Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains
Bibliographic record
Abstract
Authorities in Quebec, Canada, have been making considerable efforts to improve sewer networks across the province by using up-to-date technologies. To rehabilitate its sewer mains, Quebec has used several rehabilitation methods including slip lining, cement and epoxy lining, and cured-in-place pipe (CIPP).These replacement and rehabilitation techniques have been developed over the last four decades, with many arbitrary declarations made about the efficiency and performance of different rehabilitation techniques. This paper presents condition prediction models for CIPP rehabilitation of sewer mains. Regression analysis technique is used to develop these models, based on gathered and analyzed closed-circuit television (CCTV) inspection reports for Quebec CIPP rehabilitations. The models can predict the structural and operational conditions of CIPP rehabilitation on the basis of basic input such as pipe material, and rehabilitation type and date. They can also generate curves illustrating condition deterioration over time with respect to governing factors. A data set was randomly selected and put aside for validating the developed models. Models validation was based on the value of the coefficient of multiple determination (R2) ranging between 94 and 99%. The developed models are expected to be used by municipalities and contractors to forecast the condition of rehabilitated pipelines, plan inspections, and make informed budget allocation decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".