Life-Cycle Cost Based Rehabilitation Plan for Water Mains
Bibliographic record
Abstract
According to the Canadian National Research Council reports, the renewal and rehabilitation of infrastructure across Canada is estimated to be at least $15 billion. Life cycle cost is an essential approach to distinguish alternative rehabilitation strategies for water main rehabilitation. Therefore, Life-cycle cost is used to compare different alternative strategies among water main rehabilitation techniques. Current research identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (e.g. open trench, sleeves); (2) renovation (e.g. slip lining, cement lining, epoxy lining, CIPP); and (3) replacement (e.g. pipe bursting, micro-tunneling, directional drilling, auger boring, open cut). Stochastic life cycle cost (SLCC), using Monte Carlo simulation approach, is used to compare different rehabilitation scenarios within the same alternative. Data, related to the cash flow of each scenario, are collected from contractors and municipalities in Canada. Results show that using "Open Trench" and "Slip-Lining" are the best methods for "repair" and "renovation" categories, respectively. However, the best method for "replacement" category is pipe bursting for small pipe diameters (<30") and open cut for large pipe diameters (>30"). Accordingly, a rehabilitation plan, based on SLCC, favors repairing with "Open Trench" until the breakage rate reaches 0.5 breaks/ km/year then replaces the main.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.010 | 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".