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Machine learning-driven optimization of asphalt performance using eco-friendly waste modifiers
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DOI：10.1038/s41598-026-51700-x.png)
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Abstract
En 中文
Sustainable road construction is crucial in minimizing energy consumption, greenhouse gas emissions, and depletion of natural resources. Traditional asphalt production practices are under scrutiny due to their environmental impact, necessitating a shift towards sustainable technologies. Incorporating waste materials like Low-Density Polyethylene (LDPE) and Wood Apple Shell (WAS) powder in asphalt can improve mechanical properties, reduce material costs, and contribute to sustainability by reducing reliance on virgin materials and diverting waste from landfills. This research investigates the effects of these modifiers on asphalt properties, durability, and environmental impact, offering insights into eco-friendly practices for sustainable road infrastructure without conducting a full environmental life cycle assessment. The comprehensive analysis of LDPE + WAS modified asphalt mixtures identifies the 6% dosage as optimal, demonstrating superior performance across mechanical properties, moisture resistance, and economic viability. The mixture achieves enhanced tensile strength and stability, with a dry ITS of 2.1 MPa and a stability of 30.0 kN, indicating improved load-bearing capacity. It also shows excellent moisture resistance with a TSR of 90%, crucial for extending pavement life. Economically, the 6% dosage offers the lowest life-cycle costs and reduced maintenance expenses, making it a cost-effective choice. The use of waste materials contributes to sustainability, while improved dynamic creep performance and complex shear modulus enhance durability under heavy traffic. These findings underscore the innovative use of LDPE + WAS in asphalt mixtures, offering significant advancements in performance, cost-effectiveness, and sustainability for road construction projects.
Keywords:
Sustainable road construction
Low-Density Polyethylene
Wood Apple Shell
Asphalt modification
Machine learning
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