Sustainability Assessment of Asset Management Decisions for Wastewater Infrastructure Systems—Implementation of a System Dynamics Model
Bibliographic record
Abstract
The goal of this case study is to demonstrate the application and utility of a developed System Dynamics (SD) model to assess the sustainability of strategic decisions for managing the wastewater collection (WWC) pipe network system for a medium-size municipality in Southern Ontario. Two asset management scenarios, suggested by the research-partnered municipality, are adapted based on the acceptable maximum fraction of pipes in the worst condition (ICG5) being equal to (1) 10% of the network-length/year, and (2) the initial 2.8% of network-length/year for the entire life cycle of the asset. The urban densification scenarios are restricted to a 50% urban densification rate. The least maximum rehabilitation rates of 1.41% and 1.85% of network length/year are found necessary to keep the ICG5 pipes fractions below the selected 10% and 2.8% thresholds, respectively. The maximum and minimum user fee-hike rates for WWC and wastewater treatment (WWT) services are adjusted to support the financial self-sustainability aspect. Results from the SD model, as presented over a 100 year simulation period, show that an accelerated rehabilitation strategy will have a lower financial cost with the lowest greenhouse gas (GHG) emissions. This study highlights the implications of integrating asset management of wastewater-collection and -treatment systems. Applying such an integrated SD model will help decision makers to forecast the future trends related to social, economic, and environmental performances of wastewater infrastructure systems, and evaluate the behavior of interrelated and complex WWC and WWT systems to find synergistic cost-saving opportunities while at the same time improve sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".