Predicting fatigue performance of hot mix asphalt using artificial neural networks

Abstract

Developing predictive models for fatigue performance is a complex process and can depend on variables including material properties, test conditions and sample geometry. Several models have been developed in this regard; some of these are regression models and are related to mechanistic properties in addition to volumetric properties. In this work, a computational model, based on artificial neural networks (ANNs), is used to predict the fatigue performance of hot mix asphalt (HMA) tested in a dynamic shear rheometer (DSR) technique. Fatigue performance was evaluated according to three approaches: traditional, energy ratio and dissipated pseudo-strain energy. For predicting fatigue performance, two types of ANN models were developed: those dependent on test modes, that is, based on controlled test modes, and those independent of test modes, that is, irrespective of controlled test modes, using fundamental parameters, for example, stiffness modulus, phase angle and volumetric properties. In this work, limestone (L) and granite (G) aggregates were used with two binder grades (40/60 and 160/220) to prepare four mixtures with two different gradations: gap-graded hot rolled asphalt (HRA) and continuously graded dense bitumen macadam (DBM). The results revealed an excellent correlation between the predicted and experimental data. It was found that the prediction accuracy of the strain test mode was better than that of the stress test mode.