Deep learning based methodological approach for prediction of dynamic modulus and phase angle of asphalt concrete

Abstract

The present work proposes a deep learning approach to predict dynamic modulus and phase angle of asphalt mixtures. The dynamic modulus and phase angle data reported in national cooperative highway research program 9–19 were utilized to validate the proposed approach. Within this database 201 distinct asphalt mixtures were selected. Dynamic modulus and phase angle mastercurves were constructed for individual mixtures using combination of two sigmoidal functions and two temperature shift factor determination approaches. The input variables for deep learning model consisted of information regarding reduced frequency, binder properties, aggregate gradation, and mixture volumetrics. When compared to other input variables, reduced frequency and binder properties were highly correlated with dynamic modulus and phase angle. Further, recursive feature elimination was used to rank all input variables. Using these ranked input variables, deep learning-based models for predicting dynamic modulus and phase angle were developed. The deep learning architecture (finalized through exhaustive optimization) dependent on parameter under consideration, and mastercurve construction approach was adopted. Detailed statistical analysis indicated dynamic modulus predictive models performed better when compared to phase angle predictive models. The numerical values of goodness of fit indicators showed that accuracy of deep learning-based model was dependent on mastercurve construction approach adopted. Overall results indicated that the deep learning-based models can predict dynamic modulus and phase angle with good accuracy. The output from the proposed deep learning models can be used as direct input into pavement design framework, which can result in accurate prediction of pavement performance.