Using Artificial Neural Networks to Predict the Dynamic Modulus of Asphalt Mixtures Containing Recycled Asphalt Shingles


The difference between hot-mix asphalt (HMA) containing recycled asphalt shingles (RAS) and virgin HMA in terms of composition and properties causes difficulties when engineers try to predict the performance of HMA containing RAS. This study develops a prediction model based on artificial neural networks (ANN) to predict the dynamic modulus (E*) of HMA containing RAS. The E* database used in this study to develop the ANN model contains 1,701 sets of experimental data, which were obtained from four different demonstration projects. In order to train and test the model, the data were randomly divided into two different subsets: one is for training, containing 1,361 data points, and the other is for testing, containing 340 data points. The input parameters of the proposed model included percent passing a #200 sieve (ρ200), cumulative percent retained on a #4 sieve (ρ4), cumulative percent retained on a 9.5-mm sieve (ρ38), cumulative percent retained on a 19-mm sieve (ρ34), air voids (Va), effective binder content (Vbeff), viscosity of the asphalt binder (η), loading frequency (f), and RAS contents (pa, by weight of total aggregate). The sensitivity analysis of these parameters was performed by correlating each model parameter with E*. The proposed ANN model was compared with the Iowa model and showed significantly higher prediction accuracy than the Iowa model. It can be concluded that the proposed ANN model has great potential to be used as a tool to predict the E* of asphalt mixtures containing RAS.


Civil, Architectural and Environmental Engineering


This study was funded by the National Natural Science Foundation of China (Grant No. 51278188), Fundamental Research Funds for the Central Universities, and Young Core Instructor Foundation from the Education Commission of Hunan Province.

Keywords and Phrases

Recycled asphalt shingles; Artificial neural networks; Dynamic modulus

International Standard Serial Number (ISSN)

0899-1561; 1943-5533

Document Type

Article - Journal

Document Version


File Type





© 2018 American Society of Civil Engineers (ASCE), All rights reserved.

Publication Date

01 Apr 2018