Decision Support Model for Integrated Risk Assessment and Prioritization of Intervention Plans of Municipal Infrastructure
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a model for the integrated risk-based prioritization of municipal infrastructure assets. The model is a three-module decision-making tool for planning risk-based rehabilitation of water and sewer networks sharing the same corridor. The model is developed to identify corridor segments, assess risk of individual and integrated asset networks, and to set priorities for intervention plans of related critical corridor segments. The probability of failure of water pipe segments is calculated utilizing data from municipal inspection reports, while the probability of failure of sewer pipe segments is determined by soliciting experts’ opinions. The consequences of failure for individual water and sewer networks account for 13 economic, social, and environmental factors. Risk matrices are used to determine the criticality index of water and sewer segments depending on the combinations of probability and consequences of failure for each network measured on an ordinal scale. To integrate water and sewer indices, a novel dynamic weighting system is introduced to account for the varying impact of different pipe segments deterioration on the overall risk index. A case study from the metropolitan area of the city of Montreal in Canada is analyzed to illustrate the use of the developed model and highlight the essential features of its functions. The developed model is a well-structured decision support tool that utilizes input data commonly collected by municipalities. This 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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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 | 0.001 |
| 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 it