journal article·2025-12Open access

Comprehensive evaluation framework for long-term pavement skid resistance and texture evolution

Jingtao Zhong, Xi Jiang, Zhiqiang Cheng, Shengjia Xie, Kai Huang, Yuetan Ma, Baoshan Huang

Abstract

Understanding the long-term frictional performance of asphalt pavements is critical for designing safer, more durable road surfaces. This study introduces an integrated framework combining laboratory and field evaluations to guide friction performance–based mixture design. A novel ring-shaped specimen setup, in conjunction with the Three-Wheel Polishing Device (TWPD) and Dynamic Friction Tester (DFT), was developed to assess polishing resistance across different aggregates and asphalt mixtures. Findings highlight that micro-texture deterioration, reflected by reductions in the Coefficient of Friction (COF), dominates over macro-texture changes. Aggregates play a key role in friction retention, where limestone degrades rapidly, granite offers long-term durability, and slag incorporation enhances texture durability and skid resistance. A combined analysis of silica (SiO 2 ) and iron (Fe 2 O 3 ) contents improves the prediction of polishing behavior. Field data confirm similar COF trends across wheel paths, allowing safer data collection on the right wheel path. The proposed framework offers insight into developing performance-driven asphalt mixtures targeting long-term skid resistance and roadway safety. • The novel ring-shaped specimen setup offers a valuable tool for frictional performance-based mixture design. • Combining DFT with TWPD supports the development of performance-driven asphalt mixtures evaluation. • Slag-containing asphalt mixtures highlights the role of iron-rich aggregates in enhancing polishing resistance.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Infrastructure Maintenance and Monitoring · Asphalt Pavement Performance Evaluation · Smart Materials for Construction

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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