Dealing With Knowledge Uncertainties in Pipeline Reliability Based Design and Assessment
Bibliographic record
Abstract
Knowledge uncertainties result from limitations of the data and other information required to define parameters that are used in estimating reliability with respect to a given failure threat. The parameters affected typically represent distribution parameters of input random variables used in the calculation; for example, the mean corrosion growth rate for a given pipeline segment. Knowledge uncertainties are distinct from randomness, which is typically manifested in variations in the basic input parameters affecting a given limit state; for example, variations in the excavator force applied to the pipeline in different impact events. Randomness is reflected in the probability distributions used to model the input variables affected and is automatically built into the reliability estimate. However, the reliability estimate is conditional on the values used for parameters affected by knowledge uncertainty. Since these parameters can take a range of values with different probabilities, knowledge uncertainty is best represented as a distribution or confidence interval on the calculated failure probability. Two approaches are proposed to deal with knowledge uncertainties in Reliability Based Design and Assessment (RBDA) applications in which design and operational choices are accepted if they meet a specified reliability target. The first is a formal approach in which reliability targets must be met with a specified level of confidence (e.g. meet the reliability targets with 90% confidence). The second approach is an informal one in which a single conservative value is used for each parameter affected by knowledge uncertainties. Although this approach relies on the judgment of the user, it has the advantage of being simple. In the context of standardizing RBDA, it is recommended that epistemic uncertainty be identified as an important issue that must be considered in demonstrating compliance. It is also recommended that both formal and informal approaches be permitted as viable means of accounting for epistemic uncertainty. The informal approach should be included as a minimum requirement, whereas the formal approach should be presented as an option. This recommended strategy addresses epistemic uncertainty without creating a significant obstacle to the application of RBDA.
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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 | 0.022 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".