Modelling Tyre-Road Noise with Data Mining Techniques

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 V4 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.

Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques

“…Readers are encouraged to consult Refs. [14][15][16][17] that constitute a relevant contribution to the literature review. Most of the models presented in the literature are based on linear relationships between the considered parameters.…”

Tyre/Road Noise Annoyance Assessment Through Virtual Sounds

“…In an atempt to simplify the experimental procedure, the suitability of tyre/road noise close proximity measurements to assess annoyance through psychoacoustic parameters was examined [13,14]. Furthermore, relations between subjective annoyance ratings and the traic noise levels described by acoustic and psychoacoustic indicators LA max , LA eq and Loudness as a function of speed were established [14].…”

“…Furthermore, relations between subjective annoyance ratings and the traic noise levels described by acoustic and psychoacoustic indicators LA max , LA eq and Loudness as a function of speed were established [14].…”

“…High macro-texture [16] and low mega-texture, negative texture, high porosity [17], small and regular mineral aggregates and low stifness [17] or high damping [14,18] are the main characteristics of a low noise surface. Asphaltic surfaces with bitumen modiied with rubber [1 ] or with elastic aggregate substitute materials [20] also contribute to tyre/road noise reduction.…”

“…These conditions were used to illustrate hereafter the main results of the studies. Tyre/road noise measurement details can be found in [12] and [14].…”