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 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 | 0.002 | 0.002 |
| 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 | 0.001 |
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 teacher head, 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".