Identifying Rehabilitation Options for Optimum Improvement in Municipal Asset Condition
Bibliographic record
Abstract
Sustainability in municipal services calls for a comprehensive asset management approach that balances between the needs of a growing portfolio of aging infrastructure and the increased demand(s) arising from new growth—all while staying within the financial means of the community. Best practices for municipal asset management require municipalities and communities to clearly define and state their respective goals that reflect their expectations in terms of level of service. The challenge lies in the fact that asset performance from a community perspective may be quite different from that of a municipal perspective. There is need to interrelate the two perspectives and accordingly determine the optimum quantity of improvement required in the condition of a municipal asset. A complete solution should lead to the most appropriate technique for asset rehabilitation. A methodology to address these issues is proposed and illustrated that identifies and adopts: (1) a model to express asset level of service, (2) a model to measure asset condition based on performance, and (3) a fuzzy logic–based method that maps the level of service to the asset condition rating. Based on the inputs of these models, a structured method for analyzing the capacity and suitability of rehabilitation techniques is designed. Case study of a water main is presented to illustrate the concept and to quantitatively demonstrate the implementation of the methodology. This methodology will assist municipal asset managers to quantify the condition improvement required in their assets, in order to meet service goals, and to thereby make more informed decisions on the type and priority of rehabilitation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".