A Capacity-Based Optimal Allocation of Storm Pipes' Replacements: Considering the Effects of Climate Change and Urbanization
Bibliographic record
Abstract
Storm pipes are a major component of any municipal infrastructure system, however, poor attention is commonly paid to their management and replacement of pipes is typically guided by condition criteria. Pipes replacement also faces the issue that their capacity is not considered, and the proposed pipes replacements are of the same diameter as the previous ones. However, lagged replacements of storm pipes can compromise urban system ability to drain runoff-water from rainfall and this could lead into flooding. This research extents optimal condition-based allocation of pipes by considering demand-capacity ratios, in order to prevent flooding. The effects of urbanization and climate change are also incorporated. A general method for detecting the impacts of urbanization currently exist, however, a simplified approach due to the lack of data is suggested. A case study of the city of Kindersley in Saskatchewan is used to illustrate the application of the method. Hydrological models based on current and future land uses were used to estimate changes in demand-capacity ratios for each pipe in the system. Performance curves for capacity and condition were developed, and validated by change detection of observed historical land use cover for the past 25 years. An extra 18% rainfall intensity was used to model the impacts of climate change. It was found that CAN$40,000 were enough to sustain current levels of condition and capacity-demand ratios, however, condition was at unacceptable level. Budget was raised and weights were adjusted until a combination with 45% capacity and 55% condition with $100,000 was found to be the departure at which compliance begins to reach desirable point minimum levels of demand-capacity ratios and condition, possibly higher values of budget would be necessary as the municipality adjust such minimum requirements to the desired ones.
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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 | 0.003 |
| 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 | 0.001 |
| 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".