An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials

Abstract

A flexible pavement with an adequate Marshall mix design for the asphalt mixture surface layer(s) and appropriate subbase/base design offers a proper pavement structure for driving safety and comfortability. The conventional approach of calculating the Marshall mix design properties (e.g., stability and flow) and resilient modulus of base materials typically requires laborious, expensive, and time-consuming laboratory sample preparation and testing, such as the Marshall stability test and Resilient modulus test. Therefore, this study conducts research on the application of tree-based ensemble methods specifically, random forest (RFR) and eXtreme gradient boosting regression (XGBR) to predict the properties (mainly including Marshall stability, flow, VMA, VFA, and unit weigh) of asphalt mix following Marshall mix design and resilient modulus (Mr) of stabilized aggregate base material. To develop the prediction models, a comprehensive database was established using existing literature. The performance of the prediction models was evaluated using statistical parameters, performance index (p), and other suggested model performance criteria from external research. Additionally, the Shapley Additive exPlainations (SHAP) interpretation technique was employed to explain the models. The analysis results demonstrate that both RFR and XGBR methods have the excellent fitting capability (R > 0.9). However, XGBR has superior performance in predicting Marshall mix properties and Mr value. Comparison with previous research studies reveals that the proposed models show superior performance than genetic programming-based Marshall mix design models. The RFR model performs approximately similar to ANN, while XGBR outperforms ANN, and particle swarm optimization-extreme learning machine-based models for Mr prediction. Furthermore, the equation developed based on multi expression programming for Mr value prediction exhibits superior performance compared to previous research study gene expression programming model.