Managing the Risk of Critical Water Trunk Mains: A Municipal Perspective
Bibliographic record
Abstract
In early 2012, the City of Regina started a risk assessment program for its critical trunk mains in its water supply/distribution system. The program included the rating of initial condition and failure consequence ratings of the mains, and the development of a risk model for the system. It also recommended an annual condition assessment program, which combines desktop analysis and field inspections to maximize knowledge of the trunk’s condition in the most effective manner. As an initial and simplified step, pipe condition in the risk model was estimated based on pipe age, size and material. However, the foundation of a condition assessment program should be built on the applied loadings on the trunks versus the residual resistance strength of the trunks as determined in the condition assessment program, as this is the true measure of failure probability. Considering the high cost of a condition assessment program, an initial assessment project was conducted to rationalize both the economics and degree of certainty associated with different inspection techniques in order to develop an optimum program for the long term. This paper presents the framework developed for managing the risk of the City’s trunk mains, which includes an asset criticality model with trunks ranked based on their relative consequence of failure, a condition assessment strategy that maximizes the use of existing failure records and in-direct assessment data, and a set of decision criteria that can be used to identify and prioritize inspection, maintenance, and capital requirements for a comprehensive mitigation plan for the trunks. Also presented in the paper were the results of the initial assessment project. Both traditional and advanced techniques were deployed to inspect the condition of two trunk sections. The inspection indicated severe internal pits and potential external coating deficiencies/pipe corrosion. Field excavation was performed to verify the inspection results.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".