Improving Runway Pavement Friction Analysis through Innovative Modeling
Bibliographic record
Abstract
Available runway friction has a significant impact on aircraft landing performance. This is especially noted when aircrafts are landing on wet or otherwise contaminated runways due to the reduced braking action, which has been well documented since the dawn of the jet aircraft age. In addition, according to International Air Transport Association (IATA) statistics, runway excursions contribute nearly a quarter of all the accidents and no trends show an obvious decrease of these accidents in the past few years. In order to prevent runway landing excursion accidents and incidents, and enhance airport and airline operation safety, available runway friction should be studied. A good level of available runway friction is required for aircraft landing operations. With the presence of water film, snow, and ice, the available runway friction changes rapidly, and different measure devices provide results with a large variance on a uniform runway condition. According to the results of a survey of Canadian airline pilots in the Joint Winter Runway Friction Measurement Program, “Pilots indicated that the quality of runway friction information provided by airports varies between airports. Generally the quality is better at large airports, but each airport differs depending on various factors”. Because of the inconsistencies in runway friction measuring devices, it is better to analyze available runway friction based on aircraft measurements. In order to model the aircraft’s landing performance, a mechanistic-empirical aircraft landing deceleration equation has been developed. This equation incorporates all of the major forces that contribute to aircraft braking, and was calibrated and validated using digital flight data from dry runway aircraft landings. As a result, it is able to back calculate friction from the developed equations and evaluate the impacts of dry, wet, and contaminated runways on aircraft braking performance. The objectives of the paper are as follows: (1) Provide back ground knowledge regarding wet and contaminated runway aircraft braking; (2) Analyze aircraft braking performance on wet and contaminated runways using the built mechanistic-empirical aircraft landing deceleration equation; and (3) Study runway available braking friction under different conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".