In-Place Hot-Mix Asphalt Density Estimation Using Ground-Penetrating Radar

Abstract

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 ...

Comparison of the field measurements of asphalt concrete densities obtained by ground-penetrating radar, pavement quality indicator and the borehole coring methods

“…He presented a model to estimate the moisture content of asphalt mixture based on dielectric constant. Then, the same principle was used to measure the percentage of air voids in asphalt mixtures (Al-Qadi, Leng, & Larkin, 2011). Also Saarenketo used the GPR for air void estimation in 1993 in a project that was owned by the Technology Development Centre of Finland (TEKES) (Al-Qadi, 1992).…”

“…The software named SIDARS was designed on the basis of mixing theory that each pavement layer is formed of three types of solid, liquid and gas. Thus, the dielectric constant is a combination of dielectric constant of each of the three phases of the material (Al-Qadi, Leng, & Larkin, 2011). Based on this assumption a model of wave propagation was used in pavement systems.…”

“…Based on this assumption a model of wave propagation was used in pavement systems. An initial value was obtained by an iterative process to reduce the mean-square error between the measured and calculated values of the reflected radar signals (Al-Qadi, Leng, & Larkin, 2011). Volumetric parameters were calculated for each layer by calibrating the model that is used in the software with the real data obtained from the coring samples (Wells & Lytton, 2001).…”

Permittivity of Sub-Soil Materials Retrieved Through Transmission Line Model and GPR Data

“…In fact, the electromagnetic echo recorded by GPR is due to the different electromagnetic parameters (i.e., effective relative permittivity, permeability and conductivity) of the materials along the stratification [3], whose knowledge leads to a correct time-to-depth conversion and a correct estimation of the depth of a buried target [2]. Moreover, the knowledge of the dielectric permittivity of soil allows to infer the chemical, physical and mineralogical properties of the mixture composing the soil, being a macroscopic parameter related to the soil moisture content [4][5][6], to the mixture density of asphalt [7], and to the air voids per unit of volume [8].…”

Characterising moisture within unbound granular pavements using multi-offset Ground Penetrating Radar

“…This in turn enables calibration of layer depth estimates and determination of the bulk relative permittivity of pavement layers, from which the moisture content or other pavement parameters or their variation may be inferred, for example changes in density for new asphalt pavements (Al-Qadi et al, 2010). As the approach accounts for material inhomogeneity within layers, it enables more accurate layer depth estimates for older and more variable pavement materials compared to the surface reflection coefficient approach (Lahouar et al, 2002) as that approach is better suited to new or defect-free pavements that are non-attenuative .…”