Fuzzy Preference Programming Framework for Functional assessment of Subway Networks
Bibliographic record
Abstract
The 2019 Canadian Infrastructure report card identified 60% of the subway system to be in a very poor to a poor condition. With multiple assets competing for the limited fund, new methodologies are required to prioritize assets for rehabilitation. The report suggested that adopting an Asset Management Plan would assist municipalities in maintaining and operating infrastructure effectively. ISO 55000 emphasized the importance of risk assessment in assessing the value of an organization’s assets. Subway risk assessment models mainly focus on structural failures with minimum focus on functional failure impacts and network criticality attributes. This research presents two modules to measure the functional failure impacts of a subway network, given financial, social, and operational perspectives, in addition to the station criticality. The model uses the Fuzzy Analytical Network Process with application to Fuzzy Preference Programming to calculate the weights for seven failure impact attributers and seven criticality attributes. Data are collected using questionnaires and unstructured/structured interviews with municipality personnel. The analysis identified social impacts to have the highest score of 38%, followed by operational and financial impacts at 34% and 27.65%, respectively. The subway station criticality revealed station location to have the highest impact at 35%, followed by station nature of use and station characteristics at 30.5% and 31.82%, respectively. When integrated with probability of failure, this model provides a comprehensive risk index to optimize stations for 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".