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The complex shear modulus (G*) and phase angle (δ) are fundamental viscoelastic rheological properties used in the estimation of rutting and fatigue pavement distress in asphalt binder. In the tropical regions, rutting and fatigue cracking are major pavement distress affecting the serviceability of road infrastructure. Laboratory testing of the complex shear modulus and phase angle requires expensive and advanced equipment that is not obtainable in major laboratories within the developing countries of the region, giving rise to the need for an accurate predictive model to support quality pavement design. This research aims at developing a predictive model for the estimation of rutting and fatigue susceptive of asphalt binder at intermediate and high pavement temperatures. Asphalt rheological and ageing test was conducted on eight mixes of modified binders used to build the study database containing 1976 and 1668 data points for rutting and fatigue parameters respectively. The database was divided into training and simulation dataset. The Gaussian process regression (GPR) algorithm was used to predict the rutting and fatigue parameters using unaged and aged conditioned inputs. The proposed GPR was compared with the support vector machine (SVM), recurrent neural networks (RNN) and artificial neural network (ANN) models. Results show that the model performed better in the estimation of rutting parameter than the fatigue parameter. Further, unaged input variables show better reliability in the prediction of fatigue parameter.

Artificial Intelligence Prediction of Rutting and Fatigue Parameters in Modified Asphalt Binders