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<p>Asphalt mixture surface texture determines critical surface functions (e.g., skid resistance, noise) but is merely a post-design verification index, not integrated into composition design. This renders pre-design functional evaluation unfeasible and leads to high waste from trial-and-error tests. To clarify the composition-texture relationship, this study systematically reviews formation mechanisms, characterization methods, and predictive models. Notably, texture is regulated by binders, aggregates, gradation, and compaction, with the wall effect causing surface-internal structural differences; Fractal descriptions link texture to composition but lack unified parameters; While composition-based statistical, physical, and machine-learning models are available, enhancing their interpretability, generalization, and mechanistic accuracy remains a key future priority. These advances facilitate the transition from empirical surface validation to composition-driven functional design of asphalt pavements.</p>

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A review of relationship between mix composition and surface texture in asphalt mixture: mechanisms, characterization, and modeling

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Xuejun Liu,
  • Anxin Meng,
  • Xing Liu,
  • Mingteng Zhang,
  • Shenqing Xiao

摘要

Asphalt mixture surface texture determines critical surface functions (e.g., skid resistance, noise) but is merely a post-design verification index, not integrated into composition design. This renders pre-design functional evaluation unfeasible and leads to high waste from trial-and-error tests. To clarify the composition-texture relationship, this study systematically reviews formation mechanisms, characterization methods, and predictive models. Notably, texture is regulated by binders, aggregates, gradation, and compaction, with the wall effect causing surface-internal structural differences; Fractal descriptions link texture to composition but lack unified parameters; While composition-based statistical, physical, and machine-learning models are available, enhancing their interpretability, generalization, and mechanistic accuracy remains a key future priority. These advances facilitate the transition from empirical surface validation to composition-driven functional design of asphalt pavements.