Optimizing modified asphalt binder performance at high and intermediate temperatures using experimental and machine learning approaches

Asphalt pavements are highly susceptible to temperature-induced stresses, which can lead to significant deterioration such as rutting at high temperatures and cracking at low temperatures. These challenges are exacerbated by global warming, which raises pavement temperatures and potentially accelerates material degradation. Addressing these issues, this study utilizes two base asphalt binders, PG 64–22 and PG 76–22, and the use of phase change materials (PCMs) like styrene-butadiene-styrene, lignin, and ground tire rubber to enhance the resilience of asphalt binders. Through an extensive experimental investigation, which includes aging processes such as rolling thin film oven and pressure aging vessel, alongside a series of both traditional and advanced tests (including dynamic shear rheometer, rotational viscometer, Fourier transform infrared spectroscopy, among others), a comprehensive set of 51 samples were analysed. By incorporating these binders and PCMs into asphalt, the experimental work demonstrated a significant improvement in the thermal and mechanical properties, effectively increasing resistance to high-temperature rutting and fatigue cracking. Complementing the experimental approach, machine learning models were employed to predict complex asphalt binder properties, such as combustion value and failure temperature, based on the experimental data. This dual approach fills a significant gap in the literature by offering a more efficient method and prediction model for optimizing pavement materials amidst climate change, while also promising to significantly advance pavement technology by enhancing material design and performance. • Two base asphalt binders were modified with phase change materials. • Styrene-butadiene-styrene, lignin, and ground tire rubber were used as PCM. • Rolling thin film oven and pressure aging vessel aging processes were considered. • Viscosity, rheology, combustion heat, and chemical changes were evaluated. • Explainable ML models are used for developing the prediction model.