Asset Management Perspective in Long-Term Rehabilitation of Aged Water Distribution Networks
Bibliographic record
Abstract
Traditionally, the rehabilitation and/or upgrade of water distribution networks (WDNs) are performed with only one perspective with the aim to bring a long-term reliable system to supply the consumers. On the contrary, such systems are not isolated but interdependent with other adjacent infrastructure and networks, including urban drainage and road networks. This means that the performance and rehabilitation schemes of different networks could have direct or indirect impacts on each other, requiring coordinated infrastructure management strategies to be adopted. For example, the pipe replacement in a WDN could interrupt a traffic flow on a co-located street, or a flood could prevent water utility staff from accessing the section of a WDN pipe, thus making maintenance infeasible. Furthermore, if different infrastructural activities (operation, maintenance, rehabilitation, and upgrading) are not planned in an integrated way, there will be a high risk of redundant activities in the same location, imposing unnecessary costs. This implies that in the decision-making process for the rehabilitation of a WDN, the status and plans of other correlated urban systems should be considered simultaneously. In this study, the single and multi-utility rehabilitation of a simplified real-world WDN are dynamically planned, as a part of asset management process, with the aid of a multiobjective optimization engine in which the objectives are utility practice cost and outcome reliability measures. Then the results are compared and discussed in terms of the pros and cons of the two mentioned approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".