Value-based optimisation for cross-asset maintenance in a Canadian municipality
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it