A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events that may cause catastrophic accidents and heavy financial losses. This study develops a safety assessment model for O&G pipeline failure by incorporating fuzzy logic into Bayesian belief network. Proposed fuzzy Bayesian belief network (FBBN) model explicitly represents dependencies of events, updating probabilities and representation of uncertain knowledge (such as randomness, vagueness and ignorance). The study highlights the utility of FBBN in safety analysis of O&G pipeline because of its flexible structure, allowing it to fit a wide variety of accident scenarios. The sensitivity analysis of the proposed model indicates that construction defect, overload, mechanical damage, bad installation and quality of worker are the most significant causes for the O&G pipeline failures. The research results can help owners of transmission and distribution pipeline companies and professionals to prepare preventive safety measures and allocate proper resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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