Optimized phased planning for dynamic rehabilitation of integrated municipal infrastructure
Bibliographic record
Abstract
ABSTRACT Phased planning for municipal infrastructure is based on the time-dependent status of multiple networks, which is in contrast to the traditional approach, where one-phase construction and a single status are considered for planning system activities. This study integrates and optimizes the corridor-wise intervention planning of water, sewer, and road networks where the number of equally long phases and intervention decisions are among the decision variables showing the extent to which phase number optimization can impact the cost and coordination of the interventions in interdependent systems. Optimizing the phase number for municipal infrastructure optimization within an evolutionary algorithm is a challenging task due to the evolutionary recombination between numerous planning solutions with different decision variable lengths. A multi-phase design and construction approach is developed for the rehabilitation of the system in a real case study in Montreal, Canada. The study involves 20 corridors in which a street section is co-located with water and sewer pipes. A metaheuristic single-objective optimization engine is employed to minimize the total net present value of intervention plan costs for the whole integrated system. The results show that phased optimization could bring about a 25% cost saving for the rehabilitation master plan and coordinated multi-systems intervention activities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".