Pavement surface texture is one of the prevailing factors for friction realization on pavement surfaces. In this paper, an overview of pavement texture properties related to the pavement frictional response is given. Image analysis methods used for pavement texture characterization are thoroughly analyzed together with their potential for the establishment of a pavement texture–friction relationship. Digital pavement surface models derived from photogrammetry or laser scanning methods enable the extraction of texture parameters comparable to the ones acquired by common pavement surface measuring techniques. This paper shows the results of a preliminary small-scale research study of the pavement texture–friction relationship. This research was performed in a laboratory which produced asphalt samples, primarily to analyze the potential of developing a methodology for the digital pavement texture model setup. Furthermore, the relationship between selected 2D texture parameters calculated from the digital texture model and measured friction coefficient expressed as SRT value was analyzed. A significant correlation was established for standard texture indicator mean profile depth (MPD) and SRT values (R = 0.81). Other texture parameters showed moderate correlation with the frictional response of the surface, with absolute values of correlation coefficients varying from 0.7 to 0.75. A further analysis of this relationship will be performed by inclusion of other texture parameters that can be determined from the digital texture model acquired by the established methodology.

Crumb rubber (CR) obtained from end-of-life tyres (ELT) has gained significant attention in the sustainable design of asphalt pavements in recent years, showing a promising perspective in the enhancement of pavement performance related to its structural and functional properties. Existing research on CR influence on pavement performance mostly focused on peculiarities of asphalt mixture modification procedures—dry and wet processes, CR content in the mixture and CR particle size. In this study, a laboratory-based experimental investigation of CR effect on two different mixture gradations, namely dense-graded and gap-graded mixtures with three different binder contents, was performed. CR was added in mixtures through binder modification, with a constant CR content of 18% by binder weight in all mixtures. Volumetric properties—maximum mixture density, bulk density and void characteristics, alongside mechanical properties determined by the Marshall test method—were determined on unmodified and modified mixtures. The goal was to evaluate the influence of CR modification with respect to three different binder contents. The results showed that gap-graded mixtures are more sensitive to change in CR modified binder content in comparison to dense-graded mixtures in terms of air voids content. Furthermore, the mechanical properties of CR-modified mixtures were slightly enhanced in gap-graded mixtures, showing a promising potential of CR modification for pavement performance. However, the choice of optimal binder content in CR-modified mixtures was shown to be a critical mixture design parameter due to the increased sensitivity of binder content change to the analysed voids properties and permanent deformations.

The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The originally developed methodology is based on a photogrammetry technique for pavement surface data acquisition and analysis, named the Close-Range Orthogonal Photogrammetry (CROP) method. Texture roughness features were analyzed on pavement surface profiles extracted from surface 3D models, obtained by the CROP method. Selected non-standard roughness parameters were used as predictors in the skid resistance model. The predictive model was developed by the partial least squares (PLS) method as a feature engineering procedure in the regression analysis framework. The proposed model was compared to the simple linear regression model with a traditional texture parameter Mean Profile Depth as the predictor, showing better predictive strength when multiple non-standard texture parameters were used.

Two-dimensional profiles extracted from three-dimensional pavement surface textures acquired by close-range photogrammetry are employed as geometrical input for modeling complex interfaces in finite element contact simulations of viscoelastic materials. The methodology allows to establish a robust computational framework for the systematic investigation of possible correlations between the simulated viscoelastic friction coefficients of profiles, the profile roughness descriptors, and the measured surface frictional performance assessed through the static skid resistance tester (SRT) parameter. The study shows that the proposed methodology successfully recovers consistent values of the friction coefficient in the range of velocities typical of the equivalent experimental tests. The methodology is suitable for large-scale statistical analyses and uncertainty quantification, which emerge as key aspects for future research.