Roles of Value in the Evaluation and Modeling of Decision Strategies for Pipe Maintenance in Water Distribution Networks
Bibliographic record
Abstract
Water distribution networks (WDNs) are essential infrastructure systems, providing vital drinking and process water for urban health and economic vitality. The unfolding choices of how and when to maintain these aging networks reflect the values (priorities) invoked by decision makers, and, in turn, these choices determine the value of the service delivered, ranging from adequate water delivery to its alignment with broader environmental, social, and economic objectives. However, the extent to which asset management strategies acknowledge and incorporate diverse values through their priorities, assumptions, and objectives, remains limited both in practice and in modeling research. This paper proposes a computational model to evaluate the performance of long-term pipe maintenance strategies through Monte Carlo simulations of sequential pipe maintenance activities, capturing the probabilistic nature of pipe failure and its impacts across multidimensional performance metrics. Specifically, the model explores how operator priorities affect the choice of different repair and replacement strategies, and the effects of these value profiles across multiple dimensions including both system-level performance and the perception of different stakeholders such as users and service regulators. An illustrative example of a theoretical distribution network shows how valuing pipe replacement over short-term repair can both reduce pipe failure risk and lead to notable improvements in service, environmental, and monetary outcomes. Through an exploration of stakeholder value perception, this study shows that there is potential for alignment between societal objectives such as water losses and energy use, and between global service outcomes and direct maintenance costs for network operators. This study provides a novel exploration of the relationship between value (i.e., preference, priorities, and objectives) across stakeholders, and proposes methodological improvements in pipe maintenance modeling to better reflect the uncertain nature of WDN operation and a generalized and granular approach to pipe maintenance modeling.
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 | 0.005 | 0.021 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".