Decision making methods to prioritise asset-management plans for municipal infrastructure
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".