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In-situ asphalt mixture density is critically important to the performance of flexible airport 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 provides only 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 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 GPRpredicted 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%. iv Due to the importance of 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 ...

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A major problem in using ground penetrating radar (GPR) for estimating pavement layer thickness is assuming the dielectric properties of that layer. Pavement dielectric properties may vary significantly due to aggregate type, moisture presence, and other conditions. Therefore, uncertainties in the dielectric constant, which may vary from 3 to 15, will result in misleading thickness determination. Obtaining cores for calibration may reduce the error, but the variation in the dielectric constant along the roadway often leads to errors in the thickness determination. A method was developed to determine the dielectric constant, and therefore the thickness, of the hot-mix asphalt (HMA) layer of a pavement using GPR. Because of the different compositions and ages of the layers forming HMA in older pavements, dielectric constant estimation based on the surface reflection may not be accurate and may lead to wrong thickness estimations. The developed method uses a modified common midpoint technique (usually used in seismic testing) to estimate the dielectric constant, based on the reflections from a common point at the bottom of the layer. Data were collected from a 27-km portion of Interstate 81 and processed with this technique. Comparison between the thickness estimated by this method and that measured on cores extracted from the highway revealed a mean error of 6.8%.

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Testing at Virginia Smart Road allowed determination of the vertical compressive stress pulse induced by a moving truck and by falling weight deflectometer (FWD) loading at different locations beneath the pavement surface. Testing was performed on 12 different flexible pavement sections. Stress and temperature were measured using pressure cells and thermocouples, respectively, that had been installed during construction of the road. Target testing speeds were 8 km/h, 24 km/h, 40 km/h, and 72 km/h. The considered depths below the pavement surface were 40 mm, 190 mm, 267 mm, 419 mm, and 597 mm. A haversine or normalized bellshape equation was found to be a good representation of the measured normalized vertical compressive stress pulse for a moving vehicle. Haversine duration times varied from 0.02 s for a vehicle speed of 70 km/h at a depth of 40 mm to 1 s for a vehicle speed of 10 km/h at a depth of 597 mm. For the FWD loading, a haversine with a duration of 0.03 s was found to approximate the induced stress pulse at any depth below the pavement surface. Currently, laboratory dynamic testing on hot-mix asphalt (HMA) specimens is performed using a haversine wave at loading duration of 0.1 s. Because HMA is a viscoelastic material, the loading time affects its properties and, therefore, it is recommended that the loading time of HMA dynamic tests be reduced to 0.03 s to better match loading times obtained from moving trucks at average speed and from FWD testing.