Comparative Analysis of Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), and Polyvinyl Chloride (PVC)

In today’s rapidly evolving transportation infrastructure, developing long-lasting, high-performance pavement materials remains a significant priority. Integrating machine learning (ML) techniques provides a transformative approach to optimizing asphalt mix design and performance prediction. This study investigates the use of waste plastics, including Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), and Polyvinyl Chloride (PVC), as modifiers in asphalt concrete to enhance durability and mechanical performance. A predictive modeling approach was employed to estimate the bulk-specific gravity (Gmb) of asphalt concrete using various ML techniques, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gaussian Processes (GPs), and Reduced Error Pruning (REP) Tree. The accuracy of each model was evaluated using statistical performance metrics, including the correlation coefficient (CC), scatter index (SI), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate that the ANN model outperformed all other ML techniques, achieving the highest correlation (CC = 0.9996 for training, 0.9999 for testing) and the lowest error values (MAE = 0.0004, RMSE = 0.0006, SI = 0.00026). A comparative analysis between actual and predicted Gmb values confirmed the reliability of the proposed ANN model, with minimal error margins and superior accuracy. Additionally, sensitivity analysis identified bitumen content (BC) and volume of bitumen (Vb) as the most influential parameters affecting Gmb, emphasizing the need for precise parameter optimization in asphalt mix design. This study demonstrates the effectiveness of machine learning-driven predictive modeling in optimizing sustainable asphalt mix design, offering a cost-effective, time-efficient, and highly accurate alternative to traditional experimental methods.