Design of Drinking Water Distribution Networks with Consideration of Future Retrofit
Bibliographic record
Abstract
Drinking water distribution networks are important because they provide water to meet basic human needs, and to protect humans in the event of fires and other public emergencies. With aging infrastructure, there is an urgent need to design water distribution networks to ensure safety and reliability of service in the future. The uncertainty that surrounds future scenarios of climate-induced drought, population levels, and urbanization patterns — all factors that influence water demand — means that a system design chosen today may be inadequate to meet future demands in a water distribution network. Water utilities often have to plan the design of their water distribution network with only limited information on future water demand levels. For example, if water demand in the future is higher than the level predicted at the time of design, then a municipality will eventually have to retrofit the system, perhaps at great expense, to meet the unanticipated water demand. Thus, there is a need for methods to design networks to minimize the need for future system retrofit under uncertain demands, and to make networks economically robust. In this paper a new framework is presented to design water distribution networks for economic robustness. Economic robustness supplements the least-cost optimality criterion by providing a measure of the variability involved in retrofit costs of a system under uncertain future conditions. In the new framework, water demand projected at the end of a 20-year planning period is treated as an uncertain quantity and modeled as a random variable with an error probability density function (PDF). In the proposed framework, new criteria such as the expected value and standard deviation of retrofit cost are developed to evaluate the economic robustness of water distribution networks. The framework is applied to a simple network, with comparison of design alternatives done on the basis of pipe cost and expected retrofit cost. The results obtained indicate that surplus hydraulic capacity lowers the uncertainty of future retrofit costs and increases confidence of retrofit cost estimates. Consequently, the results substantiate the intuitive understanding of the benefits of surplus hydraulic capacity in a system.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".