In order to better evaluate the construction quality of asphalt pavement, nondestructive testing techniques are used to inspect newly paved asphalt mixture pavement. The proposed system for the evaluation of asphalt pavement construction quality uses three-dimensional ground-penetrating radar (GPR) and a non-nuclear density gauge. The GPR and the non-nuclear density gauge test results were used to establish a dielectric constant–porosity model by fitting. This approach can more accurately determine the dielectric constant selection scheme of the GPR based on the average value of every 10 dielectric constant data points in the length direction of the radar antenna and every three data channels in the width direction. The GPR collected the dielectric constants of the road surface based on the total reflection method and used the average value of the local dielectric constant to evaluate the construction quality of the road. The non-nuclear density gauge used the local porosity to assess the construction quality of the road. It is recommended that the two testing schemes described above be used to evaluate the quality of asphalt pavement construction. They can provide theoretical guidance for future applications in practical processes.

No abstract

To reduce the use of aggregates such as limestone and basalt, this paper used steel slag to replace some of the limestone aggregates in the production of SMA-13 asphalt mixes. The optimum content of steel slag in the SMA-13 asphalt mixes was investigated, and the performance of these mixes was evaluated. Five SMA-13 asphalt mixes with varying steel slag content (0%, 25%, 50%, 75%, and 100%) were designed and prepared experimentally. The high-temperature stability, low-temperature crack resistance, water stability, dynamic modulus, shear resistance, and volumetric stability of the mixes were investigated using the wheel tracking, Hamburg wheel tracking, three-point bending, freeze–thaw splitting, dynamic modulus, uniaxial penetration, and asphalt mix expansion tests. The results showed that compared to normal SMA-13 asphalt mixes, the high-temperature stability, water stability, and shear resistance of the SMA-13 asphalt mixes increased and then decreased as the steel slag content increased. All three performance indicators peaked at 75% steel slag content, and the dynamic stability, freeze–thaw splitting ratio, and uniaxial penetration strength increased by 90.48%, 7.39%, and 88.08%, respectively; however, the maximum bending tensile strain, which represents the low-temperature crack resistance of the asphalt mix, decreased by 5.98%. The dynamic modulus of the SMA-13 asphalt mixes increased with increasing steel slag content, but the volume expansion at a 75% steel slag content was 0.446% higher than at a 0% steel slag content. Based on the experimental results, the optimum content of steel slag for SMA-13 asphalt mixes was determined to be 75%.

Temperature segregation during the paving of asphalt pavements is one of the causes of asphalt pavement distress. Therefore, controlling the paving temperature is crucial in the construction of asphalt pavements. To quickly evaluate the road performance of asphalt mixtures during paving, in this work, we used unmanned aerial vehicle infrared thermal imaging technology to monitor the construction work. By analyzing the temperature distribution at the paving site, and conducting laboratory tests, the relationship between the melt temperature, high-temperature stability, and water stability of the asphalt mix was assessed. The results showed that the optimal temperature measurement height for an unmanned aerial vehicle (UAV) with an infrared thermal imager was 7–8 m. By coring the representative temperature points on the construction site and then conducting a Hamburg wheel tracking (HWT) test, the test results were verified through the laboratory test results in order to establish a prediction model for the melt temperature and high-temperature stability of y = 10.73e0.03x + 1415.78, where the predictive model for the melt temperature and water was y = −19.18e−0.02x + 98.03. The results showed that using laboratory tests combined with UAV infrared thermography could quickly and accurately predict the road performance of asphalt mixtures during paving. We hope that more extensive evaluations of the roadworthiness of asphalt mixtures using paving temperatures will provide reference recommendations in the future.

Obtaining the required homogeneity, including uniform thickness and density, is very crucial for controlling the quality of flexible asphalt layers. Although non-destructive testing (NDT) methods are time-saving and less labor-intensive, they only provide indirect measurement data under testing area conditions and strongly depend on the explanations by prediction models. In this study, in terms of the three-dimensional air-launched Ground Penetrating Radar (GPR) technique, the dielectric constant of asphalt concrete base with dry conditions in pavements was detected and calculated by different methods (the Coring Method, Reflection Amplitudes Method and Common Mid-Point Method). According to the calculated dielectric constant, the thickness and density of asphalt concrete base were further calculated and assessed. Comparing with the Coring Method, the Common Mid-Point Method was recommended to calculate dielectric constants in order to obtain reliable thickness of asphalt pavement base. Among the Birefringence, Boettcher, Linearity indicator, and Rayleigh models, the Rayleigh model was suggested to predict the density, and the predicted density exhibited a good correlation coefficient with the measured one. Furthermore, by choosing these proper calculation methods, an assessment was successfully conducted to evaluate homogeneity of a constructed field pavement in practice.