Decision making methods to prioritise asset-management plans for municipal infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a methodology for measuring and comparing the benefits associated with a change in the decision making method for infrastructure asset management. Three common methods of measuring are reactive approach (also known as worst-first), silos and trade-off optimisation. A case study is used to illustrate the impact of applying different decision making approaches. The case is based on the urban municipality of the Town of Kindersley, Canada, and contains pavements and water main, storm sewer and sanitary sewer pipes. Economic comparisons of (a) the observed levels of service under fixed budgets and (b) the expenditure required to achieve the target levels of service are presented to support the selection of the preferred decision making method and to measure the superiority of one approach over another. Results from the analysis confirmed the expected inferiority of the worst-first method. Applying the trade-off method resulted in the expenditure of 8.83% fewer resources than the use of the silo method. For a yearly budget of C$800 000 (US$590 695) applied to all types of infrastructure, the trade-off resulted in mean condition levels 12.9% higher than those resulting from the silo method. The proposed platform can be applied to other infrastructure using different performance indicators.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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