Experimental evaluation and predictive modeling of asphalt binders modified with ground tire rubber

Engineering notes

<title>Abstract</title> The use of ground tire rubber (GTR) as a modifier significantly enhances asphalt binder performance while simultaneously addressing environmental concerns associated with tire disposal. The experimental results demonstrated that incorporating GTR significantly improved the thermal and mechanical performance of the asphalt binders, enhancing resistance to rutting and fatigue cracking.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

<title>Abstract</title> The use of ground tire rubber (GTR) as a modifier significantly enhances asphalt binder performance while simultaneously addressing environmental concerns associated with tire disposal. As electric vehicle (EV) adoption rises, scrap tire volumes are projected to increase by around 30%, underscoring the need for sustainable disposal solutions. To address this issue, this study modifies two asphalt binders, unmodified asphalt binder PG 64–22 and SBS-modified asphalt binder PG 76–22, with ground tire rubber (GTR) to optimize their resilience to fatigue and distortion. To assess the impact of these modifications, a rigorous experimental study was performed, integrating aging processes with advanced rheological testing on 72 asphalt binder samples. The experimental results demonstrated that incorporating GTR significantly improved the thermal and mechanical performance of the asphalt binders, enhancing resistance to rutting and fatigue cracking. In particular, increasing the content of these modifiers in the PG 64–22 binder elevated its performance grade to PG 76–22, while their higher dosage in the SBS-modified PG 76–22 binder successfully achieved the superior classification of PG 82–22. In parallel with the laboratory investigations, artificial neural network (ANN) models were employed to predict the complex shear modulus and phase angle from the experimental dataset. These models demonstrated strong predictive capability, capturing the rheological properties of the modified binders with high precision. This combined approach bridges a key gap in the literature and provides a practical tool for enhancing binder design and improving pavement performance.

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