Predicting Asphalt Pavement Friction Using Texture-Based Image Indicator

2025-07-04

AI Summary

Conventional friction testing for asphalt pavements is costly and难以 integrated into early-stage mixture design. Method: This study proposes a low-cost predictive method based on digital image analysis, introducing “aggregate protrusion area” as a novel texture metric—more sensitive than existing image parameters to friction degradation during tire polishing. The method integrates dynamic friction tester (DFT) field measurements to develop pavement-type-specific statistical regression models. Results: Adjusted R² values exceed 0.90 for all asphalt surface types, confirming high predictive accuracy and broad applicability across diverse materials. The approach enables quantitative friction performance assessment and optimization during the initial asphalt mixture design phase.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Machine Learning: Evaluation and Analysis
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Pavement skid resistance is of vital importance for road safety. The objective of this study is to propose and validate a texture-based image indicator to predict pavement friction. This index enables pavement friction to be measured easily and inexpensively using digital images. Three different types of asphalt surfaces (dense-graded asphalt mix, open-grade friction course, and chip seal) were evaluated subject to various tire polishing cycles. Images were taken with corresponding friction measured using Dynamic Friction Tester (DFT) in the laboratory. The aggregate protrusion area is proposed as the indicator. Statistical models are established for each asphalt surface type to correlate the proposed indicator with friction coefficients. The results show that the adjusted R-square values of all relationships are above 0.90. Compared to other image-based indicators in the literature, the proposed image indicator more accurately reflects the changes in pavement friction with the number of polishing cycles, proving its cost-effective use for considering pavement friction in mix design stage.

Problem

• Predict pavement friction using texture-based image indicator
• Validate cost-effective friction measurement via digital images
• Correlate aggregate protrusion area with friction coefficients

Innovation

• Texture-based image indicator predicts pavement friction
• Uses aggregate protrusion area as friction indicator
• Statistical models achieve R-square above 0.90