Evaluating the mechanical behavior of plastic waste modified asphalt using optimized machine learning approaches.

The growing environmental challenges associated with plastic waste disposal and the need for sustainable pavement construction practices have prompted significant research interest in incorporating recycled plastics into asphalt mixtures. However, accurately predicting the performance characteristics of plastic-modified asphalt mixtures, particularly Marshall Stability (MS) and Marshall Flow (MF), remains a critical yet challenging task due to complex nonlinear relationships between mixture constituents. This study addresses this issue by developing reliable predictive models using machine learning techniques including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), further optimized through Particle Swarm Optimization (PSO). A comprehensive dataset comprising 210 samples of plastic-modified asphalt mixtures was utilized, incorporating inputs such as plastic content and size, bitumen content, maximum aggregate size, mixing temperature, and compaction effort (number of blows), to predict MS and MF as outputs. Results showed that the PSO-optimized XGB model achieved the highest accuracy, yielding R2 values of 0.82 for MS and 0.83 for MF. Model interpretability was enhanced using advanced techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, and Taylor diagrams, quantitatively highlighting optimal plastic particle sizes (2.5–4 mm), bitumen content (5.3–5.5%) and plastic content (20–30%). These findings provide actionable insights that support safer and longer-lasting pavements, promote the sustainable reuse of waste plastics, and enable cost-effective mix design strategies for modern asphalt construction.