A computational framework for evaluating tire-asphalt hysteretic friction including pavement roughness

TL;DR

This work addresses tire–pavement friction by linking real pavement roughness to hysteretic friction through a high-fidelity finite-element framework. It integrates Close-Range Orthogonal Photogrammetry (CROP)–derived textures and EN ISO 21920-2 roughness descriptors into the MPJR interface to simulate finite sliding of a viscoelastic rubber block over actual profiles, yielding the hysteretic friction coefficient $\overline{\mu}$. Key contributions include extending MPJR to handle real rough profiles under sliding, benchmarking against sinusoidal and rough textures, and showing a trend between roughness descriptors and friction while highlighting limitations of a 2D, adhesion-free formulation. The results demonstrate the potential of texture-based friction prediction and set the stage for 3D roughness analyses and model-order reduction to enable scalable pavement-friction assessments in practice.

Abstract

Pavement surface textures obtained by a photogrammetry-based method for data acquisition and analysis are employed to investigate if related roughness descriptors are comparable to the frictional performance evaluated by finite element analysis. Pavement surface profiles are obtained from 3D digital surface models created with Close-Range Orthogonal Photogrammetry. To characterize the roughness features of analyzed profiles, selected texture parameters were calculated from the profile's geometry. The parameters values were compared to the frictional performance obtained by numerical simulations. Contact simulations are performed according to a dedicated finite element scheme where surface roughness is directly embedded into a special class of interface finite elements. Simulations were performed for different case scenarios and the obtained results showed a notable trend between roughness descriptors and friction performance, indicating a promising potential for this numerical method to be consistently employed to predict the frictional properties of actual pavement surface profiles.

Figures (14)

• Figure 1: Pavement digital surface acquisition model.
• Figure 2: Profiles segmented from the digital surface model with defined segmentation properties: profile length is $100\,mm$ and profiles' lateral distance is $10\,mm$.
• Figure 3: The same $10\,mm$ length profile segmented with three different section thicknesses.
• Figure 4: Profiles elevation fields.
• Figure 5: Rheological diagrams for rubber like material.