Vulnerability of Buried Energy Pipelines Subject to Earthquake-Triggered Transverse Landslides in Permafrost Thawing Slopes
Bibliographic record
Abstract
Current regional seismic loss estimation models, for example, the United States Federal Emergency Management Agency’s (FEMA) Hazus, typically employ empirical functions for damage assessment of buried pipelines subjected to permanent ground deformation (PGD). These functions are based on a limited number of damage observations to pipelines during past strong earthquakes. They represent the repair rate per unit length in a brittle or ductile pipe segment under average structural and geotechnical conditions and ground failure phenomena. This study aims to propose an analytical method for assessment of vulnerability of ductile energy pipelines traversing permafrost regions and subject to active layer detachment (ALD) landslide hazard. Canadian ALD morphological statistics combined with the probability of pipeline exposure to transverse ALD-caused PGD and the extent of the potential PGD are used as input. A computer program is developed in order to analyze the structural behavior of pipelines and evaluate their vulnerability considering three damage mechanisms: tensile rupture, local buckling, and premature cross-sectional failure. The vulnerability functions associated with PGD, expressed in terms of repair rate, are obtained by applying Monte Carlo simulation to the structural analysis results. The novel vulnerability functions developed herein are specific to permafrost regions and can be incorporated in the Hazus-type platforms for regional seismic risk assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".