A decision support model to assess the braking performance on snow and ice contaminated runways

Klein-Paste, Alex; Bugge, Hans-Jørgen; Huseby, Arne Bang

doi:10.1016/j.coldregions.2015.06.002

Cited by 10 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pilots need accurate predictions on the quality of runway surface conditions when operating on runways disrupted by snow/ice. These predictions are typically made by friction measurements, or by expert judgments of runway inspectors [16]. Maintaining operational safety and the status of airport runways during snowfall events is a challenging issue with which many airports are grappling.…”

Section: Winter Maintenance Of Airportmentioning

confidence: 99%

Reducing the Negative Environmental Impact of Winter Airport Maintenance through Its Model Design and Simulation

Koščák

Berežný

Vajdová

et al. 2020

IJERPH

View full text Add to dashboard Cite

Airports are one of the segments within air transport and their serviceability ensures air transport efficiency. However, airports are among the largest contributors to the negative environmental impact of this kind of transport. Individual activities at airports leave a negative environmental footprint. By optimizing processes, it is possible to reduce the negative environmental impacts of these subjects. Airport winter maintenance is one of the important activities accompanying airports in geographical areas where winter season is expected due to the climate zone and weather conditions. Effective winter maintenance ensures optimal airport operation and has a significant impact on safety. However, the winter maintenance of the airport causes an excessive burden on the environment around the airports, in particular by applying de-icing agents to aircraft and movement areas, or by emissions and noise caused by equipment for snow and ice removal. The aim of the present article is to optimize the winter maintenance of the airport, namely the deployment of winter maintenance equipment with respect to meteorological conditions while maintaining the priorities for winter maintenance between individual airport areas. The aim of optimization is to achieve the saving of maintenance time and reduce the negative environmental impact of winter maintenance by effectively deploying equipment and devices.

show abstract

Section: Winter Maintenance Of Airportmentioning

confidence: 99%

Reducing the Negative Environmental Impact of Winter Airport Maintenance through Its Model Design and Simulation

Koščák

Berežný

Vajdová

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…In order to predict the surface conditions before aircrafts hit the ground, there have been conducted studies on how available surface friction is affected by weather conditions and runway contamination based on engineeringand physics-based models and basic statistical approaches. Klein-Paste et al [32] proposed a runway model for the surface conditions which interprets descriptive data from the international standardized runway reports called Snowtam reports. The model evaluates a sum of seven effects that contain information about the runway contamination as well as measurement of runway temperature and humidity, P = 7 i=1 x i .…”

Section: Introductionmentioning

confidence: 99%

“…The scenario model was further developed in Huseby & Rabbe [21], where it was shown that the scenario model could be improved by optimizing the thresholds for the scenarios according to Type 1 and Type 2 errors using weather and flight data. Both Huseby & Rabbes [20] scenario model and the runway model of Klain-Paste et al [32] are used in an integrated runway information system called IRIS, which is implemented on 16 Norwegian airports to support in safer operations of Norwegian airports.…”

Section: Introductionmentioning

confidence: 99%

“…All the models are combined to create a data-driven decision support system, which can aid airport operators and pilots in their decisions and contribute to safer and more economic operation of airports. To evaluate the performance of the XGBoost models, they are compared to state-of-the-art runway surface conditions assessment models, namely the runway model of Klein-Paste et al [32] and the scenario model of Huseby and Rabbe [20]. The models are also compared to runway assessment of airport runway inspectors reported in the Snowtam reports.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Midtfjord,

De Bin,

Huseby

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In China, more than 100 airports meet with heavy snow or frozen ice in winter, which causes brake failing or accidents easily [ 1 – 3 ]. For avoiding accidents, most airports close runways and use the deicing chemicals [ 4 ] or machines [ 5 – 7 ] to remove snow.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Concrete Runway Snow Melting Utilizing Heat Pipe Technology

Chen¹,

Su²,

Ye³

et al. 2018

The Scientific World Journal

View full text Add to dashboard Cite

A full scale snow melting system with heat pipe technology is built in this work, which avoids the negative effects on concrete structure and environment caused by traditional deicing chemicals. The snow melting, ice-freezing performance and temperature distribution characteristics of heat pipe concrete runway were discussed by the outdoor experiments. The results show that the temperature of the concrete pavement is greatly improved with the heat pipe system. The environment temperature and embedded depth of heat pipe play a dominant role among the decision variables of the snow melting system. Heat pipe snow melting pavement melts the snow completely and avoids freezing at any time when the environment temperature is below freezing point, which is secure enough for planes take-off and landing. Besides, the exportation and recovery of geothermal energy indicate that this system can run for a long time. This paper will be useful for the design and application of the heat pipe used in the runway snow melting.

show abstract

A decision support model to assess the braking performance on snow and ice contaminated runways

Cited by 10 publications

References 12 publications

Reducing the Negative Environmental Impact of Winter Airport Maintenance through Its Model Design and Simulation

Reducing the Negative Environmental Impact of Winter Airport Maintenance through Its Model Design and Simulation

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

Experimental Investigation of Concrete Runway Snow Melting Utilizing Heat Pipe Technology

Contact Info

Product

Resources

About