Developing and Implementing a PCCP Condition Assessment Program
Bibliographic record
Abstract
This paper presents the City of Ottawa’s recent experiences developing and implementing a condition assessment program for large-diameter watermains. A significant portion of the inventory is comprised of prestressed concrete cylinder pipe (PCCP) lines, and due to recent failures on these pipes, efforts have been focused on understanding the condition and failure mechanisms of the PCCP in their system. As the City has embarked on their inspection program, it has become apparent that compiling and managing available data, filling in missing critical pieces of data, selecting appropriate PCCP inspection tools, and interpreting inspection results are all critical to developing a complete program. The goal of the City’s condition assessment program is to identify distressed pipes with broken prestressing wires, evaluate the risk of failure of such distressed pipes, and repair or replace pipes at high risk of failure. In order to achieve this goal, the City needs to understand the structural capacity of the pipes, the sensitivity of the pipes to prestressing wire breaks, and which inspection technologies provide sufficient resolution in the distress estimation. This paper will present an overview of the original condition assessment approach at the City of Ottawa, data sources and gathering efforts, how the program evolved as well as the lessons learned and improvements of the program over time.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".