Alternative Approach for Predicting the Phase Angle Characteristics of Asphalt Concrete Mixtures Based on Recurrent Neural Networks

Abstract

Laboratory performance testing of the phase angle of asphalt concrete (AC) mixtures is often expensive and requires enormous human effort and time. To circumvent this problem, several regression-based methods have been proposed in the literature to model the phase angle behavior of AC mixtures using various approaches. However, these methods impose strict assumptions on the underlying relationship between phase angle and its corresponding covariates as well as how well and accurately these covariates are measured, restricting us from fully analyzing the predictive capability of any modeling method. To this end, this study proposed an alternative approach for modeling the phase angle characteristics of AC mixtures based on a recurrent neural network (RNN) that inherently and implicitly captures the effects of covariates. This approach is suitable to model the sequential nature of data recorded in laboratory testing where phase angle testing was repeated for a set of six loading frequencies forming a recurrent pattern. The proposed RNN model (P-RNN) was applied separately to wearing and base course mixtures by considering the historical values of phase angle as input and to predict its value for the next loading frequency, keeping temperature as a constant. To demonstrate the superiority of the proposed approach, the P-RNN model is compared with other competing models from the literature, and the results reveal superior performance of the P-RNN model.