Abstract
Runway surface conditions are fundamental to ensure safety during landing and takeoff operations of aircrafts. In this manner, airport operators are required to monitor the coefficient of friction and macrotexture of runways to maintain its safety and plan maintenance and rehabilitation strategies when appropriate, since both these parameters get deteriorated with time. Thus, to assist aerodrome operators and regulatory agencies in the decision-making process for conservation and monitoring of airfield pavements, this study aimed to develop a prediction model for runway friction using Artificial Neural Network. Our results were satisfactory and may contribute to the decision-making process in the context of the Airport Pavement Management System.
Highlights
The tire-pavement adherence – represented by the macrotexture and by the coef icient of friction – are essential for the safety of the operations on the runways
The success rates, Coef icient of Determination (R2), and the errors measured by the Mean Squared Error (MSE) and the Mean Absolute Error (MAE) are shown on Table 1
This paper developed a model to estimate the coef icient of friction measured on the runway of the International Airport of Fortaleza
Summary
The tire-pavement adherence – represented by the macrotexture and by the coef icient of friction – are essential for the safety of the operations on the runways. They make it possible for the aircraft to slow down after landing as they allow the airplane tire to roll until it reaches the speed to takeoff (Fwa et al, 1997) and they act in the draining of the water on the runway. In Brazil, ANAC (2019), establishes that the airport operators should keep the runway in condition to operate safely in order to guarantee: (i) skid resistance, (ii) the directional control of the airplanes, and (iii) the integrity of the aeronautical equipment. The more landings, the more measurements should be made, and as these measurements are made, more data that composes the Pavement Management System (PMS) are generated, which is one of the main characteristics of a modern PMS for Haas et al (2015)
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