Investigating the temperature reduction potential of different warm mix–incorporated polymer modified binder through systematic multi-parameters evaluation
摘要
The use of modified binders in road infrastructure is rapidly increasing due to their superior performance, but they typically require higher mixing and compaction (M&C) temperatures, leading to elevated energy consumption and greenhouse gas (GHG) emissions. This creates a conflict with global sustainability goals aimed at reducing environmental impact. Warm Mix Asphalt (WMA) technologies offer a solution by enabling lower M&C temperatures; however, accurately determining these temperatures for modified binders is challenging, especially when WMA additives do not significantly reduce viscosity. Conventional methods, such as equiviscous and steady shear flow approaches, may not accurately estimate temperature reductions for polymer-modified binders such as PMB40, as their non-Newtonian rheological behavior is highly dependent on shear rate and does not follow a linear viscosity–temperature relationship. In addition, these methods have limitations in evaluating the influence of surface tension and interfacial interactions associated with chemical WMA modifications, leading to overestimation or inconsistent prediction of achievable temperature reductions. To overcome this, the present study evaluates multi-parameter evaluation framework that integrates underexplored parameters like work of cohesion (WoC) and wettability from surface free energy analysis, along with activation energy (Ea) and gyratory compaction effort, offering a more reliable and scientifically grounded approach to determine appropriate temperatures for WMA-modified binders. Six different WMA additives were utilized to modified PMB40 binder. Results confirmed reductions in WoC of about 40–54%, improved wettability by 41–65%. When considered Ea and gyratory compaction efforts, reductions of 20–38% and 25–48% was reported at reduced temperatures. Statistical as well as correlation analysis also confirmed significant differences upon modifications and nature of relationship between parameters. Lastly multiple linear regression analysis was used to predict the mixing temperatures and observed significant predictions (R2 = 0.92) and ranking was predicted based on various parameters considered in the present study.