Decision Support Model for Integrated Intervention Plans of Municipal Infrastructure
Bibliographic record
Abstract
This paper describes a model designed to facilitate the decision-making process for corridor rehabilitation of municipal assets. The proposed model comprises four main modules encompassing identification of corridor segments, risk assessment of individual asset networks, and integrated risk assessment to identify critical corridor segments and set priorities for intervention plans. In general, risk assessment requires integration of the criticality of the asset condition and the consequences of failure values to prioritize intervention plans. Each asset network was evaluated with respect to 13 economic, social, and environmental factors using a weighted scoring system. The criticality index of each asset was developed by combining the consequence of failure index with the condition rating index. The integrated risk index for network segments was calculated by integrating the three criticality indices of the individual assets. A case study, from one of the 19 boroughs within the metropolitan area of the City of Montreal in Canada, was used to illustrate the developed modules and their respective functions. The results indicated a strong positive relationship between the integrated risk index and the criticality indices of the three networks. It also shows that the model successfully represents the integrated criticality index for the combined water, sewer, and road segments using their criticality indices as the coefficient of determination R2 was 0.9656. The implementation of the proposed model on the case study enabled condition rating of integrated segments into five main levels of criticality. The developed model is expected to assist municipal engineers and decision makers to prioritize inspections, rehabilitation, and replacement decisions and optimize budget allocation and resource usage.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".