Statistical Evaluation of the Material-Source Effects on the DSR Rheological Properties of Plant-Mix Extracted Asphalt-Binders

• Main Authors: Lubinda F. Walubita, Jose G. Gonzalez-Hernandez, Gilberto Martinez-Arguelles, Hossain Tanvir, Luis Fuentes, Seyed Amid Tahami
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Materials
• Subjects: Texas DSS; asphalt-binder; DSR; rheology properties; material-source effect; statistical analysis
• Online Access: https://www.mdpi.com/1996-1944/14/8/1931

The work presented in this paper was carried out to statistically evaluate and quantify the material-source effect on the asphalt-binder’s rheological properties using Analysis of Variance (ANOVA) and Tukey’s Honestly Significant Difference (Tukey´s HSD) test. The study focused on the Asphalt-Binders’ high-temperature rheological properties, namely, the G*, δ, G*/Sin(δ) and G*/(1 − (1/Tan(δ)Sin(δ))) parameters, measured using the Dynamic Shear Rheometer (DSR) device. The DSR data analyzed in the study were extracted from the Texas flexible pavements and overlays database, namely, the Texas Data Storage System (DSS), covering two Asphalt-Binders (ABs), performance grade (PG) 64-22 and PG 76-22 plant-mix extracted ABs that were treated as rolling thin film oven (RTFO) residue, and sourced from 14 different suppliers. The study findings substantiate that material-source has an effect on the high-temperature rheological properties of ABs. Additionally, it was also concluded that in as much as performance superiority and costs are crucial issues in deciding the AB source/provider, consistency and quality aspects cannot be disregarded. Therefore, material-source effects should be inclusively evaluated from both performance (rheological properties) and quality (consistence) standpoints as well as cost considerations when choosing a supplier. In general, the study contributes to the state-of-the-art enrichment on aspects of material-source effects on RTFO residue ABs’ high-temperature rheological properties, consistency, variability, and data quality.