Formulation of Constitutive Viscoelastic Properties of Modified Bitumen Mastic Using Genetic Programming

The objective of this study is to create explicit prediction models for the complex shear modulus (G*) and phase angle (δ) of bitumen mastic fabricated using an evolutionary machine learning approach. The dynamic shear rheometer (DSR) test in frequency sweep mode at seven test temperatures was performed to measure G* and δ. In order to create specific prediction models for each modifier, multigene genetic programming (MGGP) was employed. These models took into account various factors including the dosage of the additive, filler volume filling rate, loading frequency, temperature, as well as the G* and δ values of the neat bitumen. In general, six explicit prediction models are presented for different additives with R-squared values of more than 0.9. The results showed that the hybrid machine learning approach can effectively develop precise, meaningful, and yet simple formulas for calculating G* and δ of the bitumen mastic. To gain a deeper understanding of the developed models, a comprehensive parametric study and sensitivity analysis were carried out.