A New GIS-Based Method to Estimate Annual Probability of Pipeline Failure Resulting From Landslides Based on Actual Failure Locations
Bibliographic record
Abstract
Abstract Risk assessment and reliability models try to predict the probability of landslide-induced pipeline failures based on detailed, site-specific studies. Because these models are mainly designed to be used on a site-by-site basis, applying them over long pipelines is a challenge due to the challenges of collecting vast amounts of data for those long distances. A new GIS-based method has been developed to produce an order of magnitude approach to estimating the annual probability of landslide-caused failures (POFs) of pipelines over entire transmission systems using historical data that can range from loss of containment, loss of serviceability, and significant deformations caused by landslides. This new method uses high-resolution light detection and ranging (LiDAR) mapping to detect and delineate terrain anomalies interpreted to be the geomorphic response to ground deformations caused by landslides. The possible landslides are inventoried to record their activity, relative relationship with the pipeline, proximity to the centerline, length of intersection with a pipeline, and the angle of incidence between the perceived direction of movement of the potential landslide and the pipeline. This method also integrates regional landslide susceptibility maps depicting the relative likelihood of soil units to landslide occurrence along the pipeline corridor and its surrounding areas. In the presented study case, the developed method is applied to an approximately 19,312-kilometer (12,000-mile) pipeline system located in the United States and Canada. The application of the model yielded results that significantly help the operator to prioritize and optimize the allocation of resources for landslide management. The model can be replicated over multiple pipeline systems and customized to the particular needs of the end-users.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".