Selection Method for Rehabilitation of Water Distribution Networks
Bibliographic record
Abstract
Selecting most suitable methods for rehabilitation of water main networks has become a challenging task; considering the wide range of emerging trenchless methods. Developing decision support methods is expected to assist municipalities in North America and elsewhere in carrying out the selection process effectively. This paper introduces a new concept for choosing cost effective rehabilitation methods accounting for cost and duration of each rehabilitation method being considered along with its impact on environment using Multi-Objective evaluation methodology (MOM). The first objective, cost of rehabilitation, is calculated considering a number of cost elements including social cost. Social cost, in the developed methodology accounts for cleaning costs, loss of sales tax, number of impacted vehicles and pedestrians, etc. The second objective is the duration of the rehabilitation method under consideration. The environmental impact is considered as the third objective. It is modeled considering several factors using the Analytical Hierarchy Process (AHP). In order to demonstrate the essential features, a hypothetical example has been developed to test the model robustness. Results show that a cost effective method can be selected by combining cost, duration, and impact on environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".