Formulation of Constitutive Viscoelastic Properties of Modified Bitumen Mastic Using Genetic Programming
Publication: Journal of Engineering Mechanics
Volume 149, Issue 11
Abstract
The objective of this study is to create explicit prediction models for the complex shear modulus () and phase angle () 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 and . 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 and 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 and of the bitumen mastic. To gain a deeper understanding of the developed models, a comprehensive parametric study and sensitivity analysis were carried out.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
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Received: Sep 17, 2022
Accepted: Jun 1, 2023
Published online: Aug 17, 2023
Published in print: Nov 1, 2023
Discussion open until: Jan 17, 2024
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