Optimized planning of repair works for pipelines in water distribution networks using genetic algorithm
Bibliographic record
Abstract
Abstract One of the main reasons for pipeline breakage and leakage is aging. This results in a decline in system efficiency, and associated levels of service, which in turn may endanger public health and safety. Since the most expensive and significant part of a water supply system is the distribution network, repair strategies are necessary to protect the value of the assets when a pipeline reaches the end of its useful life. Usually, repair works are taken as a reaction to the detection of a leak, pressure improvement and other factors that eventually results in an inefficient management of allocated funds. Therefore, a careful computational analysis should be performed to efficiently utilize the allocated budge. This paper presents a near‐optimized budget allocation model that is able to find the near‐optimal scheduling plan for renewal and/or replacement. The aim of this paper is to suggest a new model in order to minimize the number of breaks over a given planning horizon and the overall cost of repair of water distribution networks (WDNs), including indirect damage cost, direct damage cost, and failure repair cost while fulfilling functional requirements. The model will identify the pipe segment that needs replacement, time of replacement, and the necessary interventions that should be carried out for the network. It also identifies those pipelines that need repair. The solution is usually constrained by the yearly limited budget. The optimization procedure results in a solution alternative that decision‐makers could use for their operational requirements. The outcome of the developed model is validated based on the available leakage and breakage data from the city of Montreal database and an existing validated model. The model forecast that, in the next 20 years, 19.7% of the network will need open trench repair, 25.3% could be repaired using trenchless techniques, while 50.9% of the pipelines will remain intact.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".