Abstract
The research aimed to establish tyre-road noise models by using a Data
Mining approach that allowed to build a predictive model and assess the
importance of the tested input variables. The data modelling took into
account three learning algorithms and three metrics to define the best
predictive model. The variables tested included basic properties of
pavement surfaces, macrotexture, megatexture, and unevenness and, for the
first time, damping. Also, the importance of those variables was measured
by using a sensitivity analysis procedure. Two types of models were set:
one with basic variables and another with complex variables, such as
megatexture and damping, all as a function of vehicles speed. More
detailed models were additionally set by the speed level. As a result,
several models with very good tyre-road noise predictive capacity were
achieved. The most relevant variables were Speed, Temperature, Aggregate
size, Mean Profile Depth, and Damping, which had the highest importance,
even though influenced by speed. Megatexture and IRI had the lowest
importance. The applicability of the models developed in this work is
relevant for trucks tyre-noise prediction, represented by the AVON V 4
test tyre, at the early stage of road pavements use. Therefore, the
obtained models are highly useful for the design of pavements and for
noise prediction by road authorities and contractors.
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