Use of Monte Carlo Simulation in Long-Term Capital Planning of Rehabilitation of Water Distribution Networks
Bibliographic record
Abstract
Long-term capital planning of intervention programs for water distribution networks involves the selection of pipe for intervention by applying the appropriate strategies such as replacement, lining, or cathodic protection as examples. A proposed program consists of both the selection of pipe and the time at which the interventions are to be performed. The assessment of the costs and benefits of long-term programs necessarily require a prediction model to forecast water main break rates into the future. However, in proposing a rehabilitation scenario the use of predicted failure rates can be problematic in the immediate short-term. There is always some variation expected between predicted failure rates and observed failure rates. Failure history rather than predicted failure rates is the preferred method for selecting pipes for intervention in the short-term. Replacing a pipe that has not failed cannot be justified on the grounds that it is predicted to fail. Therefore, long-term planning models should transition from pipe selection based on past failure history for the immediate present to selection based on predicted failure rates in the distant future. This paper describes how Monte Carlo simulation can be used to shift from short-term, history-based modeling to long-term, prediction-based modeling in a single planning scenario. The method is demonstrated using examples from the City of Hamilton, Ontario.
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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 | 0.007 |
| 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".