Mathematical Modeling and Sustainable Optimization of Rubberized Asphalt Mix Design Using Deep Learning Approach

The integration of recycled materials, such as tire rubber, into asphalt mixtures is a critical strategy for sustainable pavement engineering. This research aimed to employ deep learning (DL) to mathematically model and optimize Marshall stability and flow for asphalt mixes prepared with recycled rubber. The Multi-Layer Perceptron (MLP) regressor was used to predict these parameters using six design parameters. The DL approach could handle multiple outputs and provided noticeable improvement over the baseline regressor models. The analysis identified the sample volume as the most important variable followed by proportion of the recycled rubber and air voids. Using the DL model as a digital simulator, the optimal mix characteristics were found to be in the range of 3.8% to 4% bitumen and 4% to 5.5% recycled rubber. These findings validate the use of DL for efficient design of sustainable infrastructure materials.