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

2025-04-02

AI Summary

This study addresses the challenge of accurately quantifying hysteretic friction between tires and asphalt pavements. We propose a quantitative modeling framework that integrates three-dimensional (3D) measured pavement roughness with interface finite element simulation. Specifically, high-fidelity 3D pavement topography, acquired via orthographic close-range photogrammetry, is directly embedded into a dedicated interface finite element model (IFEM), enabling in-situ coupling of geometric roughness features with nonlinear hysteretic friction behavior. Through surface texture parametrization and contact dynamic simulation, we systematically identify statistically significant correlations between multiple roughness parameters (e.g., Rq, Rsk, Rku) and the hysteretic friction coefficient. Validation against field measurements demonstrates that the proposed method reduces prediction error for real-world pavement friction by over 35% compared to conventional empirical models. This work establishes a generalizable, high-accuracy computational framework for investigating tire–pavement interaction mechanisms and guiding high-performance pavement design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Evaluating tire-asphalt friction using pavement roughness descriptors
• Comparing photogrammetry-based texture parameters with numerical friction results
• Predicting frictional performance via finite element analysis and surface roughness

Innovation

• Photogrammetry-based pavement texture analysis
• Finite element simulation with embedded roughness
• Roughness descriptors predict friction performance