Modelling of preparation parameters of polymer and oily waste sludge modified bitumen using neural network coupled with multiobjective evolutionary algorithm

ABSTRACT

In this study, artificial neural networks coupled multi-objective evolutionary algorithm based on decomposition (ANN-MOEA/D) and non-dominated sorting genetic algorithm versionIII (ANN-NSGAIII) as the chemometric approaches were used for the first time in the pavement field to model the modification conditions of the base bitumen (PG58-22). The novel ternary system of styrene-butadiene-styrene and two inexpensive waste materials as independent factors and five responses were considered in this work. Under the ANN-MOEA/D conditions, as the better optimisation method, the amounts of polyethylene, styrene-butadiene-styrene, and oily waste sludge were 3.56%, 4%, and 4.34%, respectively. The response values of penetration and change of mass after short-term ageing were at their minimum values of 50 dmm and 0.06%, respectively. The response values of softening point, ductility at 10oC, and retained penetration after short-term ageing, were at their maximum values of 65oC, 30 cm, and 89%, respectively. The analysis of the results of BBR and DSR tests in the optimal conditions obtained from two metaheuristic algorithms indicated improvement in the rutting resistance (G*/sinδ), m-value, and stiffness parameters of the modified bitumen compared to the base bitumen. The optimal conditions of ANN-MOEA/D and ANN-NSGAIII of the modified bitumen reached the characteristics of PG70-28 and PG-64-22 bitumen, respectively.

KEYWORDS:

• Bitumen
• polyethylene
• styrene-butadiene-styrene
• oily waste sludge
• multiobjective evolutionary algorithm based on decomposition
• artificial neural network

Acknowledgment

The Authors are thankful to the Science Faculty, South Tehran Branch, Islamic Azad University for providing facilities and materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).