Qualitative and Semi-Quantitative Model for Estimating the Probability of Failure at River Crossings
Bibliographic record
Abstract
Abstract Pipeline river crossings are typically managed by using a combination of flood monitoring, ground inspections, integrity assessments, and remediations. Using a probabilistic model to assess the likelihood of failure at river crossings would enable combined consideration of all factors that contribute to the failure threat, provide site rankings to support discrete mitigation prioritizations, allow for evaluation of whether a crossing is acceptable in regard to a risk target, and provide a “check” to the deterministic integrity management methods. This paper describes two models for estimating the pipeline probability of failure at river crossings. The first model is a qualitative scoring model that can be easily implemented by operators and consultants. This model employs a weighting-factors approach to consider the multiple variables that contribute to pipeline exposures and overstress given exposure. The results may be applied to threat rank diverse crossings, as well estimate the probability of failure at a crossing relative to that at historical failure sites. The second model is a semi-quantitative model that 1) estimates the likelihood of a crossing exposure occurring, 2) estimates the associated scour length, 3) assesses the pipelines critical span length, and 4) quantifies the probability that a span length longer than the critical span length could form. This model may be applied to achieve the same goals as the qualitative model, and also compare the probability of failure at a river crossing to a reliability target. Due to the complexity of this model and the paper length limits, it is conceptually described within this paper. The results demonstrated that the model output site rankings correlated reasonably with those estimated by pipeline integrity program managers, the scour depth and length prediction results were consistent with measured historical scours, and the pipeline probability of failure at the assessed river crossings were within expected ranges.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".