Mathematical formulation for predicting moisture damage indices of asphalt mixtures treated with sustainable waste plastic modifiers using gene expression programming

Abstract

Flexible pavements are susceptible to rutting, fatigue, and moisture damage failures. As a sustainable approach, waste plastic modifiers have been used to improve the moisture damage resistance of asphalt mixtures. However, assessing moisture damage indices such as tensile strength ratio (TSR), rut depth (RD), and loss in Marshal stability (LI) for waste plastic modified asphalt mixtures (WPMAM) via traditional lab methods is time-consuming and resource intensive. As a solution, this study suggests a gene expression programming (GEP) approach for predicting moisture damage indices of WPMAM. The models were constructed based on the outcomes from qualitative lab tests on loose coated asphalt specimens, including the static water immersion test (SWIT), rolling bottle test (RBT), and boiling water test (BWT). The proposed models demonstrated robust performance with high R values and favorable low error metrics. TSR achieved 0.99 (training) and 0.98 (testing) R values, while RD and LI displayed strong predictive accuracy with 0.95, 0.93 (training), and 0.94, 0.77 (testing) R values. Notably, lower root means square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) values reinforce the models' reliability. Sensitivity analysis identified RBT as the most influential factor for TSR, RD, and LI of WPMAM. Finally, parametric analysis verified the alignment of proposed models with underlying physical processes. Thus, these models can be successfully implemented for predicting moisture damage indices of WPMAM, offering a streamlined alternatives to labor-intensive laboratory procedures, making them valuable for optimizing material choices and implementing preventive measures in construction.