Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks

Abstract

This study aims to model the complex modulus of base and styrene–butadiene–styrene (SBS) modified bitumens by using artificial neural networks (ANNs). The complex modulus of base and SBS polymer modified bitumen samples (PMB) were determined by using dynamic shear rheometer (DSRs). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with SBS Kraton D1101 copolymer at five different polymer contents. In ANN model, the bitumen temperature, frequency and SBS contents are the parameters for the input layer where as the complex modulus is the parameter for the output layer. The variants of the algorithm used in the study are the Levenberg–Marquardt (LM), scaled conjugate gradient (SCG) and Pola-Ribiere conjugate gradient (CGP) algorithms. A tangent sigmoid transfer function was used for both hidden layer and the output layer. The statistical indicators, such as the root-mean squared (RMS), the coefficient of multiple determination (R2) and the coefficient of variation (cov) was utilized to compare the predicted and measured values for model validation. The analysis indicated that the LM algorithm appeared to be the most optimal topology which gained 0.0039 mean RMS value, 20.24 mean cov value and 0.9970 mean R2 value.