Optimal Scheduling of Replacement and Rehabilitation of Water Distribution Systems
Bibliographic record
Abstract
Many municipal water distribution systems across North America are reaching or have exceeded their design lives and, therefore, require extensive upgrading through rehabilitation and/or replacement. However, these needs far surpass the available resources, and decision makers must prioritize their replacement/rehabilitation needs. One such approach is the determination of the optimal replacement time based on the minimization of the total or annual average cost during a predetermined service period. This paper describes a simple approach for the optimization of replacement/rehabilitation activities for a network of buried pipes, which is based on the assumption that the occurrence of breaks in a pipeline segment follows a nonhomogeneous Poisson process. Equations for evaluating the optimal replacement time are derived by minimizing the expected annual average cost during the service period of the pipeline segment. Predictions are compared with those obtained by minimizing the expected total (accumulated) cost during the service or planning period. The use of the proposed approach is illustrated via numerical examples. Optimal replacement time predictions, based on minimization of the annual average cost, were found to be significantly longer than those obtained based on minimization of the total cost for the case—where break occurrence rate follows an exponential function.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".