Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures

Abstract

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* and φ of graphene oxide (GO) modified asphalt at medium and high temperatures. Two data sets are gathered from previously published experiments, consisting of 357 samples for G* and 339 samples for φ, and these are used to develop the XGB model using nine inputs representing the asphalt binder components. The findings show that XGB is an excellent predictor of G* and φ of GO-modified asphalt, evaluated by the coefficient of determination R² (R² = 0.990 and 0.9903 for G* and φ, respectively) and root mean square error (RMSE = 31.499 and 1.08 for G* and φ, 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.

Graphical abstract

Keywords

complex modulus / phase angle / graphene oxide / asphalt / extreme gradient boosting / machine learning

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