Investigation on Gradation Optimization Design of Basalt Fiber Asphalt Mixture Based on Discrete Element Simulation

Basalt fiber (BF) has proven effective in enhancing the performance of asphalt mixtures, with fiber length being a critical influencing factor. However, the mechanistic relationship between fiber length distribution and performance improvement remains insufficiently studied. This research develops a systematic approach to investigate the reinforcement mechanism of BF in asphalt mixtures using discrete element (DE) modeling. Three dense gradations (AC-13, AC-20, AC-25) were simulated to characterize the aggregate skeleton gap parameters linked to BF length distribution. Additionally, CT scanning validation confirmed the reliability of the DE-derived skeleton gap characteristics. Finally, pavement performance tests with various combinations of BF lengths were conducted to validate the DE-based optimization method. The results demonstrate that the optimal BF length distribution for achieving the best pavement performance of asphalt mixtures with three different gradations can be identified through DE simulation technology. It was determined that the BF length distribution based on the longest axis of the aggregate skeleton is the most effective, significantly enhancing pavement performance. At a fiber content of 0.3%, asphalt mixtures with optimized BF distributions achieved maximum improvements in high-temperature stability (30.2%, 29.0%, and 29.1% for AC-13, AC-20, and AC-25, respectively) and crack resistance (29.1%, 35.9%, and 47.3%, respectively). Specific optimal length ratios were identified: AC-13 (3 mm∶6 mm∶9 mm=2∶5∶2), AC-20 (6 mm∶9 mm∶12 mm=2∶2∶1), and AC-25 (9 mm∶12 mm∶15 mm=2∶3∶1). This study establishes that aligning BF lengths with the geometry of the aggregate skeleton maximizes reinforcement effects, thereby validating DE simulations as an effective tool for determining optimal fiber length distributions in asphalt pavements.