Microsurfacing: a predictive macrotexture model from mix design parameters

Abstract

The main functional characteristics of pavement (friction, noise, drainability, and rolling resistance) are strictly related to the surface texture. Considering its importance, it would be appropriate to consider surface texture as a key parameter for road engineering already during the design stages. The objective of this study is confined to the setting up of a macrotexture prediction model (in terms of mean texture depth, MTD) for Microsurfacing, a preventive solution for pavement maintenance that provides a regular and even surface texture with remarkable improvement in skid resistance. Four models were developed – by using multiple linear regression (MLR) – considering a volumetric approach based on mix design-related factors (aggregate gradation, bitumen content, and mineral filler rates). In an attempt to confirm the good generalization of the models and their basic assumptions, a K-fold cross-validation method was used. Several statistical metrics aim at evaluating the goodness of fit and the errors in prediction. The results obtained reveal a strong correlation and low errors between the observed and estimated values, confirming the adequacy of the models (R2 of 0.76–0.90). A more comprehensive dataset and experimental data can further improve the performance of the model. Results can benefit both researchers and practitioners.