Probabilistic Seismic Risk Analysis of Buried Pipelines Due to Permanent Ground Deformation for Victoria, BC
Why this work is in the frame
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Bibliographic record
Abstract
Buried continuous pipelines are prone to failure due to permanent ground deformation as a result of fault rupture. Since the failure mode is dependent on a number of factors, a probabilistic approach is necessary to correctly compute the seismic risk. In this study, a novel method to estimate regional seismic risk to buried continuous pipelines is presented. The seismic risk assessment method is thereafter illustrated for buried gas pipelines in the City of Victoria, British Columbia. The illustrated example considers seismic hazard from the Leech River Valley Fault Zone (LRVFZ). The risk assessment approach considers uncertainties of earthquake rupture, soil properties at the site concerned, geometric properties of pipes and operating conditions. Major improvements in this method over existing comparable studies include the use of stochastic earthquake source modeling and analytical Okada solutions to generate regional ground deformation, probabilistically. Previous studies used regression equations to define probabilistic ground deformations along a fault. Secondly, in the current study, experimentally evaluated 3D shell and continuum pipe–soil finite element models were used to compute pipeline responses. Earlier investigations used simple soil spring–beam element pipe models to evaluate the pipeline response. Finally, the current approach uses the multi-fidelity Gaussian process surrogate model to ensure efficiency and limit required computational resources. The developed multi-fidelity Gaussian process surrogate model was successfully cross-validated with high coefficients of determination of 0.92 and 0.96. A fragility curve was generated based on failure criteria from ALA strain limits. The seismic risks of pipeline failure due to compressive buckling and tensile rupture at the given site considered were computed to be 1.5 percent and 0.6 percent in 50 years, respectively.
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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 | 0.001 |
| 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 it