Statistical optimization of crumb rubber modified bitumen performance through material blending analysis

This study presents a comprehensive investigation into the optimization of crumb rubber modified bitumen (CRMB) performance by systematically analysing the effects of blending parameters and material characteristics. The effects of blending temperature, mixing speed, blending time, and mixer type on the viscosity and stability of CRMB were systematically evaluated using a fixed 40/60 penetration grade binder and 15% CR-A (ambient ground, #40 mesh) in the first phase of this study. However, the influence of three bitumen grade (40/60, 70/100, 100/150), two crumb rubber types (CR-A and CR-B), three mesh sizes (#30, #40, #50), and four CR contents (10%, 15%, 17.5%, and 20%) was evaluated with blending protocol devised initially. Viscosity, penetration, and softening point were measured, yielding significant improvements; 15% CR addition in the 40/60 binder reduced penetration from 48 to 38 dmm and increased the softening point from 52.5 °C to 58.4 °C. Chemical interactions were assessed through SARA fraction analysis, revealing a strong inverse correlation between aromatic content and final viscosity with r = -0.78. The blending process was further optimized using factorial design and statistical analysis, identifying high shear mixing at 180 °C for 90 min (2000-3000 rpm) as the ideal condition. This setting produced homogenous, stable blends, with equilibrium viscosity values for the tested combinations ranging from 2.7 to 3.6 Pa·s. Multi-factor ANOVA confirmed significant effects (p < 0.001) of blending temperature, CR type, and bitumen grade. Regression models developed for the materials tested achieved strong predictive power (R² = 0.85), highlighting that higher aromatic content reduced viscosity, while resins and asphaltenes contributed to increased stiffness. The study demonstrates that performance optimization requires not only controlled blending conditions but also informed material selection. This dual approach provides a reliable foundation for scalable, efficient, and sustainable CRMB production.