Considering of economies of scale and adjacent infrastructure works in water main renewal planning
Bibliographic record
Abstract
Optimal strategies for scheduling rehabilitation and/or replacement of water mains can generally be classified as high-level planning of groups or cohorts of pipes, or low-level scheduling of individual water mains, which is the subject of this paper.An approach is proposed for the efficient scheduling of replacement of individual water mains. The approach receives as input the expected number of breaks forecasted for each pipe in a short to medium planning period. The present value (PV) of all costs that are associated with each individual pipe is computed and good candidate pipes for replacement within the planning period are identified. Candidate replacement policies are formulated, including economies of scale considerations as well as coordination with other known infrastructure works. A multi-objective genetic algorithm scheme is used as a tool to search a vast combinatorial solution space for feasible solutions that minimise PV of cost as well as maximise usage of available budgets. Various budget scenarios can also be considered, including global budget for the planning period as well as series of annual budgets for each year in the planning period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".