Decision Support Model for Integrated Risk Assessment and Prioritization of Intervention Plans of Municipal Infrastructure
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".