Fragility analysis of buried continuous pipelines under normal faulting through analytical solution combining with machine learning technique
Bibliographic record
Abstract
Crossing major active faults is often unavoidable for pipelines in earthquake-prone regions. This study explores a finite difference analytical solution for continuous pipelines subjected to normal faulting. The effectiveness of the proposed method is evaluated by comparing the calculated results with the data from centrifuge and full-scale laboratory tests. Combining with the Lasso regression machine learning technique, multiparameter probabilistic risk assessment of pipelines can be performed to generate fragility curves. Probability density function curves of normalized location of pipe failure are also computed. Results show that pipelines with a larger pipe wall thickness and a smaller pipe diameter buried at a shallower depth in soils with a lower elastic modulus are less prone to failure. The dominant failure mode of pipelines transits from local buckling to tensile strain, depending on the exceedance of critical diameter-to-thickness ( D/t) ratio and burial depth. Greater attention should be placed on the failure mode of local buckling, and the normalized location of pipe failure moves away from the fault plane, when a shallow burial depth is selected. The fragility relations help to understand the relative vulnerability of pipelines with different parameters, and provide implications for the design and repair of pipelines in seismic-prone regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".