Third-Party Damage Model for Gas Distribution Pipelines
Bibliographic record
Abstract
Third-party damage (TPD) is any damage to underground infrastructure that occurs during work unrelated to the asset. In 2015, there were 10,107 TPD incidents in Canada causing over a billion dollars in estimated damage. TPD is the leading cause of failure for distribution gas pipelines; since distribution pipelines are generally located in areas with high population densities, TPD has significant safety and economic implications. In this study, a probabilistic model is developed to qualify the probability of failure of distribution pipelines due to TPD. The model consists of a fault tree model to quantify the probability of hit given the occurrence of third-party excavation activities and the methodology to evaluate the probability of failure given hit. Fault tree analysis (FTA) is a top down, deductive failure analysis method which uses Boolean logic to combine a series of basic events to analyze the state of a system. Earlier prior research demonstrated the ability of a FTA to quantify the probability of TPD occurring on natural gas transmission pipeline systems. These models allow for a quantitative analysis of preventative measures and, in conjunction with current practices, facilitate a predictive method to plan and optimize resource allocation for damage mitigation and emergency preparedness. The developed TPD model is validated using the data provided from a region in Southwest Ontario. The model will provide distribution companies with a practical tool to identify third-party damage hot spots, develop proactive third-party damage prevention measures, and prioritize damage repair activities using a risk-based approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".