Real-Time Flood Monitoring and Management of a Mississippi River Pipeline Crossing
Bibliographic record
Abstract
Enbridge Liquids Pipelines (Enbridge) operates over 26,000 km of liquid pipelines in Canada and the US, administers a system-wide geohazard management program to identify, investigate and monitor geohazards, and performs remediation as required. An integral part of the geohazard management program is real-time flood monitoring, where pipeline watercourse crossings affected by flooding are identified and flood levels monitored. Watercourse crossings where the pipelines have a high potential to become exposed, to span, and potentially to fail during a flood event are studied in more detail. This flood monitoring program automatically monitors publicly available real-time stream gauge flow measurements and compares these measurements to estimated discharge thresholds for the crossing under evaluation. Thresholds are related to the current pipeline depth of cover (DOC) and the amount of scour that can occur over a range of flood magnitudes. Thresholds include: 1) the estimated peak flow to expose the top of the pipe, “exposure flow”, 2) the estimated peak discharge and associated flow velocities that could create enough free spanning pipe for the onset of vortex induced vibration (VIV) fatigue failure, “flow of concern”, and 3) where additional mechanical assessment taking account of specific pipe properties, data requirements and circumstances has been carried out, the “critical flow”, the estimated average peak flow and duration that has the potential to result in product release due to VIV once a sufficient pipe span length has developed, “critical flow”. This paper is a case study of an assessment and flood monitoring of one of Enbridge’s Mississippi River pipeline crossings, which has a history of flood-related pipeline exposure and subsequent mitigations. During real-time monitoring of a 2015 flood event the “exposure flow” and “flow of concern” thresholds for this crossing were exceeded, resulting in a decision by Enbridge to shut down the pipeline. Subsequent surveys revealed that the pipe had become exposed and was spanning adjacent to the previously remediated area. The previous mitigation likely limited the length of pipe exposure and pipe span. Added complexity was encountered during the post shutdown DOC survey, which needed to be completed as quickly and safely as possible after flood levels declined to allow for an assessment of the actual condition of the pipeline prior to restart. This paper presents a methodology that could allow pipeline operators to identify river crossings susceptible to pipe exposure, and the potential for freespan development, due to flooding, by providing an understanding of what is likely happening to the cover over the pipe at a particular crossing during a flood event. This provides a tool to better manage pipeline river crossings experiencing flooding. As far as the authors are aware, this case study represents the first time a pipeline has been shut down based on real-time flows and thresholds in the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".