摘要
Complex modulus(G^(*))is one of the important criteria for asphalt classification according to AASHTO M320-10,and is often used to predict the linear viscoelastic behavior of asphalt binders.In addition,phase angle(φ)characterizes the deformation resilience of asphalt and is used to assess the ratio between the viscous and elastic components.It is thus important to quickly and accurately estimate these two indicators.The purpose of this investigation is to construct an extreme gradient boosting(XGB)model to predict G^(*)andφof graphene oxide(GO)modified asphaltat medium and high temperatures.Two data sets are gathered from previously published experiments,consisting of 357 samples for G^(*)and 339 samples forφ,and the se are used to develop the XGB model using nine inputs representing theasphalt binder components.The findings show that XGB is an excellent predictor of G^(*)andφof GO-modified asphalt,evaluated by the coefficient of determination R^(2)(R^(2)=0.990 and 0.9903 for G^(*)andφ,respectively)and root mean square error(RMSE=31.499 and 1.08 for G^(*)andφ,respectively).In addition,the model’s performance is compared with experimental results and five other machine learning(ML)models to highlight its accuracy.In the final step,the Shapley additive explanations(SHAP)value analysis is conducted to assess the impact of each input and the correlation between pairs of important features on asphalt’s two physical properties.
