Development of asphalt mixture density estimation model applicable to wide air void content range using ground penetrating radar

•The density estimation performance of conventional models was investigated.•The full-scale test site with wide air void content range was constructed.•The proposed model considered the effect of neighbouring inclusion polarization.•The proposed model provided accuracy results in wide air void content range. Density is one of the key properties that strongly affect the service quality and life of asphalt mixture. Ground penetration radar (GPR) coupled with suitable density estimation model has been recognized as an effective method to nondestructively estimate the density of asphalt mixture. However, the estimation accuracy of conventional models was questioned for asphalt mixture with high void content. To fill this gap, this study aims at developing an electromagnetic mixing model to predict the density of asphalt mixture with a wide range of air void content based on GPR measured data. In this study, the performance of four classical models, including Al-Qadi, Lahouar and Leng (ALL) model, Rayleigh model, Böttcher model and complex refractive index (CRIM) model was compared. Then, the influence of neighboring inclusion polarization linking with the air void content was taken into account to propose the density estimation model based on the electromagnetic mixing theory. Finally, the proposed model was validated using the laboratory measured density of the field cored samples. It was found that the density predicted by the proposed model is highly consistent with that measured in laboratory, with the average ratio of estimation results to test results at 1.004 and the standard deviation at 0.015. In addition, the proposed model showed superior prediction with currently available models in terms of density estimation for asphalt mixture with different aggregate gradations and air void contents. Based on the results of this study, it is recommended to using the proposed model as an alternative to the currently available models in estimating the density of asphalt mixture based on GPR collected data.