Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks
Bibliographic record
Abstract
Different techniques have been used to maintain and rehabilitate pipes and manholes of the sewer networks in the province of Quebec, including several trenchless rehabilitation techniques in the past two decades. In an effort to predict the future performance of trenchless rehabilitations, this paper presents condition prediction models for chemical grouting rehabilitation of both pipelines and manholes in the city of Laval, Quebec, Canada. The models were developed using regression analysis, based on gathered and analyzed closed circuit television (CCTV) inspection reports for the Laval city sewer network. Different defects in the chemical grouting rehabilitated sewer mains and manholes in this city are presented. The developed regression models are capable of predicting the structural and operational conditions; they are also utilized to generate deterioration curves over time for chemical grouting rehabilitation of sewer pipes and manholes based on basic governing factors such as pipe material and rehabilitation age. Models were validated using a set of data that was randomly selected and set aside. Models validation based on the value of coefficient of multiple determinations (R2) ranged between 80 and 97%. The developed models could be used by municipalities for forecasting chemical grouting rehabilitation for network components’ conditions, planning inspections, and in decision making regarding budget allocations.
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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) | 0.001 | 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".