In the context of increasingly complex road service environments and enhanced requirements for high durability maintenance, the precise characterization of the fatigue-healing competition mechanism of nanomodified natural asphalt has become an important issue in material optimization. This paper integrates computer technologies such as molecular dynamics, cross-scale parameter mapping, damage driving function, competition state discrimination, and experimental-simulation collaborative calibration to construct a molecular-phase-state-microscopic-macroscopic coupled analysis framework. The results show that the dynamic modulus of group G3 reaches 2.36 GPa, the fatigue life is 7.10×104 times, and the healing recovery rate is 66.9%, which demonstrates better anti-damage and recovery capabilities compared to group G0; the prediction errors of the model for fatigue life and healing recovery rate are both controlled within 4%. The research indicates that cross-scale calculations can effectively reveal the main controlling path of the fatigue-healing competition of nanomodified natural asphalt, and have theoretical and engineering significance for the design and intelligent optimization of high durability asphalt materials.
Povzetek: This paper focuses on the fatigue-healing competition mechanism of nanomodified natural asphalt, and constructs a cross-scale computational and experimental collaborative analysis framework involving molecular, phase state, microstructure and macroscopic coupling. It reveals the transmission relationship among interface interaction, phase state reorganization, crack propagation and performance evolution. Through model calibration, result verification and sensitivity analysis, the key control factors are identified, providing theoretical method support for the optimization design of high durability asphalt materials.
