Subway Infrastructure Condition Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it