Soft Computing Approach for Predicting the Effects of Waste Rubber–Bitumen Interaction Phenomena on the Viscosity of Rubberized Bitumen

The ability to anticipate the effects of interaction between waste rubber particles from end-of-life tires and bitumen can encourage use rubberized bitumen, a material with proven environmental benefits, in civil engineering applications. In this study, predictive model viscosity is presented for purpose. A machine learning-based approach (Multi-Gene Genetic Programming—MGGP) more traditional multi-variable least square regression (MLSR) method are compared. statistical analysis indicates that robustness capability MGGP algorithm led better estimation bitumen’s viscosity. Additionally, returned an actual equation could be easily implemented any spreadsheet initial tuning production protocol based on desired level bitumen.