Study of in situ dynamic modulus prediction of asphalt mixture utilizing Ground penetrating radar technology

Abstract

• The small-scale specimen’s applicability in dynamic modulus test was investigated. • Empirical and ANN dynamic modulus prediction models were proposed using GPR data. • The proposed models provided acceptable results at the confidence level of 95%. It is widely known that the asphalt mixture dynamic modulus is the key mechanical property that affects the quality and service life of asphalt pavement greatly. Ground penetrating radar (GPR) technology is a prospective method commonly used in determining the material information of asphalt pavement. Its utilization is supported by studying the relationship between the characterizations of GPR waveform and the parameters representing the condition of asphalt pavement. However, it is rarely applied in dynamic modulus prediction of asphalt mixture. To fill this gap, this study focuses on developing the corresponding dynamic modulus predictive method on the basis of GPR data. First, the applicability of the ϕ 38 mm × h 110 mm small-scale specimen covering wide range of air void content in the uniaxial compression test was analyzed. Then, the empirical and artificial neural network (ANN) dynamic modulus prediction models were developed by taking into account the dielectric constant of asphalt mixture. Last, the two proposed models were validated based on four asphalt mixture lanes in the field. It was found that the test errors of small-scale specimens were smaller than 15 %, and the statistical difference of dynamic moduli and phase angles measured from the full-size and small-scale specimens is insignificant at the 95 % confidence level meaning the size influence can be ignored. According to the verification experiment, the proposed empirical model and ANN model could yield acceptable results with the average prediction error of 19.3 % and 24.8 %, respectively. It is recommended that the two proposed predictive models coupled with GPR data could be used as an alternative to the currently available methods in estimating the dynamic modulus of asphalt mixture in situ.