Value-based optimisation for cross-asset maintenance in a Canadian municipality
Bibliographic record
Abstract
Most municipalities have a disconnection between the effective usage of their budget and their ability to preserve the value of their network of assets. Long-term planning for cross-asset infrastructures is always a challenge for municipalities. This paper proposes a decision-making platform which optimises municipal assets by integrating cross-asset models with asset value to achieve an optimal solution for the maximum returns of investment over a long-term period. The method was compared with the classical condition-based optimisation approach by implementing it on a case study of the Municipality of Kindersley, Canada. It was found that the value-based optimisation model demonstrated meaningful results by integrating engineering concepts with the value of the assets to determine the optimal long-term investment planning. For the same available budget, the value-based model achieved a similar overall condition while increasing the total value relative to that of the condition-based approach. The life-cycle analysis showed that for 20 years’ investment in the case study, the value-based model obtained Can$18 million (US$13·5 million) more return, which validates the higher efficiency of the proposed model. The developed value-based optimisation technique enables municipalities to apply a multi-asset decision-making process that balances engineering and economic approaches to delivering better value for money.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".