Macrotexture Prediction for Road Mixtures with Low Nominal Maximum Aggregate Size

Abstract

Bituminous mixtures with low nominal maximum aggregate size, NMAS, have appreciable acoustic performance. Their surface texture is crucial to balance acoustic- and safety-related requirements. Unfortunately, there is a lack of models to predict surface texture and consequently, the main objective of this study is to analyze the impact of the composition and production of low-NMAS mixtures on their macrotexture. Based on the hexagonal packing model, a model for mean texture depth (MTD) was set up, calibrated and validated. To this end, low-NMAS mixtures were produced and tested. Results show that MTD variance can be explained through air void content, AV, and NMAS but their explanatory effectiveness depends on NMAS and AV range. Analyses involved different ranges of AV and NMAS. Results demonstrate that AV explains more than 70% of MTD variance, while AV and NMAS can explain up to about 90% of MTD variance. Nonlinearities and subdomains of AV and NMAS were addressed.