Development of friction coefficient prediction models for Brazilian runways using Artificial Neural Networks
DOI:
https://doi.org/10.58922/transportes.v31i2.2792Keywords:
Airfield pavements, Skid resistance, Coefficient of friction, Operational safetyAbstract
Landing and takeoff procedures are the prior most critical phases of a flight, once they are up to several factors that play a fundamental role in its performance, these include the pilot´s skill, weather states and skid resistance. In this context, the friction coefficient represents an important parameter for operational safety in terms of tirepavement adherence. In that fashion, this research aims to offer confident prediction models supported by Artificial Neural Networks as a way of quantifying friction coefficient based on 3-6 meters from the axis of runways (RWY) through different types of equipment in virtue of assisting the aerodrome operator with respect of safety procedural requirements, in addition to verifying the influence of grooving upon friction coefficient performance. The models developed have achieved some satisfactory results, given the complexity of the problem, emphasizing the significance of further improvements, even though these models might settle on ways that help guide and control RWY safety procedures
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