The Effective Depth of Skid Resistance (EDSR): A Novel Approach to Detecting Skid Resistance in Asphalt Pavements

Asphalt pavement skid resistance, governed by surface texture, is critical for traffic safety. Most research has focused on full-depth textural characteristics, often overlooking the depth of tire-pavement contact under real traffic conditions. This study introduces the concept of the Effective Depth of Skid Resistance (EDSR) to describe the effective depth of tire-asphalt contact, improving skid resistance assessment accuracy. Using blue linear laser scanning, surface textures of three common asphalt pavements with wearing courses-AC-13, AC-16, and SMA-13-were analyzed, and friction coefficients were measured using a British pendulum. After pre-processing three-dimensional texture data, fractal dimensions at various depths were calculated using the box-counting method and correlated with the friction coefficients. Previous studies show an insignificant correlation between full-depth asphalt pavement textures and skid resistance. However, this study found a significant positive correlation between skid resistance and pavement textures at specific depths or the EDSR. A depth with a correlation exceeding 0.9 was defined as the EDSR. Linear formulas were established for each pavement type within these EDSR ranges. A theoretical model was developed for predicting skid resistance, showing an over 80% accuracy against real-world data, indicating its potential for improving road surface performance detection.

Authors

Institutions

Publication Details

Journal
Materials
Published
2025-03-07
DOI
https://doi.org/10.3390/ma18061204
Primary Topic
Asphalt Pavement Performance Evaluation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Effective Depth of Skid Resistance (EDSR): A Novel Approach to Detecting Skid Resistance in Asphalt Pavements

Yongli Xu, Liming Wang, Yiming Li, Hongguang Wang et al.
Materials
Asphalt Pavement Performance Evaluation
article

The Effective Depth of Skid Resistance (EDSR): A Novel Approach to Detecting Skid Resistance in Asphalt Pavements

Yongli Xu, Liming Wang, Yiming Li, Hongguang Wang, Yi Luo
article en

Abstract

Asphalt pavement skid resistance, governed by surface texture, is critical for traffic safety. Most research has focused on full-depth textural characteristics, often overlooking the depth of tire-pavement contact under real traffic conditions. This study introduces the concept of the Effective Depth of Skid Resistance (EDSR) to describe the effective depth of tire-asphalt contact, improving skid resistance assessment accuracy. Using blue linear laser scanning, surface textures of three common asphalt pavements with wearing courses-AC-13, AC-16, and SMA-13-were analyzed, and friction coefficients were measured using a British pendulum. After pre-processing three-dimensional texture data, fractal dimensions at various depths were calculated using the box-counting method and correlated with the friction coefficients. Previous studies show an insignificant correlation between full-depth asphalt pavement textures and skid resistance. However, this study found a significant positive correlation between skid resistance and pavement textures at specific depths or the EDSR. A depth with a correlation exceeding 0.9 was defined as the EDSR. Linear formulas were established for each pavement type within these EDSR ranges. A theoretical model was developed for predicting skid resistance, showing an over 80% accuracy against real-world data, indicating its potential for improving road surface performance detection.

MaterialsVol. 18(6)
Harbin Engineering University (CN), Northeast Forestry University (CN)
Sustainable cities and communities
Openalex Percentile: Top 96%
Asphalt Pavement Performance Evaluation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.