Predicting Asphalt Pavement Friction Using Texture-Based Image Indicator

TL;DR

This study introduces a texture-based image indicator derived from digital images to predict asphalt pavement friction, offering a cost-effective and accurate method validated across different asphalt types and tire polishing cycles.

Contribution

The paper proposes a novel aggregate protrusion area indicator from images to predict pavement friction, validated with high accuracy across multiple asphalt types.

Findings

Adjusted R-square values above 0.90 for all models

The indicator accurately tracks friction changes with polishing cycles

More effective than existing image-based indicators

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.