Developing a Decision-Support System to Optimize Rehabilitation and Replacement Programs for Ferrous Distribution Mains in Municipal Water Systems
Bibliographic record
Abstract
Given the importance of watermains in supplying safe and quality drinking water to customers, municipalities are required to ensure that the distribution mains are performing at certain pre-defined levels of service (LoS). In North America, the majority of watermains are made of ferrous materials, typically ductile iron (DI), and cast iron (CI) pipelines. These assets are prone to deterioration and breaks due to aging and other influencing factors, including corrosion. Annually, municipalities confront several constraints related to budgets and the proper time to intervene to preserve water distribution mains. With the significant municipal budgets required to rehabilitate or replace assets with newer pipelines, municipalities should be focusing on prioritizing interventions based on the likelihood and consequence of failures. Besides risk assessment frameworks, optimized programs need to be developed to maximize performance while minimizing the overall funding requirements in order to maintain sustainable funding levels and infrastructure in both the short- and long-term. Therefore, the main objective of this paper was to develop a decision-support system based on the genetic algorithm (GA) optimization tool. The tool considered maximizing the network performance and minimizing the total costs during a 5-year study period. After implementing the model on part of the Municipality of Thames Centre’s ferrous network, the total required cost attained was $434,282, and the performance of the network was restored to approximately 28 out of 100. The total costs of the major intervention (structural lining) and the minor intervention (cathodic protection) were $402,603 and $31,678, respectively. This study will benefit municipalities in developing optimized rehabilitation/replacement programs considering typical municipal constraints while maximizing the existing performance of the network and minimizing the total costs.
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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".