Influence of fiber-asphalt interface property on crack resistance of asphalt mixture
Source abstract
Abstract
This paper aims to analyze the interface property between fibers and asphalt, and study its influence on the crack resistance of the asphalt mixture. Four fibers (basalt fiber A (BF-A), basalt fiber B (BF-B), polyester fiber (PEF) and polyacrylonitrile fiber (PAF)) were selected in the study. The interface adhesion parameters were obtained by conducting the novel fiber-asphalt pullout test, and the micromorphology of the fiber-asphalt interface area was also observed. The crack resistance of the mixture was determined using the indirect tensile asphalt cracking test (IDEAL-CT) and the semi-circular bending test (SCB). Basic crack resistance indicators such as CTIndex and flexibility index (FI) were used for analyzing. Meanwhile, digital image correlation (DIC) technology was adopted to quantitatively characterize the crack inhibition mechanism of fibers in the fiber-asphalt mixtures. The results showed that the interface adhesion strength between BF-A and asphalt is the largest at 25 ℃, followed by BF-B, PEF and PAF. The addition of fibers can effectively improve the CTIndex and FI of asphalt mixtures, and it is found that the interface adhesion characteristics between fibers and asphalt have a great influence on the crack propagation process. The crack opening velocity curve obtained from the DIC analysis can quantitatively characterize the crack development law of the asphalt mixture. The coefficient of determination R2 between the interface adhesion strength and the DIC index (stage I velocity change rate and timing point 1 time) exceeded 0.85. The results can provide scientific references for the design and application of fiber reinforced asphalt mixture.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Geotechnical Engineering and Underground Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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