Modeling-Based Approach to Relate Ground Friction Measurements to Aircraft Braking Performance

Cerezo, Véronique; Gerthoffert, Jonathan; Bouteldja, Mohamed; Tan, Minh

doi:10.2514/1.c033309

Cited by 5 publications

(1 citation statement)

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“…There have been several studies that attempt to make the measurements from the friction measurement devices more useful. They relate the ground friction measurements to the aircraft braking friction using correlations or adjustments [53,7,26]. However, the inconsistency and variance between friction measurement devices and different airports is a problem.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

Midtfjord,

De Bin,

Huseby

2021

Preprint

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The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways.

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Section: Introductionmentioning

confidence: 99%