Bibliographic record
Abstract
Public transit infrastructures nowadays face extensive deterioration and require large amount of capital expenditure to regain sufficient performance levels. According to one official U.S. government assessment, transit infrastructure is assigned a grade of D, which means “poor condition.” In the meantime, subway systems ridership is growing; therefore, it is crucial to assess the condition of such vital infrastructure, which greatly affects public safety. Transit providers need to create efficient management tools, including methodologies for the condition rating and performance evaluation of their assets. The objective of the present research is to develop a condition assessment model for subway stations and tunnels considering structural, electrical, and mechanical components. The condition is rated based on actual defects in which the Analytic Hierarchy and Networks Processes are utilized to estimate defect and component’s weights. A fuzzy scale is proposed to interpret the various condition grades. A customized Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is utilized to develop an integrated infrastructure condition index for various components, stations, and tunnels. Data to determine weights were collected from experts through on-line surveys. The model is implemented in a case study, where the examined subway system reported good performance, and is tested through comparison with results obtained from existing models. This study is relevant to transit authorities, subway industry practitioners, and researchers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".