Bank Erosion and Encroachment Model Development to Enhance Pipeline Integrity Management
Bibliographic record
Abstract
Abstract Bank erosion can expose pipelines at watercourse crossings or in floodplains at encroachment sites. Once a pipeline is exposed, failure can occur due to water loading, debris loading, excessive strain, or vortex-induced vibration (VIV). Empirical engineering formulae have been developed to predict scour depths in rivers and can be used at pipeline watercourse crossings to estimate the annual probability of pipeline exposure from scour. Vulnerability algorithms can then be used to estimate the annual probability of pipeline failure at watercourse crossings, which most commonly results from VIV. However, bank erosion has generally proven to be complex and time-consuming to assess, and it is therefore often considered at the site scale rather than at a screening level in system-wide assessments of hydrotechnical hazards. This paper describes a novel bank erosion model developed and validated using morphological characteristics and historical aerial imagery from over 240 detailed assessments of pipeline watercourse crossings and encroachment sites across North America. The model predicts the rate of bank erosion based on channel and floodplain morphology, as well as other site characteristics such as debris presence where data exists. Based on these characteristics, as well as the actual rate of observed erosion where possible, a time to pipeline exposure is estimated at watercourse crossings and encroachment sites. The estimated time to pipeline exposure and bank protection type is then used to assign an annual probability of pipeline exposure. This paper also addresses a gap in the current practice by providing a framework for assessing vulnerability at encroachment sites where flow is parallel or sub-parallel to the pipeline and VIV is unlikely to occur. The probability of pipeline failure at both watercourse crossings and encroachment sites can then be assessed by considering site-specific characteristics, such as the potential free span length, pipe properties, and pipeline operating conditions. As the models use morphological characteristics observable on the ground or in a desktop review and known pipeline data, the results can be used to prioritize actions (e.g., ground inspections or mitigation) at watercourse crossings and encroachment sites and to guide geohazard management strategies to enhance pipeline integrity management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".