From Inception to Completion—Rehabilitation of Large Non-Circular Combined Sewers in Winnipeg, Manitoba
Bibliographic record
Abstract
This paper details the rehabilitation of five large non-circular combined sewers in Winnipeg, Manitoba, Canada. The project is part of the City of Winnipeg’s annual sewer rehabilitation program and includes egg-shaped sewers ranging in size from 2,060 × 1,625 to 2,950 × 1,950 mm. With multiple rehabilitation technologies on the market, the preliminary design process focused on evaluating applicable rehabilitation methods. Considerations included the existing sewer geometry, liner constructability, construction risk, liner structural performance, and an evolving approach to liner design in the market. The project faced many obstacles during design and construction, including construction on leased City and railway property, construction on busy regional streets, and COVID-19-related shipping delays. This paper takes readers through the entirety of the project from preliminary design to the completion of construction. There is a focus on technology selection, risk reduction, tendering rehabilitation work utilizing the new ASCE MOP 145, and construction. The latter will focus on lessons learned, stakeholder engagement, ongoing risk reduction efforts, and dealing with a volatile post-pandemic global market. The intent is to demonstrate a holistic approach to large-diameter sewer rehabilitation, which is employed from inception to completion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".