Cost Optimization of Hydraulic and Structural Rehabilitation of Urban Drainage Network
Bibliographic record
Abstract
Urban drainage systems are prone to symptomatic decay that eventually causes surcharged flows and flooding, with important consequences for the aquatic ecosystems of receiving water bodies in addition to the safety of drinking water and recreational water activities. Rapid urban development and climate change combine with wear and tear along with a lack of network maintenance to accelerate this decay and cause a reduction in the hydraulic system’s capacity. In this context, the need for system rehabilitation becomes more pressing. Cost figures prominently take precedence in the decision-making surrounding the choice of rehabilitation method employed, but models for assessing cost-effectiveness which consider both structural and hydraulic options, in addition to real-world constraints and time-frame conditions, are lacking. This paper proposes an algorithm to maximize the benefits ensuing from the rehabilitation of urban drainage systems. Potential interventions considered in the algorithm include both traditional rehabilitation methods such as the resizing and rebuilding of damaged pipes, in addition to best management practices (BMPs) aimed at reducing runoff rate and volume. A case study from the borough of Verdun is presented, in which the algorithm identifies the best combination of volume retention, pipe rehabilitation, and resizing interventions to optimize the network’s hydraulic performance and minimize operational costs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".