Development of a unique deterioration index, prioritization methodology, and foreign object damage evaluation models for Canadian airfield pavement management
Bibliographic record
Abstract
Modelling of pavement performance deterioration is a critical engineering process in Pavement Management Systems. Most of the existing Airport Pavement Management Systems (APMS) employ limited surface distresses in their performance evaluation models. These systems may not serve the objectives of some agencies. It is essential for an effective APMS to include evaluation models that adequately address the specific needs of the agency. This paper presents the development of a unique pavement deterioration index, i.e., the Condition Rating Index, developed for 1 Canadian Air Division (1 CAD). This index is modelled to serve the specific needs of 1 CAD effectively. Performance prediction models for the various classes of pavements are developed based on Markov Chains. The prioritization methodology employed also reflects the needs of 1 CAD. Consequently, this paper investigates the quantification and prediction of Foreign Object Damage (FOD p ). The FOD p Index is developed as well as defined. Prediction models for FOD p are developed along with the establishment of critical states of the FOD p Index.Key words: airport pavement management system, Condition Rating Index, pavement performance evaluation models, Foreign Object Damage, 1 Canadian Air Division.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".