assessment situations involving limit states that apply at known specific locations. Examples include design for geotechnical loads on a particular unstable slope or integrity assessment of a corrosion defect at a specific location (as determined by in-line inspection). In previous work, reliability targets for natural gas pipelines have been developed on the basis of appropriate societal and individual risk criteria. This paper describes an approach to adapt these targets and demonstrate compliance with them, for location-specific limit states. The approach is based on using separate checks to ensure that the individual and societal risk criteria underlying the targets are met. An example is included to demonstrate application of the approach to design a pipeline on an unstable slope.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".