Effectiveness of combining gilsonite and vacuum tower bottoms in enhancing the properties of recycled binder with high RAB content
Source abstract
Abstract
The study aimed to enhance the fatigue, rheological, and cracking characteristics of Reclaimed Asphalt Pavement (RAP) binder (RAB) by employing a Gilsonite-modified soft binder (GMSB). Researchers developed and analyzed ten binder compositions, which included a base binder, three RAB-containing binders, and six blends of RAB and GMSB. They conducted rheological and performance tests to assess the temperature-dependent properties of these binders, using MSCR and LAS tests to evaluate resistance to rutting and fatigue. Findings indicated that while Gilsonite negatively affected low-temperature performance and high-strain fatigue resistance, it significantly improved high-temperature performance, rutting resistance, and fatigue life at low strains. The optimal performance was achieved with a softer binder containing 12 % Gilsonite, which enhanced elastic properties and reduced stress sensitivity. The top-performing binders were identified as G12, RAB50/G12, and G8. Combining Gilsonite with Vacuum Tower Bottoms (VTB) proved effective, with VTB compensating for the loss of lighter and oily components in RAB. This combination resulted in a binder system with improved modulus and fatigue resistance. The results highlight the substantial effects of temperature, Gilsonite, and RAB on the performance characteristics of recycled binder, revealing important interactions among these factors. The developed prediction models align well with the experimental data, offering a dependable method for estimating the properties of recycled binder. The ideal Gilsonite content varies: approximately 11 % for optimal rutting resistance and around 6 % for better low-temperature performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Recycled Aggregate Concrete Performance · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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