Data acquisition and analysis for water main rehabilitation techniques
Bibliographic record
Abstract
The ability to regularly deliver safe drinking water is a constant challenge to municipalities worldwide. In Canada, the replacement/rehabilitation cost of water mains is estimated to be $28 billion (1997–2012). Therefore, selecting cost-effective repair and/or rehabilitation scenario(s) is essential to optimise the quality of existing water mains and minimise unnecessary rehabilitation costs. The research presented in this paper identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (i.e. open trench, sleeves); (2) renovation (i.e. slip lining, cement mortar lining, epoxy lining, cured in place pipe (CIPP)); and (3) replacement (i.e. pipe bursting, micro-tunnelling, horizontal directional drilling, auger boring, open cut). Due to complexity, scarcity, and enormity of data required to perform life cycle cost (LCC) and select the cost-effective scenario(s), the research presented focuses on LCC data acquisition and analysis. Data were collected from contractors and municipalities in Canada. Rehabilitation decision trees were developed as a preparation step for future LCC implementation. Breakage rate analysis was successfully developed to predict the intervals of various rehabilitation alternatives. The research presented is relevant to researchers and practitioners (municipal engineers, consultants, and contractors) to prioritise pipe inspection and rehabilitation planning for existing water mains.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".