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Updated: Aug 5, 2026

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
Published on: May 31, 2022
Investigation of Multi-Factor Coupled Aging Mechanisms and Rheological Performance Prediction of Asphalt in Diverse
Hong Xu1, Shanglin Song2, Fangxia Wang3
1Gansu Provincial Highway Development Group Co., Ltd., Lanzhou 730070, China.
Abstract:
Aging of asphalt pavements is a complex, multi-scale degradative process driven by the synergistic effects of various environmental stressors. Traditional laboratory-accelerated aging protocols often employ static parameters that fail to accurately replicate dynamic, region-specific climatic conditions. To bridge the gap between laboratory simulations and actual field performance, this study investigates the aging behaviors of base binder and SBS-modified binder under multi-factor coupled environmental conditions. Field observations were conducted across six distinct climatic regions in Gansu Province, alongside an indoor second-order orthogonal regression composite design that evaluated the interactive effects of temperature, ultraviolet (UV) radiation, humidity, and aging time. Rheological evaluations revealed that for the base binder, the synergistic coupling of UV radiation, elevated temperatures, and high humidity significantly accelerates oxidative hardening and embrittlement far beyond the impact of any single factor. Conversely, SBS-modified binder demonstrated a non-linear, U-shaped rheological response governed by a competitive mechanism between UV/thermal-induced polymer scission and moisture/time-driven matrix oxidation. Fourier Transform Infrared (FT-IR) spectroscopy corroborated these macroscopic findings at the molecular level, tracking the simultaneous evolution of carbonyl and sulfoxide indices alongside the degradation of the polybutadiene segments in the modified binder. Ultimately, a quadratic polynomial regression model was established to precisely correlate natural field aging with equivalent indoor accelerated aging times based on specific regional climatic data.
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