Scholarlink

<p>The study explores the potential of incorporating shredded cigarette butt fibers into Stone Mastic Asphalt mixtures to improve performance. The study addresses the dual challenge of seeking durable road construction materials and managing cigarette butt waste. By assessing three CBF dosages in SMA mixes, the research introduces a sustainable solution. Key findings indicate that CBF inclusion enhances drain-down resistance, Marshall properties, and resistance to rutting and moisture-induced damage at high temperatures. Specifically, a 0.05% CBF dosage optimized elastic and dynamic modulus values, suggesting enhanced stiffness and resilience, while a 0.03% dosage improved fatigue performance. These results suggest that incorporating shredded CBFs can improve the structural integrity and durability of SMA mixtures. Furthermore, machine learning models, including XGBoost, Random Forest, and Linear Regression, were used to predict key mechanical performance parameters, with XGBoost and Random Forest demonstrating high accuracy (R² values up to 0.99, RMSE as low as 0.02, and minimal mean absolute error), thereby corroborating the experimental findings. This study offers an early experimental evaluation of shredded CBFs in SMA and uniquely uses machine learning to develop a predictive framework for performance evaluation. The approach supports the sustainable reuse of hazardous urban waste in road infrastructure, combining material innovation with environmental responsibility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Eco-friendly asphalt design: machine learning analysis of stone mastic asphalt containing shredded cigarette butt fibres

  • 期刊论文

  • 发表日期：

    2025年8月12日

查看全文
  • M. Karthik,
  • H. A. Varalakshmi,
  • J. Madhura,
  • Sharath Chandra Sathvik,
  • Rakesh Kumar

摘要

The study explores the potential of incorporating shredded cigarette butt fibers into Stone Mastic Asphalt mixtures to improve performance. The study addresses the dual challenge of seeking durable road construction materials and managing cigarette butt waste. By assessing three CBF dosages in SMA mixes, the research introduces a sustainable solution. Key findings indicate that CBF inclusion enhances drain-down resistance, Marshall properties, and resistance to rutting and moisture-induced damage at high temperatures. Specifically, a 0.05% CBF dosage optimized elastic and dynamic modulus values, suggesting enhanced stiffness and resilience, while a 0.03% dosage improved fatigue performance. These results suggest that incorporating shredded CBFs can improve the structural integrity and durability of SMA mixtures. Furthermore, machine learning models, including XGBoost, Random Forest, and Linear Regression, were used to predict key mechanical performance parameters, with XGBoost and Random Forest demonstrating high accuracy (R² values up to 0.99, RMSE as low as 0.02, and minimal mean absolute error), thereby corroborating the experimental findings. This study offers an early experimental evaluation of shredded CBFs in SMA and uniquely uses machine learning to develop a predictive framework for performance evaluation. The approach supports the sustainable reuse of hazardous urban waste in road infrastructure, combining material innovation with environmental responsibility.