Abstract

This study evaluates the viscoelastic behavior of asphalt binders modified with novel high-elastic modifiers (NHEMs) using a combined experimental and machine learning (ML) approach. Dynamic shear rheometer (DSR) frequency temperature sweeps and multiple stress creep recovery (MSCR) tests provided data for training XGBoost models with temperature, frequency, and modifier type as inputs. The models accurately predicted complex modulus (G*) and phase angle (δ), with temperature and frequency as the main predictors. This framework shows that ML can complement rheological testing, reduce experimental workload and enhance the evaluation of binder modifiers.

Keywords:Rheology; Viscoelastic behavior; XGBoost; Asphalt binder; Novel high-elastic modifiers