Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

Abstract

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree (AHA-boosted) model for predicting the dynamic modulus (E∗) of hot mix asphalt concrete. Using a substantial dataset from NCHRP Report-547, the model was trained and rigorously tested. Performance metrics, specifically RMSE, MAE, and R2, were employed to assess the model's predictive accuracy, robustness, and generalisability. When benchmarked against well-established models like support vector machines (SVM) and gaussian process regression (GPR), the AHA-boosted model demonstrated enhanced performance. It achieved R2 values of 0.997 in training and 0.974 in testing, using the traditional Witczak NCHRP 1-40D model inputs. Incorporating features such as test temperature, frequency, and asphalt content led to a 1.23% increase in the test R2, signifying an improvement in the model's accuracy. The study also explored feature importance and sensitivity through SHAP and permutation importance plots, highlighting binder complex modulus |G∗| as a key predictor. Although the AHA-boosted model shows promise, a slight decrease in R2 from training to testing indicates a need for further validation. Overall, this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete, making it a valuable asset for pavement engineering.

Keywords

• Asphalt
• Dynamic modulus
• Prediction
• Artificial hummingbird algorithm
• Boosted tree