Spill Consequence Analysis: A Method to Prioritize Integrity Excavations of Liquid Pipelines
Bibliographic record
Abstract
Corrosion and stress corrosion cracking (SCC) control programs are elements of pipeline integrity management. These programs often include in-line inspections (ILI’s) to identify, characterize and size anomalies, followed by field excavations to repair defects, remediate coating failures, and establish tool sizing accuracy. The highest priority excavations target anomalies with the lowest predicted remaining strength or deepest flaws. In cases where loss of pipeline integrity is highly consequential, selection of additional excavation sites based on the risk of failure is warranted. As specified in Annex B of CSA Z662-07, the risk of failure combines probability (typically based on the predicted remaining life of known corrosion or cracking features) with an evaluation of consequence along the pipeline length. Consequence evaluation typically considers the impacts of health and safety, environmental, property damage, public disruption, service interruption and financial loss. A practical methodology for evaluating consequence for liquid pipelines was developed for the NPS 10 Alberta Products Pipeline (APPL) in Alberta, Canada. Comprising of three (3) parts, the methodology starts with an evaluation of spill volume along the pipeline based on valve closure times and pipeline inventory drawdown. Combined with soil absorption data and topography, the spill volumes are used to model spill areas along the pipeline. Finally, the spill areas are overlaid on a classification of land use along the pipeline to quantify the relative spill consequence. The land use classification developed for this analysis has commonalities with the definitions for High Consequence Areas (HCA) and Unusually Sensitive Areas (USA) within U.S. Code of Federal Regulations (49 CFR, Part 195). Prioritized segments of the pipeline with elevated consequence levels were identified and used with the probability of failure to calculate risk and prioritize sites for ILI excavation programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".