Natural Gas Pipeline Failure Risk Prediction and Relation Analysis by Combining Rough-AHP and Rough DEMATEL Method
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
The main purpose of this research is to identify the most crucial factors, accordingly improve the security management and reduce the potential failure risks. In our daily life, natural gas is widely used for manufacturing industry and household activities such as heating, cooking and production of electricity. Last two decades natural gas consumption rate has been increasing exponentially in all over the world. As a result, day by day energy Provider Company gets pressure to supply safe and reliable distribution from the point of source to the specific consumer. Any kind of pipeline failure may cause catastrophic disaster i.e. human casualties, financial penalty, delay of manufacturing goods production and environmental pollution. Subsequently with help of Rough Analytic Hierarchy Process (Rough-AHP) the energy providers can analyze failure rank order according to the importance. In addition Rough-Decision making and Evaluation Laboratory (Rough DEMATEL) methods can analyze the cause-effect relation among gas pipeline failure criteria. Therefore, the energy supplier company can take necessary action plan and reduce the potential pipeline failure risk. As well as, company can estimate the budget for maintenance program based on priority.
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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