Tire/road noise analysis of innovative microsurfacing mixtures based on SPERoN® model and noise-related texture indicators

Abstract

Road traffic noise is a significant environmental issue in urban areas with major health and economic implications for communities. Thus, a comprehensive understanding of tire/road noise mechanism is crucial for road pavement engineering. This study evaluates the noise behaviour of six innovative microsurfacing mixtures incorporating natural and artificial aggregates (geopolymers and crumb rubber) with varying particle size distributions and binders. A 2D laser analysis aims at collecting surface texture indicators, while noise-related indicators were derived according to ISO 10844 standards. Noise levels were predicted using the SPERoN® model (statistical physical explanation of rolling noise), analysing the vibro-dynamic and the aerodynamic contributions separately. Correlations between tire/road noise levels predicted by the model and surface texture indicators elucidate the key factors influencing noise generation mechanism. The findings indicate that lower nominal maximum aggregate size (NMAS) and uniformly shaped artificial aggregates substantially mitigate rolling noise. Moreover, profiles with negative skewness and positive kurtosis exhibit reduced noise generation. The study highlights the limitations of traditional indicators like the estimated noise difference due to texture (END_T) and highlights the g-factor from the Abbott curve as a more reliable predictor of pavement noise properties. These findings provide valuable insights for designing low-noise pavements with enhanced performance, offering new perspectives on the noise behaviour and acoustic properties of microsurfacing.