Prediction of in-situ asphalt mixture density using ground penetrating radar: theoretical development and field verification

In-situ asphalt mixture density is critically important to the performance of flexible pavements: density that is too high, or too low, may cause early pavement distresses. Traditionally, two methods have been commonly used for in-situ asphalt mixture density measurement: laboratory testing on field-extracted cores, and in-situ nuclear gauge testing. However, both these methods have limitations. The coring method damages pavement, causes traffic interruption, and only provides limited data at discrete locations. The nuclear gauge method also provides limited data measurement. Moreover, it requires a license for the operators because it uses radioactive material. To overcome the limitations of these traditional methods, this study proposes to develop a nondestructive method of using ground penetrating radar (GPR) to measure in-situ asphalt mixture density accurately, continuously, and rapidly. The prediction of asphalt mixture density using GPR is based on the fact that the dielectric constant of an asphalt mixture, which can be measured by GPR, is dependent on the dielectric and volumetric properties of its components. According to the electromagnetic (EM) mixing theory, two candidate specific gravity models, namely the modified complex refractive index model (CRIM) and the modified Bottcher model, were developed to predict the bulk specific gravity of asphalt mixture from its dielectric constant. To evaluate the performance of these two models, a full-scale six-lane test site with four sections in each lane was carefully designed and constructed. Forty cores were extracted from the test site, and their densities were measured in the laboratory and compared to the GPR-predicted values using the two models. Both models were found effective in predicting asphalt mixture density, although the modified Bottcher model performed better. To account for the effect of the non-spherical inclusions in asphalt mixture and further improve the density prediction accuracy, a shape factor was introduced into the modified Bottcher model. Nonlinear least square curve fitting of the field core data indicated that a shape factor of -0.3 provided the best-performance model, which is referred to as the Al-Qadi Lahouar Leng (ALL) model. The performance of the ALL model was validated using data collected from an active pavement construction site in Chicago area. It was found that when the ALL model was employed, the prediction accuracy of the GPR was comparable to, or better than, that of the traditional nuclear gauge. For the asphalt mixtures without slags, the average density prediction errors of GPR were between 0.5% and 1.1% with two calibration cores, while those of the nuclear gauge were between 1.2% and 3.1%. Due to the importance of the accurate input of the dielectric constant of asphalt mixture to the prediction accuracy of the specific gravity model, this study also looked into alternative methods for asphalt mixture dielectric constant estimation. The extended common mid-point (XCMP) method using two air-coupled antenna systems was developed, and its implementation feasibility was explored. The XCMP method was found to provide better performance than the traditional surface-reflection method for thick pavement structures with multi-lifts. However, for thin pavement layers (less than 63 mm thick), the accuracy of this method could be improved. Factors accounting for the accuracy reduction for a thin surface layer include the sampling rate limitation of the GPR systems, as well as the possible overlap of the GPR signal reflections at the surface and bottom of the thin asphalt layer