Development and validation of a tenable process for quantifying texture spikiness for pavement noise prediction

Abstract

In pavement infrastructure, it is important to characterise the surfaces for an effective prediction of noise. One of the major influencing variables, texture orientation, also called spikiness, is a measure of how spiky the surface asperities are. Tyre–pavement interaction noise is associated with mechanisms triggered by micro-, macro- and megatexture. Of the variables within macro-texture range, texture spikiness has gained increased interest by providing explanations for scenarios with similar texture direction and mean profile depth on the same level of distress yet exhibiting very different noise levels. A tool created in this research, ‘PARSER’, facilitated computation of skewness/spikiness statistics. This paper therefore tenably quantifies texture spikiness by the method of skewness of amplitude distribution function. Consequently, a logical quantification of texture spikiness has facilitated a phenomenological noise prediction model. When properly quantified, texture spikiness is an indispensable tyre–pavement interaction variable.